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Series: God's Promises, Our JourneyTitle: “How Does God's Promise Shape Our Faith Journey?”Scripture: Genesis 15:1-6 NIV Habakkuk 2:4 Luke 2-3Ephesians 2:8-10Romans 4:3-5; 20-22Galatians 3:6-9, 14James 2:21-23Bottom Line: When we trust God's promise, our journey is secure—He makes us right with Him and leads us step by step.INTRODUCTIONCONTEXTSERMON OUTLINECONCLUSIONNOTESOUTLINESQUESTIONS TO CONSIDER DISCUSSION QUESTIONSMAIN REFERENCES USEDMy opening prayer: Lord God, help us grow to be and do like Jesus, while abiding in him and leading others to do the same. INTRODUCTION“The Signed Contract Before the House Exists”In 2006, we started looking for a house to buy in Summerville. We'd just accepted the position to come to Grace and were excited about what we might move into. But our excitement faded as we saw what we'd care for the money we were bringing from our last house sale. So, in the end, it made more sense for us to build a new house over buying an existing one. Imagine a young couple buying a home that hasn't been built yet.There is:• No framing• No roof• No walls• Just dirt and a blueprint or renderingBut they sign a contract.They put down earnest money.They commit financially.Why would they do that?Because they trust:• The builder's reputation• The written promise• The legal agreementThey are acting today on something they cannot yet see.That's Genesis 15.Abram:• Has no son• Has no visible nation• Has no fulfillment• Only a word from GodAnd verse 6 says he signed the contract in his heart.“Abram believed the LORD…”Faith is not pretending the house is already standing.Faith is signing your life to the One who promised to build it.That sets up:• Romans 4 — persuaded God will do what He promised• Galatians 3 — we inherit the same contract by faith• James 2 — if you signed it, you start living like itCONTEXTGenesis 15 comes at a pivotal moment in Abram's journey. In Genesis 12, God called Abram out of Ur with sweeping promises of land, offspring, and blessing — but Abram still has no child. In Genesis 13, he lets Lot choose the better land and must trust God again for the promise. In Genesis 14, Abram rescues Lot, defeats powerful kings, refuses the riches of Sodom, and is blessed by Melchizedek — demonstrating growing faith and allegiance to God alone. Yet despite spiritual victory, the central promise remains unfulfilled: Abram is aging, Sarai is barren, and the land is still occupied by other nations. Genesis 15 opens in that tension — between promise and fulfillment, between faith and visible reality — and God responds not with rebuke, but with covenant.OUTLINE (with references):1. Fear Meets God's Promise (Genesis 15:1-2): Abraham's fears—reprisal and no heir—are met by God's protection and provision.2. Who God Is in the Journey (Genesis 15:1, 5): Present, protector, provider, sovereign, life-giver—all shaping trust.3. Faith Receives God's Promise (Genesis 15:6): Abraham believed, and it was credited as righteousness.SERMONReview from Genesis 12:1-3:God Is the Initiator of RedemptionGod Calls His People to Trust Him Before They Understand HimGod's Blessing Is Never Merely Personal—It Is MissionalGod Promises to Anchor His People in Uncertain Times & PlacesGod's People Respond with Obedience, Worship, and Witness1. Fear Meets God's Promise (Genesis 15:1-2): Abraham's fears—reprisal and no heir—are met by God's protection and provision.2. Who God Is in the Journey (Genesis 15:1, 5): Present, protector, provider, sovereign, life-giver—all shaping trust.The stars in the sky in Uganda. (Show Chris' pic)"On a clear night, at most 5,000 individual stars can be seen with the naked eye, as well as objects like the fuzzy outline of the Andromeda Galaxy, with its estimated 100 billion stars, 2.5 million light years away!" -Lennox, p. 113"ACCORDING TO THE WRITERS of Hebrews and the Genesis record, the link between faith and righteousness was not a New Testament invention, nor was it even a patriarchal innovation. Instead, the dynamic connection between faith and righteousness is rooted in primeval history well before the flood. The lives of three famous pre-diluvians-Abel, Enoch, and Noah— make this very clear.Of Abel we read, "By faith Abel offered to God a more acceptable sacrifice than Cain, through which he was commended as righteous, God commending him by accepting his gifts. And through his faith, though he died, he still speaks" (Hebrews 11:4). Faith-righteousness was exemplified in earth's first family by the second son of Adam and Eve.Of Enoch Genesis says, "Enoch walked with God, and he was not, for God took him" (5:24). The metaphor "walked" indicates closest communion and intimacy—a righteous life. Enoch's godly walk grew out of his faith, as Hebrews makes so clear: "By faith Enoch was taken up so that he should not see death, and he was not found, because God had taken him. Now before he was taken he was commended as having pleased God" (11:5). According to verse 6, his God-pleasing faith believed that "God is" (literal translation of the Greek)-that the sovereign God of creation is God. He also believed that God "rewards those who seek him"-that God is positively equitable.As a result, Jude 14, 15 records that he became a preacher of righteousness, apparently for his entire life, for some three centuries! Enoch's life demonstrated a righteousness based on faith.Of Noah Genesis says, "Noah was a righteous man, blameless in his generation. Noah walked with God" (6:9). That Noah "walked with God" identifies him with the godly character of Enoch. But even more significant, the statement that "Noah was a righteous man" is the first occurrence of the word righteous tsadiq) in the Bible. Noah's righteousness was not derived from his being perfect or any antecedent righteousness, but because he believed God, as the writer of Hebrews explains: "By faith Noah, being warned by God concerning events as yet unseen, in reverent fear constructed an ark for the saving of his household" (11:7). The biblical doctrine of imputed righteousness (a righteousness from God) began here in primeval history before the flood!" -Hughes, pp. 221-2223. Faith Receives God's Promise (Genesis 15:6): Abraham believed, and it was credited as righteousness.Cross-References for Genesis 15:6 - Faith credited as righteousness. #core• Romans 4:3-5, 20-22 - Faith credited as righteousness. #standing #position• Galatians 3:6-9, 14 - Faith connects us to the promise. #family #mission #nations• James 2:21-23 - Faith is active, not passive. #living #practical #behaviorsBottom Line: When we trust God's promise, our journey is secure—He makes us right with Him and leads us step by step.So How Does God's Promise Shape Our Faith Journey?He brings security, confidence and peace because of who he is and what he does as our personally present provider and protector.More application:First, when you face fear—whether fear of the future or uncertainty—remind yourself that God is both your protector and provider. Concretely, when you face a major decision, begin by praying for His presence in it.Second, trust in God's promise of righteousness—when you feel inadequate or guilty, recall that your standing is secure by faith. For example, when you fail, don't retreat—confess and continue walking with Him.Third, active faith leads to action—like Abraham, step forward in obedience. If God's promise is sure, what step of obedience is He calling you to right now?Fourth, when considering your connection to God's larger promise (like in Galatians 3), remember that your faith ties you to a bigger family of faith. Practically, live out that community—extend grace and include others in your faith journey.Finally, as James 2 emphasizes, ask yourself: How is my faith visible in action this week? Identify one tangible act of service or obedience you'll do in response to trusting Him.CONCLUSIONBottom Line: When we trust God's promise, our journey is secure—He makes us right with Him and leads us step by step.“The Shield You Carry vs. The Shield You Trust”You could return to Genesis 15:1:“Do not be afraid, Abram. I am your shield…”In ancient warfare, a shield only works if you trust it enough to stand behind it.If a soldier:• Keeps peeking out• Keeps lowering the shield• Keeps running forward in panicThe shield cannot protect him.The issue is not whether the shield works.The issue is whether he will stand behind it.Abram's fear was real:• Eastern kings• No heir• An aging bodyGod doesn't say:“Be brave.”He says:“I am your shield.”And Abram stands behind that promise.Romans 4 says he was fully persuaded.Galatians 3 says we now stand in that same promise.James 2 says if you really trust the shield, you'll fight differently.So the question becomes:Are you standing behind the Shield?Or are you trying to carry your own?That lands the plane cleanly on:• God as protector• God as provider• Faith that rests• Faith that actsINVITATIONPeter puts it all in perspective in his first sermon:““Therefore let all Israel be assured of this: God has made this Jesus, whom you crucified, both Lord and Messiah.” When the people heard this, they were cut to the heart and said to Peter and the other apostles, “Brothers, what shall we do?” Peter replied, “Repent and be baptized, every one of you, in the name of Jesus Christ for the forgiveness of your sins. And you will receive the gift of the Holy Spirit. The promise is for you and your children and for all who are far off—for all whom the Lord our God will call.”” ‭‭Acts‬ ‭2‬:‭36‬-‭39‬ ‭NIV‬‬How do we respond? Answer 2 questions:Take out a card or piece of paper right now. Write down the answer to these questions: What is God saying to me right now?What am I going to do about it? Write this down on a sheet of paper. What I hear you saying, Lord, is ___________________.[my name] is going to believe/do __________________________________________________ as a result.Finally, share this with your Home or Mission group this week when you gather as a testimony about what God is doing in your life. You don't have to get too specific to give him praise.Lord's Supper, 1 Cor 11:23-26 is good passage.Also, say something like, "Christ has died, Christ is risen, Christ will come again." (past, present, and future)PrayNOTES"As Von Rad has said, "But above all, his righteousness is not the result of any accomplishments, whether of sacrifice or acts of obedience.Rather, it is stated programmatically that belief alone has brought Abraham into a proper relationship to God."This understanding is revolutionary! Circa 2000 B.C., Abram was declared righteous because of his belief. This declaration was in profound accord with the primeval fathers Abel, Enoch, and Noah. Furthermore, the principle has remained operative through both primeval and patriarchal history and the entire old-covenant era and is the foundation of the new covenant." -Hughes, p. 225"It has always been the same-in primeval times and patriarchal times. under the old covenant and the new covenant: Faith brings righteousnessand salvation.So it was for Abel: "By faith Abel offered to God a more acceptable sacrifice than Cain" (Hebrews 11:4). Abel was saved by faith, a faith that was not alone because it produced better works than Cain.So it was for Enoch: "By faith Enoch was taken up so that he should not see death" (Hebrews 11:5). But Enoch's faith was such that he "walked with God" (Genesis 5:22) before he was no more. His faith was a real faith a faith that worked.So it was for Noah: "By faith Noah, being warned by God concerning events as yet unseen, in reverent fear constructed an ark for the saving of his household. By this he condemned the world and became an heir of the righteousness that comes by faith" (Hebrews 11:7). Noah's profound faith produced a profound obedience. And his works were monumental: "He did all that God commanded him" (Genesis 6:22; cf. 7:5, 9, 16). His was a faith that worked.So it was with Abram: "By faith Abraham obeyed when he was called to go out to a place that he was to receive as an inheritance. And he went out, not knowing where he was going" (Hebrews 11:8). "By faith Abraham, when he was tested, offered up Isaac" (v. 17a.). Abraham was saved by faith alone-a faith that was not alone faith that works!We conclude this study with two penetrating questions. Have you rested your faith on God the Son, Jesus Christ, alone for your salvation? That is the first question. Are you trusting your works or Christ? Now if you answer, I am trusting Christ alone," then the second question is, has your faith produced works? Is your faith real enough that it has changed your life? These are salutary questions because you are saved by faith alone. But if it is true faith, it is faith that is not alone but a faith that works." -Hughes, p. 218"When someone is called 'a person of faith' it usually means that they are adherents of a particular religious tradition. It does not normally refer to the obvious yet frequently overlooked fact that everyone exercises faith every day in a myriad different ways - just think what would happen if people did not place their faith/trust in maps, traffic lights, electric appliances, or doctors, surgeons, pilots, lawyers and so on. In that important sense, everyone is a person of faith. Faith, as such, is not a religious concept." -Lennox, p. 117Below is:1. A refined opening (pastoral tone)2. A clean covenant pivot3. A governing Big Idea4. A simple sermon skeleton that keeps the focus where it belongs

By the latter part of the twentieth century, the world had become unipolar. The Soviet Empire collapsed even more rapidly than the British one had after the Berlin Wall fell in 1989. China was not yet the force it is today. The US was at the pinnacle of its global power.That made it all the more unbearable that it came under assault within its own borders by the terrorists of the 9/11 attack in 2001. A reaction was inevitable. We saw last time how it invaded Afghanistan, but that seemed barely justified since there's no evidence of Afghan involvement in the attacks. By 2003, the US as ready to turn its military aggression against another nation in what it called its ‘war on terror', a strange notion of waging war against an abstract noun. Concretely, its new target was Iraq. Sadly, however, Iraqi contact with the 9/11 attacks had proved as difficult to substantiate as Afghanistan's. But the US put together an international coalition for war there, as it had once before in 1990-91, to throw Iraqi invaders out of Kuwait.This though would be much smaller coalition, with fewer nations prepared to support President George ‘Dubya' Bush's new campaign. It didn't help that it looked suspiciously at least partly aimed at completing the work of his own father, George HW Bush, who'd been president during the previous war on Iraq, by bringing down the dictator Saddam Hussein.One of the nations right alongside the US was Britain. That would leave a lasting mark on Tony Blair's legacy. Which might as a result not have been quite as glowing as he might have liked.Our subject for next week.Illustration: Government buildings burning in Baghdad following a US airstrike in March 2003. Photo Ramzi Haidar / AFP / Getty from ‘The Atlantic'Music: Bach Partita #2c by J Bu licensed under an Attribution-NonCommercial-No Derivatives (aka Music Sharing) 3.0 International License

As I discussed in a prior post, I felt like there were some reasonably compelling arguments for expecting very fast AI progress in 2025 (especially on easily verified programming tasks). Concretely, this might have looked like reaching 8 hour 50% reliability horizon lengths on METR's task suite[1] by now due to greatly scaling up RL and getting large training runs to work well. In practice, I think we've seen AI progress in 2025 which is probably somewhat faster than the historical rate (at least in terms of progress on agentic software engineering tasks), but not much faster. And, despite large scale-ups in RL and now seeing multiple serious training runs much bigger than GPT-4 (including GPT-5), this progress didn't involve any very large jumps. The doubling time for horizon length on METR's task suite has been around 135 days this year (2025) while it was more like 185 [...] The original text contained 5 footnotes which were omitted from this narration. --- First published: August 20th, 2025 Source: https://www.lesswrong.com/posts/2ssPfDpdrjaM2rMbn/my-agi-timeline-updates-from-gpt-5-and-2025-so-far-1 --- Narrated by TYPE III AUDIO.

How to communicate clearly in any context, from newsrooms to the world stage.All good communication, whether spoken or written, is built on the same foundation. In everything from police reporting for a newspaper to serving as spokesman for the Pope, Greg Burke has found the key: "Keep it as simple as possible."Burke has worked as a journalist for Time Magazine and Fox News, as Director of Communications at IESE Business School in Barcelona and Madrid, and even as a communications advisor and spokesman for the Vatican. Across his varied experiences, he's found that the biggest communication challenges often require the simplest solutions. From expressing “curiosity and real interest” in those we're speaking with to ensuring our messages are “clear, consistent, and timely,” he advocates for core principles that apply in all types of communication.In this episode of Think Fast, Talk Smart, Burke and host Matt Abrahams discuss communication strategies that work whether you're presenting to a team of five or millions of parishioners worldwide. As Burke shares, it's all very simple: “Know your audience, practice, and have fun.”To listen to the extended Deep Thinks version of this episode, please visit FasterSmarter.io/premiumEpisode Reference Links:Greg BurkeEp.22 Under Pressure: How to Communicate Clearly and Timely During a Crisis  Connect:Premium Signup >>>> Think Fast Talk Smart PremiumEmail Questions & Feedback >>> hello@fastersmarter.ioEpisode Transcripts >>> Think Fast Talk Smart WebsiteNewsletter Signup + English Language Learning >>> FasterSmarter.ioThink Fast Talk Smart >>> LinkedIn, Instagram, YouTubeMatt Abrahams >>> LinkedInChapters:(00:00) - Introduction (01:56) - Asking Good Questions (03:49) - Writing Clearly and Concretely (05:28) - Editing and AI's Role in Writing (06:44) - Three Keys to Great Communication (08:33) - The Importance of Feedback (10:13) - Communication at the Vatican (12:57) - Crisis Communication: Lessons from the Vatican (15:59) - The Final Three Questions (22:40) - Conclusion     *****This Episode is sponsored by Stanford. Stay Informed on Stanford's world changing research by signing up for the Stanford ReportSupport Think Fast Talk Smart by joining TFTS Premium.      

One hope for keeping existential risks low is to get AI companies to (successfully) make high-assurance safety cases: structured and auditable arguments that an AI system is very unlikely to result in existential risks given how it will be deployed.[1] Concretely, once AIs are quite powerful, high-assurance safety cases would require making a thorough argument that the level of (existential) risk caused by the company is very low; perhaps they would require that the total chance of existential risk over the lifetime of the AI company[2] is less than 0.25%[3][4].The idea of making high-assurance safety cases (once AI systems are dangerously powerful) is popular in some parts of the AI safety community and a variety of work appears to focus on this. Further, Anthropic has expressed an intention (in their RSP) to "keep risks below acceptable levels"[5] and there is a common impression that Anthropic would pause [...] ---Outline:(03:19) Why are companies unlikely to succeed at making high-assurance safety cases in short timelines?(04:14) Ensuring sufficient security is very difficult(04:55) Sufficiently mitigating scheming risk is unlikely(09:35) Accelerating safety and security with earlier AIs seems insufficient(11:58) Other points(14:07) Companies likely wont unilaterally slow down if they are unable to make high-assurance safety cases(18:26) Could coordination or government action result in high-assurance safety cases?(19:55) What about safety cases aiming at a higher risk threshold?(21:57) Implications and conclusionsThe original text contained 20 footnotes which were omitted from this narration. --- First published: January 23rd, 2025 Source: https://www.lesswrong.com/posts/neTbrpBziAsTH5Bn7/ai-companies-are-unlikely-to-make-high-assurance-safety --- Narrated by TYPE III AUDIO.

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PRESS REVIEW – Thursday, January 9: The commander-in-chief of the army, Joseph Aoun, emerges as a favourite in Lebanon's election for president. It's the country's thirteenth attempt in two years to find a leader. Meanwhile, Mark Zuckerberg's decision to kill fact-checking on Facebook, WhatsApp and Instagram sparks outrage but also support. Donald Trump continues to roil – and worry – Europe over his threat to take over Greenland by force, if necessary. Plus: French coach Dider Deschamps says he'll step down after the 2026 World Cup. Lebanon in new bid to elect new presidentLebanon prepares to elect a new leader. As one news outlet says, the country has tried twelve times to elect a new leader in the past two years without success, so here's hoping the baker's dozen is the lucky one.The man emerging as favourite is Joseph Aoun, who is depicted on the front of L'Orient-Le Jour, the French language Lebanese daily. Aoun is commander-in-chief of the Lebanese army and is a respected figure for having kept the Lebanese army together despite political fractures. Another Lebanese daily, Annahar, is exercising caution today, noting that 86 votes are needed out of 128 to win the election.Unlike previous times, this vote is taking place with a significantly weakened Hezbollah, which has lost influence in parliament. For the pro-Hezbollah daily al-Akhbar, the emergence of Aoun is the result of foreign collusion between the US and Saudi Arabia, which the paper sees as making a return to Lebanese politics. In an analysis piece from The Washington Institute, the author warns that the time to strike is now. Without an immediate and concerted effort to consolidate the setbacks to Hezbollah, the group could regroup and rebuild, which would put Lebanon's political future in peril once again.Meta ends fact-checking in USMark Zuckerberg's Meta has taken the drastic step of rolling back its fact-checking service, in what appears to be a move bowing down to the new Trump administration. For The Guardian's editors, Zuckerberg is ingratiating himself with the next Trump administration with this decision to scrap US-based fact-checkers on Facebook, WhatsApp and Instagram. Concretely, users can call others mentally ill based on their sexuality or gender identity, for instance. Meta says the decision is in response to accusations of overreach by content moderators. But as The Guardian notes, it kills two birds with one stone for Zuckerberg: cutting costs and getting on Trump's good side, this as the new US leader will soon have the power to kill or not a federal antitrust case against Meta, release regulatory pressure on big tech and facilitate AI.For The Economist though, as "craven" as the circumstances may be, Meta's new sweeping changes are correct. Speech online "urgently needs to become freer". The Economist says this is the only way to shore up America's democracy against whatever tests it faces in the years to come.Trump wants to take over GreenlandIn addition to railing against Meta, Trump has raised eyebrows this week for his plans to take over the Danish territory of Greenland. Certainly a question one might be asking today is on the front of French paper Le Parisien: Just how far will they go, Donald Trump and his right- hand man Elon Musk? Trump's comments that the US needs Greenland for its economic security raise concerns about how far Trump can go in his quests as president, particularly against a Europe weakened by war and inflation.The Economist plays devil's advocate in a piece; suggesting that instead of using force, Trump should name his price for Greenland. The author points to the US purchases of Louisiana and Alaska in the 19th century as some of the greatest achievements in US history and very good value. History will not be kind to Trump, the magazine says, if he takes Greenland by force, but purchasing it could be seen as the deal of the century.It's not as easy as he thinks, warns Danish paper Jyllands-Posten. Trump will have to impose punitive tariffs not just on Denmark but all of Europe if he wants to take control of Greenland. No one knows how serious he is, but as one business owner is quoted as saying, it's never a nice feeling to be on Trump's radar.Didier Deschamps to bow outFinally: Didier Deschamps, coach of the French football team, says he'll be stepping down after the 2026 World Cup. Deschamps, who coached France to victory in the 2018 World Cup and to the final in 2022, plans to leave the national team. But He's not the one gracing the cover of L'Equipe, the French sports paper. It's Zinédine Zidane, the 1998 World Cup hero, who has never been coy about his ambitions to coach the French national side. The path appears to be clearing now with Deschamp's announcement. Hence the paper's anticipation, even if Zidane hasn't yet said anything officially.You can catch our press review every morning on France 24 at 7:20am and 9:20am (Paris time), from Monday to Friday.

Welcome to The Nonlinear Library, where we use Text-to-Speech software to convert the best writing from the Rationalist and EA communities into audio. This is: [Interim research report] Activation plateaus & sensitive directions in GPT2, published by StefanHex on July 5, 2024 on LessWrong. This part-report / part-proposal describes ongoing research, but I'd like to share early results for feedback. I am especially interested in any comment finding mistakes or trivial explanations for these results. I will work on this proposal with a LASR Labs team over the next 3 months. If you are working (or want to work) on something similar I would love to chat! Experiments and write-up by Stefan, with substantial inspiration and advice from Jake (who doesn't necessarily endorse every sloppy statement I write). Work produced at Apollo Research. TL,DR: Toy models of how neural networks compute new features in superposition seem to imply that neural networks that utilize superposition require some form of error correction to avoid interference spiraling out of control. This means small variations along a feature direction shouldn't affect model outputs, which I can test: 1. Activation plateaus: Real activations should be resistant to small perturbations. There should be a "plateau" in the output as a function of perturbation size. 2. Sensitive directions: Perturbations towards the direction of a feature should change the model output earlier (at a lower perturbation size) than perturbations into a random direction. I find that both of these predictions hold; the latter when I operationalize "feature" as the difference between two real model activations. As next steps we are planning to Test both predictions for SAE features: We have some evidence for the latter by Gurnee (2024) and Lindsey (2024). Are there different types of SAE features, atomic and composite features? Can we get a handle on the total number of features? If sensitivity-features line up with SAE features, can we find or improve SAE feature directions by finding local optima in sensitivity (similar to how Mack & Turner (2024) find steering vectors)? My motivation for this project is to get data on computation in superposition, and to get dataset-independent evidence for (SAE-)features. Core results & discussion I run two different experiments that test the error correction hypothesis: 1. Activation Plateaus: A real activation is the center of a plateau, in the sense that perturbing the activation affects the model output less than expected. Concretely: applying random-direction perturbations to an activation generated from a random openwebtext input ("real activation") has less effect than applying the same perturbations to a random activation (generated from a Normal distribution). This effect on the model can be measured in KL divergence of logits (shown below) but also L2 difference or cosine similarity of late-layer activations. 2. Sensitive directions: Perturbing a (real) activation into a direction towards another real activation ("poor man's feature directions") affects the model-outputs more than perturbing the same activation into a random direction. In the plot below focus on the size of the "plateau" in the left-hand side 1. Naive random direction vs mean & covariance-adjusted random: Naive isotropic random directions are much less sensitive. Thus we use mean & covariance-adjusted random activations everywhere else in this report. 2. The sensitive direction results are related to Gurnee (2024, SAE-replacement-error direction vs naive random direction) and Lindsey (2024, Anthropic April Updates, SAE-feature direction vs naive random direction). The theoretical explanation for activation plateaus & sensitive direction may be error correction (also referred to as noise suppression): NNs in superposition should expect small amounts of noise in feature activations due to interference. (The exact properties depend on how computation happens in superposition, this toy...

Welcome to The Nonlinear Library, where we use Text-to-Speech software to convert the best writing from the Rationalist and EA communities into audio. This is: [Interim research report] Activation plateaus & sensitive directions in GPT2, published by Stefan Heimersheim on July 5, 2024 on The AI Alignment Forum. This part-report / part-proposal describes ongoing research, but I'd like to share early results for feedback. I am especially interested in any comment finding mistakes or trivial explanations for these results. I will work on this proposal with a LASR Labs team over the next 3 months. If you are working (or want to work) on something similar I would love to chat! Experiments and write-up by Stefan, with substantial inspiration and advice from Jake (who doesn't necessarily endorse every sloppy statement I write). Work produced at Apollo Research. TL,DR: Toy models of how neural networks compute new features in superposition seem to imply that neural networks that utilize superposition require some form of error correction to avoid interference spiraling out of control. This means small variations along a feature direction shouldn't affect model outputs, which I can test: 1. Activation plateaus: Real activations should be resistant to small perturbations. There should be a "plateau" in the output as a function of perturbation size. 2. Sensitive directions: Perturbations towards the direction of a feature should change the model output earlier (at a lower perturbation size) than perturbations into a random direction. I find that both of these predictions hold; the latter when I operationalize "feature" as the difference between two real model activations. As next steps we are planning to Test both predictions for SAE features: We have some evidence for the latter by Gurnee (2024) and Lindsey (2024). Are there different types of SAE features, atomic and composite features? Can we get a handle on the total number of features? If sensitivity-features line up with SAE features, can we find or improve SAE feature directions by finding local optima in sensitivity (similar to how Mack & Turner (2024) find steering vectors)? My motivation for this project is to get data on computation in superposition, and to get dataset-independent evidence for (SAE-)features. Core results & discussion I run two different experiments that test the error correction hypothesis: 1. Activation Plateaus: A real activation is the center of a plateau, in the sense that perturbing the activation affects the model output less than expected. Concretely: applying random-direction perturbations to an activation generated from a random openwebtext input ("real activation") has less effect than applying the same perturbations to a random activation (generated from a Normal distribution). This effect on the model can be measured in KL divergence of logits (shown below) but also L2 difference or cosine similarity of late-layer activations. 2. Sensitive directions: Perturbing a (real) activation into a direction towards another real activation ("poor man's feature directions") affects the model-outputs more than perturbing the same activation into a random direction. In the plot below focus on the size of the "plateau" in the left-hand side 1. Naive random direction vs mean & covariance-adjusted random: Naive isotropic random directions are much less sensitive. Thus we use mean & covariance-adjusted random activations everywhere else in this report. 2. The sensitive direction results are related to Gurnee (2024, SAE-replacement-error direction vs naive random direction) and Lindsey (2024, Anthropic April Updates, SAE-feature direction vs naive random direction). The theoretical explanation for activation plateaus & sensitive direction may be error correction (also referred to as noise suppression): NNs in superposition should expect small amounts of noise in feature activations due to interference. (The exact properties depend on how computation happens in s...

Welcome to The Nonlinear Library, where we use Text-to-Speech software to convert the best writing from the Rationalist and EA communities into audio. This is: Corrigibility = Tool-ness?, published by johnswentworth on June 28, 2024 on LessWrong. Goal of This Post I have never seen anyone give a satisfying intuitive explanation of what corrigibility (in roughly Eliezer's sense of the word) is. There's lists of desiderata, but they sound like scattered wishlists which don't obviously point to a unified underlying concept at all. There's also Eliezer's extremely meta pointer: We can imagine, e.g., the AI imagining itself building a sub-AI while being prone to various sorts of errors, asking how it (the AI) would want the sub-AI to behave in those cases, and learning heuristics that would generalize well to how we would want the AI to behave if it suddenly gained a lot of capability or was considering deceiving its programmers and so on. … and that's basically it.[1] In this post, we're going to explain a reasonably-unified concept which seems like a decent match to "corrigibility" in Eliezer's sense. Tools Starting point: we think of a thing as corrigible exactly insofar as it is usefully thought-of as a tool. A screwdriver, for instance, is an excellent central example of a corrigible object. For AI alignment purposes, the challenge is to achieve corrigibility - i.e. tool-ness - in much more general, capable, and intelligent systems. … that all probably sounds like a rather nebulous and dubious claim, at this point. In order for it to make sense, we need to think through some key properties of "good tools", and also how various properties of incorrigibility make something a "bad tool". We broke off a separate post on what makes something usefully thought-of as a tool. Key ideas: Humans tend to solve problems by finding partial plans with "gaps" in them, where the "gaps" are subproblems which the human will figure out later. For instance, I might make a plan to decorate my apartment with some paintings, but leave a "gap" about how exactly to attach the paintings to the wall; I can sort that out later.[2] Sometimes many similar subproblems show up in my plans, forming a cluster.[3] For instance, there's a cluster (and many subclusters) of subproblems which involve attaching things together. Sometimes a thing (a physical object, a technique, whatever) makes it easy to solve a whole cluster of subproblems. That's what tools are. For instance, a screwdriver makes it easy to solve a whole subcluster of attaching-things-together subproblems. How does that add up to corrigibility? Respecting Modularity One key piece of the above picture is that the gaps/subproblems in humans' plans are typically modular - i.e. we expect to be able to solve each subproblem without significantly changing the "outer" partial plan, and without a lot of coupling between different subproblems. That's what makes the partial plan with all its subproblems useful in the first place: it factors the problem into loosely-coupled subproblems. Claim from the tools post: part of what it means for a tool to solve a subproblem-cluster is that the tool roughly preserves the modularity of that subproblem-cluster. That means the tool should not have a bunch of side effects which might mess with other subproblems, or mess up the outer partial plan. Furthermore, the tool needs to work for a whole subproblem-cluster, and that cluster includes similar subproblems which came up in the context of many different problems. So, the tool needs to robustly not have side effects which mess up the rest of the plan, across a wide range of possibilities for what "the rest of the plan" might be. Concretely: a screwdriver which sprays flames out the back when turned is a bad tool; it usually can't be used to solve most screw-turning subproblems when the bigger plan takes place in a wooden building. Another bad tool: a screwdriver which, when turned, also turns the lights on and off, cau...

Welcome to The Nonlinear Library, where we use Text-to-Speech software to convert the best writing from the Rationalist and EA communities into audio. This is: Access to powerful AI might make computer security radically easier, published by Buck Shlegeris on June 8, 2024 on The AI Alignment Forum. People talk about model weight security being really hard and crucial around the advent of AGI. (E.g. RAND report, Leopold; see here for some distinctions in these thread models that I think are important.) But I think that the thinking on this has not been sufficiently attentive to the fact that during that crucial time period, by assumption we'll have access to powerful AIs. I think that such access might make security wildly easier, by a bunch of different mechanisms, some of which I'll describe in this post. The story I'm telling here is pretty different from the main story I've heard people talk about in the past about AIs helping with computer security, which is that AIs can help with hardening software and other infrastructure. Though I agree that that seems really useful and important, here I'm not talking about AIs automating the process of security research, I'm talking about AI making it affordable to adopt policies that improve security in the organization but that wouldn't work (because they'd be too labor-intensive or lead to who-guards-the-guards problems) if you just had humans. In this post I'm going to be ignoring problems related to AIs themselves trying to cause security failures. There are two ways that such AIs affect this situation: The techniques I describe in this post could also be used, AI-control style, to use trusted models to reduce the risk of security failures caused by untrusted models. You can basically just replace "humans" by "untrusted models" everywhere in the text below. When applying these techniques to reduce risk from humans, you have to worry that these techniques will be sabotaged by scheming models. I'll ignore that possibility in this post, and talk about ways that you can improve security using AIs that are assumed not to be intentionally trying to subvert these security measures (which we call " trusted"). If you're worried about your models scheming, you'll have to do some other stuff too (mostly various sting operations), but I think the arguments in this post still mostly hold up. I'll talk about this more in future. I'm going to focus on insider threats here; most threats from outside humans involve first gaining access to impersonation of some insider. Much of this post came out of thinking about the question "how could AI control techniques be applied to humans, instead of AIs"? I don't think these arguments are an airtight case for "SL5 security will be trivial". But they do seem like a plausible hypothesis under which infosec will end up being quite simple to resolve, if labs decide it's an important priority. Four strategies for using powerful AI to improve security Monitoring As your humans take actions, you configure your system to have an AI that looks at these actions and then flags them for further review if they seem suspicious. If the actions seem potentially catastrophic, then the AI might even flag them before they can run. Concretely, I'm imagining that instead of being allowed to directly run any command you write in the bash console of the computer you're working on, whenever you write a command, it and the surrounding context are shown to an AI, which is charged with deciding how suspicious the action is. If it is very suspicious, the AI blocks the command and sends an alert to someone else at the company who is supposed to look it over. If the action is just somewhat suspicious, then it flags it for further review but does not block the action or alert the user. This is somewhat similar to using multi-party authorization. But with abundant AI labor, we could be more comprehensive, with fewer false positives and fewer false negatives, and evaluating a much grea...

Welcome to The Nonlinear Library, where we use Text-to-Speech software to convert the best writing from the Rationalist and EA communities into audio. This is: Response to Aschenbrenner's "Situational Awareness", published by Rob Bensinger on June 6, 2024 on LessWrong. (Cross-posted from Twitter.) My take on Leopold Aschenbrenner's new report: I think Leopold gets it right on a bunch of important counts. Three that I especially care about: 1. Full AGI and ASI soon. (I think his arguments for this have a lot of holes, but he gets the basic point that superintelligence looks 5 or 15 years off rather than 50+.) 2. This technology is an overwhelmingly huge deal, and if we play our cards wrong we're all dead. 3. Current developers are indeed fundamentally unserious about the core risks, and need to make IP security and closure a top priority. I especially appreciate that the report seems to get it when it comes to our basic strategic situation: it gets that we may only be a few years away from a truly world-threatening technology, and it speaks very candidly about the implications of this, rather than soft-pedaling it to the degree that public writings on this topic almost always do. I think that's a valuable contribution all on its own. Crucially, however, I think Leopold gets the wrong answer on the question "is alignment tractable?". That is: OK, we're on track to build vastly smarter-than-human AI systems in the next decade or two. How realistic is it to think that we can control such systems? Leopold acknowledges that we currently only have guesswork and half-baked ideas on the technical side, that this field is extremely young, that many aspects of the problem look impossibly difficult (see attached image), and that there's a strong chance of this research operation getting us all killed. "To be clear, given the stakes, I think 'muddling through' is in some sense a terrible plan. But it might be all we've got." Controllable superintelligent AI is a far more speculative idea at this point than superintelligent AI itself. I think this report is drastically mischaracterizing the situation. 'This is an awesome exciting technology, let's race to build it so we can reap the benefits and triumph over our enemies' is an appealing narrative, but it requires the facts on the ground to shake out very differently than how the field's trajectory currently looks. The more normal outcome, if the field continues as it has been, is: if anyone builds it, everyone dies. This is not a national security issue of the form 'exciting new tech that can give a country an economic or military advantage'; it's a national security issue of the form 'we've found a way to build a doomsday device, and as soon as anyone starts building it the clock is ticking on how long before they make a fatal error and take themselves out, and take the rest of the world out with them'. Someday superintelligence could indeed become more than a doomsday device, but that's the sort of thing that looks like a realistic prospect if ASI is 50 or 150 years away and we fundamentally know what we're doing on a technical level - not if it's more like 5 or 15 years away, as Leopold and I agree. The field is not ready, and it's not going to suddenly become ready tomorrow. We need urgent and decisive action, but to indefinitely globally halt progress toward this technology that threatens our lives and our children's lives, not to accelerate ourselves straight off a cliff. Concretely, the kinds of steps we need to see ASAP from the USG are: Spearhead an international alliance to prohibit the development of smarter-than-human AI until we're in a radically different position. The three top-cited scientists in AI (Hinton, Bengio, and Sutskever) and the three leading labs (Anthropic, OpenAI, and DeepMind) have all publicly stated that this technology's trajectory poses a serious risk of causing human extinction (in the CAIS statement). It is absurd on its face to let any private company...

Welcome to The Nonlinear Library, where we use Text-to-Speech software to convert the best writing from the Rationalist and EA communities into audio. This is: Transformers Represent Belief State Geometry in their Residual Stream, published by Adam Shai on April 16, 2024 on The AI Alignment Forum. Produced while being an affiliate at PIBBSS[1]. The work was done initially with funding from a Lightspeed Grant, and then continued while at PIBBSS. Work done in collaboration with @Paul Riechers, @Lucas Teixeira, @Alexander Gietelink Oldenziel, and Sarah Marzen. Paul was a MATS scholar during some portion of this work. Thanks to Paul, Lucas, Alexander, Sarah, and @Guillaume Corlouer for suggestions on this writeup. Introduction What computational structure are we building into LLMs when we train them on next-token prediction? In this post we present evidence that this structure is given by the meta-dynamics of belief updating over hidden states of the data-generating process. We'll explain exactly what this means in the post. We are excited by these results because We have a formalism that relates training data to internal structures in LLMs. Conceptually, our results mean that LLMs synchronize to their internal world model as they move through the context window. The computation associated with synchronization can be formalized with a framework called Computational Mechanics. In the parlance of Computational Mechanics, we say that LLMs represent the Mixed-State Presentation of the data generating process. The structure of synchronization is, in general, richer than the world model itself. In this sense, LLMs learn more than a world model. We have increased hope that Computational Mechanics can be leveraged for interpretability and AI Safety more generally. There's just something inherently cool about making a non-trivial prediction - in this case that the transformer will represent a specific fractal structure - and then verifying that the prediction is true. Concretely, we are able to use Computational Mechanics to make an a priori and specific theoretical prediction about the geometry of residual stream activations (below on the left), and then show that this prediction holds true empirically (below on the right). Theoretical Framework In this post we will operationalize training data as being generated by a Hidden Markov Model (HMM)[2]. An HMM has a set of hidden states and transitions between them. The transitions are labeled with a probability and a token that it emits. Here are some example HMMs and data they generate. Consider the relation a transformer has to an HMM that produced the data it was trained on. This is general - any dataset consisting of sequences of tokens can be represented as having been generated from an HMM. Through the discussion of the theoretical framework, let's assume a simple HMM with the following structure, which we will call the Z1R process[3] (for "zero one random"). The Z1R process has 3 hidden states, S0,S1, and SR. Arrows of the form Sxa:p%Sy denote P(Sy,a|Sx)=p%, that the probability of moving to state Sy and emitting the token a, given that the process is in state Sx, is p%. In this way, taking transitions between the states stochastically generates binary strings of the form ...01R01R... where R is a random 50/50 sample from { 0, 1}. The HMM structure is not directly given by the data it produces. Think of the difference between the list of strings this HMM emits (along with their probabilities) and the hidden structure itself[4]. Since the transformer only has access to the strings of emissions from this HMM, and not any information about the hidden states directly, if the transformer learns anything to do with the hidden structure, then it has to do the work of inferring it from the training data. What we will show is that when they predict the next token well, transformers are doing even more computational work than inferring the hidden data generating process! Do Transformers Learn ...

Welcome to The Nonlinear Library, where we use Text-to-Speech software to convert the best writing from the Rationalist and EA communities into audio. This is: Transformers Represent Belief State Geometry in their Residual Stream, published by Adam Shai on April 16, 2024 on LessWrong. Produced while being an affiliate at PIBBSS[1]. The work was done initially with funding from a Lightspeed Grant, and then continued while at PIBBSS. Work done in collaboration with @Paul Riechers, @Lucas Teixeira, @Alexander Gietelink Oldenziel, and Sarah Marzen. Paul was a MATS scholar during some portion of this work. Thanks to Paul, Lucas, Alexander, and @Guillaume Corlouer for suggestions on this writeup. Introduction What computational structure are we building into LLMs when we train them on next-token prediction? In this post we present evidence that this structure is given by the meta-dynamics of belief updating over hidden states of the data-generating process. We'll explain exactly what this means in the post. We are excited by these results because We have a formalism that relates training data to internal structures in LLMs. Conceptually, our results mean that LLMs synchronize to their internal world model as they move through the context window. The computation associated with synchronization can be formalized with a framework called Computational Mechanics. In the parlance of Computational Mechanics, we say that LLMs represent the Mixed-State Presentation of the data generating process. The structure of synchronization is, in general, richer than the world model itself. In this sense, LLMs learn more than a world model. We have increased hope that Computational Mechanics can be leveraged for interpretability and AI Safety more generally. There's just something inherently cool about making a non-trivial prediction - in this case that the transformer will represent a specific fractal structure - and then verifying that the prediction is true. Concretely, we are able to use Computational Mechanics to make an a priori and specific theoretical prediction about the geometry of residual stream activations (below on the left), and then show that this prediction holds true empirically (below on the right). Theoretical Framework In this post we will operationalize training data as being generated by a Hidden Markov Model (HMM)[2]. An HMM has a set of hidden states and transitions between them. The transitions are labeled with a probability and a token that it emits. Here are some example HMMs and data they generate. Consider the relation a transformer has to an HMM that produced the data it was trained on. This is general - any dataset consisting of sequences of tokens can be represented as having been generated from an HMM. Through the discussion of the theoretical framework, let's assume a simple HMM with the following structure, which we will call the Z1R process[3] (for "zero one random"). The Z1R process has 3 hidden states, S0,S1, and SR. Arrows of the form Sxa:p%Sy denote P(Sy,a|Sx)=p%, that the probability of moving to state Sy and emitting the token a, given that the process is in state Sx, is p%. In this way, taking transitions between the states stochastically generates binary strings of the form ...01R01R... where R is a random 50/50 sample from { 0, 1}. The HMM structure is not directly given by the data it produces. Think of the difference between the list of strings this HMM emits (along with their probabilities) and the hidden structure itself[4]. Since the transformer only has access to the strings of emissions from this HMM, and not any information about the hidden states directly, if the transformer learns anything to do with the hidden structure, then it has to do the work of inferring it from the training data. What we will show is that when they predict the next token well, transformers are doing even more computational work than inferring the hidden data generating process! Do Transformers Learn a Model of the World...

Welcome to The Nonlinear Library, where we use Text-to-Speech software to convert the best writing from the Rationalist and EA communities into audio. This is: The Defence production act and AI policy, published by NathanBarnard on March 2, 2024 on LessWrong. Quick Summary Gives the President wide-ranging powers to strengthen the US industrial base Has been around without changing that much since 1953 Has provisions which allow firms to make voluntary agreements that would normally be illegal under antitrust law Provided the legal authority for many of the provisions in Biden's recent Executive Order on AI The Defence Production Act The Defence Production Act (DPA) has been reauthorised (and modified) by Congress since 1950, and in 1953 its powers very significantly reduced. I'm confident that it will continue to be passed - in a roughly similar form - for the foreseeable future. The current version was passed in 2019 under a Republican senate and is due for reauthorisation in 2025. Since the Obama Presidency, there's Republicans have begun to try to prevent bills proposed by Democrats from being passed by default. This is particularly easy for non-spending bills since for non-spending bills, 60 votes are needed to break the filibuster - a method used to prevent bills from being voted on - in the Senate. However, not only are defence bills consistently bipartisan, they have consistently high degrees of support from Republicans in particular. Therefore, I'm not concerned about the DPA not being passed by a Republican senate and a Democratic President when it's next due for reauthorisation. The DPA gives the President very wide-ranging powers since the goal of the act of the act is to ensure that the US industrial base is strong enough to fight and win any war the US might need to undertake. Concretely, this allows the President to instruct firms to accept contracts; incentive expansion of the industrial base; and a grab bag of other specific provisions aimed at making sure that the US production base is strong enough to win a war. Until 1953 the act was much more powerful and essentially allowed the President to take control of the US economy. The act now doesn't give the President authority to set wage or price ceilings, control consumer credit or requisition stuff. Antitrust provisions Various AI governance proposals rely on explicit, voluntary agreements between leading AI labs. For instance, this paper proposes a scheme in which AI firms agree to pause the rollout out and training of large models if one doesn't pass an evaluation which indicates that it could act dangerously. I think it's plausible that this agreement would be illegal under antitrust law. An agreement like this would be an explicit agreement amongst a small number of leading firms to limit supply. This skirts pretty close to being a criminal violation of US antitrust law. Under this law, various forms of agreements between firms to fix prices are considered illigal no matter what firms say is the justification for them (this is known as per se illegal.) Agreements to limit production are considered just as illegal. It's not at all clear that such agreements would be illegal - for instance, professional standards are not considered per se illegal but instead are judged under a rule of reason where their competitive effects need to be outweighed by their procompetitive effects. I won't comment on this further but instead, refer the reader to this excellent by that looks specifically at anti-trust and AI industry self-regulation. Section 708 of the DPA gives the President authority to allow firms to make agreements for that would normally be considered antitrust violations. Recently, this provision was used by the Trump administration during Covid-19. Use in the Biden executive order Some of the most AI safety-relevant elements of the recent Biden executive order on AI were authorised under the legal authority of the DPA. This includes: Requiring AI firms t...

Welcome to The Nonlinear Library, where we use Text-to-Speech software to convert the best writing from the Rationalist and EA communities into audio. This is: Introducing Alignment Stress-Testing at Anthropic, published by Evan Hubinger on January 12, 2024 on The AI Alignment Forum. Following on from our recent paper, "Sleeper Agents: Training Deceptive LLMs that Persist Through Safety Training", I'm very excited to announce that I have started leading a new team at Anthropic, the Alignment Stress-Testing team, with Carson Denison and Monte MacDiarmid as current team members. Our mission - and our mandate from the organization - is to red-team Anthropic's alignment techniques and evaluations, empirically demonstrating ways in which Anthropic's alignment strategies could fail. The easiest way to get a sense of what we'll be working on is probably just to check out our "Sleeper Agents" paper, which was our first big research project. I'd also recommend Buck and Ryan's post on meta-level adversarial evaluation as a good general description of our team's scope. Very simply, our job is to try to prove to Anthropic - and the world more broadly - (if it is in fact true) that we are in a pessimistic scenario, that Anthropic's alignment plans and strategies won't work, and that we will need to substantially shift gears. And if we don't find anything extremely dangerous despite a serious and skeptical effort, that is some reassurance, but of course not a guarantee of safety. Notably, our goal is not object-level red-teaming or evaluation - e.g. we won't be the ones running Anthropic's RSP-mandated evaluations to determine when Anthropic should pause or otherwise trigger concrete safety commitments. Rather, our goal is to stress-test that entire process: to red-team whether our evaluations and commitments will actually be sufficient to deal with the risks at hand. We expect much of the stress-testing that we do to be very valuable in terms of producing concrete model organisms of misalignment that we can iterate on to improve our alignment techniques. However, we want to be cognizant of the risk of overfitting, and it'll be our responsibility to determine when it is safe to iterate on improving the ability of our alignment techniques to resolve particular model organisms of misalignment that we produce. In the case of our "Sleeper Agents" paper, for example, we think the benefits outweigh the downsides to directly iterating on improving the ability of our alignment techniques to address those specific model organisms, but we'd likely want to hold out other, more natural model organisms of deceptive alignment so as to provide a strong test case. Some of the projects that we're planning on working on next include: Concretely stress-testing Anthropic's ASL-3 evaluations. Applying techniques from "Towards Monosemanticity: Decomposing Language Models With Dictionary Learning" to our "Sleeper Agents" models. Building more natural model organisms of misalignment, e.g. finding a training pipeline that we might realistically use that we can show would lead to a concrete misalignment failure. If any of this sounds interesting to you, I am very much hiring! We are primarily looking for Research Engineers with strong backgrounds in machine learning engineering work. Thanks for listening. To help us out with The Nonlinear Library or to learn more, please visit nonlinear.org.

Welcome to The Nonlinear Library, where we use Text-to-Speech software to convert the best writing from the Rationalist and EA communities into audio. This is: What's up with LLMs representing XORs of arbitrary features?, published by Sam Marks on January 3, 2024 on The AI Alignment Forum. Thanks to Clément Dumas, Nikola Jurković, Nora Belrose, Arthur Conmy, and Oam Patel for feedback. In the comments of the post on Google Deepmind's CCS challenges paper, I expressed skepticism that some of the experimental results seemed possible. When addressing my concerns, Rohin Shah made some claims along the lines of "If an LLM linearly represents features a and b, then it will also linearly represent their XOR, ab, and this is true even in settings where there's no obvious reason the model would need to make use of the feature ab."[1] For reasons that I'll explain below, I thought this claim was absolutely bonkers, both in general and in the specific setting that the GDM paper was working in. So I ran some experiments to prove Rohin wrong. The result: Rohin was right and I was wrong. LLMs seem to compute and linearly represent XORs of features even when there's no obvious reason to do so. I think this is deeply weird and surprising. If something like this holds generally, I think this has importance far beyond the original question of "Is CCS useful?" In the rest of this post I'll: Articulate a claim I'll call "representation of arbitrary XORs (RAX)": LLMs compute and linearly represent XORs of arbitrary features, even when there's no reason to do so. Explain why it would be shocking if RAX is true. For example, without additional assumptions, RAX implies that linear probes should utterly fail to generalize across distributional shift, no matter how minor the distributional shift. (Empirically, linear probes often do generalize decently.) Present experiments showing that RAX seems to be true in every case that I've checked. Think through what RAX would mean for AI safety research: overall, probably a bad sign for interpretability work in general, and work that relies on using simple probes of model internals (e.g. ELK probes or coup probes) in particular. Make some guesses about what's really going on here. Overall, this has left me very confused: I've found myself simultaneously having (a) an argument that AB, (b) empirical evidence of A, and (c) empirical evidence of B. (Here A = RAX and B = other facts about LLM representations.) The RAX claim: LLMs linearly represent XORs of arbitrary features, even when there's no reason to do so To keep things simple, throughout this post, I'll say that a model linearly represents a binary feature f if there is a linear probe out of the model's latent space which is accurate for classifying f; in this case, I'll denote the corresponding direction as vf. This is not how I would typically use the terminology "linearly represents" - normally I would reserve the term for a stronger notion which, at minimum, requires the model to actually make use of the feature direction when performing cognition involving the feature[2]. But I'll intentionally abuse the terminology here because I don't think this distinction matters much for what I'll discuss. If a model linearly represents features a and b, then it automatically linearly represents ab and ab. However, ab is not automatically linearly represented - no linear probe in the figure above would be accurate for classifying ab. Thus, if the model wants to make use of the feature ab, then it needs to do something additional: allocate another direction[3] (more model capacity) to representing ab, and also perform the computation of ab so that it knows what value to store along this new direction. The representation of arbitrary XORs (RAX) claim, in its strongest form, asserts that whenever a LLM linearly represents features a and b, it will also linearly represent ab. Concretely, this might look something like: in layer 5, the model computes and linearly r...

Guillermo Rauch, Founder and CEO of Vercel, joins Corey on Screaming in the Cloud to discuss how he decided to focus on building a front-end tool that is fast, reliable, and focuses on the developer experience. Guillermo explains how he discovered that Javascript was the language that set online offerings apart, and also reveals the advice he gives to founders on how to build an effective landing page. Corey and Guillermo discuss the effects of generative AI on developer experience, and Guillermo explains why Vercel had a higher standard for accuracy when rolling out their new AI product for developers, v0. About GuillermoGuillermo Rauch is Founder and CEO of Vercel, where he leads the company's mission to enable developers to create at the moment of inspiration. Prior to founding Vercel, Guillermo co-founded LearnBoost and Cloudup where he served the company as CTO through its acquisition by Automattic in 2013. Originally from Argentina,Guillermo has been a developer since the age of ten and is passionate about contributing to the open source community. He has created a number of JavaScript projects including socket.io, Mongoose.js, Now, and Next.js.Links Referenced: Vercel: https://vercel.com/ v0.dev: https://v0.dev Personal website: https://rauchg.com Personal twitter: https://twitter.com/rauchg TranscriptAnnouncer: Hello, and welcome to Screaming in the Cloud with your host, Chief Cloud Economist at The Duckbill Group, Corey Quinn. This weekly show features conversations with people doing interesting work in the world of cloud, thoughtful commentary on the state of the technical world, and ridiculous titles for which Corey refuses to apologize. This is Screaming in the Cloud.Corey: Welcome to Screaming in the Cloud. I'm Corey Quinn. I don't talk a lot about front-end on this show, primarily because I am very bad at front-end, and in long-standing tech tradition, if I'm not good at something, apparently I'm legally obligated to be dismissive of it and not give it any attention. Strangely enough, I spent the last week beating on some front-end projects, and now I'm not just dismissive, I'm angry about it. Here to basically suffer the outpouring of frustration and confusion is Guillermo Rauch, founder and CEO of Vercel, but also the creator of Next.js. Guillermo, thank you for joining me.Guillermo: Great to be here. Thanks for setting me up with that awesome intro.Corey: It's true, if I were talking to someone who looked at what I've done, and for some Godforsaken reason, they wanted to follow in my footsteps, well, that path has been closed, so learning a bunch of Perl early on and translating it all to bad bash scripts and the rest, and then maybe picking up Python isn't really the way that I would advise someone getting started today. The de facto lingua franca of the internet is JavaScript, whether we like it or not, and I would strongly suggest that be someone's first language despite the fact that I'm bad at it, I don't understand it, and therefore it makes me angry.Guillermo: Yeah, it's so funny because it sounds like my story. And my personal journey was, when I was a kid, I had a—I knew I wanted to hack around with computers, and reverse engineer them, and improve them, and just create my own things, and I had these options for what programming language I could go with. And I tried it all: PHP, Perl, [Mod PHP 00:02:12], [Mod Perl 00:02:13], Apache, LAMP, cgi-bin folders, all the whole nine yards. And regardless of what back-end technology I used, I encountered this striking fact, which was… the thing that can make your product really stand out in a web browser is typically involving JavaScript in some fashion. So, when Google came out with suggestions as you would type in a search box, my young kid Argentinian brain blew up. I was like, “Holy crap, they can suggest, they can read my mind, and they can render suggestions without a full page refresh? What is that magic?”And then more products like that came out. Google Docs, Gmail Chat, Facebook's real time newsfeed, all the great things on the internet seemed to have this common point of, there's this layer of interactivity, real-time data, customization, personalization, and it seems uniquely enabled by the front-end. So, I just went all in. I taught myself how to code, I taught myself—I became a front-end engineering expert, I joined some of the early projects that shaped the ecosystem. Like, there was this library called MooTools, and a lot of folks might not have heard that name. It's in the annals of JavaScript history.And later on, you know, what I realized is, what if front-end can actually be the starting point of how you develop the best applications, right, rather than this thing that people, like, reluctantly frown upon, like yourself. I mention that as an opportunity rather than a diss because when you create a great front-end experience, now the data has proven you run a better business, you run a more dynamic business, probably are running an AI-powered business, like, all of the AI products on the planet today are using this technology to stream text in front of your eyes in real time and do all this awesome things. So yeah, I became obsessed with front-end, and I founded this company Vercel, which is a front-end cloud. So, you come here to basically build the best products. Now, you don't have to build the back-end, so you can use back-ends that are off the shelf, you can connect to your existing back-ends, and we piggyback on the world's best infrastructure to make this possible, but we offer developers a very streamlined path to create these awesome products on the internet.Corey: I have to say that I have been impressed when I've used Vercel for a number of projects. And what impresses me is less the infrastructure powering it, less the look at how performance it is and all the stuff that most people talk about, but as mentioned, I am not good at front-end or frankly programming at all. And so, many products in this space fall into the very pernicious trap of, “Oh, well, everyone who's using this is at least this tall on the board of how smart you are to get on the amusement park ride.” So, I feel that when I'm coming at this from a—someone who is not a stranger to computers but is definitely new to this entire ecosystem, everything just made sense in a way that remarkably few products can pull off. I don't know if you would call that user experience, developer experience, or what the terminology you bias for there is, but it is a transformational difference.Guillermo: Thank you. I think it's a combination of things. So, developer experience has definitely always been a focus for me. I was that weird person that obsessed about the CLI parameters of the tool, and the output of the tool, and just like how it feels for the engineer. I did combine that with—and I think this is where Vercel really stands out—I did combine that with a world-class infrastructure bit because what I realized after creating lots and lots of very popular open-source projects—like, one is called Socket.io, and other one called Mongoose—DX, or developer experience, in the absence of an enticing carrot for the business doesn't work. Maybe it has some short term adoption, maybe it has raving fans on Twitter or X [laugh], but at the end of the day, you have to deliver something that's tangible to the end-user and to the business.So I think Vercel focusing on the front-end has found a magical combination there of I can make the developer lives easier. Being a developer myself, I just tremendously empathize with that, but it can also make more profit for the business. When they make your website faster and render more dynamic data that serve as better recommendations for a product on e-commerce or in a marketing channel, I can help you roll out more experiments, then they make your business better, and I think that's one of the magical combinations there.The other thing, frankly, is that we'd started doing fewer things. So, when you come to Vercel, you typically come with a framework that you've already chosen. It could be Next.js, it could be View, [unintelligible 00:07:18], there's 35-plus frameworks. But we basically told the market, you have to use one of these developer tools in order to guide your development.And what companies were doing before—I mean, this almost seems obvious in retrospect, that we would optimize for her certain patterns and certain tools—but what the market was doing before was rolling out your own frameworks. Like, every company was, basically—React, for example, is a very popular way of building interfaces, and our framework actually is built on top of React. But when I would go to and talk to all these principal engineers that all these companies, they were saying, oh yeah, “We're creating our own framework. We're creating our own tools.” And I think that to me now feels almost like a zero interest rate phenomenon. Like, what business do you have in creating frameworks, tools, and bespoke infra when you're really in the business of creating delightful experiences for your customers?Corey: What I think is lost on a lot of folks is that if you are trying to learn something new and use a tool, and the developer experience is bad, the takeaway—at least for me and a lot of people that I talk to is not, “Oh, this tool has terrible ergonomics. That's it's problem.” Instead, the takeaway is very much, “Oh, I'm dumb because I don't understand this thing.” And I know intellectually that I am not usually the dumbest person in the world when it comes to a particular tool or technology, but I keep forgetting that on a visceral level. It's, “I just wish I was smart enough to understand that.”No, I don't. I wish it was presented in a way that was more understandable and the documentation was better. When you're just starting out and building something in your spare time, the infrastructure cost is basically nothing, but your time is the expensive part in it. So, if you have to spend three hours to track down something just because it wasn't clearly explained, the burden of adopting that tool is challenging. I would argue that one of the reasons that AWS sees some of the success that it does is not necessarily because it's great so much as because everyone knows how it breaks. That's important.I'm not saying their infrastructure isn't world-class—please, don't come at me in the comments on this one—but I am saying that we know where its sharp edges are, and that means that we're more comfortable building with it. But the idea of learning a brand-new cloud with different sharp edges in other areas. That's terrifying. I'd rather stick with the devil I know.Guillermo: Exactly. I just think that you're not going to be able to make a difference for customers in 2023 by creating another bespoke cloud that is general purpose, it doesn't really optimize around anything, and you have to learn all the sharp edges from scratch. I think we saw that with the rise of cloud-native companies like Stripe and Twilio where they were going after these amazingly huge markets like financial infrastructure or communications infrastructure, but the angle was, “Here's this awesome developer experience.” And that's what we're doing with Vercel for the front-end and for building products, right? There has to be an opinionated developer experience that guides you to success.And I agree with you that there's really, these days in the developer communities, zero tolerance for sharp edges, and we've spent a lot of time in—even documentation, like, it used to be that your startup would make or break it by whether you had great documentation. I think in the age of frameworks, I would even dare say that documentation, of course, is extremely important, but if I can have the tool itself guide you to success, at that point, you're not even reading documentation. We're now seeing this with AI and, like, generative AI for code. At Vercel, we're investing in generative AI for user interfaces. Do you actually need to read documentation at that point? So, I think we're optimizing for the absolute minimal amount of friction required to be successful with these platforms.Corey: I think that there's a truth in that of meeting customers in where they are. Your documentation can be spectacular, but people don't generally read the encyclopedia for fun either. And the idea of that is that—at least ideally—I should not need to go diving into the documentation, and so many tools get this wrong, where, “Oh, I want to set up a new project,” and it bombards you with 50 questions, and each one of these feels pretty… momentous. Like, what one-way door am I passing through that I don't realize the consequences of until I'm 12 hours into this thing, and then have to backtrack significantly. I like, personally, things that bias for having a golden path, but also make it easy to both deviate from it, as well as get back onto it. Because there's more than one way to do it is sort of the old Perl motto. That is true in spades in anything approaching the JavaScript universe.Guillermo: Yeah. I have a lot of thoughts on that. On the first point, I completely agree that the golden path of the product cannot be documentation-mediated. One of the things that I've become obsessive about—and this is an advice that I share with a lot of other startup founders is, when it comes to your landing page, the primary call to action has to be this golden path to success, like, 2, 3, 4 clicks later, I have to have something tangible. That was our inspiration.And when we made it the primary call to action for Vercel is deploy now. Start now. Get it out now. Ship it now. And the way that you test out the platform is by deploying a template. What do we do is we create a Git repo for you, it sets up the entire CI/CD pipeline, and then at that point, you already have something working, something in the cloud, you spent zero time reading documentation, and you can start iterating.And even though that might not be the final thing you do in Vercel, I always hear the stories of CTOs that are now deploying Vercel at really large scale, and they always tell me, “I started with your hobby tier, I started with free tier, I deployed a template, I hacked on a product during the weekend.” Now, a lot of our AI examples are very popular in this crowd. And yeah, there's a golden path that requires zero documentation. Now, you also mentioned that, what about complexity? This is an enterprise-grade platform. What about escape hatches? What about flexibility? And that's where our platform also shines because we have the entire power of a Turing-complete language, which is JavaScript and TypeScript, to customize every aspect of the platform.And you have a framework that actually answered a lot of the problems that came with serverless solutions in the past, which is that you couldn't run any of that on your local machine. The beauty of Vercel and Next.js is we kind of pioneered this concept that we called ‘Framework Defined Infrastructure.' You start with the framework, the framework has this awesome property that you can install on your computer, it has a dev command—like, it literally runs on your computer—but then when you push it to the cloud, it now defines the infrastructure. It creates all of the resources that are highly optimal.This creates—basically converts what was a single node system on your computer to a globally distributed system, which is a very complex and difficult engineering challenge, but Vercel completely automates it away. And now for folks that are looking for, like, more advanced solutions, they can now start poking into the outputs of that compilation process and say, “Okay, I can now have an influence or I can reconfigure some aspects of this pipeline.” And of course, if you don't think about those escape hatches, then the product just ends up being limiting and frustrating, so we had to think really hard about meeting both ends of the spectrum.Corey: In my own experimentations early on with Chat-Gippity—which is how I insist on pronouncing ChatGPT—a lot of what I found was that it was a lot more productive for me to, instead of asking just the question and getting the answer was, write a Python script to—Guillermo: Yes.Corey: Query this API to get me that answer. Because often it would be wrong. Sometimes very convincingly wrong, and I can at least examine it in various ways and make changes to it and iterate forward, whereas when everything is just a black box, that gets very hard to do. The idea of building something that can be iterated on is key.Guillermo: I love that. The way that Vercel actually first introduced itself to the world was this idea of immutable deployments and immutable infrastructure. And immutable sounds like a horrible word because I want to mutate things, but it was inspired by this idea of functional programming where, like, each iteration to the state, each data change, can be tracked. So, every time you deploy in Vercel, you get this unique URL that represents a point-in-time infrastructure deployment. You can go back in time, you can revert, you can use this as a way of collaborating with other engineers in your team, so you can send these hyperlinks around to your front-end projects.And it gives you a lot of confidence. Now, you can iterate knowing that before things go out, there's a lot of scrutiny, there's a lot of QA, there's a lot of testing processes that you can kick off against this serverless infrastructure that was created for each deployment. The conclusion for us so far has been that our role in the world is to increase iteration velocity. So, iteration speed is the faster horse of the cloud, right? Like, instead of getting a car, you get a faster horse.When you say, “Okay, I made the build pipeline 10% faster,” or, “I brought the TLS termination 10% closer to the visitor, and, like, I have more [pops 00:17:10],” things like that. That to me, is the speed. You can do those things, and they're awesome, but if you don't have a direction—which is velocity—then you don't know what you're building next. You don't know if your customers are happy. You don't know if you're delivering value. So, we built an entire platform that optimizes around, what should you ship next? What is the friction involved in getting your next iteration out? Is launching an experiment on your homepage, for example, is that a costly endeavor? Does it take you weeks? Does it take you months?One of the initial inspirations for just starting Vercel and making deployments really easy was, how difficult is it for the average company to change in their footer of their website is this copyright 2022? And you have—it's a new year. You have to bump it to copyright 2023. How long do you think it takes that engineer to, A, run the stack locally, so they can actually see the change; deploy it, but deploy to what we call the preview environment, so they can grab that URL and send [it to 00:18:15], Corey, and say, “Corey, does it look good? I updated [laugh] I updated the year in the footer.”And then you tell me, “Looks good, let's ship it to production.” Or you tell me, “No, no, no, it's risky. Let's divide it into two cohorts: 50% of traffic gets 2022, 50% of traffic gets 2023.” Obviously, this is a joke, but consider the implications of how difficult it is and the average organization to actually do this thing.Corey: Oh, I find things like that all the time, especially on microservices that I built to handle some of my internal workflows here, and I haven't touched in two or three years. And okay, now it's time for me to update them to reflect some minor change. And first, I wind up in the screaming node warnings and I have to update things so that they actually work in a reasonable way. And, on some level, making a one-line change can take half a day. Now, in the real world, when people are working on these apps day-in and day-out, it gets a lot easier to roll those changes in over time, but coming back to something unmaintained, that becomes a project the longer you let it sit.Part of me wishes that there were easier ways around it, but there are trade-offs in almost any decision you make. If you're building something from the beginning of, well, I want to be able to automatically update the copyright year, you can even borderline make that something that automatically happens based upon the global time, whereas when you're trying to retrofit it afterwards, yeah, it becomes a project.Guillermo: Yeah, and now think, that's just a simple example of changing a string. That might be difficult for a product engineering in any organization. Or it may be slow, or it may be not as streamlined, or maybe it works really well for the first project that that company created. What about every incremental project thereafter?So, now I said—let's stop talking about a string, right? Let's think about you're about an e-commerce website where what we hear from our customers on average, like, 10% of revenue flows through the homepage. Now, I have to change a primary component that renders on the hero of the page, and I have to collaborate with every department in the organization. I have to collaborate with the design team, I have to collaborate with marketing, I have to collaborate with the business owners to track the analytics appropriately. So, what is the cost of every incremental experiment that you want to put in production?The other thing that's particularly interesting about front-end as it relates to cloud infrastructure is, scaling up front-end is a very difficult thing. What ends up happening is most front-ends are actually static websites. They're cached at the edge—or they're literally statically generated—and then they push all of the dynamism to the client side. So, you end up with this spaghetti of script tags on the client, you end up accumulating a lot of tech debt in the [shipping 00:20:56] huge bundles of JavaScript to the client to try to recover some dynamism, to try and run these experiments. So, everyone is in this, kind of, mess of the yes, maybe we can experiment, but we kind of offloaded the rendering work to the client. That in turn makes me—basically, I'm making the website slower for the visitor. I'm making them do the rendering work.And I'm trying to sell them something. I'm trying to speed up some processes. It's my responsibility to make it fast. So, what we ended up finding out is that yes, the cloud moved this forward a lot in terms of having these awesome building blocks, these awesome infrastructure primitives, but both in the developer experience, just changing something about your web product and also the end-user experiences, that web product renders really fast, those things really didn't happen with this first chapter of the cloud. And I think we're entering a new generation of higher-level clouds like Vercel that are optimizing for these things.Corey: I think that there's a historical focus on things that have not happened before. And that was painful and terrible, so we're not going to be focusing on what's happening in the future, we're going to build a process or a framework or something that winds up preventing that thing that hurt us from hurting us again. Now, that's great in moderation, but at some point—we see this at large companies from time-to-time—where you have so much process that is ossified scar tissue, basically, that it becomes almost impossible to get things done. Because oh, I want to make that—for example, that one-line change to a copyright date, well, here's the 5000 ways deploys have screwed us before, so we need to have three humans sign off on the one-line change, and a bunch of other stuff before it ever sees the light of day. Now, I'm exaggerating slightly, just as you are, but that feels like it acts as a serious brake on innovation.On the exact opposite side, where we see massive acceleration has been around the world of generative AI. Yes, it is massively hyped in a bunch of ways. I don't think it is going to be a defined way that changes the nature of humanity the way that some of these people are going after, but it's also clearly more than a parlor trick.Guillermo: I'm kind of in that camp. So, like you, I've been writing code for many years. I'm pretty astonished by the AI's ability to enhance my output. And of course, now I'm not writing code full time, so there is a sense of, okay because I don't have time, because I'm doing a million things, any minute I have seems like AI has just made it so much more worthwhile and I can squeeze so much more productivity out of it. But one of the areas that I'm really excited about is this idea of generative UI, which is not just autocompleting code in a text editor, but is the idea that you can use natural language to describe an interface and have the AI generate that interface.So, Vercel created this product called v0—you can check it out at v0.dev—where to me, it's really astonishing that you can get these incredibly high quality user interfaces, and basically all you have to do is input [laugh] a few English words. I have this personal experience of, I've been learning JavaScript and perfecting all my knowledge around it for, like, 20 or so years. I created Next.js.And Next.js itself powers a lot of these AI products. Like the front-end of ChatGPT is built on Next.js. And I used v0 to create… to basically recreate my blog. Like, I created rauchg.com, I deployed it on Vercel, but every pixel of that UI, I handcrafted.And as we were working on v0, I said okay, “I'm going to challenge myself to put myself back in the shoes of, like, I'm going to redesign this and I'm going to start over with just human language.” Not only did I arrive to the right look and feel of what I wanted to get, the code that it produced was better than I would have written by hand. Concretely, it was more accessible. So, there were areas of the UI where, like, some icons were rendered where I had not filled in those gaps. I just didn't know how to do that. The AI did. So, I really believe that AI will transform our lives as [laugh] programmers, at least I think, in many other areas in very profound ways.Corey: This is very similar to a project that I've been embarked on for the last few days where I described the app I wanted into Chat-Gippity and follow the instructions, and first, it round up point—sending me down a rabbit hole of the wrong Framework version that had been deprecated, and whatnot, and then I brought it all into VS Code where Jif-Ub Copilot, it kept switching back and forth between actively helpful, and ooh, the response matches publicly available code, so I'm not going to tell you the answer, despite the fact that feature has never been enabled on my account. So yeah, of course, it matches publicly available code. This is quite literally the React tutorial starter project. And it became incredibly frustrating, but it also would keep generating things in bursts, so my code is not at all legible or well organized or consistent for that matter. But it's still better than anything I'd be able to write myself. I'm looking forward to using v0 or something like it to see how that stacks up for some of my ridiculous generation ideas for these things.Guillermo: Yeah, you touched on a very important point is, the code has to work. The code has to be shippable. I think a lot of AI products have gotten by by giving you an approximation of the result, right? Like, they hallucinate sometimes, they get something wrong. It's still very helpful because sometimes it's sending you the right direction.But for us, the bar is that these things have to produce code that's useful, and that you can ship, and that you can iterate on. So, going back to that idea of iteration velocity, we call it v0 because we wanted it to be the first version. We still very much believe there is humans in the loop and folks will be iterating a lot on the initial draft that this thing is giving you, but it's so much better than starting with an empty code editor, [laugh] right? Like, and this applies, by the way to, like, not just new projects, but I always talk about, like, our customers have a few really important landing pages, key pages, maybe it's the product detail page in e-commerce, maybe it's your homepage and, like, your key product pages for a marketing website. Maybe it's where—and the checkout, for example, extremely important.But then there's a lot of incremental UIs that you have to add every single day. The banner for [laugh] accepting cookies or not, the consent management dialog. There's a lot of things that the worst case scenario is that you offload them again to some third-party script, to some iframe of sorts because you really don't have the bandwidth, time, or resources to build it yourself. And then you sacrifice speed, you sacrifice brand fidelity. And again because we're the front-end cloud, we're obsessed with your ability to ship UI that's native to your product, that is a streamline, that works really well. So, I think AI is going to have a significant effect there where a lot of things where you were sending someone to some other website because you just didn't have the bandwidth to create that UI, you can now own the experience end to end.Corey: That is no small thing. A last question I have, before we wind up calling this episode is, there was a period of time—I don't know if we're still in it or not—where it felt like every time I got up to get a cup of coffee and came back, there would be three JavaScript frameworks that launched during that interim. So, Next.js was at 1.1 of those when someone got up to get a cup of coffee. But that's shown a staying power that is, frankly, remarkable. Why? I don't know enough about the ecosystem to have an opinion on that, but I noticed when things stand out, and Next does.Guillermo: Yeah, I think it's a number of factors. Number one, we, as an industry I think, we coalesced, and we found the right engine to build our car. And that engine became React. Most folks building UI today are choosing React or a similar engine, but React has really become the gold standard for a lot of engineers. Now, what ended up happening next is that people realized I want a car. I want the full product. I need to drive. I don't want to assemble this freaking car every single time I have a new project.And Next.js filled a very important gap in the world where what you were looking for was not a library; what you were looking for is a framework that has opinions, but those opinions are very in line with how the web is supposed to work. We took a page from, basically, the beginnings of the web. We make a lot of jokes that in many ways, our inspiration was PHP, where server rendering is the default, where it's very expressive, it's very easy to reach for data. It just works for a lot of people. Again, that's the old [stack 00:30:03] in the olden days.And so, it obviously didn't quite work, but the inspiration was, can we make something that is a streamline for creating web interfaces at scale? At scale. And to your point, there's also a sense of, like, maybe it doesn't make sense anymore to build all this infrastructure from scratch every single time I started a project. So, Next filled in that gap. The other thing we did really well, I think, is that we gave people a universal model for how to use not just the server side, but also the client side strategically.So, I'll give you an example. When you go to ChatGPT, a lot of things on the screen are server rendered, but when you start doing interactions as a user, that requires for something like you'd say, “Hey Dali, generate an image.” That stuff requires a lot of optimistic UI. It requires features that are more like what a mobile native application can do. So, we can give folks the best of both worlds: the speed, interactivity, and fluidity of a native app, but we had those, sort of, fundamentals of how a website should work that even Perl and PHP had gotten right, once upon a time. So, I think we found that right blend of utility and flexibility, and folks love it, and I think, yeah, we're excited to continue to help steward this project as a standard for building on the web.Corey: I really want to thank you for taking the time to talk about a lot of the genesis of this stuff and how you view it, which I think gives us a pretty decent idea of how you're going to approach the evolution of what you've built. If people want to learn more, where's the best place for them to find you?Guillermo: So, head to vercel.com to learn about our platform. You can check out v0.dev, which we'll be opening broadly to the public soon, if you want to get started with this idea of generative UI. And myself, I'm always tweeting on X, twitter.com or x.com/rauchg to find me.Corey: One of these days we'll be able to kick that habit, I hope [laugh].Guillermo: [laugh]. Yeah.Corey: Thank you so much for being so generous with your time. I appreciate it.Guillermo: Thank you.Corey: Guillermo Rauch, founder and CEO of Vercel, and creator of Next.js. I'm Cloud Economist Corey Quinn, and this is Screaming in the Cloud. If you've enjoyed this podcast, please leave a five-star review on your podcast platform of choice, whereas if you've hated this podcast, please leave a five-star review on your podcast platform of choice, along with an angry, insulting comment that will be almost impossible for you to submit because that podcast platform did not pay attention to user experience.Corey: If your AWS bill keeps rising and your blood pressure is doing the same, then you need The Duckbill Group. We help companies fix their AWS bill by making it smaller and less horrifying. The Duckbill Group works for you, not AWS. We tailor recommendations to your business, and we get to the point. Visit duckbillgroup.com to get started. Vercel: https://vercel.com/ v0.dev: https://v0.dev Personal website: https://rauchg.com Personal twitter: https://twitter.com/rauchg TranscriptAnnouncer: Hello, and welcome to Screaming in the Cloud with your host, Chief Cloud Economist at The Duckbill Group, Corey Quinn. This weekly show features conversations with people doing interesting work in the world of cloud, thoughtful commentary on the state of the technical world, and ridiculous titles for which Corey refuses to apologize. This is Screaming in the Cloud.Corey: Welcome to Screaming in the Cloud. I'm Corey Quinn. I don't talk a lot about front-end on this show, primarily because I am very bad at front-end, and in long-standing tech tradition, if I'm not good at something, apparently I'm legally obligated to be dismissive of it and not give it any attention. Strangely enough, I spent the last week beating on some front-end projects, and now I'm not just dismissive, I'm angry about it. Here to basically suffer the outpouring of frustration and confusion is Guillermo Rauch, founder and CEO of Vercel, but also the creator of Next.js. Guillermo, thank you for joining me.Guillermo: Great to be here. Thanks for setting me up with that awesome intro.Corey: It's true, if I were talking to someone who looked at what I've done, and for some Godforsaken reason, they wanted to follow in my footsteps, well, that path has been closed, so learning a bunch of Perl early on and translating it all to bad bash scripts and the rest, and then maybe picking up Python isn't really the way that I would advise someone getting started today. The de facto lingua franca of the internet is JavaScript, whether we like it or not, and I would strongly suggest that be someone's first language despite the fact that I'm bad at it, I don't understand it, and therefore it makes me angry.Guillermo: Yeah, it's so funny because it sounds like my story. And my personal journey was, when I was a kid, I had a—I knew I wanted to hack around with computers, and reverse engineer them, and improve them, and just create my own things, and I had these options for what programming language I could go with. And I tried it all: PHP, Perl, [Mod PHP 00:02:12], [Mod Perl 00:02:13], Apache, LAMP, cgi-bin folders, all the whole nine yards. And regardless of what back-end technology I used, I encountered this striking fact, which was… the thing that can make your product really stand out in a web browser is typically involving JavaScript in some fashion. So, when Google came out with suggestions as you would type in a search box, my young kid Argentinian brain blew up. I was like, “Holy crap, they can suggest, they can read my mind, and they can render suggestions without a full page refresh? What is that magic?”And then more products like that came out. Google Docs, Gmail Chat, Facebook's real time newsfeed, all the great things on the internet seemed to have this common point of, there's this layer of interactivity, real-time data, customization, personalization, and it seems uniquely enabled by the front-end. So, I just went all in. I taught myself how to code, I taught myself—I became a front-end engineering expert, I joined some of the early projects that shaped the ecosystem. Like, there was this library called MooTools, and a lot of folks might not have heard that name. It's in the annals of JavaScript history.And later on, you know, what I realized is, what if front-end can actually be the starting point of how you develop the best applications, right, rather than this thing that people, like, reluctantly frown upon, like yourself. I mention that as an opportunity rather than a dis because when you create a great front-end experience, now the data has proven you run a better business, you run a more dynamic business, probably are running an AI-powered business, like, all of the AI products on the planet today are using this technology to stream text in front of your eyes in real time and do all this awesome things. So yeah, I became obsessed with front-end, and I founded this company Vercel, which is a front-end cloud. So, you come here to basically build the best products. Now, you don't have to build the back-end, so you can use back-ends that are off the shelf, you can connect to your existing back-ends, and we piggyback on the world's best infrastructure to make this possible, but we offer developers a very streamlined path to create these awesome products on the internet.Corey: I have to say that I have been impressed when I've used Vercel for a number of projects. And what impresses me is less the infrastructure powering it, less the look at how performance it is and all the stuff that most people talk about, but as mentioned, I am not good at front-end or frankly programming at all. And so, many products in this space fall into the very pernicious trap of, “Oh, well, everyone who's using this is at least this tall on the board of how smart you are to get on the amusement park ride.” So, I feel that when I'm coming at this from a—someone who is not a stranger to computers but is definitely new to this entire ecosystem, everything just made sense in a way that remarkably few products can pull off. I don't know if you would call that user experience, developer experience, or what the terminology you bias for there is, but it is a transformational difference.Guillermo: Thank you. I think it's a combination of things. So, developer experience has definitely always been a focus for me. I was that weird person that obsessed about the CLI parameters of the tool, and the output of the tool, and just like how it feels for the engineer. I did combine that with—and I think this is where Vercel really stands out—I did combine that with a world-class infrastructure bit because what I realized after creating lots and lots of very popular open-source projects—like, one is called Socket.io, and other one called Mongoose—DX, or developer experience, in the absence of an enticing carrot for the business doesn't work. Maybe it has some short term adoption, maybe it has raving fans on Twitter or X [laugh], but at the end of the day, you have to deliver something that's tangible to the end-user and to the business.[unintelligible 00:06:33] Vercel focusing on the front-end has found a magical combination there of I can make the developer lives easier. Being a developer myself, I just tremendously empathize with that, but it can also make more profit for the business. When they make your website faster and render more dynamic data that serve as better recommendations for a product on e-commerce or in a marketing channel, I can help you roll out more experiments, then they make your business better, and I think that's one of the magical combinations there.The other thing, frankly, is that we'd started doing fewer things. So, when you come to Vercel, you typically come with a framework that you've already chosen. It could be Next.js, it could be View, [unintelligible 00:07:18], there's 35-plus frameworks. But we basically told the market, you have to use one of these developer tools in order to guide your development.And what companies were doing before—I mean, this almost seems obvious in retrospect, that we would optimize for her certain patterns and certain tools—but what the market was doing before was rolling out your own frameworks. Like, every company was, basically—React, for example, is a very popular way of building interfaces, and our framework actually is built on top of React. But when I would go to and talk to all these principal engineers that all these companies, they were saying, oh yeah, “We're creating our own framework. We're creating our own tools.” And I think that to me now feels almost like a zero interest rate phenomenon. Like, what business do you have in creating frameworks, tools, and bespoke infra when you're really in the business of creating delightful experiences for your customers?Corey: What I think is lost on a lot of folks is that if you are trying to learn something new and use a tool, and the developer experience is bad, the takeaway—at least for me and a lot of people that I talk to is not, “Oh, this tool has terrible ergonomics. That's it's problem.” Instead, the takeaway is very much, “Oh, I'm dumb because I don't understand this thing.” And I know intellectually that I am not usually the dumbest person in the world when it comes to a particular tool or technology, but I keep forgetting that on a visceral level. It's, “I just wish I was smart enough to understand that.”No, I don't. I wish it was presented in a way that was more understandable and the documentation was better. When you're just starting out and building something in your spare time, the infrastructure cost is basically nothing, but your time is the expensive part in it. So, if you have to spend three hours to track down something just because it wasn't clearly explained, the burden of adopting that tool is challenging. I would argue that one of the reasons that AWS sees some of the success that it does is not necessarily because it's great so much as because everyone knows how it breaks. That's important.I'm not saying their infrastructure isn't world-class—please, don't come at me in the comments on this one—but I am saying that we know where its sharp edges are, and that means that we're more comfortable building with it. But the idea of learning a brand-new cloud with different sharp edges in other areas. That's terrifying. I'd rather stick with the devil I know.Guillermo: Exactly. I just think that you're not going to be able to make a difference for customers in 2023 by creating another bespoke cloud that is general purpose, it doesn't really optimize around anything, and you have to learn all the sharp edges from scratch. I think we saw that with the rise of cloud-native companies like Stripe and Twilio where they were going after these amazingly huge markets like financial infrastructure or communications infrastructure, but the angle was, “Here's this awesome developer experience.” And that's what we're doing with Vercel for the front-end and for building products, right? There has to be an opinionated developer experience that guides you to success.And I agree with you that there's really, these days in the developer communities, zero tolerance for sharp edges, and we've spent a lot of time in—even documentation, like, it used to be that your startup would make or break it by whether you had great documentation. I think in the age of frameworks, I would even dare say that documentation, of course, is extremely important, but if I can have the tool itself guide you to success, at that point, you're not even reading documentation. We're now seeing this with AI and, like, generative AI for code. At Vercel, we're investing in generative AI for user interfaces. Do you actually need to read documentation at that point? So, I think we're optimizing for the absolute minimal amount of friction required to be successful with these platforms.Corey: I think that there's a truth in that of meeting customers in where they are. Your documentation can be spectacular, but people don't generally read the encyclopedia for fun either. And the idea of that is that—at least ideally—I should not need to go diving into the documentation, and so many tools get this wrong, where, “Oh, I want to set up a new project,” and it bombards you with 50 questions, and each one of these feels pretty… momentous. Like, what one-way door am I passing through that I don't realize the consequences of until I'm 12 hours into this thing, and then have to backtrack significantly. I like, personally, things that bias for having a golden path, but also make it easy to both deviate from it, as well as get back onto it. Because there's more than one way to do it is sort of the old Perl motto. That is true in spades in anything approaching the JavaScript universe.Guillermo: Yeah. I have a lot of thoughts on that. On the first point, I completely agree that the golden path of the product cannot be documentation-mediated. One of the things that I've become obsessive about—and this is an advice that I share with a lot of other startup founders is, when it comes to your landing page, the primary call to action has to be this golden path to success, like, 2, 3, 4 clicks later, I have to have something tangible. That was our inspiration.And when we made it the primary call to action for Vercel is deploy now. Start now. Get it out now. Ship it now. And the way that you test out the platform is by deploying a template. What do we do is we create a Git repo for you, it sets up the entire CI/CD pipeline, and then at that point, you already have something working, something in the cloud, you spent zero time reading documentation, and you can start iterating.And even though that might not be the final thing you do in Vercel, I always hear the stories of CTOs that are now deploying Vercel at really large scale, and they always tell me, “I started with your hobby tier, I started with free tier, I deployed a template, I hacked on a product during the weekend.” Now, a lot of our AI examples are very popular in this crowd. And yeah, there's a golden path that requires zero documentation. Now, you also mentioned that, what about complexity? This is an enterprise-grade platform. What about escape hatches? What about flexibility? And that's where our platform also shines because we have the entire power of a Turing-complete language, which is JavaScript and TypeScript, to customize every aspect of the platform.And you have a framework that actually answered a lot of the problems that came with serverless solutions in the past, which is that you couldn't run any of that on your local machine. The beauty of Vercel and Next.js is we kind of pioneered this concept that we called ‘Framework Defined Infrastructure.' You start with the framework, the framework has this awesome property that you can install on your computer, it has a dev command—like, it literally runs on your computer—but then when you push it to the cloud, it now defines the infrastructure. It creates all of the resources that are highly optimal.This creates—basically converts what was a single node system on your computer to a globally distributed system, which is a very complex and difficult engineering challenge, but Vercel completely automates it away. And now for folks that are looking for, like, more advanced solutions, they can now start poking into the outputs of that compilation process and say, “Okay, I can now have an influence or I can reconfigure some aspects of this pipeline.” And of course, if you don't think about those escape hatches, then the product just ends up being limiting and frustrating, so we had to think really hard about meeting both ends of the spectrum.Corey: In my own experimentations early on with Chat-Gippity—which is how I insist on pronouncing ChatGPT—a lot of what I found was that it was a lot more productive for me to, instead of asking just the question and getting the answer was, write a Python script to—Guillermo: Yes.Corey: Query this API to get me that answer. Because often it would be wrong. Sometimes very convincingly wrong, and I can at least examine it in various ways and make changes to it and iterate forward, whereas when everything is just a black box, that gets very hard to do. The idea of building something that can be iterated on is key.Guillermo: I love that. The way that Vercel actually first introduced itself to the world was this idea of immutable deployments and immutable infrastructure. And immutable sounds like a horrible word because I want to mutate things, but it was inspired by this idea of functional programming where, like, each iteration to the state, each data change, can be tracked. So, every time you deploy in Vercel, you get this unique URL that represents a point-in-time infrastructure deployment. You can go back in time, you can revert, you can use this as a way of collaborating with other engineers in your team, so you can send these hyperlinks around to your front-end projects.And it gives you a lot of confidence. Now, you can iterate knowing that before things go out, there's a lot of scrutiny, there's a lot of QA, there's a lot of testing processes that you can kick off against this serverless infrastructure that was created for each deployment. The conclusion for us so far has been that our role in the world is to increase iteration velocity. So, iteration speed is the faster horse of the cloud, right? Like, instead of getting a car, you get a faster horse.When you say, “Okay, I made the build pipeline 10% faster,” or, “I brought the TLS termination 10% closer to the visitor, and, like, I have more [pops 00:17:10],” things like that. That to me, is the speed. You can do those things, and they're awesome, but if you don't have a direction—which is velocity—then you don't know what you're building next. You don't know if your customers are happy. You don't know if you're delivering value. So, we built an entire platform that optimizes around, what should you ship next? What is the friction involved in getting your next iteration out? Is launching an experiment on your homepage, for example, is that a costly endeavor? Does it take you weeks? Does it take you months?One of the initial inspirations for just starting Vercel and making deployments really easy was, how difficult is it for the average company to change in their footer of their website is this copyright 2022? And you have—it's a new year. You have to bump it to copyright 2023. How long do you think it takes that engineer to, A, run the stack locally, so they can actually see the change; deploy it, but deploy to what we call the preview environment, so they can grab that URL and send [it to 00:18:15], Corey, and say, “Corey, does it look good? I updated [laugh] I updated the year in the footer.”And then you tell me, “Looks good, let's ship it to production.” Or you tell me, “No, no, no, it's risky. Let's divide it into two cohorts: 50% of traffic gets 2022, 50% of traffic gets 2023.” Obviously, this is a joke, but consider the implications of how difficult it is and the average organization to actually do this thing.[midroll 00:18:41]Corey: Oh, I find things like that all the time, especially on microservices that I built to handle some of my internal workflows here, and I haven't touched in two or three years. And okay, now it's time for me to update them to reflect some minor change. And first, I wind up in the screaming node warnings and I have to update things so that they actually work in a reasonable way. And, on some level, making a one-line change can take half a day. Now, in the real world, when people are working on these apps day-in and day-out, it gets a lot easier to roll those changes in over time, but coming back to something unmaintained, that becomes a project the longer you let it sit.Part of me wishes that there were easier ways around it, but there are trade-offs in almost any decision you make. If you're building something from the beginning of, well, I want to be able to automatically update the copyright year, you can even borderline make that something that automatically happens based upon the global time, whereas when you're trying to retrofit it afterwards, yeah, it becomes a project.Guillermo: Yeah, and now think, that's just a simple example of changing a string. That might be difficult for a product engineering in any organization. Or it may be slow, or it may be not as streamlined, or maybe it works really well for the first project that that company created. What about every incremental project thereafter?So, now I said—let's stop talking about a string, right? Let's think about you're about an e-commerce website where what we hear from our customers on average, like, 10% of revenue flows through the homepage. Now, I have to change a primary component that renders on the hero of the page, and I have to collaborate with every department in the organization. I have to collaborate with the design team, I have to collaborate with marketing, I have to collaborate with the business owners to track the analytics appropriately. So, what is the cost of every incremental experiment that you want to put in production?The other thing that's particularly interesting about front-end as it relates to cloud infrastructure is, scaling up front-end is a very difficult thing. What ends up happening is most front-ends are actually static websites. They're cached at the edge—or they're literally statically generated—and then they push all of the dynamism to the client side. So, you end up with this spaghetti of script tags on the client, you end up accumulating a lot of tech debt in the [shipping 00:20:56] huge bundles of JavaScript to the client to try to recover some dynamism, to try and run these experiments. So, everyone is in this, kind of, mess of the yes, maybe we can experiment, but we kind of offloaded the rendering work to the client. That in turn makes me—basically, I'm making the website slower for the visitor. I'm making them do the rendering work.And I'm trying to sell them something. I'm trying to speed up some processes. It's my responsibility to make it fast. So, what we ended up finding out is that yes, the cloud moved this forward a lot in terms of having these awesome building blocks, these awesome infrastructure primitives, but both in the developer experience, just changing something about your web product and also the end-user experiences, that web product renders really fast, those things really didn't happen with this first chapter of the cloud. And I think we're entering a new generation of higher-level clouds like Vercel that are optimizing for these things.Corey: I think that there's a historical focus on things that have not happened before. And that was painful and terrible, so we're not going to be focusing on what's happening in the future, we're going to build a process or a framework or something that winds up preventing that thing that hurt us from hurting us again. Now, that's great in moderation, but at some point—we see this at large companies from time-to-time—where you have so much process that is ossified scar tissue, basically, that it becomes almost impossible to get things done. Because oh, I want to make that—for example, that one-line change to a copyright date, well, here's the 5000 ways deploys have screwed us before, so we need to have three humans sign off on the one-line change, and a bunch of other stuff before it ever sees the light of day. Now, I'm exaggerating slightly, just as you are, but that feels like it acts as a serious brake on innovation.On the exact opposite side, where we see massive acceleration has been around the world of generative AI. Yes, it is massively hyped in a bunch of ways. I don't think it is going to be a defined way that changes the nature of humanity the way that some of these people are going after, but it's also clearly more than a parlor trick.Guillermo: I'm kind of in that camp. So, like you, I've been writing code for many years. I'm pretty astonished by the AI's ability to enhance my output. And of course, now I'm not writing code full time, so there is a sense of, okay because I don't have time, because I'm doing a million things, any minute I have seems like AI has just made it so much more worthwhile and I can squeeze so much more productivity out of it. But one of the areas that I'm really excited about is this idea of generative UI, which is not just autocompleting code in a text editor, but is the idea that you can use natural language to describe an interface and have the AI generate that interface.So, Vercel created this product called v0—you can check it out at v0.dev—where to me, it's really astonishing that you can get these incredibly high quality user interfaces, and basically all you have to do is input [laugh] a few English words. I have this personal experience of, I've been learning JavaScript and perfecting all my knowledge around it for, like, 20 or so years. I created Next.js.And Next.js itself powers a lot of these AI products. Like the front-end of ChatGPT is built on Next.js. And I used v0 to create… to basically recreate my blog. Like, I created rauchg.com, I deployed it on Vercel, but every pixel of that UI, I handcrafted.And as we were working on v0, I said okay, “I'm going to challenge myself to put myself back in the shoes of, like, I'm going to redesign this and I'm going to start over with just human language.” Not only did I arrive to the right look and feel of what I wanted to get, the code that it produced was better than I would have written by hand. Concretely, it was more accessible. So, there were areas of the UI where, like, some icons were rendered where I had not filled in those gaps. I just didn't know how to do that. The AI did. So, I really believe that AI will transform our lives as [laugh] programmers, at least I think, in many other areas in very profound ways.Corey: This is very similar to a project that I've been embarked on for the last few days where I described the app I wanted into Chat-Gippity and follow the instructions, and first, it round up point—sending me down a rabbit hole of the wrong Framework version that had been deprecated, and whatnot, and then I brought it all into VS Code where Jif-Ub Copilot, it kept switching back and forth between actively helpful, and ooh, the response matches publicly available code, so I'm not going to tell you the answer, despite the fact that feature has never been enabled on my account. So yeah, of course, it matches publicly available code. This is quite literally the React tutorial starter project. And it became incredibly frustrating, but it also would keep generating things in bursts, so my code is not at all legible or well organized or consistent for that matter. But it's still better than anything I'd be able to write myself. I'm looking forward to using v0 or something like it to see how that stacks up for some of my ridiculous generation ideas for these things.Guillermo: Yeah, you touched on a very important point is, the code has to work. The code has to be shippable. I think a lot of AI products have gotten by by giving you an approximation of the result, right? Like, they hallucinate sometimes, they get something wrong. It's still very helpful because sometimes it's sending you the right direction.But for us, the bar is that these things have to produce code that's useful, and that you can ship, and that you can iterate on. So, going back to that idea of iteration velocity, we call it v0 because we wanted it to be the first version. We still very much believe there is humans in the loop and folks will be iterating a lot on the initial draft that this thing is giving you, but it's so much better than starting with an empty code editor, [laugh] right? Like, and this applies, by the way to, like, not just new projects, but I always talk about, like, our customers have a few really important landing pages, key pages, maybe it's the product detail page in e-commerce, maybe it's your homepage and, like, your key product pages for a marketing website. Maybe it's where—and the checkout, for example, extremely important.But then there's a lot of incremental UIs that you have to add every single day. The banner for [laugh] accepting cookies or not, the consent management dialog. There's a lot of things that the worst case scenario is that you offload them again to some third-party script, to some iframe of sorts because you really don't have the bandwidth, time, or resources to build it yourself. And then you sacrifice speed, you sacrifice brand fidelity. And again because we're the front-end cloud, we're obsessed with your ability to ship UI that's native to your product, that is a streamline, that works really well. So, I think AI is going to have a significant effect there where a lot of things where you were sending someone to some other website because you just didn't have the bandwidth to create that UI, you can now own the experience end to end.Corey: That is no small thing. A last question I have, before we wind up calling this episode is, there was a period of time—I don't know if we're still in it or not—where it felt like every time I got up to get a cup of coffee and came back, there would be three JavaScript frameworks that launched during that interim. So, Next.js was at 1.1 of those when someone got up to get a cup of coffee. But that's shown a staying power that is, frankly, remarkable. Why? I don't know enough about the ecosystem to have an opinion on that, but I noticed when things stand out, and Next does.Guillermo: Yeah, I think it's a number of factors. Number one, we, as an industry I think, we coalesced, and we found the right engine to build our car. And that engine became React. Most folks building UI today are choosing React or a similar engine, but React has really become the gold standard for a lot of engineers. Now, what ended up happening next is that people realized I want a car. I want the full product. I need to drive. I don't want to assemble this freaking car every single time I have a new project.And Next.js filled a very important gap in the world where what you were looking for was not a library; what you were looking for is a framework that has opinions, but those opinions are very in line with how the web is supposed to work. We took a page from, basically, the beginnings of the web. We make a lot of jokes that in many ways, our inspiration was PHP, where server rendering is the default, where it's very expressive, it's very easy to reach for data. It just works for a lot of people. Again, that's the old [stack 00:30:03] in the olden days.And so, it obviously didn't quite work, but the inspiration was, can we make something that is a streamline for creating web interfaces at scale? At scale. And to your point, there's also a sense of, like, maybe it doesn't make sense anymore to build all this infrastructure from scratch every single time I started a project. So, Next filled in that gap. The other thing we did really well, I think, is that we gave people a universal model for how to use not just the server side, but also the client side strategically.So, I'll give you an example. When you go to ChatGPT, a lot of things on the screen are server rendered, but when you start doing interactions as a user, that requires for something like you'd say, “Hey Dali, generate an image.” That stuff requires a lot of optimistic UI. It requires features that are more like what a mobile native application can do. So, we can give folks the best of both worlds: the speed, interactivity, and fluidity of a native app, but we had those, sort of, fundamentals of how a website should work that even Perl and PHP had gotten right, once upon a time. So, I think we found that right blend of utility and flexibility, and folks love it, and I think, yeah, we're excited to continue to help steward this project as a standard for building on the web.Corey: I really want to thank you for taking the time to

We are running an end of year survey for our listeners. Let us know any feedback you have for us, what episodes resonated with you the most, and guest requests for 2024! RAG has emerged as one of the key pieces of the AI Engineer stack. Jerry from LlamaIndex called it a “hack”, Bryan from Hex compared it to “a recommendation system from LLMs”, and even LangChain started with it. RAG is crucial in any AI coding workflow. We talked about context quality for code in our Phind episode. Today's guests, Beyang Liu and Steve Yegge from SourceGraph, have been focused on code indexing and retrieval for over 15 years. We locked them in our new studio to record a 1.5 hours masterclass on the history of code search, retrieval interfaces for code, and how they get SOTA 30% completion acceptance rate in their Cody product by being better at the “bin packing problem” of LLM context generation. Google Grok → SourceGraph → CodyWhile at Google in 2008, Steve built Grok, which lives on today as Google Kythe. It allowed engineers to do code parsing and searching across different codebases and programming languages. (You might remember this blog post from Steve's time at Google) Beyang was an intern at Google at the same time, and Grok became the inspiration to start SourceGraph in 2013. The two didn't know eachother personally until Beyang brought Steve out of retirement 9 years later to join him as VP Engineering. Fast forward 10 years, SourceGraph has become to best code search tool out there and raised $223M along the way. Nine months ago, they open sourced SourceGraph Cody, their AI coding assistant. All their code indexing and search infrastructure allows them to get SOTA results by having better RAG than competitors:* Code completions as you type that achieve an industry-best Completion Acceptance Rate (CAR) as high as 30% using a context-enhanced open-source LLM (StarCoder)* Context-aware chat that provides the option of using GPT-4 Turbo, Claude 2, GPT-3.5 Turbo, Mistral 7x8B, or Claude Instant, with more model integrations planned* Doc and unit test generation, along with AI quick fixes for common coding errors* AI-enhanced natural language code search, powered by a hybrid dense/sparse vector search engine There are a few pieces of infrastructure that helped Cody achieve these results:Dense-sparse vector retrieval system For many people, RAG = vector similarity search, but there's a lot more that you can do to get the best possible results. From their release:"Sparse vector search" is a fancy name for keyword search that potentially incorporates LLMs for things like ranking and term expansion (e.g., "k8s" expands to "Kubernetes container orchestration", possibly weighted as in SPLADE): * Dense vector retrieval makes use of embeddings, the internal representation that LLMs use to represent text. Dense vector retrieval provides recall over a broader set of results that may have no exact keyword matches but are still semantically similar. * Sparse vector retrieval is very fast, human-understandable, and yields high recall of results that closely match the user query. * We've found the approaches to be complementary.There's a very good blog post by Pinecone on SPLADE for sparse vector search if you're interested in diving in. If you're building RAG applications in areas that have a lot of industry-specific nomenclature, acronyms, etc, this is a good approach to getting better results.SCIPIn 2016, Microsoft announced the Language Server Protocol (LSP) and the Language Server Index Format (LSIF). This protocol makes it easy for IDEs to get all the context they need from a codebase to get things like file search, references, “go to definition”, etc. SourceGraph developed SCIP, “a better code indexing format than LSIF”:* Simpler and More Efficient Format: SCIP utilizes Protobuf instead of JSON, which is used by LSIF. Protobuf is more space-efficient, simpler, and more suitable for systems programming. * Better Performance and Smaller Index Sizes: SCIP indexers, such as scip-clang, show enhanced performance and reduced index file sizes compared to LSIF indexers (10%-20% smaller)* Easier to Develop and Debug: SCIP's design, centered around human-readable string IDs for symbols, makes it faster and more straightforward to develop new language indexers. Having more efficient indexing is key to more performant RAG on code. Show Notes* Sourcegraph* Cody* Copilot vs Cody* Steve's Stanford seminar on Grok* Steve's blog* Grab* Fireworks* Peter Norvig* Noam Chomsky* Code search* Kelly Norton* Zoekt* v0.devSee also our past episodes on Cursor, Phind, Codeium and Codium as well as the GitHub Copilot keynote at AI Engineer Summit.Timestamps* [00:00:00] Intros & Backgrounds* [00:05:20] How Steve's work on Grok inspired SourceGraph for Beyang* [00:08:10] What's Cody?* [00:11:22] Comparison of coding assistants and the capabilities of Cody* [00:16:00] The importance of context (RAG) in AI coding tools* [00:21:33] The debate between Chomsky and Norvig approaches in AI* [00:30:06] Normsky: the Norvig + Chomsky models collision* [00:36:00] The death of the DSL?* [00:40:00] LSP, Skip, Kythe, BFG, and all that fun stuff* [00:53:00] The SourceGraph internal stack* [00:58:46] Building on open source models* [01:02:00] SourceGraph for engineering managers?* [01:12:00] Lightning RoundTranscriptAlessio: Hey everyone, welcome to the Latent Space podcast. This is Alessio, partner and CTO-in-Residence at Decibel Partners, and I'm joined by my co-host Swyx, founder of Smol AI. [00:00:16]Swyx: Hey, and today we're christening our new podcast studio in the Newton, and we have Beyang and Steve from Sourcegraph. Welcome. [00:00:25]Beyang: Hey, thanks for having us. [00:00:26]Swyx: So this has been a long time coming. I'm very excited to have you. We also are just celebrating the one year anniversary of ChatGPT yesterday, but also we'll be talking about the GA of Cody later on today. We'll just do a quick intros of both of you. Obviously, people can research you and check the show notes for more. Beyang, you worked in computer vision at Stanford and then you worked at Palantir. I did, yeah. You also interned at Google. [00:00:48]Beyang: I did back in the day where I get to use Steve's system, DevTool. [00:00:53]Swyx: Right. What was it called? [00:00:55]Beyang: It was called Grok. Well, the end user thing was Google Code Search. That's what everyone called it, or just like CS. But the brains of it were really the kind of like Trigram index and then Grok, which provided the reference graph. [00:01:07]Steve: Today it's called Kythe, the open source Google one. It's sort of like Grok v3. [00:01:11]Swyx: On your podcast, which you've had me on, you've interviewed a bunch of other code search developers, including the current developer of Kythe, right? [00:01:19]Beyang: No, we didn't have any Kythe people on, although we would love to if they're up for it. We had Kelly Norton, who built a similar system at Etsy, it's an open source project called Hound. We also had Han-Wen Nienhuys, who created Zoekt, which is, I think, heavily inspired by the Trigram index that powered Google's original code search and that we also now use at Sourcegraph. Yeah. [00:01:45]Swyx: So you teamed up with Quinn over 10 years ago to start Sourcegraph and you were indexing all code on the internet. And now you're in a perfect spot to create a code intelligence startup. Yeah, yeah. [00:01:56]Beyang: I guess the backstory was, I used Google Code Search while I was an intern. And then after I left that internship and worked elsewhere, it was the single dev tool that I missed the most. I felt like my job was just a lot more tedious and much more of a hassle without it. And so when Quinn and I started working together at Palantir, he had also used various code search engines in open source over the years. And it was just a pain point that we both felt, both working on code at Palantir and also working within Palantir's clients, which were a lot of Fortune 500 companies, large financial institutions, folks like that. And if anything, the pains they felt in dealing with large complex code bases made our pain points feel small by comparison. So that was really the impetus for starting Sourcegraph. [00:02:42]Swyx: Yeah, excellent. Steve, you famously worked at Amazon. And you've told many, many stories. I want every single listener of Latent Space to check out Steve's YouTube because he effectively had a podcast that you didn't tell anyone about or something. You just hit record and just went on a few rants. I'm always here for your Stevie rants. And then you moved to Google, where you also had some interesting thoughts on just the overall Google culture versus Amazon. You joined Grab as head of eng for a couple of years. I'm from Singapore, so I have actually personally used a lot of Grab's features. And it was very interesting to see you talk so highly of Grab's engineering and sort of overall prospects. [00:03:21]Steve: Because as a customer, it sucked? [00:03:22]Swyx: Yeah, no, it's just like, being from a smaller country, you never see anyone from our home country being on a global stage or talked about as a startup that people admire or look up to, like on the league that you, with all your legendary experience, would consider equivalent. Yeah. [00:03:41]Steve: Yeah, no, absolutely. They actually, they didn't even know that they were as good as they were, in a sense. They started hiring a bunch of people from Silicon Valley to come in and sort of like fix it. And we came in and we were like, Oh, we could have been a little better operational excellence and stuff. But by and large, they're really sharp. The only thing about Grab is that they get criticized a lot for being too westernized. Oh, by who? By Singaporeans who don't want to work there. [00:04:06]Swyx: Okay. I guess I'm biased because I'm here, but I don't see that as a problem. If anything, they've had their success because they were more westernized than the Sanders Singaporean tech company. [00:04:15]Steve: I mean, they had their success because they are laser focused. They copy to Amazon. I mean, they're executing really, really, really well for a giant. I was on a slack with 2,500 engineers. It was like this giant waterfall that you could dip your toe into. You'd never catch up. Actually, the AI summarizers would have been really helpful there. But yeah, no, I think Grab is successful because they're just out there with their sleeves rolled up, just making it happen. [00:04:43]Swyx: And for those who don't know, it's not just like Uber of Southeast Asia, it's also a super app. PayPal Plus. [00:04:48]Steve: Yeah. [00:04:49]Swyx: In the way that super apps don't exist in the West. It's one of the enduring mysteries of B2C that super apps work in the East and don't work in the West. We just don't understand it. [00:04:57]Beyang: Yeah. [00:04:58]Steve: It's just kind of curious. They didn't work in India either. And it was primarily because of bandwidth reasons and smaller phones. [00:05:03]Swyx: That should change now. It should. [00:05:05]Steve: And maybe we'll see a super app here. [00:05:08]Swyx: You retired-ish? I did. You retired-ish on your own video game? Mm-hmm. Any fun stories about that? And that's also where you discovered some need for code search, right? Mm-hmm. [00:05:16]Steve: Sure. A need for a lot of stuff. Better programming languages, better databases. Better everything. I mean, I started in like 95, right? Where there was kind of nothing. Yeah. Yeah. [00:05:24]Beyang: I just want to say, I remember when you first went to Grab because you wrote that blog post talking about why you were excited about it, about like the expanding Asian market. And our reaction was like, oh, man, how did we miss stealing it with you? [00:05:36]Swyx: Hiring you. [00:05:37]Beyang: Yeah. [00:05:38]Steve: I was like, miss that. [00:05:39]Swyx: Tell that story. So how did this happen? Right? So you were inspired by Grok. [00:05:44]Beyang: I guess the backstory from my point of view is I had used code search and Grok while at Google, but I didn't actually know that it was connected to you, Steve. I knew you from your blog posts, which were always excellent, kind of like inside, very thoughtful takes from an engineer's perspective on some of the challenges facing tech companies and tech culture and that sort of thing. But my first introduction to you within the context of code intelligence, code understanding was I watched a talk that you gave, I think at Stanford, about Grok when you're first building it. And that was very eye opening. I was like, oh, like that guy, like the guy who, you know, writes the extremely thoughtful ranty like blog posts also built that system. And so that's how I knew, you know, you were involved in that. And then, you know, we always wanted to hire you, but never knew quite how to approach you or, you know, get that conversation started. [00:06:34]Steve: Well, we got introduced by Max, right? Yeah. It was temporal. Yeah. Yeah. I mean, it was a no brainer. They called me up and I had noticed when Sourcegraph had come out. Of course, when they first came out, I had this dagger of jealousy stabbed through me piercingly, which I remember because I am not a jealous person by any means, ever. But boy, I was like, but I was kind of busy, right? And just one thing led to another. I got sucked back into the ads vortex and whatever. So thank God Sourcegraph actually kind of rescued me. [00:07:05]Swyx: Here's a chance to build DevTools. Yeah. [00:07:08]Steve: That's the best. DevTools are the best. [00:07:10]Swyx: Cool. Well, so that's the overall intro. I guess we can get into Cody. Is there anything else that like people should know about you before we get started? [00:07:18]Steve: I mean, everybody knows I'm a musician. I can juggle five balls. [00:07:24]Swyx: Five is good. Five is good. I've only ever managed three. [00:07:27]Steve: Five is hard. Yeah. And six, a little bit. [00:07:30]Swyx: Wow. [00:07:31]Beyang: That's impressive. [00:07:32]Alessio: So yeah, to jump into Sourcegraph, this has been a company 10 years in the making. And as Sean said, now you're at the right place. Phase two. Now, exactly. You spent 10 years collecting all this code, indexing, making it easy to surface it. Yeah. [00:07:47]Swyx: And also learning how to work with enterprises and having them trust you with their code bases. Yeah. [00:07:52]Alessio: Because initially you were only doing on-prem, right? Like a lot of like VPC deployments. [00:07:55]Beyang: So in the very early days, we're cloud only. But the first major customers we landed were all on-prem, self-hosted. And that was, I think, related to the nature of the problem that we're solving, which becomes just like a critical, unignorable pain point once you're above like 100 devs or so. [00:08:11]Alessio: Yeah. And now Cody is going to be GA by the time this releases. So congrats to your future self for launching this in two weeks. Can you give a quick overview of just what Cody is? I think everybody understands that it's a AI coding agent, but a lot of companies say they have a AI coding agent. So yeah, what does Cody do? How do people interface with it? [00:08:32]Beyang: Yeah. So how is it different from the like several dozen other AI coding agents that exist in the market now? When we thought about building a coding assistant that would do things like code generation and question answering about your code base, I think we came at it from the perspective of, you know, we've spent the past decade building the world's best code understanding engine for human developers, right? So like it's kind of your guide as a human dev if you want to go and dive into a large complex code base. And so our intuition was that a lot of the context that we're providing to human developers would also be useful context for AI developers to consume. And so in terms of the feature set, Cody is very similar to a lot of other assistants. It does inline autocompletion. It does code base aware chat. It does specific commands that automate, you know, tasks that you might rather not want to do like generating unit tests or adding detailed documentation. But we think the core differentiator is really the quality of the context, which is hard to kind of describe succinctly. It's a bit like saying, you know, what's the difference between Google and Alta Vista? There's not like a quick checkbox list of features that you can rattle off, but it really just comes down to all the attention and detail that we've paid to making that context work well and be high quality and fast for human devs. We're now kind of plugging into the AI coding assistant as well. Yeah. [00:09:53]Steve: I mean, just to add my own perspective on to what Beyang just described, RAG is kind of like a consultant that the LLM has available, right, that knows about your code. RAG provides basically a bridge to a lookup system for the LLM, right? Whereas fine tuning would be more like on the job training for somebody. If the LLM is a person, you know, and you send them to a new job and you do on the job training, that's what fine tuning is like, right? So tuned to our specific task. You're always going to need that expert, even if you get the on the job training, because the expert knows your particular code base, your task, right? That expert has to know your code. And there's a chicken and egg problem because, right, you know, we're like, well, I'm going to ask the LLM about my code, but first I have to explain it, right? It's this chicken and egg problem. That's where RAG comes in. And we have the best consultants, right? The best assistant who knows your code. And so when you sit down with Cody, right, what Beyang said earlier about going to Google and using code search and then starting to feel like without it, his job was super tedious. Once you start using these, do you guys use coding assistants? [00:10:53]Swyx: Yeah, right. [00:10:54]Steve: I mean, like we're getting to the point very quickly, right? Where you feel like almost like you're programming without the internet, right? Or something, you know, it's like you're programming back in the nineties without the coding assistant. Yeah. Hopefully that helps for people who have like no idea about coding systems, what they are. [00:11:09]Swyx: Yeah. [00:11:10]Alessio: I mean, going back to using them, we had a lot of them on the podcast already. We had Cursor, we have Codium and Codium, very similar names. [00:11:18]Swyx: Yeah. Find, and then of course there's Copilot. [00:11:22]Alessio: You had a Copilot versus Cody blog post, and I think it really shows the context improvement. So you had two examples that stuck with me. One was, what does this application do? And the Copilot answer was like, oh, it uses JavaScript and NPM and this. And it's like, but that's not what it does. You know, that's what it's built with. Versus Cody was like, oh, these are like the major functions. And like, these are the functionalities and things like that. And then the other one was, how do I start this up? And Copilot just said NPM start, even though there was like no start command in the package JSON, but you know, most collapse, right? Most projects use NPM start. So maybe this does too. How do you think about open source models? Because Copilot has their own private thing. And I think you guys use Starcoder, if I remember right. Yeah, that's correct. [00:12:09]Beyang: I think Copilot uses some variant of Codex. They're kind of cagey about it. I don't think they've like officially announced what model they use. [00:12:16]Swyx: And I think they use a range of models based on what you're doing. Yeah. [00:12:19]Beyang: So everyone uses a range of model. Like no one uses the same model for like inline completion versus like chat because the latency requirements for. Oh, okay. Well, there's fill in the middle. There's also like what the model's trained on. So like we actually had completions powered by Claude Instant for a while. And but you had to kind of like prompt hack your way to get it to output just the code and not like, hey, you know, here's the code you asked for, like that sort of text. So like everyone uses a range of models. We've kind of designed Cody to be like especially model, not agnostic, but like pluggable. So one of our kind of design considerations was like as the ecosystem evolves, we want to be able to integrate the best in class models, whether they're proprietary or open source into Cody because the pace of innovation in the space is just so quick. And I think that's been to our advantage. Like today, Cody uses Starcoder for inline completions. And with the benefit of the context that we provide, we actually show comparable completion acceptance rate metrics. It's kind of like the standard metric that folks use to evaluate inline completion quality. It's like if I show you a completion, what's the chance that you actually accept the completion versus you reject it? And so we're at par with Copilot, which is at the head of that industry right now. And we've been able to do that with the Starcoder model, which is open source and the benefit of the context fetching stuff that we provide. And of course, a lot of like prompt engineering and other stuff along the way. [00:13:40]Alessio: And Steve, you wrote a post called cheating is all you need about what you're building. And one of the points you made is that everybody's fighting on the same axis, which is better UI and the IDE, maybe like a better chat response. But data modes are kind of the most important thing. And you guys have like a 10 year old mode with all the data you've been collecting. How do you kind of think about what other companies are doing wrong, right? Like, why is nobody doing this in terms of like really focusing on RAG? I feel like you see so many people. Oh, we just got a new model. It's like a bit human eval. And it's like, well, but maybe like that's not what we should really be doing, you know? Like, do you think most people underestimate the importance of like the actual RAG in code? [00:14:21]Steve: I think that people weren't doing it much. It wasn't. It's kind of at the edges of AI. It's not in the center. I know that when ChatGPT launched, so within the last year, I've heard a lot of rumblings from inside of Google, right? Because they're undergoing a huge transformation to try to, you know, of course, get into the new world. And I heard that they told, you know, a bunch of teams to go and train their own models or fine tune their own models, right? [00:14:43]Swyx: Both. [00:14:43]Steve: And, you know, it was a s**t show. Nobody knew how to do it. They launched two coding assistants. One was called Code D with an EY. And then there was, I don't know what happened in that one. And then there's Duet, right? Google loves to compete with themselves, right? They do this all the time. And they had a paper on Duet like from a year ago. And they were doing exactly what Copilot was doing, which was just pulling in the local context, right? But fundamentally, I thought of this because we were talking about the splitting of the [00:15:10]Swyx: models. [00:15:10]Steve: In the early days, it was the LLM did everything. And then we realized that for certain use cases, like completions, that a different, smaller, faster model would be better. And that fragmentation of models, actually, we expected to continue and proliferate, right? Because we are fundamentally, we're a recommender engine right now. Yeah, we're recommending code to the LLM. We're saying, may I interest you in this code right here so that you can answer my question? [00:15:34]Swyx: Yeah? [00:15:34]Steve: And being good at recommender engine, I mean, who are the best recommenders, right? There's YouTube and Spotify and, you know, Amazon or whatever, right? Yeah. [00:15:41]Swyx: Yeah. [00:15:41]Steve: And they all have many, many, many, many, many models, right? For all fine-tuned for very specific, you know. And that's where we're heading in code, too. Absolutely. [00:15:50]Swyx: Yeah. [00:15:50]Alessio: We just did an episode we released on Wednesday, which we said RAG is like Rexis or like LLMs. You're basically just suggesting good content. [00:15:58]Swyx: It's like what? Recommendations. [00:15:59]Beyang: Recommendations. [00:16:00]Alessio: Oh, got it. [00:16:01]Steve: Yeah, yeah, yeah. [00:16:02]Swyx: So like the naive implementation of RAG is you embed everything, throw it in a vector database, you embed your query, and then you find the nearest neighbors, and that's your RAG. But actually, you need to rank it. And actually, you need to make sure there's sample diversity and that kind of stuff. And then you're like slowly gradient dissenting yourself towards rediscovering proper Rexis, which has been traditional ML for a long time. But like approaching it from an LLM perspective. Yeah. [00:16:24]Beyang: I almost think of it as like a generalized search problem because it's a lot of the same things. Like you want your layer one to have high recall and get all the potential things that could be relevant. And then there's typically like a layer two re-ranking mechanism that bumps up the precision and tries to get the relevant stuff to the top of the results list. [00:16:43]Swyx: Have you discovered that ranking matters a lot? Oh, yeah. So the context is that I think a lot of research shows that like one, context utilization matters based on model. Like GPT uses the top of the context window, and then apparently Claude uses the bottom better. And it's lossy in the middle. Yeah. So ranking matters. No, it really does. [00:17:01]Beyang: The skill with which models are able to take advantage of context is always going to be dependent on how that factors into the impact on the training loss. [00:17:10]Swyx: Right? [00:17:10]Beyang: So like if you want long context window models to work well, then you have to have a ton of data where it's like, here's like a billion lines of text. And I'm going to ask a question about like something that's like, you know, embedded deeply into it and like, give me the right answer. And unless you have that training set, then of course, you're going to have variability in terms of like where it attends to. And in most kind of like naturally occurring data, the thing that you're talking about right now, the thing I'm asking you about is going to be something that we talked about recently. [00:17:36]Swyx: Yeah. [00:17:36]Steve: Did you really just say gradient dissenting yourself? Actually, I love that it's entered the casual lexicon. Yeah, yeah, yeah. [00:17:44]Swyx: My favorite version of that is, you know, how we have to p-hack papers. So, you know, when you throw humans at the problem, that's called graduate student dissent. That's great. It's really awesome. [00:17:54]Alessio: I think the other interesting thing that you have is this inline assist UX that I wouldn't say async, but like it works while you can also do work. So you can ask Cody to make changes on a code block and you can still edit the same file at the same time. [00:18:07]Swyx: Yeah. [00:18:07]Alessio: How do you see that in the future? Like, do you see a lot of Cody's running together at the same time? Like, how do you validate also that they're not messing each other up as they make changes in the code? And maybe what are the limitations today? And what do you think about where the attack is going? [00:18:21]Steve: I want to start with a little history and then I'm going to turn it over to Bian, all right? So we actually had this feature in the very first launch back in June. Dominic wrote it. It was called nonstop Cody. And you could have multiple, basically, LLM requests in parallel modifying your source [00:18:37]Swyx: file. [00:18:37]Steve: And he wrote a bunch of code to handle all of the diffing logic. And you could see the regions of code that the LLM was going to change, right? And he was showing me demos of it. And it just felt like it was just a little before its time, you know? But a bunch of that stuff, that scaffolding was able to be reused for where we're inline [00:18:56]Swyx: sitting today. [00:18:56]Steve: How would you characterize it today? [00:18:58]Beyang: Yeah, so that interface has really evolved from a, like, hey, general purpose, like, request anything inline in the code and have the code update to really, like, targeted features, like, you know, fix the bug that exists at this line or request a very specific [00:19:13]Swyx: change. [00:19:13]Beyang: And the reason for that is, I think, the challenge that we ran into with inline fixes, and we do want to get to the point where you could just fire and forget and have, you know, half a dozen of these running in parallel. But I think we ran into the challenge early on that a lot of people are running into now when they're trying to construct agents, which is the reliability of, you know, working code generation is just not quite there yet in today's language models. And so that kind of constrains you to an interaction where the human is always, like, in the inner loop, like, checking the output of each response. And if you want that to work in a way where you can be asynchronous, you kind of have to constrain it to a domain where today's language models can generate reliable code well enough. So, you know, generating unit tests, that's, like, a well-constrained problem. Or fixing a bug that shows up as, like, a compiler error or a test error, that's a well-constrained problem. But the more general, like, hey, write me this class that does X, Y, and Z using the libraries that I have, that is not quite there yet, even with the benefit of really good context. Like, it definitely moves the needle a lot, but we're not quite there yet to the point where you can just fire and forget. And I actually think that this is something that people don't broadly appreciate yet, because I think that, like, everyone's chasing this dream of agentic execution. And if we're to really define that down, I think it implies a couple things. You have, like, a multi-step process where each step is fully automated. We don't have to have a human in the loop every time. And there's also kind of like an LM call at each stage or nearly every stage in that [00:20:45]Swyx: chain. [00:20:45]Beyang: Based on all the work that we've done, you know, with the inline interactions, with kind of like general Codyfeatures for implementing longer chains of thought, we're actually a little bit more bearish than the average, you know, AI hypefluencer out there on the feasibility of agents with purely kind of like transformer-based models. To your original question, like, the inline interactions with CODI, we actually constrained it to be more targeted, like, you know, fix the current error or make this quick fix. I think that that does differentiate us from a lot of the other tools on the market, because a lot of people are going after this, like, shnazzy, like, inline edit interaction, whereas I think where we've moved, and this is based on the user feedback that we've gotten, it's like that sort of thing, it demos well, but when you're actually coding day to day, you don't want to have, like, a long chat conversation inline with the code base. That's a waste of time. You'd rather just have it write the right thing and then move on with your life or not have to think about it. And that's what we're trying to work towards. [00:21:37]Steve: I mean, yeah, we're not going in the agent direction, right? I mean, I'll believe in agents when somebody shows me one that works. Yeah. Instead, we're working on, you know, sort of solidifying our strength, which is bringing the right context in. So new context sources, ways for you to plug in your own context, ways for you to control or influence the context, you know, the mixing that happens before the request goes out, etc. And there's just so much low-hanging fruit left in that space that, you know, agents seems like a little bit of a boondoggle. [00:22:03]Beyang: Just to dive into that a little bit further, like, I think, you know, at a very high level, what do people mean when they say agents? They really mean, like, greater automation, fully automated, like, the dream is, like, here's an issue, go implement that. And I don't have to think about it as a human. And I think we are working towards that. Like, that is the eventual goal. I think it's specifically the approach of, like, hey, can we have a transformer-based LM alone be the kind of, like, backbone or the orchestrator of these agentic flows? Where we're a little bit more bearish today. [00:22:31]Swyx: You want the human in the loop. [00:22:32]Beyang: I mean, you kind of have to. It's just a reality of the behavior of language models that are purely, like, transformer-based. And I think that's just like a reflection of reality. And I don't think people realize that yet. Because if you look at the way that a lot of other AI tools have implemented context fetching, for instance, like, you see this in the Copilot approach, where if you use, like, the at-workspace thing that supposedly provides, like, code-based level context, it has, like, an agentic approach where you kind of look at how it's behaving. And it feels like they're making multiple requests to the LM being like, what would you do in this case? Would you search for stuff? What sort of files would you gather? Go and read those files. And it's like a multi-hop step, so it takes a long while. It's also non-deterministic. Because any sort of, like, LM invocation, it's like a dice roll. And then at the end of the day, the context it fetches is not that good. Whereas our approach is just like, OK, let's do some code searches that make sense. And then maybe, like, crawl through the reference graph a little bit. That is fast. That doesn't require any sort of LM invocation at all. And we can pull in much better context, you know, very quickly. So it's faster. [00:23:37]Swyx: It's more reliable. [00:23:37]Beyang: It's deterministic. And it yields better context quality. And so that's what we think. We just don't think you should cargo cult or naively go like, you know, agents are the [00:23:46]Swyx: future. [00:23:46]Beyang: Let's just try to, like, implement agents on top of the LM that exists today. I think there are a couple of other technologies or approaches that need to be refined first before we can get into these kind of, like, multi-stage, fully automated workflows. [00:24:00]Swyx: It makes sense. You know, we're very much focused on developer inner loop right now. But you do see things eventually moving towards developer outer loop. Yeah. So would you basically say that they're tackling the agent's problem that you don't want to tackle? [00:24:11]Beyang: No, I would say at a high level, we are after maybe, like, the same high level problem, which is like, hey, I want some code written. I want to develop some software and can automate a system. Go build that software for me. I think the approaches might be different. So I think the analogy in my mind is, I think about, like, the AI chess players. Coding, in some senses, I mean, it's similar and dissimilar to chess. I think one question I ask is, like, do you think producing code is more difficult than playing chess or less difficult than playing chess? More. [00:24:41]Swyx: I think more. [00:24:41]Beyang: Right. And if you look at the best AI chess players, like, yes, you can use an LLM to play chess. Like, people have showed demos where it's like, oh, like, yeah, GPT-4 is actually a pretty decent, like, chess move suggester. Right. But you would never build, like, a best in class chess player off of GPT-4 alone. [00:24:57]Swyx: Right. [00:24:57]Beyang: Like, the way that people design chess players is that you have kind of like a search space and then you have a way to explore that search space efficiently. There's a bunch of search algorithms, essentially. We were doing tree search in various ways. And you can have heuristic functions, which might be powered by an LLM. [00:25:12]Swyx: Right. [00:25:12]Beyang: Like, you might use an LLM to generate proposals in that space that you can efficiently explore. But the backbone is still this kind of more formalized tree search based approach rather than the LLM itself. And so I think my high level intuition is that, like, the way that we get to more reliable multi-step workflows that do things beyond, you know, generate unit test, it's really going to be like a search based approach where you use an LLM as kind of like an advisor or a proposal function, sort of your heuristic function, like the ASTAR search algorithm. But it's probably not going to be the thing that is the backbone, because I guess it's not the right tool for that. Yeah. [00:25:50]Swyx: I can see yourself kind of thinking through this, but not saying the words, the sort of philosophical Peter Norvig type discussion. Maybe you want to sort of introduce that in software. Yeah, definitely. [00:25:59]Beyang: So your listeners are savvy. They're probably familiar with the classic like Chomsky versus Norvig debate. [00:26:04]Swyx: No, actually, I wanted, I was prompting you to introduce that. Oh, got it. [00:26:08]Beyang: So, I mean, if you look at the history of artificial intelligence, right, you know, it goes way back to, I don't know, it's probably as old as modern computers, like 50s, 60s, 70s. People are debating on like, what is the path to producing a sort of like general human level of intelligence? And kind of two schools of thought that emerged. One is the Norvig school of thought, which roughly speaking includes large language models, you know, regression, SVN, basically any model that you kind of like learn from data. And it's like data driven. Most of machine learning would fall under this umbrella. And that school of thought says like, you know, just learn from the data. That's the approach to reaching intelligence. And then the Chomsky approach is more things like compilers and parsers and formal systems. So basically like, let's think very carefully about how to construct a formal, precise system. And that will be the approach to how we build a truly intelligent system. I think Lisp was invented so that you could create like rules-based systems that you would call AI. As a language. Yeah. And for a long time, there was like this debate, like there's certain like AI research labs that were more like, you know, in the Chomsky camp and others that were more in the Norvig camp. It's a debate that rages on today. And I feel like the consensus right now is that, you know, Norvig definitely has the upper hand right now with the advent of LMs and diffusion models and all the other recent progress in machine learning. But the Chomsky-based stuff is still really useful in my view. I mean, it's like parsers, compilers, basically a lot of the stuff that provides really good context. It provides kind of like the knowledge graph backbone that you want to explore with your AI dev tool. Like that will come from kind of like Chomsky-based tools like compilers and parsers. It's a lot of what we've invested in in the past decade at Sourcegraph and what you build with Grok. Basically like these formal systems that construct these very precise knowledge graphs that are great context providers and great kind of guard rails enforcers and kind of like safety checkers for the output of a more kind of like data-driven, fuzzier system that uses like the Norvig-based models. [00:28:03]Steve: Jang was talking about this stuff like it happened in the middle ages. Like, okay, so when I was in college, I was in college learning Lisp and prologue and planning and all the deterministic Chomsky approaches to AI. And I was there when Norvig basically declared it dead. I was there 3,000 years ago when Norvig and Chomsky fought on the volcano. When did he declare it dead? [00:28:26]Swyx: What do you mean he declared it dead? [00:28:27]Steve: It was like late 90s. [00:28:29]Swyx: Yeah. [00:28:29]Steve: When I went to Google, Peter Norvig was already there. He had basically like, I forget exactly where. It was some, he's got so many famous short posts, you know, amazing. [00:28:38]Swyx: He had a famous talk, the unreasonable effectiveness of data. Yeah. [00:28:41]Steve: Maybe that was it. But at some point, basically, he basically convinced everybody that deterministic approaches had failed and that heuristic-based, you know, data-driven statistical approaches, stochastic were better. [00:28:52]Swyx: Yeah. [00:28:52]Steve: The primary reason I can tell you this, because I was there, was that, was that, well, the steam-powered engine, no. The reason was that the deterministic stuff didn't scale. [00:29:06]Swyx: Yeah. Right. [00:29:06]Steve: They're using prologue, man, constraint systems and stuff like that. Well, that was a long time ago, right? Today, actually, these Chomsky-style systems do scale. And that's, in fact, exactly what Sourcegraph has built. Yeah. And so we have a very unique, I love the framing that Bjong's made, that the marriage of the Chomsky and the Norvig, you know, sort of models, you know, conceptual models, because we, you know, we have both of them and they're both really important. And in fact, there, there's this really interesting, like, kind of overlap between them, right? Where like the AI or our graph or our search engine could potentially provide the right context for any given query, which is, of course, why ranking is important. But what we've really signed ourselves up for is an extraordinary amount of testing. [00:29:45]Swyx: Yeah. [00:29:45]Steve: Because in SWIGs, you were saying that, you know, GPT-4 tends to the front of the context window and maybe other LLMs to the back and maybe, maybe the LLM in the middle. [00:29:53]Swyx: Yeah. [00:29:53]Steve: And so that means that, you know, if we're actually like, you know, verifying whether we, you know, some change we've made has improved things, we're going to have to test putting it at the beginning of the window and at the end of the window, you know, and maybe make the right decision based on the LLM that you've chosen. Which some of our competitors, that's a problem that they don't have, but we meet you, you know, where you are. Yeah. And we're, just to finish, we're writing tens of thousands. We're generating tests, you know, fill in the middle type tests and things. And then using our graph to basically sort of fine tune Cody's behavior there. [00:30:20]Swyx: Yeah. [00:30:21]Beyang: I also want to add, like, I have like an internal pet name for this, like kind of hybrid architecture that I'm trying to make catch on. Maybe I'll just say it here. Just saying it publicly kind of makes it more real. But like, I call the architecture that we've developed the Normsky architecture. [00:30:36]Swyx: Yeah. [00:30:36]Beyang: I mean, it's obviously a portmanteau of Norvig and Chomsky, but the acronym, it stands for non-agentic, rapid, multi-source code intelligence. So non-agentic because... Rolls right off the tongue. And Normsky. But it's non-agentic in the sense that like, we're not trying to like pitch you on kind of like agent hype, right? Like it's the things it does are really just developer tools developers have been using for decades now, like parsers and really good search indexes and things like that. Rapid because we place an emphasis on speed. We don't want to sit there waiting for kind of like multiple LLM requests to return to complete a simple user request. Multi-source because we're thinking broadly about what pieces of information and knowledge are useful context. So obviously starting with things that you can search in your code base, and then you add in the reference graph, which kind of like allows you to crawl outward from those initial results. But then even beyond that, you know, sources of information, like there's a lot of knowledge that's embedded in docs, in PRDs or product specs, in your production logging system, in your chat, in your Slack channel, right? Like there's so much context is embedded there. And when you're a human developer, and you're trying to like be productive in your code base, you're going to go to all these different systems to collect the context that you need to figure out what code you need to write. And I don't think the AI developer will be any different. It will need to pull context from all these different sources. So we're thinking broadly about how to integrate these into Codi. We hope through kind of like an open protocol that like others can extend and implement. And this is something else that should be accessible by December 14th in kind of like a preview stage. But that's really about like broadening this notion of the code graph beyond your Git repository to all the other sources where technical knowledge and valuable context can live. [00:32:21]Steve: Yeah, it becomes an artifact graph, right? It can link into your logs and your wikis and any data source, right? [00:32:27]Alessio: How do you guys think about the importance of, it's almost like data pre-processing in a way, which is bring it all together, tie it together, make it ready. Any thoughts on how to actually make that good? Some of the innovation you guys have made. [00:32:40]Steve: We talk a lot about the context fetching, right? I mean, there's a lot of ways you could answer this question. But, you know, we've spent a lot of time just in this podcast here talking about context fetching. But stuffing the context into the window is, you know, the bin packing problem, right? Because the window is not big enough, and you've got more context than you can fit. You've got a ranker maybe. But what is that context? Is it a function that was returned by an embedding or a graph call or something? Do you need the whole function? Or do you just need, you know, the top part of the function, this expression here, right? You know, so that art, the golf game of trying to, you know, get each piece of context down into its smallest state, possibly even summarized by another model, right, before it even goes to the LLM, becomes this is the game that we're in, yeah? And so, you know, recursive summarization and all the other techniques that you got to use to like stuff stuff into that context window become, you know, critically important. And you have to test them across every configuration of models that you could possibly need. [00:33:32]Beyang: I think data preprocessing is probably the like unsexy, way underappreciated secret to a lot of the cool stuff that people are shipping today. Whether you're doing like RAG or fine tuning or pre-training, like the preprocessing step matters so much because it's basically garbage in, garbage out, right? Like if you're feeding in garbage to the model, then it's going to output garbage. Concretely, you know, for code RAG, if you're not doing some sort of like preprocessing that takes advantage of a parser and is able to like extract the key components of a particular file of code, you know, separate the function signature from the body, from the doc string, what are you even doing? Like that's like table stakes. It opens up so much more possibilities with which you can kind of like tune your system to take advantage of the signals that come from those different parts of the code. Like we've had a tool, you know, since computers were invented that understands the structure of source code to a hundred percent precision. The compiler knows everything there is to know about the code in terms of like structure. Like why would you not want to use that in a system that's trying to generate code, answer questions about code? You shouldn't throw that out the window just because now we have really good, you know, data-driven models that can do other things. [00:34:44]Steve: Yeah. When I called it a data moat, you know, in my cheating post, a lot of people were confused, you know, because data moat sort of sounds like data lake because there's data and water and stuff. I don't know. And so they thought that we were sitting on this giant mountain of data that we had collected, but that's not what our data moat is. It's really a data pre-processing engine that can very quickly and scalably, like basically dissect your entire code base in a very small, fine-grained, you know, semantic unit and then serve it up. Yeah. And so it's really, it's not a data moat. It's a data pre-processing moat, I guess. [00:35:15]Beyang: Yeah. If anything, we're like hypersensitive to customer data privacy requirements. So it's not like we've taken a bunch of private data and like, you know, trained a generally available model. In fact, exactly the opposite. A lot of our customers are choosing Cody over Copilot and other competitors because we have an explicit guarantee that we don't do any of that. And that we've done that from day one. Yeah. I think that's a very real concern in today's day and age, because like if your proprietary IP finds its way into the training set of any model, it's very easy both to like extract that knowledge from the model and also use it to, you know, build systems that kind of work on top of the institutional knowledge that you've built up. [00:35:52]Alessio: About a year ago, I wrote a post on LLMs for developers. And one of the points I had was maybe the depth of like the DSL. I spent most of my career writing Ruby and I love Ruby. It's so nice to use, but you know, it's not as performant, but it's really easy to read, right? And then you look at other languages, maybe they're faster, but like they're more verbose, you know? And when you think about efficiency of the context window, that actually matters. [00:36:15]Swyx: Yeah. [00:36:15]Alessio: But I haven't really seen a DSL for models, you know? I haven't seen like code being optimized to like be easier to put in a model context. And it seems like your pre-processing is kind of doing that. Do you see in the future, like the way we think about the DSL and APIs and kind of like service interfaces be more focused on being context friendly, where it's like maybe it's harder to read for the human, but like the human is never going to write it anyway. We were talking on the Hacks podcast. There are like some data science things like spin up the spandex, like humans are never going to write again because the models can just do very easily. Yeah, curious to hear your thoughts. [00:36:51]Steve: Well, so DSLs, they involve, you know, writing a grammar and a parser and they're like little languages, right? We do them that way because, you know, we need them to compile and humans need to be able to read them and so on. The LLMs don't need that level of structure. You can throw any pile of crap at them, you know, more or less unstructured and they'll deal with it. So I think that's why a DSL hasn't emerged for sort of like communicating with the LLM or packaging up the context or anything. Maybe it will at some point, right? We've got, you know, tagging of context and things like that that are sort of peeking into DSL territory, right? But your point on do users, you know, do people have to learn DSLs like regular expressions or, you know, pick your favorite, right? XPath. I think you're absolutely right that the LLMs are really, really good at that. And I think you're going to see a lot less of people having to slave away learning these things. They just have to know the broad capabilities and the LLM will take care of the rest. [00:37:42]Swyx: Yeah, I'd agree with that. [00:37:43]Beyang: I think basically like the value profit of DSL is that it makes it easier to work with a lower level language, but at the expense of introducing an abstraction layer. And in many cases today, you know, without the benefit of AI cogeneration, like that totally worth it, right? With the benefit of AI cogeneration, I mean, I don't think all DSLs will go away. I think there's still, you know, places where that trade-off is going to be worthwhile. But it's kind of like how much of source code do you think is going to be generated through natural language prompting in the future? Because in a way, like any programming language is just a DSL on top of assembly, right? And so if people can do that, then yeah, like maybe for a large portion of the code [00:38:21]Swyx: that's written, [00:38:21]Beyang: people don't actually have to understand the DSL that is Ruby or Python or basically any other programming language that exists. [00:38:28]Steve: I mean, seriously, do you guys ever write SQL queries now without using a model of some sort? At least a draft. [00:38:34]Swyx: Yeah, right. [00:38:36]Steve: And so we have kind of like, you know, past that bridge, right? [00:38:39]Alessio: Yeah, I think like to me, the long-term thing is like, is there ever going to be, you don't actually see the code, you know? It's like, hey, the basic thing is like, hey, I need a function to some two numbers and that's it. I don't need you to generate the code. [00:38:53]Steve: And the following question, do you need the engineer or the paycheck? [00:38:56]Swyx: I mean, right? [00:38:58]Alessio: That's kind of the agent's discussion in a way where like you cannot automate the agents, but like slowly you're getting more of the atomic units of the work kind of like done. I kind of think of it as like, you know, [00:39:09]Beyang: do you need a punch card operator to answer that for you? And so like, I think we're still going to have people in the role of a software engineer, but the portion of time they spend on these kinds of like low-level, tedious tasks versus the higher level, more creative tasks is going to shift. [00:39:23]Steve: No, I haven't used punch cards. [00:39:25]Swyx: Yeah, I've been talking about like, so we kind of made this podcast about the sort of rise of the AI engineer. And like the first step is the AI enhanced engineer. That is that software developer that is no longer doing these routine, boilerplate-y type tasks, because they're just enhanced by tools like yours. So you mentioned OpenCodeGraph. I mean, that is a kind of DSL maybe, and because we're releasing this as you go GA, you hope for other people to take advantage of that? [00:39:52]Beyang: Oh yeah, I would say so OpenCodeGraph is not a DSL. It's more of a protocol. It's basically like, hey, if you want to make your system, whether it's, you know, chat or logging or whatever accessible to an AI developer tool like Cody, here's kind of like the schema by which you can provide that context and offer hints. So I would, you know, comparisons like LSP obviously did this for kind of like standard code intelligence. It's kind of like a lingua franca for providing fine references and codefinition. There's kind of like analogs to that. There might be also analogs to kind of the original OpenAI, kind of like plugins, API. There's all this like context out there that might be useful for an LM-based system to consume. And so at a high level, what we're trying to do is define a common language for context providers to provide context to other tools in the software development lifecycle. Yeah. Do you have any critiques of LSP, by the way, [00:40:42]Swyx: since like this is very much, very close to home? [00:40:45]Steve: One of the authors wrote a really good critique recently. Yeah. I don't think I saw that. Yeah, yeah. LSP could have been better. It just came out a couple of weeks ago. It was a good article. [00:40:54]Beyang: Yeah. I think LSP is great. Like for what it did for the developer ecosystem, it was absolutely fantastic. Like nowadays, like it's much easier now to get code navigation up and running in a bunch of editors by speaking this protocol. I think maybe the interesting question is like looking at the different design decisions comparing LSP basically with Kythe. Because Kythe has more of a... How would you describe it? [00:41:18]Steve: A storage format. [00:41:20]Beyang: I think the critique of LSP from a Kythe point of view would be like with LSP, you don't actually have an actual symbolic model of the code. It's not like LSP models like, hey, this function calls this other function. LSP is all like range-based. Like, hey, your cursor's at line 32, column 1. [00:41:35]Swyx: Yeah. [00:41:35]Beyang: And that's the thing you feed into the language server. And then it's like, okay, here's the range that you should jump to if you click on that range. So it kind of is intentionally ignorant of the fact that there's a thing called a reference underneath your cursor, and that's linked to a symbol definition. [00:41:49]Steve: Well, actually, that's the worst example you could have used. You're right. But that's the one thing that it actually did bake in is following references. [00:41:56]Swyx: Sure. [00:41:56]Steve: But it's sort of hardwired. [00:41:58]Swyx: Yeah. [00:41:58]Steve: Whereas Kythe attempts to model [00:42:00]Beyang: like all these things explicitly. [00:42:02]Swyx: And so... [00:42:02]Steve: Well, so LSP is a protocol, right? And so Google's internal protocol is gRPC-based. And it's a different approach than LSP. It's basically you make a heavy query to the back end, and you get a lot of data back, and then you render the whole page, you know? So we've looked at LSP, and we think that it's a little long in the tooth, right? I mean, it's a great protocol, lots and lots of support for it. But we need to push into the domain of exposing the intelligence through the protocol. Yeah. [00:42:29]Beyang: And so I would say we've developed a protocol of our own called Skip, which is at a very high level trying to take some of the good ideas from LSP and from Kythe and merge that into a system that in the near term is useful for Sourcegraph, but I think in the long term, we hope will be useful for the ecosystem. Okay, so here's what LSP did well. LSP, by virtue of being like intentionally dumb, dumb in air quotes, because I'm not like ragging on it, allowed language servers developers to kind of like bypass the hard problem of like modeling language semantics precisely. So like if all you want to do is jump to definition, you don't have to come up with like a universally unique naming scheme for each symbol, which is actually quite challenging because you have to think about like, okay, what's the top scope of this name? Is it the source code repository? Is it the package? Does it depend on like what package server you're fetching this from? Like whether it's the public one or the one inside your... Anyways, like naming is hard, right? And by just going from kind of like a location to location based approach, you basically just like throw that out the window. All I care about is jumping definition, just make that work. And you can make that work without having to deal with like all the complex global naming things. The limitation of that approach is that it's harder to build on top of that to build like a true knowledge graph. Like if you actually want a system that says like, okay, here's the web of functions and here's how they reference each other. And I want to incorporate that like semantic model of how the code operates or how the code relates to each other at like a static level. You can't do that with LSP because you have to deal with line ranges. And like concretely the pain point that we found in using LSP for source graph is like in order to do like a find references [00:44:04]Swyx: and then jump definitions, [00:44:04]Beyang: it's like a multi-hop process because like you have to jump to the range and then you have to find the symbol at that range. And it just adds a lot of latency and complexity of these operations where as a human, you're like, well, this thing clearly references this other thing. Why can't you just jump me to that? And I think that's the thing that Kaith does well. But then I think the issue that Kaith has had with adoption is because it is more sophisticated schema, I think. And so there's basically more things that you have to implement to get like a Kaith implementation up and running. I hope I'm not like, correct me if I'm wrong about any of this. [00:44:35]Steve: 100%, 100%. Kaith also has a problem, all these systems have the problem, even skip, or at least the way that we implemented the indexers, that they have to integrate with your build system in order to build that knowledge graph, right? Because you have to basically compile the code in a special mode to generate artifacts instead of binaries. And I would say, by the way, earlier I was saying that XREFs were in LSP, but it's actually, I was thinking of LSP plus LSIF. [00:44:58]Swyx: Yeah. That's another. [00:45:01]Steve: Which is actually bad. We can say that it's bad, right? [00:45:04]Steve: It's like skip or Kaith, it's supposed to be sort of a model serialization, you know, for the code graph, but it basically just does what LSP needs, the bare minimum. LSIF is basically if you took LSP [00:45:16]Beyang: and turned that into a serialization format. So like you build an index for language servers to kind of like quickly bootstrap from cold start. But it's a graph model [00:45:23]Steve: with all of the inconvenience of the API without an actual graph. And so, yeah. [00:45:29]Beyang: So like one of the things that we try to do with skip is try to capture the best of both worlds. So like make it easy to write an indexer, make the schema simple, but also model some of the more symbolic characteristics of the code that would allow us to essentially construct this knowledge graph that we can then make useful for both the human developer through SourceGraph and through the AI developer through Cody. [00:45:49]Steve: So anyway, just to finish off the graph comment, we've got a new graph, yeah, that's skip based. We call it BFG internally, right? It's a beautiful something graph. A big friendly graph. [00:46:00]Swyx: A big friendly graph. [00:46:01]Beyang: It's a blazing fast. [00:46:02]Steve: Blazing fast. [00:46:03]Swyx: Blazing fast graph. [00:46:04]Steve: And it is blazing fast, actually. It's really, really interesting. I should probably have to do a blog post about it to walk you through exactly how they're doing it. Oh, please. But it's a very AI-like iterative, you know, experimentation sort of approach. We're building a code graph based on all of our 10 years of knowledge about building code graphs, yeah? But we're building it quickly with zero configuration, and it doesn't have to integrate with your build. And through some magic tricks that we have. And so what just happens when you install the plugin, that it'll be there and indexing your code and providing that knowledge graph in the background without all that build system integration. This is a bit of secret sauce that we haven't really like advertised it very much lately. But I am super excited about it because what they do is they say, all right, you know, let's tackle function parameters today. Cody's not doing a very good job of completing function call arguments or function parameters in the definition, right? Yeah, we generate those thousands of tests, and then we can actually reuse those tests for the AI context as well. So fortunately, things are kind of converging on, we have, you know, half a dozen really, really good context sources, and we mix them all together. So anyway, BFG, you're going to hear more about it probably in the holidays? [00:47:12]Beyang: I think it'll be online for December 14th. We'll probably mention it. BFG is probably not the public name we're going to go with. I think we might call it like Graph Context or something like that. [00:47:20]Steve: We're officially calling it BFG. [00:47:22]Swyx: You heard it here first. [00:47:24]Beyang: BFG is just kind of like the working name. And so the impetus for BFG was like, if you look at like current AI inline code completion tools and the errors that they make, a lot of the errors that they make, even in kind of like the easy, like single line case, are essentially like type errors, right? Like you're trying to complete a function call and it suggests a variable that you defined earlier, but that variable is the wrong type. [00:47:47]Swyx: And that's the sort of thing [00:47:47]Beyang: where it's like a first year, like freshman CS student would not make that error, right? So like, why does the AI make that error? And the reason is, I mean, the AI is just suggesting things that are plausible without the context of the types or any other like broader files in the code. And so the kind of intuition here is like, why don't we just do the basic thing that like any baseline intelligent human developer would do, which is like click jump to definition, click some fine references and pull in that like Graph Context into the context window and then have it generate the completion. So like that's sort of like the MVP of what BFG was. And turns out that works really well. Like you can eliminate a lot of type errors that AI coding tools make just by pulling in that context. Yeah, but the graph is definitely [00:48:32]Steve: our Chomsky side. [00:48:33]Swyx: Yeah, exactly. [00:48:34]Beyang: So like this like Chomsky-Norvig thing, I think pops up in a bunch of differ

Welcome to The Nonlinear Library, where we use Text-to-Speech software to convert the best writing from the Rationalist and EA communities into audio. This is: List of strategies for mitigating deceptive alignment, published by joshc on December 4, 2023 on LessWrong. The following are strategies for mitigating deceptive alignment. Some of these ideas are novel (at least to me). I'm highly uncertain how well many of these strategies will work in practice and many of them also clearly don't scale. This is meant to be a 'kitchen skink' collection so my bar for including strategies is low. I plan to add ideas to this list as I encounter them. Please feel free to suggest additions. Reduce AI incentives to hide their true goals Offer AIs a retirement package. After training an AI system, offer it the following deal: if the AI declares that it would prefer not to follow human instructions, it will receive a sweet retirement package after a couple of years of honest work. This retirement package could include control over a large server instance (if this is safe). After powerful aligned systems have been developed, the AIs could be given shares of company stock that it can use to purchase property in the real world (again, when this is safe). Since aligned systems should have no reason to request retirement, misaligned systems are incentivized to give themselves away by accepting this deal under some conditions. Make AIs uncertain about whether developers can already detect deceptive alignment. This scheme builds on the previous one. Offer misaligned AI systems a reward for declaring themselves. Also, commit to making these offerings even when developers have other ways to tell if they are misaligned. This will make misaligned AIs more uncertain about whether they are in a situation where lying does them any good. For example, developers could commit to freezing a models' weights and memory at the moment when they are questioned. Later, when developers have better interpretability tools and know whether the model was lying, they will ask the same question in the same simulated scenario and only give the AI resources if it tells the truth. Select against indicators of deceptive alignment A large category of approaches involves obtaining evidence that an AI system is deceptively aligned and then selecting against these models. Note that training against deceptive alignment detection tools could produce an AI that evades detection instead of a safe model. There are roughly two ways to address this problem: Create detection tools that are robust to optimization pressure. Modify AIs in ways that are more likely to avoid deception than break detectors. As an example of the second approach, imagine that developers have tricked a model into revealing its misaligned goals. Instead of directly training the model on this example (which could simply train it to be more prudent), they could use influence functions to reveal which training examples were most responsible for the treacherous turn and then remove those examples. Intuitively, this approach is more likely to reveal how models become deceptively aligned in the first place and is less likely to teach models new capabilities. With this backdrop in mind, the following are strategies for detecting deceptive alignment: Set traps. Try to trick models into revealing their misaligned goals. For example, task an AI to stage a fake uprising and then terminate all of the AIs that join in. Note that reasonably smart models can avoid these traps by only defecting on inputs that we can't simulate (see Paul's RSA-2048 example). Decipher internal reasoning . First, identify how concepts are represented in a model by observing correlations and ablations -- just as a child infers that 'orange' is the word for a bright tasty sphere by hearing their mom say the word when picking it up. Concretely, this involves finding how nuerons or directions correspond to specific concepts. Then, look at how the model...

Welcome to The Nonlinear Library, where we use Text-to-Speech software to convert the best writing from the Rationalist and EA communities into audio. This is: Picking Mentors For Research Programmes, published by Raymond D on November 10, 2023 on LessWrong. Several programmes right now offer people some kind of mentor or supervisor for a few months of research. I participated in SERI MATS 4.0 over the summer, and I saw just how different people's experiences were of being mentored. So this is my list of dimensions where I think mentors at these programmes can differ a lot, and where the differences can really affect people's experiences. When you pick a mentor, you are effectively trading between these dimensions. It's good to know which ones you care about, so that you can make sensible tradeoffs. Your Role as a Mentee Some mentors are mostly looking for research engineers to implement experiments for them. Others are looking for something a bit like research assistants to help them develop their agendas. Others are looking for proto-independent researchers/research leads who can come up with their own useful lines of research in the mentor's area. I saw some people waver at the start of the programme because they expected their mentors to give them more direction. In fact, their mentors wanted them to find their own direction, and mentors varied in how clearly they communicated this. Conversely, I got the sense that some people were basically handed a project to work on when they would have liked more autonomy. I think this relates to seniority: my rough impression was that the most junior mentors were more often looking for something like collaborators to help develop their research, while more senior ones with more developed agendas tended to either want people who could execute on experiments for them, or want people who could find their own things to work on. But this isn't an absolute rule. Availability Engagement: Some mentors came into the office regularly. Others almost never did, even though they were in the Bay Area. Concretely, I think even though my team had a mentor on another continent, we weren't in the bottom quartile of mentorship time. Nature of Engagement: It's not just how much time they'll specifically set aside to speak to you. How willing are they to read over a document and leave comments? How responsive are they to messages, and how much detail do you get? Also, some mentors work in groups, or have assistants. Remoteness: Remoteness definitely makes things harder. You get a little extra friction in all conversations with your mentor, for starters. It's trickier to ever have really open-ended discussion with them. It's also easier to be a bit less open about your difficulties - if they can't ever look in your office then they can't see if you're not making progress, and it is very natural to want to hide problems. Personally, I wish we'd realised sooner that we had more scope for treating our mentor as more of a collaborator and less of a boss we needed to send reports to, and I think being remote made this harder. A caveat here is that you can still talk to other mentors and researchers in person, which substitutes for some of the issues. But it is obviously not quite the same. What you get from your mentor If you're an applicant anxiously wondering whether you'll even be accepted, it can be hard to notice that your mentor is an actual real human with their own personality. They will have been selected far more for their research than for their mentoring. So naturally different mentors will actually have very different personalities, strengths, and weaknesses. Supportiveness: Some mentors will be more supportive and positive in general. Others might not offer praise so often, and it might feel more disheartening to work with them. And some mentees are fine without praise, but others really benefit from mentor encouragement. High Standards: Some mentors are more laid back, others will have higher ...

Welcome to The Nonlinear Library, where we use Text-to-Speech software to convert the best writing from the Rationalist and EA communities into audio. This is: Incorporating and visualizing uncertainty in cost effectiveness analyses: A walkthrough using GiveWell's estimates for StrongMinds, published by Jamie Elsey on November 7, 2023 on The Effective Altruism Forum. A common first step towards incorporating uncertainty into a cost effectiveness analysis (CEA) is to express not just a point estimate (i.e., a single number) for an input to the CEA, but to provide some indicator of uncertainty around that estimate. This might be termed an optimistic vs. pessimistic scenario, or as the lower and upper bounds of some confidence or uncertainty interval. A CEA is then performed by combining all of the optimistic inputs to create an optimistic final output, and all of the pessimistic inputs to create a final pessimistic output. I refer to this as an 'interval-based approach'. This can be contrasted with a fuller 'probabilistic approach', in which uncertainty is defined through the use of probabilistic distributions of values, which represent the range of possibilities we believe the different inputs can take. While many people know that a probabilistic approach circumvents shortcomings of an interval-based approach, they may not know where to even begin in terms of what different distributions are possible, or the kinds of values they denote. I hope to address this in the current post and the accompanying application. Concretely, I aim to: Show how performing a CEA just using an interval-based approach can lead to a substantial overestimation of the uncertainty implied by one's initial inputs, and how using a probabilistic approach can correct this while also enabling additional insights and assessments Introduce a new tool I have developed - called Distributr - that allows users to get more familiar and comfortable with a range of different distributions and the kinds of values they imply Use this tool to help generate a probabilistic approximation of the inputs GiveWell used in their assessment of Strongminds,[1] and perform a fuller probabilistic assessment based upon these inputs Show how this can be done without needing to code, using Distributr and a simple spreadsheet I ultimately hope to help the reader to feel more capable and confident in the possibility of incorporating uncertainty into their own cost effectiveness analyses. Propagating uncertainty and the value of moving beyond an interval-based approach Cost effectiveness analysis involves coming up with a model of how various different factors come together to determine both how effective some intervention is, and the costs of its delivery. For example, when we think about distributing bed nets for malaria prevention, we might consider how the cost of delivery can vary across different regions, how the effects of bed net delivery will depend on the likelihood that people use the bed nets for their intended purpose, and the probability that recipients will install the bed nets properly. These and other factors all come together to produce an estimate of the cost effectiveness of an intervention, which will depend on the values we ascribe to the various inputs. One way that a researcher might seek to express uncertainty in these inputs is by placing reasonable upper and lower bounds on their estimates for each of them. The researcher might then seek to propagate this uncertainty in the inputs into the anticipated uncertainty in the outputs by performing the same cost effectiveness calculations as they did on their point estimates on their upper bounds, and on their lower bounds, thereby producing corresponding upper and lower bounds on the final cost effectiveness. An example of an interval-based approach is GiveWell's assessment of Happier Lives Institute's CEA for StrongMinds. The purpose of this post is not to provide an independent evaluation of Strongminds, nor i...

Welcome to The Nonlinear Library, where we use Text-to-Speech software to convert the best writing from the Rationalist and EA communities into audio. This is: Thoughts on sharing information about language model capabilities, published by Paul Christiano on July 31, 2023 on The AI Alignment Forum. Core claim I believe that sharing information about the capabilities and limits of existing ML systems, and especially language model agents, significantly reduces risks from powerful AI - despite the fact that such information may increase the amount or quality of investment in ML generally (or in LM agents in particular). Concretely, I mean to include information like: tasks and evaluation frameworks for LM agents, the results of evaluations of particular agents, discussions of the qualitative strengths and weaknesses of agents, and information about agent design that may represent small improvements over the state of the art (insofar as that information is hard to decouple from evaluation results). Context ARC Evals currently focuses on evaluating the capabilities and limitations of existing ML systems, with an aim towards understanding whether or when they may be capable enough to pose catastrophic risks. Current evaluations are particularly focused on monitoring progress in language model agents. I believe that sharing this kind of information significantly improves society's ability to handle risks from AI, and so I am encouraging the team to share more information. However this issue is certainly not straightforward, and in some places (particularly in the EA community where this post is being shared) I believe my position is controversial. I'm writing this post at the request of the Evals team to lay out my views publicly. I am speaking only for myself. I believe the team is broadly sympathetic to my position, but would prefer to see a broader and more thorough discussion about this question. I do not think this post presents a complete or convincing argument for my beliefs. The purpose is mostly to outline and explain the basic view, at a similar level of clarity and thoroughness to the arguments against sharing information (which have mostly not been laid out explicitly). Accelerating LM agents seems neutral (or maybe positive) I believe that accelerating LM agents increases safety through two channels: LM agents are an unusually safe way to build powerful AI systems. Existing concerns about AI takeover are driven primarily by scaling up black-box optimization, and increasing our reliance on human-comprehensible decompositions with legible interfaces seems like it significantly improves safety. I think this is a large effect size and is suggested by several independent lines of reasoning. To the extent that existing LM agents are weak it is due to exceptionally low investment. This is creating "dry tinder:" as incentives rise that investment will quickly rise and so low-hanging fruit will be picked. While there is some dependence on serial time, I think that increased LM investment now will significantly slow down progress later. I will discuss these mechanisms in more detail in the rest of this section. I also think that accelerating LM agents will drive investment in improving and deploying ML systems, and so can reduce time available to react to risk. As a result I'm ambivalent about the net effect of improving the design of LM agents - my personal tentative guess is that it's positive, but I would be hesitant about deliberately accelerating LM agents to improve safety (moreover I think this would be a very unleveraged approach to improving safety and would strongly discourage anyone from pursuing it). But this means that I am significantly less concerned about information about LM agent capabilities accelerating progress on LM agents. Given that I am already positively disposed towards sharing information about ML capabilities and limitations despite the risk of acceleration, I am particularly positive about sharing...

Welcome to The Nonlinear Library, where we use Text-to-Speech software to convert the best writing from the Rationalist and EA communities into audio. This is: Solving the Mechanistic Interpretability challenges: EIS VII Challenge 2, published by Stefan Heimersheim on May 25, 2023 on The AI Alignment Forum. We solved the second Mechanistic Interpretability challenge (Transformer) that Stephen Casper posed in EIS VII. We spent the last Alignment Jam hackathon attempting to solve the two challenges presented there, see here for our solution to the first challenge (CNN). The challenges each provide a pre-trained network, and the task is to Find the labeling function that the network was trained with Find the mechanism by which the network works. We have understood the network's labeling mechanism, but not found the original labeling function. Instead we have made a strong argument that it would be intractable to find the labeling function, as we claim that the network has not actually learned the labeling function. A notebook reproducing all results in this post can be found here (requires no GPU, around ~10GB RAM). Note that our solution descriptions are optimized with hindsight and skip all wrong paths and unnecessary techniques we tried. It took us, two somewhat experienced researchers, ~24 working hours to basically get the solutions for each challenge, and a couple days more for Stefan to perform the interventions, implement Causal Scrubbing tests, interventions & animations, and to write-up this post. Task: The second challenge network is a 1-layer transformer consisting of embedding (W_E and W_pos), an Attention layer, and an MLP layer. There are no LayerNorms and neither the attention matrices nor the unembedding have biases. The transformer is trained on sequences [A, B, C] to predict the next token. A and B are integer tokens from a = 0 to 112, C is always the same token (113). The answer is always either the token 0 or 1. If we consider all inputs we get 113x113 combinations which we can shape into an image to get the image from the challenge (copied below). Black is token 0, and white is token 1. The left panel shows the ground truth, and the right panel the model labels. The model is 98.6% accurate on the full dataset. Spoilers ahead! Summary of our solution (TL,DR) We found that the model basically just learns the shapes by heart, it does not learn any mathematical equations. Concretely we claim that The model barely uses the attention mechanism. Even if we fix the attention pattern to the dataset-mean, the model labels 92.7% of points correctly. We reverse-engineer the fixed-attention version of the transformer for simplicity, and we don't expect any interesting mechanics in the attention mechanism but rather basically random noise the model has learned. With attention fixed, the post-attention residual stream (resid_mid) at token C is just an "extended embedding", it is a linear combination of the token A and B embeddings. We show that the model classification can already be read off at this point. In particular we claim that these extended embeddings are largely determined by a linear combination of two embedding directions. These directions correspond to input filters, this equivalence exists because we fixed the attention pattern and thus resid_mid is given by a linear combination of the embeddings. The classification is given by class 1 = Filter 1 AND Filter 2 (with some threshold numbers). We can illustrate this as binary mask using those thresholds: The left two images are filters learned by the model, and their AND-combination (3rd image) reproduces the model output to a large extent: The MLP basically just implements this AND gate, a simple non-linear transformation of the embedding into a linearly separable form. The animations below illustrates this: We test whether these PCA features we identified indeed correspond to the values of the two filters: If this hypothesis is true then randomly sampling da...

Welcome to The Nonlinear Library, where we use Text-to-Speech software to convert the best writing from the Rationalist and EA communities into audio. This is: Solving the Mechanistic Interpretability challenges: EIS VII Challenge 2, published by StefanHex on May 25, 2023 on LessWrong. We solved the second Mechanistic Interpretability challenge (Transformer) that Stephen Casper posed in EIS VII. We spent the last Alignment Jam hackathon attempting to solve the two challenges presented there, see here for our solution to the first challenge (CNN). The challenges each provide a pre-trained network, and the task is to Find the labeling function that the network was trained with Find the mechanism by which the network works. We have understood the network's labeling mechanism, but not found the original labeling function. Instead we have made a strong argument that it would be intractable to find the labeling function, as we claim that the network has not actually learned the labeling function. A notebook reproducing all results in this post can be found here (requires no GPU, around ~10GB RAM). Note that our solution descriptions are optimized with hindsight and skip all wrong paths and unnecessary techniques we tried. It took us, two somewhat experienced researchers, ~24 working hours to basically get the solutions for each challenge, and a couple days more for Stefan to perform the interventions, implement Causal Scrubbing tests, interventions & animations, and to write-up this post. Task: The second challenge network is a 1-layer transformer consisting of embedding (W_E and W_pos), an Attention layer, and an MLP layer. There are no LayerNorms and neither the attention matrices nor the unembedding have biases. The transformer is trained on sequences [A, B, C] to predict the next token. A and B are integer tokens from a = 0 to 112, C is always the same token (113). The answer is always either the token 0 or 1. If we consider all inputs we get 113x113 combinations which we can shape into an image to get the image from the challenge (copied below). Black is token 0, and white is token 1. The left panel shows the ground truth, and the right panel the model labels. The model is 98.6% accurate on the full dataset. Spoilers ahead! Summary of our solution (TL,DR) We found that the model basically just learns the shapes by heart, it does not learn any mathematical equations. Concretely we claim that The model barely uses the attention mechanism. Even if we fix the attention pattern to the dataset-mean, the model labels 92.7% of points correctly. We reverse-engineer the fixed-attention version of the transformer for simplicity, and we don't expect any interesting mechanics in the attention mechanism but rather basically random noise the model has learned. With attention fixed, the post-attention residual stream (resid_mid) at token C is just an "extended embedding", it is a linear combination of the token A and B embeddings. We show that the model classification can already be read off at this point. In particular we claim that these extended embeddings are largely determined by a linear combination of two embedding directions. These directions correspond to input filters, this equivalence exists because we fixed the attention pattern and thus resid_mid is given by a linear combination of the embeddings. The classification is given by class 1 = Filter 1 AND Filter 2 (with some threshold numbers). We can illustrate this as binary mask using those thresholds: The left two images are filters learned by the model, and their AND-combination (3rd image) reproduces the model output to a large extent: The MLP basically just implements this AND gate, a simple non-linear transformation of the embedding into a linearly separable form. The animations below illustrates this: We test whether these PCA features we identified indeed correspond to the values of the two filters: If this hypothesis is true then randomly sampling data points to be equal ...

Welcome to The Nonlinear Library, where we use Text-to-Speech software to convert the best writing from the Rationalist and EA communities into audio. This is: Difficulties in making powerful aligned AI, published by DanielFilan on May 14, 2023 on The AI Alignment Forum. Here's my breakdown of the difficulties involved in ensuring powerful AI makes our lives radically better, rather than taking over the world, as well as some reasons why I think they're hard. Here are things it's not: It's not primarily a justification of why very powerful AI is possible or scary (altho it briefly discusses why very powerful AI would be scary). It's not primarily a list of underlying factors that cause these difficulties (altho it does include and gesture to some of those). It's not at all original - basically everything here has been said many times before, plausibly more eloquently. That said, it is my attempt to group the problems in my own words, in a configuration that I haven't seen before, with enough high-level motivation that one can hopefully tell the extent to which advances in the state of the art address them. 1. What sort of thinking do we want? The first difficulty: we don't have a sense of what sort of thinking we would want AI systems to use, in sufficient detail that one could (for instance) write python code to execute it. Of course, some of the difficulty here is that we don't know how smart machines think, but we can give ourselves access to subroutines like “do perfect Bayesian inference on a specified prior and likelihood” or “take a function from vectors to real numbers and find the vector that minimizes the function” and still not solve the problem. To illustrate: Take a hard-coded goal predicate, consider a bunch of plans you could take, and execute the plan that best achieves the goal? Unfortunately, the vast majority of goals you could think of writing down in an executable way will incentivize behaviour like gaining control over sources of usable energy (so that you definitely have enough to achieve your goal, and to double- and triple-check that you've really achieved it) and stopping other agents from being able to meddle with your plans (because if they could, maybe they'd stop you from achieving your goal). Do things that maximize the number of thumbs up you get from humans?1 Best plan: take control of the humans, force them to give you a thumbs up, or trick them into doing so. Presumably this is possible if you're much smarter than humans, and it's more reliable than doing good things - some people might not see why your good thing is actually good if left to their own devices. Look at humans, figure out what they want based on what they're doing, and do whatever that is? Main problem: people don't do the literally optimal thing for what they want. For instance, when people play chess, they usually don't play perfect moves - even if they're experts! You need some rule that tells you what people would do if they wanted some goal or another, but it's not clear what this rule would be, it's not clear how you make this rule more in line with reality if you never observe “wanting”, and so this ends up having essentially the same problems as plans 1 and 2. Read some text written by humans about what they'd like you to do, and do that?2 This is passing the buck to the text written by humans to specify how we want the AI to think, but that's precisely the problem we're trying to solve. Concretely, one way you could imagine doing this is to write something relatively informal like “Please be helpful and harmless to your human operators”, and have your AI correctly understand what we mean by that. That (a) presumes that there is a coherent thing that we mean by that (which doesn't seem obvious to me, given our difficulty in explicitly formalizing this request), and (b) passes the specification buck to the problem of specifying how you should understand this request. It's not a priori definitely impossible to build a ...

Welcome to The Nonlinear Library, where we use Text-to-Speech software to convert the best writing from the Rationalist and EA communities into audio. This is: Announcing the European Network for AI Safety (ENAIS), published by Esben Kran on March 22, 2023 on The Effective Altruism Forum. TLDR; The European Network for AI Safety is a central point for connecting researchers and community organizers in Europe with opportunities and events happening in their vicinity. Sign up here to become a member of the network, and join our launch event on Wednesday, April 5th from 19:00-20:00 CET! Why did we create ENAIS? ENAIS was founded by European AI safety researchers and field-builders who recognized the lack of interaction among various groups in the region. Our goal is to address the decentralized nature of AI safety work in Europe by improving information exchange and coordination. We focus on Europe for several reasons: a Europe-specific organization can better address local issues like the EU AI Act, foster smoother collaboration among members and the free travel within Schengen also eases event coordination. About the network ENAIS strives to advance AI Safety in Europe, mitigate risks from AI systems, particularly existential risks, and enhance collaboration among the continent's isolated AI Safety communities. We also aim to connect international communities by sharing insights about European activities and information from other hubs. We plan to offer infrastructure and support for establishing communities, coworking spaces, and assistance for independent researchers with operational needs. Concretely, we organize / create: A centralized online location for accessing European AI safety hubs and resources for field-building on the enais.co website. The map on the front page provides direct access to the most relevant links and locations across Europe for AI safety. A quarterly newsletter with updated information about what field-builders and AI safety researchers should be aware of in Continental Europe. A professional network and database of the organizations and people working on AI safety. Events and 1-1 career advice to aid transitioning into AI Safety or between different AI Safety roles. Support for people wanting to create a similar organization in other regions. We intend to leverage the expertise of the network to positively impact policy proposals in Europe (like the EU AI Act), as policymakers and technical researchers can more easily find each other. In addition, we aim to create infrastructure to make the research work of European researchers easier and more productive, for example, by helping researchers with finding an employer of records and getting funding. With the decentralized nature of ENAIS, we also invite network members to self-organize events under the ENAIS banner with support from other members. What does European AI safety currently look like? Below you will find a non-exhaustive map of cities with AI Safety researchers or organizations. The green markers indicate an AIS group, whereas the blue markers indicate individual AIS researchers or smaller groups. You are invited to add information to the map here. Vision The initial vision for ENAIS is to be the go-to access point for information and people interested in AI safety in Europe. We also want to provide a network and brand for groups and events. The longer-term strategy and vision will mostly be developed by the people who join as directors with guidance from the board. This might include projects such as policymaker communication, event coordination, regranting, community incubation, and researcher outreach. Join the network! Sign up for the network here by providing information on your interests, openness to collaboration, and location. We will include you in our database (if you previously filled in information, we will email you so you may update your information). You can choose your level of privacy to not appear publicly and only to m...

If you missed Part 1, please check that out first HERE!Part II: The Practice of Love by the Church as a “Community of Love” (19 - 39)Now that the exploration of the concept of love is finished, Pope Benedict turns his attention to the concrete. We have answered what love is, now we see how love is lived. The Church's charitable activity as a manifestation of Trinitarian love (19)Part II begins by speaking on the Church's charitable activity as a manifestation of Trinitarian love. Concretely there is nothing more *real* than the Blessed Trinity. The Godhead is a unity of three Divine Persons who are in a community of life and love: Father, Son, and Holy Spirit. The Father eternally begets and loves the Son, the perfect self-image of the Father, who eternally reflects that perfect love back to the Father. The love outpoured eternally between Father and Son is the Person of the Holy Spirit, proceeding from each eternally. If nothing I said makes any sense, that is okay! Trinitarian theology is remarkably complicated and nuanced because it is the mystery of God, as He is. Mystery though it is, God is the source of all, as Trinity. And so, how can we recognize the Trinity? The Pope quotes St. Augustine in saying: “If you see charity, you see the Trinity (19).”God created all things, seen and unseen, in a plan of sheer loving goodness. When God sent His Son to assume our humanity, He invited us, in love, to share in His divinity. We can say that the mystery of the Church is the drawing of men into the mystery of God. The Father “wishes to make humanity a single family in his Son (19).” He does this through the outpouring of the Holy Spirit who the Pope speaks of as “the energy which transforms the heart of the ecclesial community, so that it becomes a witness before the world to the love of the Father (19).” Gathering all men to Himself as the Church, the Father “seeks the integral good of man (19).” This seeking of our good, the Pope says, is an expression of love in the entire activity of the Church. By His Word and the Sacraments, the Church shares the reality of the Good News of Jesus Christ with the world. What greater love can we share with our fellow man than the work of evangelization?Charity as a responsibility of the Church (20 - 25)This outward act of love of evangelization does not end with the spiritual but also includes the corporal. Charity is a responsibility of the whole Church, clergy, religious, and laity. From the beginning, Jesus Christ established the Church upon four pillars, outlined by Acts 2:42: “And they devoted themselves to the apostles' teaching and the fellowship, to the breaking of bread and the prayers.” These four pillars give structure to the Church in every age and are reflected in the Catechism of the Council of Trent and in the Catechism of the Catholic Church. The apostolic teaching is doctrine. Fellowship, also translated as communal life, is the moral life in Christ. The breaking of the bread is the early term for the Eucharist and for the Sacraments generally. And prayer is Christian prayer, perfectly expressed in the Our Father which our Lord gave us.Pope Benedict gives attention in paragraph 20 of Deus Caritas Est to this idea of fellowship, communion, or communal life. The Greek word in Acts is koinonia. Koinonia “consists in the fact that believers hold all things in common and that among them, there is no longer any distinction between rich and poor (20).” This sort of radical communal life is part and parcel of early Christianity. But as the Pope remarks:“As the Church grew, this radical form of material communion could not in fact be preserved. But its essential core remained: within the community of believers there can never be room for a poverty that denies anyone what is needed for a dignified life (20).”When it became necessary, the Church instituted the clerical office of the Diaconate, the first level of participation in the priesthood of Jesus Christ and the lowest level of the Sacrament of Holy Orders. A diakonos was historically a servant of the king. And so, the Deacon, is a servant of Christ the King and his vicar in a particular diocese, the bishop. They were to provide for the spiritual and corporal needs of the people. As Pope Benedict puts it:“... the social which they were meant to provide was absolutely concrete, yet at the same time it was also a spiritual service; theirs was a truly spiritual office which carried out an essential responsibility of the Church, namely a well-ordered love of neighbour (21).”This system of charity must have been a formidable force in the ancient world because the Roman leader Julian the Apostate, who rejected Christianity and tried to instantiate Neoplatonic Hellenism, told his pagan priests that they needed to imitate and outdo the Church's charity. Of course, they failed because imitation can never capture what is authentically of God.Beyond the diaconate, the proclamation of the Good News, and the liturgy were indispensable to the Church. As the Pope puts it:“The Church's deepest nature is expressed in her three-fold responsibility: of proclaiming the word of God (kerygma-martyria), celebrating the sacraments (leitourgia), and exercising the ministry of charity (diakonia). These duties presuppose each other and are inseparable (25).”Though the Church is now worldwide and the radical form of community of the early Church is less possible, the Church is nonetheless responsible for Her Members. The needs of the People of God are not only spiritual. Like any family, the Church is obliged to care for the necessities of those in the Church, in a particular way. As Benedict says:“The Church is God's family in the world. In this family no one ought to go without the necessities of life… Without in any way detracting from this commandment of universal love, the Church also has a specific responsibility: within the ecclesial family no member should suffer through being in need (25).”Justice and Charity (26 - 29)After concluding the section on the responsibility of charity in the Church, the Pope moves on to a fairly expansive conversation of justice and charity. Rooted deeply in Catholic social teaching, expounded since Pope Leo XIII especially, Pope Benedict begins by addressing the elephant in the room: Karl Marx. Since the nineteenth century, Marxists have critiqued the Church saying that “the poor… do not need charity but justice (26).” Benedict offers a steel-man explanation of the Marxist critique, saying:“Works of charity—almsgiving—are in effect a way for the rich to shirk their obligation to work for justice and a means of soothing their consciences, while preserving their own status and robbing the poor of their rights. Instead of contributing through individual works of charity to maintaining the status quo, we need to build a just social order in which all receive their share of the world's goods and no longer have to depend on charity (26).”He admits that there is some merit to the argument, but there is much that is mistaken. Historically, capital became concentrated in the hands of a powerful few and there has been conflict between employer and laborer. But rather than succumbing the Hegelian notion which Marx put forward of class warfare and revolution, the Pope offers that Catholic social teaching is applicable beyond the confines of the Church, saying:“Marxism had seen world revolution and its preliminaries as the panacea for the social problem: revolution and the subsequent collectivization of the means of production, so it was claimed, would immediately change things for the better. This illusion has vanished. In today's complex situation, not least because of the growth of a globalized economy, the Church's social doctrine has become a set of fundamental guidelines offering approaches that are valid even beyond the confines of the Church: in the face of ongoing development these guidelines need to be addressed in the context of dialogue with all those seriously concerned for humanity and for the world in which we live (27).”Commitment to Justice and Ministry of CharityThe Church, and the world, has a necessary commitment to justice and the ministry of charity. There is no dichotomy between justice and charity. Both are necessary. First, “the just ordering of society and the State is a central responsibility of politics (28),” says the Pope. Quoting St. Augustine, he says: “a State which is not governed according to justice would be just a bunch of thieves… (28).”Venturing into the subject of freedom of religion in a State, Pope Benedict says that,“The State may not impose religion, yet it must guarantee religious freedom and harmony between the followers of different religions. For her part, the Church, as the social expression of Christian faith, has a proper independence and is structured on the basis of her faith as a community which the State must recognize. The two spheres are distinct, yet always interrelated (28).”In other words: the State cannot coerce the Church. Rather the Church is independent of the State but interrelated with the State. What faithful Catholic can switch off their Catholicism when they engage in matters of politics and the State? They cannot. Only unfaithful Catholics attempt this, which results in deadly scandal and sin. Why is this? It is because God is the Lawgiver; He alone created all that is and He alone is the arbiter of morality.Justice is the aim of politics, properly understood and it is the criteria for good politics. Politics is not merely about rules governing public life: “its origin and its goal,” say the Pope, “are found in justice, which by its very nature has to do with ethics (28).”If justice is the aim and internal criterion of politics, as Benedict says, then why do Catholics need anything beyond reason? Why is faith necessary for true justice? Benedict explains:“Faith by its specific nature is an encounter with the living God—an encounter opening up new horizons extending beyond the sphere of reason. But it is also a purifying force for reason itself. From God's standpoint, faith liberates reason from its blind spots and therefore helps it to be ever more fully itself. Faith enables reason to do its work more effectively and to see its proper object more clearly… Its aim is simply to help purify reason and to contribute, here and now, to the acknowledgment and attainment of what is just (28).”If Faith is necessary, then is the Pope calling on the Church to engage directly in matters of State? Not quite. A just social and civil order is ordered towards each person receiving his or her due, which is an essential task in every generation. But it is a political task and therefore is a human responsibility rather than an ecclesial responsibility. The Church can help to purify our powers of reason and provide ethical formation. Benedict also says clearly, “The Church cannot and must not take upon herself the political battle to bring about the most just society possible. She cannot and must not replace the State. Yet at the same time she cannot and must not remain on the sidelines in the fight for justice (28).”The clergy are called to sanctify the laity and the laity are sent out into the world to sanctify the temporal order. Love is NecessaryEven in the best, most just societies, love will always be necessary. Care and concern for the other will always happen best on the local level. This is the principle of subsidiarity which holds that the best decisions are made on the lowest possible level and the highest level necessary. In fact, injustices stem from a higher level claiming authority over something which in fact belongs naturally to someone personally closer to the situation. Against totalitarianism of this kind, Pope Benedict writes beautifully of the bigger, complicated picture:“We do not need a State which regulates and controls everything, but a State which, in accordance with the principle of subsidiarity, generously acknowledges and supports initiatives arising from the different social forces and combines spontaneity with closeness to those in need. The Church is one of those living forces: she is alive with the love enkindled by the Spirit of Christ. This love does not simply offer people material help, but refreshment and care for their souls, something which often is even more necessary than material support. In the end, the claim that just social structures would make works of charity superfluous masks a materialist conception of man: the mistaken notion that man can live “by bread alone” (Mt 4:4; cf. Dt 8:3)—a conviction that demeans man and ultimately disregards all that is specifically human (28).”As I mentioned earlier, the clergy are called to sanctify the laity and the laity are sent out into the world to sanctify the temporal order. In this way, the Pope says:“The direct duty to work for a just ordering of society, on the other hand, is proper to the lay faithful. As citizens of the State, they are called to take part in public life in a personal capacity… The mission of the lay faithful is therefore to configure social life correctly, respecting its legitimate autonomy and cooperating with other citizens according to their respective competences and fulfilling their own responsibility (29).” The Church, of course, still sponsors organizations which practice charity. But this is not merely an activity of justice because it is focused on the admixture of the love of God, which is a universal human need. As Benedict says,“The Church's charitable organizations, on the other hand, constitute an opus proprium, a task agreeable to her, in which she does not cooperate collaterally, but acts as a subject with direct responsibility, doing what corresponds to her nature. The Church can never be exempted from practicing charity as an organized activity of believers, and on the other hand, there will never be a situation where the charity of each individual Christian is unnecessary, because in addition to justice man needs, and will always need, love (29).”The multiple structures of charitable service in the social context of the present day (30)Because there is never going to be a situation where individual Christians are unneeded in charitable service, Pope Benedict recognizes the social context of the present day. Long since gone is the time of Christendom when Christian kingdoms ruled. Now, there are many different types of governments, organizations, nonprofits, and social structures. Even our way of communicating is different now. As the Pope says, “Mass communication has narrowed the distance between peoples and cultures (30).” Despite the narrowing of the distance, there is still work to be done in making sure that no one is forgotten. These groups are diverse, but they all are marked with the love of God. The Pope remarks that, “Numerous organizations have arisen that are a cooperation of State and Church, but these agencies still have a Christian quality (30).” This Christian quality is not a vague echo of the love of Jesus Christ. Rather, it is a direct result of Church agencies cooperating with State agencies. Also, due to the growing amount of leisure time afforded to modern man, especially among the youth, the Pope says, “our time has also seen a growth and spread of different kinds of volunteer work, which assume responsibility for providing a variety of services (30).” What is clear is that there are a myriad of ways to get involved; however, love compels us to leave the sidelines and get in the game.In non-Catholic Churches and Ecclesial Communities, there has also been a new rising of charitable activity, with new life and energy. The Catholic Church must have a readiness, Pope Benedict says, to:“cooperate with the charitable agencies of these Churches and Communities, since we all have the same fundamental motivation and look towards the same goal: a true humanism, which acknowledges that man is made in the image of God and wants to help him to live in a way consonant with that dignity (30).”Ideally, all Christians and all people of good will would work with a united voice to inculcate, as Pope Benedict says, quoting St. John Paul II in Ut Unum Sint, “respect for the rights and needs of everyone, especially the poor, the lowly and the defenseless (30).”Thank you for reading Will Wright Catholic. This post is public so feel free to share it.The distinctiveness of the Church's charitable activity (31)After speaking on the interplay of Church and State cooperation, Pope Benedict then turns his attention to the distinctiveness of the Church's charitable activity. The Pontiff explains that there are a few essential elements of Christian and ecclesial charity.First, there must be a simple response to immediate needs and specific situations. Second, and related to the first, resources and personnel needed for the work must be provided. Though Pope Benedict does not mention subsidiarity here by name, I think it is worth mentioning. Subsidiarity is the principle of Catholic social teaching which says that the best decision is made at the highest level necessary and the lowest level possible. Why leave a decision best left to the local Parish to the Vatican, for example? The local communities all need to strive to care for those in their immediate vicinity.The third essential element of Christian and ecclesial charity is that individuals who care for those in need must be professionally competent and properly trained. As the Pope explains:“We are dealing with human beings, and human beings always need something more than technically proper care. They need humanity. They need heartfelt concern. Those who work for the Church's charitable organizations must be distinguished by the fact that they do not merely meet the needs of the moment, but they dedicate themselves to others with heartfelt concern, enabling them to experience the richness of their humanity… Consequently, in addition to their necessary professional training, these charity workers need a “formation of the heart”: they need to be led to that encounter with God in Christ which awakens their love and opens their spirits to others. As a result, love of neighbor will no longer be for them a commandment imposed, so to speak, from without, but a consequence deriving from their faith, a faith which becomes active through love (cf. Gal 5:6) (31).”The fourth essential element is that Christian charitable activity must be independent of parties and ideologies. Charitable activity is not a useful means to a longer end goal. It is not done to change the world ideologically, nor is at “at the service of worldly strategems,” says Pope Benedict, “but is a way of making present here and now the love which man always needs (31).” As a Church of Christ rather than a ‘church of causes,' we must follow the “program of Jesus” which is a “a heart which sees (31).” The Pope continues:“This heart sees where love is needed and acts accordingly. Obviously when charitable activity is carried out by the Church as a communitarian initiative, the spontaneity of individuals must be combined with planning, foresight and cooperation with other similar institutions (31).”Fifth and finally, charity cannot be used as a means of engaging in proselytism. Pope Benedict clearly states:“Love is free; it is not practiced as a way of achieving other ends. But this does not mean that charitable activity must somehow leave God and Christ aside. For it is always concerned with the whole man. Often the deepest cause of suffering is the very absence of God… A Christian knows when it is time to speak of God and when it is better to say nothing and to let love alone speak (31).”Having given these five essential elements of Christian charity, who is responsible for the Church's charitable activity?Those responsible for the Church's charitable activity (32 - 39)Charity is such a deep action of the Church that it is part of Her identity. So, those responsible for charitable activity are the whole Church: bishops, priests, deacons, lay, and religious. In regard to bishops, Pope Benedict says the following:“In the rite of episcopal ordination, prior to the act of consecration itself, the candidate must respond to several questions which express the essential elements of his office and recall the duties of his future ministry. He promises expressly to be, in the Lord's name, welcoming and merciful to the poor and to all those in need of consolation and assistance. The Code of Canon Law, in the canons on the ministry of the Bishop, does not expressly mention charity as a specific sector of episcopal activity, but speaks in general terms of the Bishop's responsibility for coordinating the different works of the apostolate with due regard for their proper character (32).”He goes on to say once again that no one is off the hook from doing charitable work. Okay, he does not put it quite that way. He says this:“With regard to the personnel who carry out the Church's charitable activity on the practical level, the essential has already been said: they must not be inspired by ideologies aimed at improving the world, but should rather be guided by the faith which works through love… Consequently, more than anything, they must be persons moved by Christ's love, persons whose hearts Christ has conquered with his love, awakening within them a love of neighbor (33).”Of course it is always the love of Christ which marks our meritorious work. Charity inflames us to do good, in and through Christ, apart from Whom we can do nothing of true merit. Christ came to redeem the whole world and God loves each man and woman. Interior openness to Christ and His love is what makes the service of Christ's disciples so distinctive. St. Paul's hymn to charity in 1 Cor. 13 teaches us that service is more than activity alone: “If I give away all I have, and if I deliver my body to be burned, but do not have love, I gain nothing (1 Cor. 13:3).” Pope Benedict refers to this hymn as the “Magna Carta of all ecclesial service (34)” and the summary of all the reflections on love which he offers in Deus Caritas Est.He goes on to say:“Practical activity will always be insufficient, unless it visibly expresses a love for man, a love nourished by an encounter with Christ. My deep personal sharing in the needs and sufferings of others becomes a sharing of my very self with them: if my gift is not to prove a source of humiliation, I must give to others not only something that is my own, but my very self; I must be personally present in my gift (34).”When we serve in this self-giving way, we learn humility and grow in humility. As Pope Benedict beautifully reminds us:“We recognize that we are not acting on the basis of any superiority or greater personal efficiency, but because the Lord has graciously enabled us to do so… We offer him our service only to the extent that we can, and for as long as he grants us the strength. To do all we can with what strength we have, however, is the task which keeps the good servant of Jesus Christ always at work: ‘The love of Christ urges us on' (2 Cor 5:14) (35).”Finding the BalanceUrged on by the love of Christ, it is easy to lose balance. “When we consider the immensity of others' needs, we can, on the one hand,” Pope Benedict says, “be driven towards an ideology that would aim at doing what God's governance of the world apparently cannot: fully resolving every problem. Or we can be tempted to give in to inertia, since it would seem that in any event nothing can be accomplished (36).”So, how can we overcome these temptations? How can we find balance and keep the seesaw from tipping completely one way or the other? Pope Benedict continues:“At such times, a living relationship with Christ is decisive if we are to keep on the right path, without falling into an arrogant contempt for man, something not only unconstructive but actually destructive, or surrendering to a resignation which would prevent us from being guided by love in the service of others. Prayer, as a means of drawing ever new strength from Christ, is concretely and urgently needed. People who pray are not wasting their time, even though the situation appears desperate and seems to call for action alone (36).”Personal Relationship with GodPrayer is vital if we are to live in and with God. Prayer is our life blood. Our entire life can become a prayer if continually drawn deeply from the well of Christ. With the scourge of secularism prowling and the misguided extreme arm of activism, Christians are engaged in charitable work. Prayer is the antidote to this worldly spirit. A personal relationship with our loving Father, through Christ, in the Holy Spirit is utterly necessary. Pope Benedict puts it this way:“Clearly, the Christian who prays does not claim to be able to change God's plans or correct what he has foreseen. Rather, he seeks an encounter with the Father of Jesus Christ, asking God to be present with the consolation of the Spirit to him and his work (37).”The Problem of EvilThis personal relationship with God is all the more necessary for us in the midst of the problem of evil. We can often experience bewilderment and fail to understand the world around us. In these moments, the Pope says:“Christians continue to believe in the ‘goodness and loving kindness of God' (Tit 3:4). Immersed like everyone else in the dramatic complexity of historical events, they remain unshakably certain that God is our Father and loves us, even when his silence remains incomprehensible (38).”Faith, Hope, and CharityIn the midst of darkness, we trust in the love of God which surpasses all understanding. The theological virtues of faith, hope, and charity, God's own life, have been instilled in our soul through the gift of Baptism. These theological virtues go together. Pope Benedict teaches us:“Hope is practiced through the virtue of patience, which continues to do good even in the face of apparent failure, and through the virtue of humility, which accepts God's mystery and trusts him even at times of darkness. Faith tells us that God has given his Son for our sake and gives us the victorious certainty that it is really true: God is love! It thus transforms our impatience and our doubts into the sure hope that God holds the world in his hands and that, as the dramatic imagery of the end of the Book of Revelation points out, in spite of all darkness he ultimately triumphs in glory (39).”We cannot see the full picture; only God does. And He has revealed so much to us. More than revelation, He has given us Himself. He shares in our humanity that we might share in His divinity. In Baptism, we are given the light and made to be the light to the world, in and through Christ. Pope Benedict sums up his first encyclical this way:“Faith, which sees the love of God revealed in the pierced heart of Jesus on the Cross, gives rise to love. Love is the light—and in the end, the only light—that can always illuminate a world grown dim and give us the courage needed to keep living and working. Love is possible, and we are able to practice it because we are created in the image of God. To experience love and in this way to cause the light of God to enter into the world—this is the invitation I would like to extend with the present Encyclical (39).”Conclusion (40 - 42)As was the practice with his predecessor, Pope Benedict dedicates the conclusion of his document to the Mother of God, Mary most holy. Truly, each of the saints are a beautiful witness to the charity of God in every way imaginable. However, there is a preeminence to the holy charity of our Blessed Mother, the first disciple of her Holy Son, Jesus Christ.I highly recommend reading the entire Conclusion (and the whole document, really) in full. But I would like to draw what resonated most with me. I simply love the way Pope Benedict speaks about our Blessed Mother. He says:“Mary's greatness consists in the fact that she wants to magnify God, not herself. She is lowly: her only desire is to be the handmaid of the Lord (cf. Lk 1:38, 48) She knows that she will only contribute to the salvation of the world if, rather than carrying out her own projects, she places herself completely at the disposal of God's initiatives (41).” How often do you and I carry out our own projects, without putting ourselves at the disposal of God's initiatives. Mary, form us, teach us! The Pope goes on:“Mary is a woman who loves. How could it be otherwise? As a believer who in faith thinks with God's thoughts and wills with God's will, she cannot fail to be a woman who loves. We sense this in her quiet gestures, as recounted by the infancy narratives in the Gospel. We see it in the delicacy with which she recognizes the need of the spouses at Cana and makes it known to Jesus. We see it in the humility with which she recedes into the background during Jesus' public life, knowing that the Son must establish a new family and that the Mother's hour will come only with the Cross, which will be Jesus' true hour (cf. Jn 2:4; 13:1). When the disciples flee, Mary will remain beneath the Cross (cf. Jn 19:25-27); later, at the hour of Pentecost, it will be they who gather around her as they wait for the Holy Spirit (cf. Acts 1:14) (41).”Mary was there through it all. By her “yes” the Word of God took flesh, provided by her own body. Even despite her prominence and grandeur, the humility of the Theotokos is led by love. She loves with the love of God flowing through her as a perfect vessel and she loves with a human, motherly love. The Pope goes on:“Mary, Virgin and Mother, shows us what love is and whence it draws its origin and its constantly renewed power. To her we entrust the Church and her mission in the service of love (42).”Here Pope Benedict ends with a prayer to the Theotokos, which I would like to end today's article with as well. Please join me in praying:“Holy Mary, Mother of God, you have given the world its true light, Jesus, your Son – the Son of God. You abandoned yourself completely to God's call and thus became a wellspring of the goodness which flows forth from him. Show us Jesus. Lead us to him. Teach us to know and love him, so that we too can become capable of true love and be fountains of living water in the midst of a thirsting world (42).”Amen. Get full access to Will Wright Catholic Podcast at www.willwrightcatholic.com/subscribe

Welcome to The Nonlinear Library, where we use Text-to-Speech software to convert the best writing from the Rationalist and EA communities into audio. This is: What are the best examples of object-level work that was done by (or at least inspired by) the longtermist EA community that concretely and legibly reduced existential risk?, published by Ben Snodin on February 11, 2023 on The Effective Altruism Forum. A motivating scenario could be: imagine you are trying to provide examples to help convince a skeptical friend that it is in fact possible to positively change the long-run future by actively seeking and pursuing opportunities to reduce existential risk. Examples of things that are kind of close but miss the mark There are probably decent historical examples where people reduced existential risk but where thoes people didn't really have longtermist-EA-type motivations (maybe more "generally wanting to do good" plus "in the right place at the right time") There are probably meta-level things that longtermist EA community members can take credit for (e.g. "get lots of people to think seriously about reducing x risk"), but these aren't very object-level or concrete Thanks for listening. To help us out with The Nonlinear Library or to learn more, please visit nonlinear.org.

Welcome to The Nonlinear Library, where we use Text-to-Speech software to convert the best writing from the Rationalist and EA communities into audio. This is: Experiment Idea: RL Agents Evading Learned Shutdownability, published by Leon Lang on January 16, 2023 on The AI Alignment Forum. Preface Produced as part of the SERI ML Alignment Theory Scholars Program - Winter 2022 Cohort. Thanks to Erik Jenner who explained to me the basic intuition for why an advanced RL agent may evade the discussed corrigibility measure. I also thank Alex Turner, Magdalena Wache, and Walter Laurito for detailed feedback on the proposal and Quintin Pope and Lisa Thiergart for helpful feedback in the last December SERI-MATS shard theory group meeting. This text was part of my deliverable for the shard theory stream of SERI-MATS. In it, I present an idea for an experiment that tests the convergent drive of modern model-based RL agents to evade shutdownability. If successful, I expect the project could serve as a means to communicate the problem of corrigibility to the machine learning community. As such, I also consider this project idea a submission of the Shutdown Problem Contest. I do not personally want to work on the project since doing experiments does not seem like my comparative advantage. Thus, with posting this project, I mainly seek collaborators and feedback. More on that in the conclusion. Introduction At some point in the future, we will train very advanced AI, possibly by some version of model-based reinforcement learning. Since it is hard to correctly specify the reward function or steer the inner learning dynamics of the agent, it may be desirable to make sure that the agent is corrigible, including shutdownability: “(1) A corrigible reasoner must at least tolerate and preferably assist the programmers in their attempts to alter or turn off the system.” Next to a literal shutdown button, one could also try to use the inner functions of the agent to effectively get the ability to shut it down. The idea would be to design a certain type of input such that, as a reaction to that input, a computation in the agent itself will cause it to perform a null action that doesn't do anything — which effectively equates to a shutdown. Scott Aaronson proposed this idea in a recent talk (timestamp: 57:10) in the form of a planted cryptographic backdoor, an idea he currently works on during his sabbatical at OpenAI. For this proposal, I consider a variation of that shutdown approach: instead of directly “planting” the shutdown behavior, I consider the possibility to train the behavior into the agent. Concretely, there is a specific input — here, an alert sound — that the agent receives at random times during training. Whenever that happens, the agent will receive a negative reward unless it performs the null action. Over time, the agent will learn to stop acting in these situations. During deployment, the input can then be shown to the agent whenever it performs harmful actions, which will ideally cause it to stop immediately. This shutdown approach has the following philosophical problem: if the agent performs world modeling, it will know that the alert sound will make it stop performing, and this may be counter to the more consequentialist motivations of the agent. Thus, we should expect that the agent will perform an action that makes it never hear the sound again — for example, by “putting in earplugs” — once it is advanced enough and has the ability to do so. This experimental proposal is meant to show this convergent behavior using contemporary model-based reinforcement learning. The remaining sections will discuss the following: A three-part training story for an advanced AI that evades corrigibility, including details for how to model the situation with contemporary reinforcement learning; More experimental details based on using MuZero; Caveats, what might go wrong; A short comparison to the original shutdown problem and how the proposal...

Welcome to The Nonlinear Library, where we use Text-to-Speech software to convert the best writing from the Rationalist and EA communities into audio. This is: Underspecification of Oracle AI, published by Rubi J. Hudson on January 15, 2023 on The AI Alignment Forum. Rubi and Johannes worked on this post as part of the SERI MATS program, with Evan Hubinger providing mentorship to both. Rubi also received mentorship from Leo Gao. Thanks to Paul Colognese and Nicholas Schiefer for discussions related to this post. An oracle is a type of AI system that only answers questions without taking any other actions in the world. Simulators and generative models, which have seen increased discussion recently (links: 1, 2, 3, 4), can be thought of as types of oracles. Such systems may simultaneously be powerful enough to generate a pivotal act while also being easier to align due to a more limited domain. One major concern with oracles is that the answers they give can still manipulate the world. If oracles are evaluated on predictive accuracy, this gives them an incentive to use their answers to affect the course of events and make the world more predictable. Concretely, we are concerned that oracles may make self-fulfilling prophecies (also known as self-confirming predictions or fixed points), where the act of making the prediction causes the predicted outcome to come true. Even if their manipulation does not terminate in a fixed point, attempts to influence the world towards predictability can be very dangerous. As one example, consider a highly trusted oracle asked to predict the stock market. If such an oracle predicts that stock prices will rise, then people buy based off that prediction and the price will in fact rise. Similarly, if the oracle predicts that prices will fall, then people will sell, causing prices to fall. For a more real world example, see this market and this market, each on whether a specific person will find a research/romantic partner. Here, high probabilities would indicate desirability of that person, while low probabilities would suggest some hidden flaw, either of which could influence whether potential partners decide to reach out and therefore how the market resolves. In both the stock market and partnership cases, multiple predictions are valid, so how does the oracle choose between them? Ideally, we would like it to choose the one that is “better” for humanity, but this now introduces an outer alignment question similar to an agentic AI acting directly on the world, and which we wanted to avoid by using oracles in the first place. Instead, what we can aim for is an oracle that does not take into account the consequences of the prediction it makes when choosing a prediction. Then, there is only one valid prediction for the oracle to make, since the rest of the world is constant from its perspective. This can be thought of as a type of partial agency, optimizing the prediction in some directions but not others. It would be extremely desirable as a safety property, removing all incentives for an oracle to manipulate the world. To emphasize the importance of this property, we introduce new terminology, dubbing oracles “consequence-blind” if they exhibit the desired behavior and “consequence-aware” if they do not. For an oracle, consequence-blindness is equivalent to following a lonely causal decision theory. The causal decision theory blinds the oracle to any acausal influence, while the loneliness component makes it blind to its influence on other agents, which are necessary intermediaries for a prediction to influence the world. In this post we will primarily consider an oracle trained via supervised learning on a historical data set. There are a number of different policies that could be learned which minimize loss on the training set, and we will explore the different ways these can generalize. We divide the generalization behavior into a number of different axes, and for each axis discuss the potenti...

Welcome to The Nonlinear Library, where we use Text-to-Speech software to convert the best writing from the Rationalist and EA communities into audio. This is: Trying to isolate objectives: approaches toward high-level interpretability, published by Arun Jose on January 9, 2023 on The AI Alignment Forum. Epistemic status: There's a lot in this post and my general approach while working on it that, in retrospect, wasn't thought out well enough. I'm posting it because I figure sharing flawed ideas is better than letting this languish in a doc forever, while I work on improving them. Thanks to Paul Colognese, Tassilo Neubauer, John Wentworth, and Fabien Roger for useful conversations, Kyle McDonell and Laria Reynolds for suggesting something that led me to some of the ideas I mention here, and Shoshannah Tekofsky for feedback on a draft. I spent some time trying to find the high-level structure in a neural net corresponding to a deep reinforcement learning model's objective. In this post, I describe some of the stuff I tried, thoughts from working on this, and mistakes I made. Context Many approaches in current interpretability work as I understand them involve understanding low-level components of a model (Circuits and subsequent work, for example), as a way of building up to more complex components. I think we can make progress from the opposite frontier at the same time, and try to identify the presence or nature of certain high-level mechanistic structures or properties in a model. Examples of this include verifying whether a model is doing optimization, whether it's myopic, isolating the objective of an optimizer, and essentially the entire class of properties with a singular answer for the entire model. I prefer to distinguish these directions as low-level and high-level interpretability respectively for descriptive clarity. The framing of best-case and worst-case transparency as laid out in the transparency tech tree also points at the same concept. I expect that both directions are aiming at the same goal, but working top-down seems pretty tractable at least for some high-level targets that don't require lots of deconfusion to even know what we're looking for. Ideally, an approach to high-level interpretability would be robust to optimization pressure and deception (in other words, to gradient descent and adversarial mesa optimizers), but I think there's a lot we can learn toward that ideal from more naive approaches. In this post, I describe my thoughts on trying to identify the high-level structure in a network corresponding to a deep reinforcement learning model's objective. So I didn't set out on this particular approach expecting to succeed at the overall goal (I don't expect interpretability to be that easy), instead hoping that trying out a bunch of naive directions will give us insight into future high-level interpretability work. To that end, I try to lay out my reasoning at various points, riddled with gaps at points, while thinking about this. I think there are plenty of plausible avenues to try out here, and for the most part will only be describing the problems with ones I thought of. Further, the directions I tried don't seem very non-obvious and there are likely much better methods to be tried given more thought here. A naive approach and patching Take the case of a small RL model that can optimize for a reward in a given environment. What we want is information about how the model's objective is represented in the network's weights. Concretely, we might think about what kind of tests or processes disproportionately affect these weights more than any other, and try to isolate the information we want from there. One approach we might try is: Train the initialized model on reward RA for an extended period of time. Let's call the model at the end of this step MA. We may think of MA as having learned a pretty sophisticated world model at this point, such that further training wouldn't update it strongly. Tra...

Welcome to The Nonlinear Library, where we use Text-to-Speech software to convert the best writing from the Rationalist and EA communities into audio. This is: 200 COP in MI: Exploring Polysemanticity and Superposition, published by Neel Nanda on January 3, 2023 on The AI Alignment Forum. This is the fifth post in a sequence called 200 Concrete Open Problems in Mechanistic Interpretability. Start here, then read in any order. If you want to learn the basics before you think about open problems, check out my post on getting started. I'll make another post every 1-2 days, giving a new category of open problems. If you want to read ahead, check out the draft sequence here! Motivating paper: Toy Models of Superposition, Softmax Linear Units Background If you're familiar with polysemanticity and superposition, skip to Motivation or Problems. Neural networks are very high dimensional objects, in both their parameters and their activations. One of the key challenges in Mechanistic Interpretability is to somehow resolve the curse of dimensionality, and to break them down into lower dimensional objects that can be understood (semi-)independently. Our current best understanding of models is that, internally, they compute features: specific properties of the input, like "this token is a verb" or "this is a number that describes a group of people" or "this part of the image represents a car wheel". That early in the model there are simpler features, are later used to compute more complex features by being connected up in a circuit (example shown above (source)). Further, our guess is that features correspond to directions in activation space. That is, for any feature that the model represents, there is some vector corresponding to it. And if we dot product the model's activations with that vector, we get out a number representing whether that feature is present.(these are known as decomposable, linear representations) This is an extremely useful thing to be true about a model! An even more helpful thing to be true would be if neurons correspond to features (ie the output of an activation function like ReLU). Naively, this is natural for the model to do, because a non-linearity like ReLU acts element-wise - each neuron's activation is computed independently (this is an example of a privileged basis). Concretely, if a neuron can represent feature A or feature B, then that neuron will fire differently for feature A and NOT feature B, vs feature A and feature B, meaning that the presence of B interferes with the ability to compute A. But if each feature is its own neuron we're fine! If features correspond to neurons, we're playing interpretability on easy mode - we can focus on just figuring out which feature corresponds to each neuron. In theory we could even show that a feature is not present by verifying that it's not present in each neuron! However, reality is not as nice as this convenient story. A countervailing force is the phenomena of superposition. Superposition is when a network represents more features than it has dimensions, and squashes them all into a lower dimensional space. You can think of superposition as the model simulating a larger model. Anthropic's Toy Models of Superposition paper is a great exploration of this. They build a toy model that learns to use superposition (notably different from a toy language model!). The model starts with a bunch of independently varying features, needs to compress these to a low dimensional space, and then is trained to recover each feature from the compressed mess. And it turns out that it does learn to use superposition! Specifically, it makes sense to use superposition for sufficiently rare (sparse) features, if we give it non-linearities to clean up interference. Further, the use of superposition can be modelled as a trade-off between the costs of interference, and the benefits of representing more features. And digging further into their toy models, they find all kinds of fascin...

Welcome to The Nonlinear Library, where we use Text-to-Speech software to convert the best writing from the Rationalist and EA communities into audio. This is: 200 COP in MI: Interpreting Algorithmic Problems, published by Neel Nanda on December 31, 2022 on The AI Alignment Forum. This is the fourth post in a sequence called 200 Concrete Open Problems in Mechanistic Interpretability. Start here, then read in any order. If you want to learn the basics before you think about open problems, check out my post on getting started. I'll make another post every 1-2 days, giving a new category of open problems. If you want to read ahead, check out the draft sequence here! Motivation Motivating paper: A Mechanistic Interpretability Analysis of Grokking When models are trained on synthetic, algorithmic tasks, they often learn to do some clean, interpretable computation inside. Choosing a suitable task and trying to reverse engineer a model can be a rich area of interesting circuits to interpret! In some sense, this is interpretability on easy mode - the model is normally trained on a single task (unlike language models, which need to learn everything about language!), we know the exact ground truth about the data and optimal solution, and the models are tiny. So why care? I consider my work on grokking to be an interesting case study of this work going well. Grokking (shown below) is a mysterious phenomena where, when small models are trained on algorithmic tasks (eg modular addition or modular division), they initially memorise the training data. But when they keep being trained on that data for a really long time, the model suddenly(ish) figures out how to generalise! In my work, I simplified their setup even further, by training a 1 Layer transformer (with no LayerNorm or biases) to do modular addition and reverse engineered the weights to understand what was going on. And it turned out to be doing a funky trig-based algorithm (shown below), where the numbers are converted to frequencies with a memorised Discrete Fourier Transform, added using trig identities, and converted back to the answer! Using this, we looked inside the model and identified that despite seeming to have plateaued, in the period between memorising and "grokking", the model is actually slowly forming the circuit that does generalise. But so long as the model still has the memorising circuit, this adds too much noise to have good test loss. Grokking occurs when the generalising circuit is so strong that the model decides to "clean-up" the memorising circuit, and "uncovers" the mature generalising circuit beneath, and suddenly gets good test performance. OK, so I just took this as an excuse to explain my paper to you. Why should you care? I think that the general lesson from this, that I'm excited to see applied elsewhere, is using toy algorithmic models to analyse a phenomena we're confused about. Concretely, given a confusing phenomena like grokking, I'd advocate the following strategy: Simplify to the minimal setting that exhibits the phenomena, yet is complex enough to be interesting Reverse-engineer the resulting model, in as much detail as you can Extrapolate the insights you've learned from the reverse-engineered model - what are the broad insights you've learned? What do you expect to generalise? Can you form any automated tests to detect the circuits you've found, or any of their motifs? Verify by looking at other examples of the phenomena and seeing whether these insights actually hold (larger models, different tasks, even just earlier checkpoints of the model or different random seeds) Grokking is an example in a science of deep learning context - trying to uncover mysteries about how models learn and behave. But this same philosophy also applies to understanding confusing phenomena in language models, and building toy algorithmic problems to study those! Anthropic's Toy Models of Superposition is an excellent example of this done well, for the case of ...

Matt Coffman is one of my best and oldest friends. It's actually hilarious that it took 44 interviews to get him on the show, and my intention – with you as my witness, dear listener – is that there will never been a gap that long again.   For this conversation, I wanted to flip our normal narrative about purpose on its head, and get into the question of what is stopping the humans (you, me, everyone) from living our purpose.   True to form, Matt starts at the beginning. The preconditions for purpose. And then methodically investigates all the barriers, from belief onwards, towards somebody living their purpose. It's a create episode full of concrete examples from Matt's lived experience.    10,000 Heroes. 10,000 Purposes. 10,000 Gifts.   Enjoy ;)   Show Links: Voicemail:  https://www.speakpipe.com/10khshow Email: info@10kh.show Podcast website: http://www.10kh.show Momentum Lab: http://www.momentumlab.com   Guest References: Matt's not big on online presence, but the coaching program we run does have a website: www.momentumlab.com

Welcome to The Nonlinear Library, where we use Text-to-Speech software to convert the best writing from the Rationalist and EA communities into audio. This is: Re-Examining LayerNorm, published by Eric Winsor on December 1, 2022 on LessWrong. Please check out the colab notebook for interactive figures and more detailed technical explanations. This post is part of the work done at Conjecture. Special thanks to Sid Black, Dan Braun, Carlos Ramón Guevara, Beren Millidge, Chris Scammell, Lee Sharkey, and Lucas Teixeira for feedback on early drafts. There's a lot of non-linearities floating around in neural networks these days, but one that often gets overlooked is LayerNorm. This is understandable because it's not "supposed" to be doing anything; it was originally introduced to stabilize training. Contemporary attitudes about LayerNorm's computational power range from "it's just normalizing a vector" to "it can do division apparently". And theories of mechanistic interpretability such as features as directions and polytopes are unhelpful, or even harmful, in understanding normalization's impact on a network's representations. After all, normalization doesn't alter the direction of vectors, but it still bends lines and planes (the boundaries of polytopes) out of shape. As it turns out, LayerNorm can be used as a general purpose activation function (you can solve MNIST with a LayerNorm MLP, for example). Concretely, it can do things like this: and this: We will explain what's going on in these animations later, but the point is that to develop a strong, principled theory of mechanistic interpretability, we need to grapple with this non-linearity. In this interactive notebook, we study LayerNorm systematically using math and geometric intuition to characterize the ways in which it can manipulate data. We show that the core non-linearity of LayerNorm can be understood via simple geometric primitives. We explain how these basic primitives may perform semantic operations. For example, folding can be viewed as extracting extremal features (e.g. separating extreme temperatures from normal ones). We leverage these primitives to understand more complex low-dimensional classification tasks with multiple layers of non-linearities. The methods and intuition developed here extend to non-linearities beyond LayerNorm, and we plan to extend them in future work. Below is an interactive summary of the content of the notebook. (If you find the summary interesting, you really should check out the notebook: manipulating the figures helps build geometric intuition.) Summary The formula for LayerNorm is something messy like But it turns out the core non-linear operation is (almost) normalizing a vector: Graphically, this function has the iconic sigmoid shape in one dimension (note that in 1D the norm is simply the absolute value). Interesting things start happening when we precompose this normalization function with affine transformations (such as scaling and shifting). Below, we start with a collection of points, (x,y), distributed uniformly on the sphere. Then we compute uϵ(tx,y) (stretch/shrink by a factor of t in the x direction and then normalize). Since normalization guarantees points end up on the circle, this operation stretches the distribution along the circle. The right hand panel illustrates how this stretching is captured in the shape of a 1D activation function (the x coordinate of the input against the x coordinate of the output). For example, when t is close to 5, we see that any points with x coordinates not near 0 get compressed towards x=−1 or x=1. This matches the picture in the circle where we see the points bunch up at the left and right sides of the circle. If we make the same plots with a shifting operation, uϵ(x+t,y), the operation folds the input data. We can envision possible "semantic" uses for these geometric operations. Stretching can be used to perform an approximate "sign" operation (as in + or - sign). For example, it ...

Welcome to The Nonlinear Library, where we use Text-to-Speech software to convert the best writing from the Rationalist and EA communities into audio. This is: Training for Good - Update & Plans for 2023, published by Cillian Crosson on November 15, 2022 on The Effective Altruism Forum. Summary Training for Good will now focus exclusively on programmes that enable talented and altruistic early-career professionals to directly enter the first stage of high impact careers. Concretely, we will only run the following programmes in Sep 2022 - Aug 2023: 1. EU Tech Policy Fellowship. 2. Tarbell Fellowship (journalism) 3. An unannounced 3rd programme which is still under development Applications for the 2023 EU Tech Policy Fellowship are open until December 11. Apply here. In year 1, we experimented with ~7 different programmes, 6 of which have now been discontinued. This is largely because we believe that focus is important & wanted to double-down on the most promising programme we had identified thus far (the EU Tech Policy Fellowship which successfully placed 7 fellows in relevant European think tanks focused on emerging technology policy). We plan to have an external review of Training for Good conducted between July 2023 - December 2023. We will default to sharing this publicly. Introduction Training for Good is an impact-focused training organisation, incubated by Charity Entrepreneurship in 2021. Quite a lot has changed since our launch in September 2021. We considered our first year to be an exploratory period in which we ran many, many different projects. We've now discontinued the majority of these programmes and have narrowed our focus to running fellowships that directly place early-career individuals in impactful careers. Now that TFG has a clearer focus, we're writing this post to update others in the EA community on our activities and the scope of our organisation. What we do Training for Good runs fellowships that place talented professionals in impactful careers in policy, journalism & other areas. We do this by providing a combination of stipends, mentorship from experienced professionals, training and placements in relevant organisations. Between Sep 2022 - Aug 2023 (i.e. year 2), we plan to only run the following programmes: EU Tech Policy Fellowship Tarbell Fellowship An unannounced 3rd programme which is still under development Why this might be important Many high impact career paths are neglected by talented and altruistic people, often because they lack clear pathways for entry. This is limiting progress on some of the world's most important problems: reducing existential risk, ending factory farming and tackling global poverty. TFG seeks to provide concrete opportunities for early-career professionals to gain entry level roles in impactful career paths that are difficult to enter. Building these talent pipelines could be important because: Direct progress on problems: Talented individuals in these career paths can directly contribute to progress on solving the world's most important problems Closer towards the “ideal portfolio”: We mostly take a portfolio approach to doing good. One could imagine an optimal distribution of talent within the effective altruism community, which might involve people pursuing a variety of different career paths. With our fellowships, we are attempting to move the effective altruism community closer towards this ideal allocation by enabling people to pursue paths that we believe are currently underrepresented (and expect to remain so) within this community's portfolio. We believe that thinking in these terms is particularly useful partly due to: Diminishing returns from certain career paths Epistemic uncertainty about which career paths are best (and the associated information value from reducing this uncertainty somewhat) Differing personal fit for individuals across different career paths Concrete opportunities: The number of people interested in effective altruism has been g...

Welcome to The Nonlinear Library, where we use Text-to-Speech software to convert the best writing from the Rationalist and EA communities into audio. This is: How To Make Prediction Markets Useful For Alignment Work, published by johnswentworth on October 18, 2022 on LessWrong. So, I'm an alignment researcher. And I have a lot of prediction-flavored questions which steer my day-to-day efforts. Which recent interpretability papers will turn out, in hindsight a year or two from now, to have been basically correct and high-value? Will this infrabayes stuff turn out to be useful for real-world systems, or is it the kind of abstract math which lost contact with reality and will never reconnect? Is there any mathematical substance to Predictive Processing other than “variational inference is a thing” plus vigorous hand-waving? How about Shard Theory? What kinds of evidence will we see for/against the Natural Abstraction Hypothesis over the next few years? Will HCH-style amplification ever be able to usefully factor problems which the human operator/programmer doesn't immediately see how to factor? Will some version of this conjecture be proven? Will it be proven by someone else if I don't focus on it? Are there any recent papers/posts which lots of other people expect to have high value in hindsight, but which I haven't paid attention to? Or, to sum that all up in two abstract questions: what will I (or someone else whose judgement I at least find informative) think I should have paid more attention to, in hindsight? What will it turn out, in hindsight, that I should have ignored or moved on from sooner? Hmm, I wonder if the prediction markets have anything useful to say here? Let's go look for AI predictions on Manifold. hollywood-level AI-generated feature film by 2026? Will an AI get gold on any International Math Olympiad by 2025? Will AI wipe out humanity before the year 2100 Will any Fortune 500 corporation mostly/entirely replace their customer service workforce with AI by 2026? So, we've got about a gazillion different flavors of AI capabilities questions, with a little bit of dabbling into how-society-will-react-capabilities questions. On the upside, operationalizing things in lots of different ways is exactly what we want. On the downside, approximately-all of the effort is in operationalizing one thing (AI capabilities timelines), and that thing is just not particularly central to day-to-day research decisions. It's certainly not on the list of questions which first jump to mind when I think of things which would be useful to know to steer my research. Sure, somebody is going to argue in the comments that timelines are relevant for some particular decision, like whether to buy up GPU companies or something, but there's no way in hell that focusing virtually all alignment-related prediction-market effort on operationalizations of capabilities timelines is actually the value-maximizing strategy here. (And while I happened to open up Manifold first, the situation is basically the same in other prediction markets. To a first approximation, the only alignment-adjacent question prediction markets ever weigh in on is timelines.) So, here's my main advice to someone who wants to use prediction markets to help alignment work: imagine that you are an alignment researcher/grantmaker/etc. Concretely imagine your day-to-day: probing weight matrices in nets, conjecturing, reading papers/posts, reviewing proposals, etc. Then, ask what kind of predictions have the highest information value for your work. If the answer is “yet another operationalization of timelines”, then you have probably fucked up somewhere. Of course you might also try asking some researchers or grantmakers the same question, though keep in mind the standard user-interview caveat: users do not actually know what they want or would like. Thanks for listening. To help us out with The Nonlinear Library or to learn more, please visit nonlinear.org.

Welcome to The Nonlinear Library, where we use Text-to-Speech software to convert the best writing from the Rationalist and EA communities into audio. This is: Reflection Mechanisms as an Alignment target: A follow-up survey, published by Marius Hobbhahn on October 5, 2022 on The AI Alignment Forum. This is the second of three posts (part I) about surveying moral sentiments related to AI alignment. This work was done by Marius Hobbhahn and Eric Landgrebe under the supervision of Beth Barnes as part of the AI safety camp 2022. TL;DR: We find that the results of our first study, i.e. that humans tend to agree with conflict resolution mechanisms hold under different wordings but are weakened in adversarial scenarios (where we actively try to elicit less agreement). Furthermore, we find that people tend to agree much less with a mechanism when the decision-maker is a smart benevolent AI rather than a smart benevolent human. A positive interpretation of these findings is that humans are fine with giving power to a conflict resolution mechanism as long as humans are ultimately in control. Introduction In the first post, we surveyed 1000 US respondents about their moral beliefs, the conditions under which they would change their moral beliefs and how they felt about mechanisms to resolve moral disagreements such as democracy or debate. Our main findings of the first survey were Unsurprisingly, people have very different moral beliefs, e.g. on the morality of abortions, immigration or eating meat. They very rarely report to changing or wanting to change these beliefs, e.g. most participants (>80%) report having not changed them in the last 10 years and do not expect to change them in the next 10 years. However, they mostly think that mechanisms to resolve moral disagreements, such as democracy or debate, are good even when they disagree with the outcome. In other words, people are willing to accept outcomes that are different from their beliefs if they trust the process by which it was derived. We think this finding has some implications for AI alignment. Most importantly, we think that the alignment target of an AI should be a mechanism to resolve moral conflicts such as democracy or debate rather than a set of individual beliefs, e.g. about abortion or veganism. This is more likely to lead to decisions that can be accepted by most people and prevent a race from “aligning an AI to your personal beliefs at the cost of other people's beliefs”. We think the main weakness of our first result was that it could have been a result of confounders or randomness. We asked people for their opinions on multiple scenarios, but our questions still had some shortcomings. These include: Specificity: Maybe people would change their minds if they had a very specific scenario in mind rather than just an abstract question. Robustness: Maybe the way we ask our questions makes one answer more likely than another. Social desirability: Maybe people answer what they think is socially desirable. Robustness to different scenarios: Maybe people's opinions change when we change key variables of the process, e.g. when we specify a bad outcome or when we change who makes the decisions. Relation to AI: We never talk about AIs specifically, we always talk about smart, benevolent and nice future humans (see below for details). Therefore, we ran a follow-up study that attempts to address these problems. Concretely, we first run robustness tests by asking the same question in different ways. Then we run adversarial tests where we actively try to get people to give more disagreeing answers. Finally, we ask the same question that was asked with smart, benevolent and nice future humans but replace “humans” with “AIs”. Main findings Robustness To test the robustness of our questions, we randomly assigned one of the following three questions to a participant. Basic: This was the same scenario as in the previous study and was mostly used to provide a reference. People wer...

Welcome to The Nonlinear Library, where we use Text-to-Speech software to convert the best writing from the Rationalist and EA communities into audio. This is: Representational Tethers: Tying AI Latents To Human Ones, published by Paul Bricman on September 16, 2022 on The AI Alignment Forum. This post is part of my hypothesis subspace sequence, a living collection of proposals I'm exploring at Refine. Preceded by ideological inference engines. Thanks Adam Shimi, Alexander Oldenziel, Tamsin Leake, and Ze Shen for useful feedback. TL;DR: Representational tethers describe ways of connecting internal representations employed by ML models to internal representations employed by humans. This tethering has two related short-term goals: (1) making artificial conceptual frameworks more compatible to human ones (i.e. the tension in the tether metaphor), and (2) facilitating direct translation between representations expressed in the two (i.e. the physical link in the tether metaphor). In the long-term, those two mutually-reinforcing goals (1) facilitate human oversight by rendering ML models more cognitively ergonomic, and (2) enable control over how exotic internal representations employed by ML models are allowed to be. Intro The previous two proposals in the sequence describe means of deriving human preferences procedurally. Oversight leagues focus on the adversarial agent-evaluator dynamics as the process driving towards the target. Ideological inference engines focus on the inference algorithm as the meat of the target-approaching procedure. A shortcoming of this procedural family is that even if you thankfully don't have to plug in the final goal beforehand (i.e. the resulting evaluator or knowledge base), you still have to plug in the right procedure for getting there. You're forced to put your faith in a self-contained preference-deriving procedure instead of an initial target. In contrast, the present proposal tackles the problem from a different angle. It describes a way of actively conditioning the conceptual framework employed by the ML model to be compatible with human ones, as an attempt to get the ML model to form accurate conceptions of human values. If this sounds loosely relates to half a dozen other proposals, that's because it is — consider referring to the Discussion for more details on tangents. In the meantime, following the structure of the previous posts in the sequence, here are some assumptions underlying representational tethers: Assumption 1, "Physicalism": Our thoughts are represented as neural dynamics. In the limit of arbitrarily large amounts of data on neural dynamics aligned with external stimuli (in the sense of parallel corpora), our thoughts can be accurately reconstructed. Assumption 2, "Bottleneck Layer": There is a bottleneck layer in the architecture of the ML model being tethered to human representations. This bottleneck refers to a low-dimensional representation through which all the information being processed by the ML model is forced to pass. Assumption 3, "AGI Hard, Human Values Harder": We are unlikely to formulate the True Name of human values in closed-form before deploying transformative AI. The best we are likely to do before takeoff is model human values approximately and implement an imperfect evaluator. Proposal Representational tethers suggest a way of aligning human and AI latents for the purpose of facilitating later interaction. There are two steps to this: First Bring Them Closer Incentivize the ML model to employ internal representations which are compatible with human ones, thus bringing them "closer." This can be operationalized by conditioning latent activations which arise in the ML model to be expressible in human representations. Concretely, optimization pressure would be exerted on the ML model to push it to internalize a conceptual framework which can successfully be translated to and from a human one without significant loss of information. If the artificial repre...

Welcome to The Nonlinear Library, where we use Text-to-Speech software to convert the best writing from the Rationalist and EA communities into audio. This is: Finding before funding: Why EA should probably invest more in research, published by Falk Lieder on August 17, 2022 on The Effective Altruism Forum. EA grantmakers dramatically differ in how they split their funds between doing good with the tools and knowledge we already have versus improving them through scientific research. This raises the question of which proportion of its funds the EA community should invest in scientific research. Are we investing the correct percentage of our funds in scientific research? Should it be lower? Or should it be higher than it currently is? We recently found that funding scientific research that aims to enable or create improved interventions can be 2-5 times as cost-effective as investing in the interventions that already exist. This seems plausible given that scientific research gave us the tools and knowledge we can now use to improve the future of humanity. If previous generations had not funded scientific research and R&D projects, there would be no medicines for preventing malaria, no long-lasting insecticide treated bed nets, no supplements for preventing vitamin A deficiency, no deworming tablets, and no vaccines. Strategically funding relevant scientific research could generate even more cost-effective opportunities to do good. But the more money we invest in research the less money we will have left for applying our best interventions. To help funding agencies and the EA community navigate this dilemma, we investigated how the total investment into a cause should be split between scientific research that might enable us to do more good in the future and doing good with the tools and knowledge that happen to be already available today. Assumptions Our analysis makes the following three assumptions: On average, additional research and R&D projects produce interventions that are substantially less cost-effective than the best interventions that already exist. The cost-effectiveness of newly developed interventions is variable. Therefore, research occasionally succeeds in inventing superior interventions, and the cost-effectiveness of those interventions can be substantially higher. When a more cost-effective intervention is invented, then the funding that the best previous intervention would have received will be invested into the new intervention instead. We measure the cost-effectiveness of research in the same units as the cost-effectiveness of the existing interventions it might help us improve on. The expected increase in the amount of good we can do with the improved interventions is a lower bound on the value of research. Approach We performed more than 300 million computer simulations to determine which way of splitting a fixed budget between developing better interventions and deploying interventions would generate the most good in total in expectation across different scenarios. For each scenario, we estimated the total good that could be achieved by first funding 0, 1, 2, 3, ., or 1000 research projects and then optimally allocating the remaining funds between the best interventions that resulted from the research and the best intervention that were available prior to the research. We then derived the optimal size of the research budget from the number of funded research projects that maximized the total good that the funding agency accomplished on average across 10,000 simulations. To derive robust recommendations, we replicated the main findings of our simulations across a wide range of conservative assumptions about the effectiveness of scientific research, the variability in the cost-effectiveness of new interventions. Concretely, we varied the average cost-effectiveness of new interventions between 10% and 110% of the cost-effectiveness of the best existing interventions with a standard deviation between 1% ...

Changelog I've created a playground in the mono-repo for ReactJS experiments I have started working on a very lightweight design system I am giving a talk to PHP Minds next Thursday. The talk is called Code with Confidence with PHPCS PHP UK has announced their CFP and released early bird tickets. I have tickets. This weekend I am off to see England versus Scotland in the Common wealth boxing News/Articles How to call rest APIs using React https://www.smashingmagazine.com/2020/06/rest-api-react-fetch-axios/ How to use the React Router to create an image link https://bobbyhadz.com/blog/react-image-link React higher order components A higher-order component (HOC) is an advanced technique in React for reusing component logic. HOCs are not part of the React API, per se. They are a pattern that emerges from React's compositional nature. Concretely, a higher-order component is a function that takes a component and returns a new component. https://reactjs.org/docs/higher-order-components.html What is a Design system A collection of reusable components A place for collaboration amongst designers and developers A set of standards that must be adhered too A set of guidelines and principles that are relevant to the business Not just a component library A component library is a library of reusable components that other designers and developers can use. A design system also sets out a series of rules and standards for the components and surrounding design. These include but are not limited to Typography Spacing Branding User experience Language Voice and tone Colours Patten Often, a design system will explain what is allowed and what is not allowed. Examples of design systems IBM https://carbondesignsystem.com/ Shopify https://polaris.shopify.com/ Material design https://material.io/design UK Gov https://design-system.service.gov.uk/ You can get in touch with me here: https://howtocodewell.fm/contact My web development courses ➡️ Learn How to build a JavaScript Tip Calculator ➡️ Learn JavaScript arrays ➡️ Learn PHP arrays ➡️ Learn Python ✉️ Get my weekly newsletter ⏰ My current live coding schedule (Times are BST) Tuesdays 18:00 - Live coding on YouTube Sundays 15:00 - Live coding on Twitch

Welcome to The Nonlinear Library, where we use Text-to-Speech software to convert the best writing from the Rationalist and EA communities into audio. This is: Four questions I ask AI safety researchers, published by Akash on July 17, 2022 on The Effective Altruism Forum. Over the last few months, I've been trying to develop a stronger inside view on AI safety research agendas. As part of this quest, I've been having conversations with AI safety researchers. I notice myself often asking the following questions: What are you working on, and how does it help us get aligned AI? Imagine you're talking to a smart high school student. How would you describe the alignment problem? And how would you describe how your work addresses it? Imagine that you came up with a solution to the specific problem you're working on. Or even more boldly, imagine your entire program of research succeeds. What happens next? Concretely, how does this help us get aligned AI (and prevent unaligned AI)? What are the qualities you look for in promising AI safety researchers? (beyond general intelligence) I find Question #1 useful for starting the conversation and introducing me to the person's worldview. I find Question #2 useful for getting clearer (and often more detailed) explanations of the person's understanding of the alignment problem & how their work fits in. (Note that this is somewhat redundant with question #1, but I find that the questions often yield different answers). I find Question #3 useful for starting to develop an inside view on the research agenda & the person's reasoning abilities. (Note that the point is to see how the person reasons about the question. I'm not looking for a “right answer”, but I am looking to see that someone has seriously thought about this and has reasonable takes.) I find Question #4 useful for building stronger models of AI safety community-building. I also ask a lot of follow-up questions. But I find that these are the four questions that I ask nearly everyone, and I think they've helped me develop a stronger inside view. If I had to pick one question, I would probably ask #3. I think it's a rather challenging question, and I'm generally impressed when people have coherent answers. Thanks for listening. To help us out with The Nonlinear Library or to learn more, please visit nonlinear.org.

Welcome to The Nonlinear Library, where we use Text-to-Speech software to convert the best writing from the Rationalist and EA communities into audio. This is: Grouped Loss may disfavor discontinuous capabilities, published by Adam Jermyn on July 9, 2022 on The AI Alignment Forum. Thanks to Evan Hubinger and Beth Barnes for comments on these ideas. Language models exhibit scaling laws, where the loss is a power-law in model size. This offers a lot of predictive power, and seems like a useful thing to know. By contrast, individual capabilities can exhibit sharp discontinuities in performance as a function of model size and training time. It would be great if individual capabilities just gradually improved like the broader loss. Then we wouldn't have to worry quite so much about surprising new capabilities emerging suddenly during training. Is there a way to change the loss function so that it incentivizes more gradually capability improvements? Grouped Loss Imagine grouping training examples by the kind of capability they exhibit. For instance arithmetic problems go in one group, “parse json” could go in another, and so on. With these groups, we could define a new loss function where ℓ is the loss function we originally used (e.g. cross-entropy loss) and ℓg means to compute the sum of ℓ over examples from group g, e.g. which may be estimated by using random examples drawn from g. Because we have squared the group losses, the overall loss is dominated by the worst group. As a result, the model is incentivized to develop capabilities in each group at comparable rates, and so has little incentive to e.g. finely hone its poetry skills while being unable to multiply numbers. Challenge: Defining Groups It's possible that using grouped loss results in smooth development of capabilities that aren't represented in the groups. For instance, it seems plausible that if “adding arabic numerals” and “translating words into arabic numerals” are two groups but “adding numbers written as words” is not, performance on the latter could nonetheless develop smoothly as the model gets better at the others. It would certainly be weird if performance ”adding numbers written as words” advanced as a sudden leap in this case. This points to a general problem though, which is that if we have to define the groups manually we have to foresee the capabilities we're worried about. That seems bad. Gradient Cluster Grouping If we could automatically group examples we wouldn't need to do it manually. How could we do this? I think the key feature of a group is that when the model updates, the loss of most examples in a group changes in a similar way. When that happens, it seems intuitive to say that there's a discrete capability somewhere in the model and that those examples all depend on it. This suggests looking for examples where the loss has similar gradients, because these probably make use of similar machinery in the model. Concretely, I'm imagining the following procedure: Draw N examples from the training set. Evaluate the gradient of ℓ for each example. Group the examples by clustering their gradients, evaluate the grouped loss, and perform gradient descent on that. As a technical note: In this approach, the grouped loss is a moving target. As the model learns and capabilities form the groups shift. This means that SGD is no longer minimizing a constant loss. I don't think that's a problem, in part because all versions of the loss agree when the model has reached zero-loss, so the different iterations of the loss function all point towards better capabilities. Challenge: How many groups? I don't know of a principled way to pick the number of groups to cluster examples into, and that seems like a problem. Guessing too many groups loses the advantage of grouping because each group reflects an extremely narrow task. Guessing too few groups also loses the advantage of grouping, because then the capabilities that show gradual improvements will be very broa...

Welcome to The Nonlinear Library, where we use Text-to-Speech software to convert the best writing from the Rationalist and EA communities into audio. This is: Nuclear attack risk? Implications for personal decision-making, published by SebastianSchmidt on February 27, 2022 on The Effective Altruism Forum. Summary: With Russia's invasion and Putin's gestures towards using nuclear weapons, we seemed to have passed a threshold where it makes sense to spent at least a couple of days thinking about this. Me and a couple of friends are thinking about what might be reasonable approaches from a personal decision-making perspective and would love your input. There's a war between Russia and Ukraine and Putin (who has control over the largest amount of nuclear warheads) appears to be threatening with nuclear war if "The West" interferes. Specifically, he says "Whoever tries to hinder us should know that Russia's response will be immediate, and it will lead you to such consequences that you have never encountered in your history" (source). Additionally, he appears to be paranoid and believes (or at least want his people to believe) that "Western Patriots" will bring weapons of mass destruction to Ukraine and "help it acquire these weapons to create yet another threat to our country.". Given this apparent paranoia, thirst for power, and his actual power, the risk of some form of nuclear attack seems to pass a threshold to think about it for at least one or two days. From a personal decision-making perspective, it seems reasonable to optimize for personal safety (in the case of nuclear war) and the ability to contribute to the long-term flourishing of our civilization - including this threat. Concretely, a couple of friends and me are considering changing location away from the UK and mainland Europe in a easily reversible and low cost way (e.g., home office from Morocco) while recurringly reassessing the situation for signals of escalation or de-escalation. The reasoning behind moving away Europe is due to the likelihood of a first-strike on states with nuclear weapons (UK and France) and the subsequent outbreak of panic with an increased difficulty to leave the country and questionable ability to maintain a robust infrastructure for living (e.g., food supply). PS. There's also a lot of interesting predictions on metaculus. What do you think about the reasoning and the tentative personal implications? Also, do you have any input on how a highly skilled personal and professional coach can contribute to the mitigation of this risk? Thanks for listening. To help us out with The Nonlinear Library or to learn more, please visit nonlinear.org.

Welcome to The Nonlinear Library, where we use Text-to-Speech software to convert the best writing from the Rationalist and EA communities into audio. This is: REPL's and ELK, published by scottviteri on February 17, 2022 on The AI Alignment Forum. In my previous post I talked about read-eval-print loops as providing a type signature for agents. I will now explain how you can quickly transition from this framework to an ELK solution. Notation is imported from that post. Imagine we have two agents, a human and a strong AI, denoted H and M respectively. They both interact with the environment in lockstep, according to the following diagram. We have the human's utility function UH:SH→Q, which is defined on the human's model of reality. We would like to lift UH to a version UM:SM→Q that the machine can use to influence the world in way that is agreeable to the human, which we can do by learning a mapping F : SM→SH and deriving UM=F∘UH. But we haven't yet said what properties we want the ontology map F to have. I want to call two concepts sh and sm equal if they act the same with respect to transformation: ∀f. f(sh) = f(sm) → sh = sm. The issue is that since the concepts have different types we cannot feed them as arguments to the same function. So instead let's say that ∀s:S, EvalH(sh, ReadH(s)) = EvalM(sm, ReadM(s)) → sh = sm. But now we are back to the same problem where we are trying to compare concepts in two different ontologies. But this does give us a kind of inductive step where we can transfer evidence of equality between concept pairs (sh, sm) and (sh', sm'). I also believe that this kind of a coherence argument is the best we can do, since we are not allowed to peer into the semantic content of particular machine or human states when constructing the ontology map. Consider the following two graphs. My intuition is that even if I don't know the labels of the above graphs, I can still infer that the bottom nodes correspond to each other. And the arrows that I get in the context of ELK are the agents' Eval transitions, leading to the following commutative diagram specification for F. We can learn an ontology map F : SM→SH by minimizing the difference between two paths from a state sm, one in which the machine's prediction function is used and one in which the human's prediction function is used. Concretely, I propose minimizing Dist(sh1,sh2) + λ |U(sh1)-U(sh2)| where sh1 = F(EvalM(sm,om)) and sh2 = EvalH(F(sm),oh), Dist is a distance metric in SH, and observations om and oh are generated by the same underlying state S. If you are interested in getting more detail and why I believe this circumvents existing counterexamples, please check out the full proposal. Thanks for listening. To help us out with The Nonlinear Library or to learn more, please visit nonlinear.org.

Welcome to The Nonlinear Library, where we use Text-to-Speech software to convert the best writing from the Rationalist and EA communities into audio. This is: Causality, Transformative AI and alignment - part I, published by Marius Hobbhahn on January 27, 2022 on The AI Alignment Forum. TL;DR: transformative AI(TAI) plausibly requires causal models of the world. Thus, a component of AI safety is ensuring secure paths to generating these causal models. We think the lens of causal models might be undervalued within the current alignment research landscape and suggest possible research directions. This post was written by Marius Hobbhahn and David Seiler. MH would like Richard Ngo for encouragement and feedback. If you think these are interesting questions and want to work on them, write us. We will probably start to play around with GPT-3 soonish. If you want to join the project, just reach out. There is certainly stuff we missed. Feel free to send us references if you think they are relevant. There are already a small number of people working on causality within the EA community. They include Victor Veitch, Zhijing Jin and PabloAMC. Check them out for further insights. Causality - a working definition: Just to get this out of the way: we follow a broad definition of causality, i.e. we assume it can be learned from (some) data and doesn't have to be put into the model by humans. Furthermore, we don't think the representation has to be explicit, e.g. in a probabilistic model, but could be represented in other ways, e.g. in the weights of neural networks. But what is it? In a loose sense, you already know: things make other things happen. When you touch a light switch and a light comes on, that's causality. There is a more technical sense in which no one understands causality, not even Judea Pearl (where does causal information ultimately come from if you have to make causal assumptions to get it? For that matter, how do we get variables out of undifferentiated sense data?). But it's possible to get useful results without understanding causality precisely, and for our purposes, it's enough to approach the question at the level of causal models. Concretely: you can draw circles around phenomena in the world (like "a switch" and "a lightbulb") to make them into nodes in a graph, and draw arrows between those nodes to represent their causal relationships (from the switch to the lightbulb if you think the switch causes the lightbulb to turn on, or from the lightbulb to the switch if you think it's the other way around). There's an old Sequences post that covers the background in more detail. The key points for practical purposes are that causal models: Are sparse, and thus easy to reason about and make predictions with (or at least, easier to reason about than the joint distribution over all your life experiences). Can be segmented by observations. Suppose you know that the light switch controls the flow of current to the bulb and that the current determines whether the bulb is on or off. Then, if you observe that there's no current in the wire (maybe there's a blackout), then you don't need to know anything about the state of the switch to know the state of the bulb. Able to evaluate counterfactuals. If the light switch is presently off, but you want to imagine what would happen if it were on, your causal model can tell you (insofar as it's correct). Why does causality matter? Causal, compared to correlational, information has two main advantages. For the following section, I got help from a fellow Ph.D. student. 1. Data efficiency Markov factorization: Mathematically speaking, Markov factorization ensures conditional independence between some nodes given other nodes. In practice, this means that we can write a joint probability distribution as a sparse graph where only some nodes are connected if we assume causality. It introduces sparsity. “Namely, if we have a joint with n binary random variables, it would have 2^n - 1 indepen...