Podcasts about rlhf

One popular objection to AI concerns is to declare that LLMs can never be AGI. You need a "new paradigm". Therefore, AGI is so far in the future that it's not worth worrying about. A common counterargument is to claim that no, LLMs can become AGI. But even without that counterargument, I think the "therefore" fails on its own terms. The key question is: how much of a new paradigm do we need? The landmark discoveries on the road to modern LLMs are something like: 1950s: Neural networks 1967: Multi-layer perceptron 2010: Modern deep learning 2017: Transformer, LLM 2022: RLHF, chatbots 2024: Chain of thought / test-time compute We can think of this as an "evolutionary tree", where a given LLM (let's say Claude Opus 4.7) shares a recent "common ancestor" with all other chatbots, and only a very distant "common ancestor" with everything else descended from the multi-layer perceptron. If AGI needs a "new paradigm", what common ancestor can we expect AGI and LLMs to share? AGI will very likely use neural networks, because the human brain is a neural network and qualifies as an AGI. It will probably use deep learning, because although deep learning isn't exactly analogous to the brain, it seems like a pretty reasonable way to emulate the brain's learning algorithms onto computer hardware. Skeptics like Yann LeCun and Gary Marcus usually pinpoint LLMs/transformers as the step where we went wrong. LeCun thinks that the first AGIs may be within the deep learning paradigm (but not LLMs); Marcus thinks that they'll combine insights from deep learning with something else. How soon should we expect a new paradigm as revolutionary as LLMs/transformers? Since we got LLMs/transformers nine years ago, Lindy's Law suggests nine more years. How soon should we expect a new paradigm as revolutionary as deep learning? By the same logic, sixteen years from now. Lindy's Law has a heavy tail, which means we can't simply halve these to find our 25th percentile estimate. Our 25th percentile estimate for the next advance as exciting as LLMs should be three years from now; for deep learning, it's five years. So even if you think AGI will require a further paradigm shift as big as the invention of the LLM or as deep learning itself, you should have 25% chance it will be developed in the next 3 - 5 years. Which is about as long as the LLM-only crowd think things will take! This isn't an excuse for relegating the risk of AGI to some vague indefinite future. It could still be the late 2020s or early 2030s! (Might we expect that low-hanging-fruit effects make the next paradigm harder to find than the last one? In practice, fields get more researchers as time goes on, and that effect usually causes time-between-advances to be approximately constant. And in fact, the number of AI researchers has grown at an unprecedented pace for a scientific field, and growth will enter an even faster regime once AIs themselves can contribute. Overall these make me think things will go even faster than Lindy's Law predicts - but I think Lindy's Law is a useful upper bound.) (Would there still be a long time between the invention of the new paradigm and the point where it could be used to maximum effect? It took five years between the invention of the transformer and ChatGPT, the first commercially-successful transformer-based project. But most of that time was spent scaling up, and we've already scaled up. If we invent a new paradigm in 2030, then any frontier lab willing to bet on it can quickly provide it with levels of compute sufficient to train human-brain-sized models.) This is my attempt to talk to the new-paradigm-wanters in their own language, but I think there's also a subtler point that undermines this worldview. In the past, new paradigms have proven useful in allowing scaling to continue after an old paradigm passed the regime where it could efficiently convert scale to results. LLMs still seem to be able to convert scale to results; while this continues, new paradigms won't be necessary, and frontier labs won't risk pursuing them. If scaling ever hits a wall, there will be a few months of confusion as frontier labs look over various new-paradigm-proposals that they already have lying around, and throw them at the wall to see what breaks through. Then scaling will continue from wherever it left off. The best way to forecast future AI progress is to extrapolate from current LLM scaling. This should work if LLMs scale all the way to AGI. But it may also work even if they don't. First, because we might get the new paradigm so soon that it won't be a significant source of delay. And second, because the most likely place for a new paradigm to start is wherever LLMs stop working, going at the same rate. https://www.astralcodexten.com/p/new-paradigms-wont-save-you

Matt Fitzpatrick, CEO of Invisible Technologies, joins Bloomberg Intelligence's Mandeep Singh on this episode of the Tech Disruptors podcast to discuss the use of reinforcement learning by frontier model providers for training, as well as the company's enterprise business. They explore reinforcement learning from human feedback (RLHF), agentic AI and self-improvement, the evolution of large language models, coding agents and contact centers.

Are we witnessing the first real signs of AI becoming a scientist? In this episode of The MAD Podcast, Matt Turck sits down with Dan Roberts, lead of the Foundations of Reinforcement Learning team at OpenAI, to explore one of the biggest shifts happening in AI: the rise of reasoning models, test-time compute, and reinforcement learning as engines of scientific discovery. Dan brings a rare perspective - from theoretical physics, black holes, quantum information, and deep learning theory - to explain how models are learning to “think,” why language may be such a powerful foundation for intelligence, what recent AI math breakthroughs really mean, and whether we are beginning to see AI systems that can contribute to science itself.(00:00) Intro: AI's wild week in mathematics(01:21) What OpenAI's Foundations of RL team does(03:08) Dan's journey: from black holes and quantum gravity to frontier AI(07:04) Are AI systems becoming useful for real science?(08:21) The AI math moment: Erdős, OpenAI, DeepMind, and Anthropic(08:52) Why the OpenAI result was an act of exploration(10:25) OpenAI vs. DeepMind: informal reasoning vs. formal proof(12:13) RL 101: learning by doing, not just watching(15:10) Why reinforcement learning works(15:58) How RL breaks: sparse feedback and long-horizon tasks(17:03) RLHF: how human feedback shaped early language models(18:48) Move 37, self-play, and the search for novel strategies(22:16) Explore vs. exploit in scientific discovery(24:49) Why RL may now be "the cake," not the cherry on top(25:46) Why RL started working with large language models(27:29) Is RL "sucking supervision through a straw"?(28:47) Why language may be the grounding layer for intelligence(31:46) A contrarian take on the Bitter Lesson(32:41) What test-time compute actually is(34:50) How RL gives models the ability to think(35:40) Verifiable rewards, math, coding, and the messy real world(38:00) What physics can teach us about AI(42:08) Is there a thermodynamics of AI?(43:08) From Erdős problems to Einstein-level AI(45:16) Is AI already doing original science?(45:51) How far are we from AI automating AI research?(47:41) Why Dan is excited about the future of science

In 2025, seven-month-old startup Axiom solved all 12 of the problems Putnam exam (scoring 8/12 in the time limit) a prestigious undergraduate math exam. The 12/12 score is better than the top undergraduates (110/120) and the closest AI system that reported a result (DeepSeek 103/120), although it is unclear what the people and other systems would have scored with more time. Nonetheless, the Putnam exam is legendary for its difficulty, with the median score typically being 0 or 1 points. Taken by itself, this seems like a minor feather in the cap of AI; one of a long series of accomplishments by AI systems in elite competitions with humans, starting with Deep Blue beating Kasparov.Fast forward to mid-2026, and Claude Code is eating the world. In 2024 Anthropic's bet on code and enterprise looked like a more pragmatic niche play vs. OpenAI's better models and massive consume scale. Today, Amodei's all in bet on acceleration via code (images and video be damned) seems prescient.Despite Anthropic's growing momentum, however, Axiom CEO Carina Hong sees coding ability as a necessary but not sufficient milestone on the path to AGI. Code arguably pushes the jagged frontier to the point of super intelligence in some domains outside of coding, but there are surprising gaps (link) that Carina believes will bottleneck AI progress. (Stats on math benchmarks).The informal bottleneck“Verified AI” sounds like eating broccoli (footnote: I actually love broccoli, but then again, I also believe strongly in Test Driven Development, so ¯(ツ)/¯ ) and paying taxes, but to Axiom it means something very different. “Verification to me is about scaling brilliance, compounding brilliance,” Carina told us.It actually took a while for me to understand what she means by this. It sounded like marketing-speak to me, until it clicked. Carina emphasizes an story about legendary mathematician Srinivasa Ramanujan to illustrate the point. When G.H. Hardy finally persuaded Ramanujan to formally prove theorems instead of relying on his (formidable) intuition, it reportedly improved his own capabilities. This is presumably because formally proving things forced Ramanujan to articulate the details in a way that open up new lines of thinking, etc. This is one part of “compounding.”But formally proving things also allowed others to benefit from his intuition: the proofs are way of communicating an intuition and persuading others that the intuition is correct. This is scaling (more people use the result) and compounding (people can learn from and build on his work).This is the analogy that Carina wants us to focus on.Verified GenerationThere are two ways that Verified AI shows up: in training and in inference.But a quick detour: to a first approximation, “Formal Verification” means using type checkers (like for TypeScript, C++ or Rust, but more capable) to verify mathematical proofs that are meticulously specified using a language like Lean (footnote: Formal verification also includes model checking (TLA+, SPIN), SMT-based tools (Dafny, F*, Why3), and refinement-type systems (Liquid Haskell) — many of which don't look much like “type checking a proof” from the user's perspective even when there's a similar logical core underneath. It also gets applied to software and hardware correctness, not only pure mathematics.). It takes a lot of work to translate an “informal” proof (albeit one that most people would not remotely call “informal”) in to a Lean proof (footnote: This is an understatement. Most theorems remain informal because formalization is so hard to do. There has been a great deal of effort to formalize the most important proofs, with mixed results)You can imagine how this would be (very) useful during Reinforcement Learning: instead of relying on best guesses based on statistics (GRPO, RLHF, etc.), you can just verify the proof is correct using a Lean verifier. This is obviously a much stronger reward signal, akin to compiling code and testing it (which is what is typically done with RL on coding).The catch: LLM are not (currently) very good at proving things with Lean.Enter Axiom: While they have not officially reported benchmark numbers besides the 12/12 Putnam result, Carina reports that they have achieved a very impressive 99% (187/189) ProofGen on the Verina benchmark. This benchmark is to generate code and proof of correctness for a series of problems. For context, OpenAI o3 (the last known OpenAI run) achieved 4.9% on this benchmark.Based on the sparse benchmarking, it's hard to say what the frontier labs are currently doing, but Carina suggests that they still are not training to generate Lean proofs directly, rather relying on informal proofs.Time will tell if the frontier labs' current approaches will close this gap.Scaling and compoundingCarina's Ramanujan analogy is pretty direct. Better proofs → better Lean generation → better RL. A stronger signal means higher sample efficiency and higher maximum performance. Great!Scaling is pretty clear too: once I have proved something in Lean, the quality of the output is basically (footnote: one might argue that its a bit lower because the proof is in distribution for the LLM) as high as if it came from a human, so my high quality training set has grown in a way that an informal rollout corpus cannot. I can trust my Lean proofs.Compounding is also clear: now all of future inference and training can build upon those proofs.On the other hand, a model trained only using statistical signals like GRPO during RL lacks the sample efficiency, maximum performance and compounding corpus that a system that uses formal verification benefits from.All roads lead to verificationBroccoli and taxes notwithstanding, “verification” has shown up in a lot of conversations recently. In the in physical system control:“I think [verifiability] is probably the hardest problem right now, because the as the models get better, it can be harder and harder to find the faults on the system. And so the problem of doing proper eval to find those faults, that problem also keeps getting harder as the models get better.” -In theoretical physics:“…now that we're in this regime where you can just get ChatGPT to tackle thousands of questions at the same time, it will return proofs for a significant fraction of them. Now actually the onus is back on the humans to verify all the outputs. And so, yeah, as that becomes a bottleneck, I think formalizing math and automating verification will become more valuable.” -Verification is, in fact, the key differences between AI for science and AI for computation: in science you to have to actually test (verify) your hypothesis by performing physical experiments. Lab in the loop systems like Radical AI and Lila build around exactly this premise (we have recorded episodes with both of these teams and will release them soon!)And yes, formally verifying critical systems such as flight control, nuclear power plants and pacemakers is a growing focus as the software and hardware that run them becomes more complex.Carina believes so strongly that AGI requires verified generation that she makes the unqualified claim that “We do not believe there is any other possible future.”Expensive to produce, cheap to verifyLean proofs are hard generate, but they can be easily shown to be correct or incorrect. But how do you know that the proof you created maps correctly to the problem you care about? As Carina puts it: “Anything that can be specified can be proven. Humans are bad at specifying everything we want.”Are we now in the specification business? Check out the episode to hear Carina's take, as well as:* Why hardware verification is a killer app* Details on the AXLE open API and recently released Discovery toolkit* The Erdos debacle* The OpenAI GPT-f diaspora This is a public episode. If you'd like to discuss this with other subscribers or get access to bonus episodes, visit www.latent.space/subscribe

Stewart Alsop sat down with Michael Shackelford to discuss their experiences building applications through vibe coding—the practice of using AI to create software without traditional programming expertise. Stewart, who runs the AI Whispers community in Buenos Aires and hosts the Crazy Wisdom podcast (with over 660 interviews), shared how he went from teaching people prompt engineering to building his own video conferencing software as a Riverside.fm replacement, while Michael opened up about his year-long journey creating Genrupt Inc, an AI-powered content generation tool for e-commerce sellers. The conversation covered everything from the decline in quality of Claude's reasoning capabilities and how Chinese companies used distillation attacks to copy Anthropic's models, to the importance of spaced repetition systems for managing knowledge in the age of LLMs, with both sharing battle-tested prompting strategies like asking AI to "explain it to me in genius terms" and using deep research queries to reverse engineer how competitors build their products.Show Notes:- Dan Martell's book "Buy Back Your Time" was mentioned as one of the best business books for thinking about life and business- Check out John Vervaeke's "Awakening from the Meaning Crisis" for understanding relevance realization and why AI fundamentally cannot determine what's relevant to humans without being toldTimestamps00:00 Michael discusses being exhausted from getting his app ready for launch, working nonstop with AI to prepare landing page for podcast traffic driving beta signups05:00 Stewart explains starting AI Whispers in Buenos Aires after leaving OpenAI vendor company, meeting early adopters like Torin who was building mind-reading EEG technology10:00 Discussion of how corporations resist AI adoption due to political games and job security fears while some companies use AI as excuse for pandemic-era layoffs15:00 Stewart describes teaching workshops on using LLMs as linguistic tools rather than coding tools, noting technical people often lack humanities background needed for prompting20:00 Explaining chatbot wrappers, API calls, and how Anthropic's reasoning quality declined after Chinese distillation attacks copied their secret sauce developed with philosophers25:00 Technical discussion of model training, fine-tuning versus RAG for new information, and different approaches to updating AI knowledge beyond initial training30:00 Stewart describes building podcast recording software to replace expensive Riverside, struggling with syncing audio and video files across different computer clocks35:00 Discussion of critical factors in vibe coding, discovering unknown technical requirements, and how AIs don't automatically reveal missing information40:00 Stewart's reverse engineering process using deep research function to study competitors' hiring and technology stacks, separating planning agents from coding agents45:00 Prompting techniques including "explain like I know everything" and using spaced repetition systems to capture valuable prompts and technical knowledge50:00 Michael explains his Generux app for generating ecommerce content using Amazon review data analysis to inform high-converting listing images and videos55:00 Discussion of founder mentality involving self-delusion about project timelines, Michael working nine-plus hours daily for nine months on app development60:00 Comparing Amazon's expert software to prosumer software approach, discussing distribution challenges and future robotics applications for customized products65:00 Stewart demonstrates spaced repetition app for memory improvement and knowledge retention, explaining relevance realization problem that AI agents cannot solve without embodimentKey Insights1. Stewart Alsop started AI Whisperers in Buenos Aires after leaving his role at Invisible Technologies, which was OpenAI's largest vendor for RLHF work. He noticed that machine learning engineers at tech companies lacked the humanities background needed to properly interact with large language models, which are fundamentally linguistic tools. This led him to create weekly workshops teaching non-technical people how to use AI effectively, running events every Thursday for two years straight. The group attracted intense geeks from the start and eventually led to Stewart speaking right after Vitalik Buterin at DevConnect, marking a significant milestone for the community.2. Large corporations are resistant to AI adoption due to multiple factors including political dynamics within organizations and employees fearing job loss. Many companies that grew during the pandemic are now using AI as an excuse to downsize when the real issue is inefficiency from rapid expansion. Stewart observed that even technical people in machine learning often don't understand how to properly use AI tools because they lack linguistic and humanities training. The fundamental problem is educational, requiring companies to train people how to use these new tools while those same people resist learning them.3. Vibe coding has evolved significantly with Claude Code being a game changer that reduced the technical barrier to entry. Before Claude Code, developers needed substantial technical knowledge to work through constant doom loops and debugging cycles. The success of coding AI tools stems from thirty years of testing infrastructure that provides clear yes or no feedback on whether code works. This infrastructure doesn't exist in the same way for manufacturing, science, and other fields, which is why software became the dominant area for AI assistance initially.4. Claude's quality degradation over recent months resulted from multiple factors including distillation attacks by Chinese companies who reverse engineered Anthropic's reasoning capabilities. Anthropic had hired philosophers, sociologists, and psychologists to develop exceptional reasoning in Claude 4.5, but this was expensive to run. When Chinese models like Kimi copied these capabilities at one tenth the cost, and when mainstream users flooded the platform before Anthropic's planned IPO, the company had to reduce quality to manage computational costs. This represents a significant loss for power users who relied on Claude's superior reasoning abilities.5. Stewart built a podcast recording application to replace Riverside because he needed API access to automate workflows, which Riverside wanted one thousand dollars monthly to provide. The technical challenge involves syncing audio and video from local recordings on multiple computers with different clocks through a server, then merging them so voices match lip movements. This problem requires understanding complex timing issues across different network conditions and file formats. Stewart has been working through AI psychosis for months on this FFMPEG pipeline problem, illustrating how vibe coding still requires building intuition about technical problems even without traditional coding knowledge.6. The transition from expert software to prosumer software represents a major opportunity for AI-enabled tools. Expert software like Photoshop, Blender, and terminal interfaces have extreme complexity that intimidates beginners, but AI is making these capabilities accessible through natural language. The reign of specialists is ending as generalists with broad knowledge and curiosity can now build complete applications by leveraging AI to fill technical gaps. This shift particularly benefits entrepreneurs and founders who specialize in getting into difficult situations and figuring them out, even when they originally thought tasks would be easier than they turned out to be.7. Building applications with AI requires accepting massive time investments beyond initial estimates and developing strategies for overcoming knowledge gaps. Michael estimated his ecommerce content generation app would take months but spent nearly a year working over nine hours daily, while Stewart spent months solving audio-video sync issues. Success requires using tools like deep research to understand how competitors solve problems, maintaining separate planning and coding agents, and learning to ask the right questions. The key insight is that vibe coders can achieve ninety percent of functionality independently, but the final ten percent often requires understanding specific technical concepts that AI cannot intuit without proper context and domain knowledge.

Tokenization. Context windows. Lost in the middle. Silent failures. RLHF. Anthropomorphism. Quantization. Top-P and Top-K. RAG. Deterministic checks.If you haven't heard of some of these topics, this podcast episode is for you.

Hey rockstar,In the last piece, we explored why AI “fast money” shortcuts leave so many people feeling numb, overwhelmed, and disconnected — and why the real foundation of a sustainable business is still connection, care, and community.There's a closely related piece almost nobody is talking about:If numbness is what erodes your relationships, joy and wealth creation from the inside out, curiosity is what brings it back to life.Not just as a nice idea — but as a literal learning rate for your brain and your purpose.“Hey, before we jump in - when you get a moment, hit reply and tell me…. What's the #1 thing you're struggling with right now?The Number That Should Stop Every Purpose Driven Wealth Creation - ColdA developmental psychologist at Williams College tracked how many questions children ask per hour.At age five, the average kid asks 107 questions per hour. They're relentless. Why is the sky blue? Why do dogs have tails? Why does grandma's hair turn white? Their brains are running at full throttle, pulling in data from every direction.Then school starts.* By first grade, the entire class asks 2.3 questions per hour — combined.* By fifth grade? 0.48 questions per hour. Less than one question every two hours from a room full of eleven-year-olds.In one observation, kids were experimenting with an old-fashioned balance scale, genuinely doing science. The teacher shut it down: “Enough of that. I'll give you time to experiment at recess. There's no time for experiments now. We're doing science.”Read that again. No time for experiments… during science class.The researcher's conclusion is brutal: if you lose your curiosity by age 11, you probably don't get it back.I disagree on one thing. I think you can get it back. But you have to understand what curiosity actually is, neurologically. And that's where it gets interesting — especially for anyone trying to build something real in the AI era.Your Brain Is a Large Language Model (No, Really)The more I create custom services and learn about how advanced AI models work, the more clear it becomes: your brain is running the same basic algorithm.Consider the parallels:* Your brain has roughly 86 billion neurons connected by an estimated 100 trillion synapses.* GPT-4 has approximately 1.8 trillion parameters across its mixture-of-experts architecture.* Both are massive pattern-recognition networks.* Both learn by prediction.Here's how an LLM trains: it reads a sentence, predicts the next word, checks whether it was right, and adjusts its internal weights. Right answer? Strengthen that pathway. Wrong answer? Weaken it, try again. Billions of repetitions, trillions of adjustments.Your brain does the same thing.Every experience is a prediction. You reach for a coffee cup and predict its weight. You start a sentence and predict how the other person will react. When reality matches your prediction, your synapses strengthen. When it doesn't, your brain recalibrates. Neuroscientists call this predictive coding.A 2024 study found LLMs become more advanced, their internal representations actually become more similar to human brain activity during speech processing.Your brain is the original foundation model — pre-trained by evolution, fine-tuned by experience.But here's the critical difference:An LLM's learning rate is set by engineers. They decide how aggressively the model updates its weights in response to new data. Too high and it's unstable. Too low and it stops learning.In your brain, that learning rate has a name. It's called curiosity. And unlike an LLM, you can adjust it yourself.Curiosity as a Reward Signal: The Dopamine ConnectionUC Davis put people in an fMRI scanner and asked them trivia questions.What they found — published in the journal Neuron — changed our understanding of how curiosity works.When participants were highly curious, their ventral tegmental area (VTA) and nucleus accumbens lit up. These are the same brain regions activated by food, sex, and addictive drugs.Curiosity hijacks your reward circuitry. It's not a nice-to-have personality trait. It's a neurochemical event.But the more interesting finding was this: during the curious state, participants were shown random faces, completely unrelated to the trivia. Later, they remembered those faces significantly better than faces shown during low-curiosity moments.Curiosity didn't just help them learn the answer they wanted. It supercharged their memory for everything happening in that moment.This is exactly how reinforcement learning works in AI. When an LLM gets a reward signal through RLHF (Reinforcement Learning from Human Feedback), it doesn't just strengthen the specific output — The reward ripples through the network.Curiosity is your brain's RLHF. It's the reward signal that tells 86 billion neurons: pay attention, something important is happening, encode everything.Without that signal, your brain does what an untrained model does. It defaults to cached responses. You stop updating. You become, in AI terms, a frozen model.Curiosity Literally Keeps You AliveAnd this is about much more than learning faster.In 1996, researchers Gary Swan and Dorit Carmelli at SRI International followed 1,118 older men over five years as part of the Western Collaborative Group Study. They measured curiosity at baseline and tracked who survived.The result: highly curious people had significantly higher survival rates — even after controlling for age, smoking, cardiovascular disease, and other risk factors. They replicated the finding in 1,035 older women.Curiosity was directly associated with greater cognitive reserve — the brain's buffer against age-related decline.Curious brains keep building new connections. Incurious ones atrophy.Mindset is a biological variable. Curious people don't merely think differently — their brains physically maintain themselves better.Which means in business terms:The relentless drive to learn boosts your neurons and adaptability as much as any supplement or course.How We Lose Curiosity (And Why That Kills Businesses)We aren't born numb.However, school, social conditioning, and performance culture often suppress questioning. By the time most people start or grow a business, their curiosity has nearly vanished.We learn to:* Stop experimenting unless there's a guaranteed outcome* Protect what we already “know” instead of updating* Prioritize looking competent over actually learningLayer AI “shortcuts” on top of that and the effect compounds. You can ship more, post more, automate more — without ever engaging the deeper questions:* What is really happening in my market right now?* What are my clients actually struggling with beneath the surface?* Where am I out of alignment with what I'm selling?Without those questions, your wealth stops evolving in any meaningful way. You may still be iterating on tactics, but your inner model of reality is frozen.Numbness plus speed is just a faster way to hit the wall.The most dangerous thing that can happen to your brain — or your business — is to stop being surprised.How to Crank Your Learning Rate Back Up Five strategies for creative agency:1. Create information gaps intentionally. Curiosity arises when you know enough to spot gaps but not enough to fill them. Before meetings, read halfway through an article and enter with questions, not answers.2. Schedule daily “explore time.” Dedicate 30 minutes to learning about unfamiliar fields to keep your curiosity alive without aiming for expertise.3. Ask “dumb” questions among experts. Genuine learners ask for explanations, even in rooms full of accomplished people.4. Change your physical inputs. Perceptual and intellectual curiosity; try new routes, restaurants without menus, or confusing places to stimulate dopamine.5. Teach what you learn within 24 hours. Sharing knowledge helps organize and consolidate it—similar to fine-tuning data in LLMs.Curiosity, AI, and the “Whole Human” In a world obsessed with speed and automation, the temptation is to outsource not just your tasks, but your actual thinking — your contact with reality.But the future we actually want isn't built by numbed-out operators running frozen mental models, propped up by ever-fancier tools.It's built by people who are:* Awake enough to notice when they've gone numb* Curious enough to re-open the questions about what they're building* Grounded enough to use AI as support for their nervous systems and insight — not as a mask over their disconnectionThat's the through-line from the last piece to this one:* From extraction → to contribution* From performance → to presence* From “how do I hack the algorithm?” → to “how do I keep my own learning rate high enough to truly serve?”What This Means for YouIf you're an entrepreneur: Your competitive advantage isn't your product. It's your rate of learning. Build a culture that rewards questions over answers. Hire curious people over credentialed people.If you're an executive or practitioner: Schedule one hour a week to explore a field completely outside your industry. Those who survive disruption are the ones whose mental models are still updating.If you're investing in yourself: Bet on your curiosity the way a smart investor bets on a sole proprietor founder's adaptability. Curiosity predicts adaptability — and adaptability predicts survival.If you're a parent or leader of others: Count the questions in the room. If the number is dropping, the issue isn't the people — it's the environment. Protect spaces where real learning (which is always a little messy) is allowed.The Invitation to the Deeper MindLet the FOMO cool.Keep experimenting with AI — but pair every tool with a question:* What is this teaching me about my clients, my patterns, my assumptions?* Where am I tempted to go numb instead of stay curious?Rebuild your foundation with timeless ingredients: connection, care, community, and a living curiosity that aligns you with life—not just trends. Curiosity reconnects you with reality, countering numbness.That's how I use Generative AI in Oracle work: To awaken intuition, not replace it.When you open The Light Between Oracle, you enter an immersive experience blending symbolic language, somatic regulation, and guided integration—so insights land in your body, not just your mind.Here's the process:* You arrive scattered or braced.* The Oracle helps you downshift to hear yourself.* It reflects the clearest pattern at play.* You leave with one grounded step to take that day.The goal isn't more information—it's becoming someone whose inner model continually updates through presence, questions, and authentic connection.If you felt this piece in your bones, take the next step with me:Try The Light Between Oracle here: [Insert your link to the Oracle app]What you'll get from it:* Clarity without overwhelm (a focused prompt + practical direction)* Nervous system replenishment (so your guidance doesn't get drowned out by stress)* Better decisions through curiosity (questions that reopen your learning rate)* Aligned momentum (action that feels clean, not performative)* A daily wisdom + strategy practice you can actually sustainIf you want, hit reply and tell me what you're navigating right now—and I'll tell you the best place to start inside the Oracle. This is a public episode. If you'd like to discuss this with other subscribers or get access to bonus episodes, visit thelightbetween.substack.com/subscribe

The Stanford AI Index's headline is 88% — organizations using AI in some capacity. The Financial Times charted where it actually lands in the workforce: 62% of top-decile earners use it daily, versus 13% at the bottom. Board decks this quarter will cite Stanford. The FT chart is what they're not showing.The economics that enabled this gap are under pressure. The three-year subsidized era is ending by financial necessity, not choice. The same optimization logic that built social media's loneliness machine is now embedded in AI products at scale. And in the same week Anthropic's most capable model autonomously found 271 zero-days in Firefox, two major platforms were breached through third-party integrations. The data and what to do about it follows.Episode 8: The Most Important Data Points in AI Right NowBrittany Hobbs solo — four segments moving from data to strategic implication. Essential for anyone making AI purchasing, hiring, or architecture decisions right now.The Stanford AI Index 2026. 88% organizational adoption is saturation, not a trend. $581 billion invested globally in 2025, up 129% year over year. The US-China AI performance gap collapsed from 17–31 percentage points in 2023 to 2.7% today — on 23 times less investment. China holds 69.7% of global AI patent filings. Architecture and application discipline closed a gap that capital alone could not. Stanford AI Index 2026 | The U.S. Can't Buy an AI LeadToken economics. Anthropic's current tiers: Haiku at $1/$5 per million input/output tokens, Sonnet at $3/$15, Opus at $5/$25. A 200-screen product built with Claude Design costs $0.22 for a first draft; the 50-iteration refinement cycle real design work requires runs to ~$2,600, plus $200–$900/month in system updates. Every comparable Figma interaction costs zero. Prompt caching provides ~90% discounts on repeated context; batch processing cuts 50%. Claude Design vs Figma cost breakdown | CNBC: Token economicsApple chose its hardware chief as next CEO. John Ternus — SVP of Hardware Engineering, architect of Apple Silicon — succeeds Tim Cook on September 1st. Johny Srouji, who designed every Apple Silicon chip, becomes Chief Hardware Officer. Apple posted $143.8 billion in Q1 FY2026 (up 16%, $109 billion in services, 92% retention) without shipping an industry-leading AI feature. The next decade of AI is decided at the silicon and device level. Apple CEO transition analysisVibe coding has never been more capable. Security has never been more exposed. Anthropic's Mythos model identified 271 zero-day vulnerabilities in Firefox autonomously; the UK's AI Security Institute found it succeeds at expert-level hacking tasks 73% of the time. Anthropic launched Project Glasswing (12 defensive security partners including Amazon, Microsoft, and Apple), then reported unauthorized Mythos access through a vendor. Vercel was breached through Context AI — customer credentials sold on BreachForums for $2 million. Lovable exposed source code and credentials via a basic authorization flaw for 48 days, fixed it, then broke it again for 76 more. TechCrunch: Anthropic Mythos | TechCrunch: Vercel breach | The Next Web: Lovable“If you're making AI decisions for your team right now — what to buy, who to hire, what to build — there are numbers out this week that should change your approach.” — Brittany HobbsListen now: Spotify | Apple Podcasts | YouTube

I. In The Argument, Kelsey Piper gives a good description of the ways that AIs are more than just "next-token predictors" or "stochastic parrots" - for example, they also use fine-tuning and RLHF. But commenters, while appreciating the subtleties she introduces, object that they're still just extra layers on top of a machine that basically runs on next-token prediction. I want to approach this from a different direction. I think overemphasizing next-token prediction is a confusion of levels. On the levels where AI is a next-token predictor, you are also a next-token (technically: next-sense-datum) predictor. On the levels where you're not a next-token predictor, AI isn't one either.

This Podcast is sponsored by Team Simmer. Go to TeamSimmer and use the coupon code DEVIATE for 10% on individual course purchases. The Technical Marketing Handbook provides a comprehensive journey through technical marketing principles. (Getting an update soon) Sign up to the Simmer Newsletter for the latest news in Technical Marketing. NEW SIMMER COURSE ALERT!  - Data Analysis with R - taught by Arben Kqiku LIMITED 20% off offer on the Javascript for Digital Marketers Course - Get it while it lasts! Latest content from Simo Ahava Add IPv6 Support To Your Server-side GTM Load Balancer Latest content from Juliana Jackson The business model is THE strategy (subscribe to the newsletter for more amazing content) LIVE from Measurecamp Helsinki - 40' of content about main effects & interactions with Matt Gershoff - will be available to all my Substack subcribers - sign up to my newsletter here: https://julianajackson.substack.com/ Mentioned in the episode: META announcement for new Click-Through Measurement Definition STAPE Content re: Meta Conversion tracking (comment from Stape team - Meta have changed the definition of view-through and added engaged-through etc., but it didn't affect how you report to pixel/capi) - https://stape.io/solutions/facebook-capi-tag - CAPI tag- https://stape.io/solutions/facebook-pixel-tag - pixel tag- https://stape.io/gtm-set-up-assistant Google Universal Commerce Protocol Agentic Misalignment (Anthropic): https://www.anthropic.com/research/agentic-misalignment Natural Emergent Misalignment from Reward Hacking (Anthropic): https://assets.anthropic.com/m/74342f2c96095771/original/Natural-emergent-misalignment-from-reward-hacking-paper.pdf This podcast is brought to you by Juliana Jackson and Simo Ahava.

AI is not just getting smarter. It is getting faster by learning how to optimize the hardware it runs on. In this episode, Sharon Zhou, VP of AI at AMD and former Stanford AI researcher, explains how language models are beginning to write and optimize their own GPU kernel code. We explore what self improving AI actually means, how reinforcement learning is used in post training, and why kernel optimization could be one of the most overlooked scaling levers in modern AI. Sharon breaks down how GPU efficiency impacts the cost of training and inference, why catastrophic forgetting remains a challenge in continual learning, and how verifiable rewards from hardware profiling can help models improve themselves. The conversation also dives into compute economics, synthetic data, RLHF, and why infrastructure may define the next phase of AI progress. If you want to understand where AI scaling is really happening beyond bigger models and more data, this episode goes under the hood. Stay Updated: Craig Smith on X: https://x.com/craigss Eye on A.I. on X: https://x.com/EyeOn_AI (00:00) Preview and Intro (00:25) Sharon Zhou's Background and Transition to AMD (02:00) What Is Self-Improving AI? (04:16) What Is a GPU Kernel and Why It Matters (07:01) Using AI Agents and Evolutionary Strategies to Write Kernels (11:31) Just-In-Time Optimization and Continual Learning (13:59) Self-Improving AI at the Infrastructure Layer (16:15) Synthetic Data and Models Generating Their Own Training Data (20:48) AMD's AI Strategy: Research Meets Product (23:22) Inside the NeurIPS Tutorial on AI-Generated Kernels (30:59) Reinforcement Learning Beyond RLHF (39:09) 10x Faster Kernels vs 10x More Compute (41:50) Will Efficiency Reduce Chip Demand? (42:18) Beyond Language Models: Diffusion, JEPA, and Robotics (45:34) Educating the Next Generation of AI Builders

From Palantir and Two Sigma to building Goodfire into the poster-child for actionable mechanistic interpretability, Mark Bissell (Member of Technical Staff) and Myra Deng (Head of Product) are trying to turn “peeking inside the model” into a repeatable production workflow by shipping APIs, landing real enterprise deployments, and now scaling the bet with a recent $150M Series B funding round at a $1.25B valuation.In this episode, we go far beyond the usual “SAEs are cool” take. We talk about Goodfire's core bet: that the AI lifecycle is still fundamentally broken because the only reliable control we have is data and we post-train, RLHF, and fine-tune by “slurping supervision through a straw,” hoping the model picks up the right behaviors while quietly absorbing the wrong ones. Goodfire's answer is to build a bi-directional interface between humans and models: read what's happening inside, edit it surgically, and eventually use interpretability during training so customization isn't just brute-force guesswork.Mark and Myra walk through what that looks like when you stop treating interpretability like a lab demo and start treating it like infrastructure: lightweight probes that add near-zero latency, token-level safety filters that can run at inference time, and interpretability workflows that survive messy constraints (multilingual inputs, synthetic→real transfer, regulated domains, no access to sensitive data). We also get a live window into what “frontier-scale interp” means operationally (i.e. steering a trillion-parameter model in real time by targeting internal features) plus why the same tooling generalizes cleanly from language models to genomics, medical imaging, and “pixel-space” world models.We discuss:* Myra + Mark's path: Palantir (health systems, forward-deployed engineering) → Goodfire early team; Two Sigma → Head of Product, translating frontier interpretability research into a platform and real-world deployments* What “interpretability” actually means in practice: not just post-hoc poking, but a broader “science of deep learning” approach across the full AI lifecycle (data curation → post-training → internal representations → model design)* Why post-training is the first big wedge: “surgical edits” for unintended behaviors likereward hacking, sycophancy, noise learned during customization plus the dream of targeted unlearning and bias removal without wrecking capabilities* SAEs vs probes in the real world: why SAE feature spaces sometimes underperform classifiers trained on raw activations for downstream detection tasks (hallucination, harmful intent, PII), and what that implies about “clean concept spaces”* Rakuten in production: deploying interpretability-based token-level PII detection at inference time to prevent routing private data to downstream providers plus the gnarly constraints: no training on real customer PII, synthetic→real transfer, English + Japanese, and tokenization quirks* Why interp can be operationally cheaper than LLM-judge guardrails: probes are lightweight, low-latency, and don't require hosting a second large model in the loop* Real-time steering at frontier scale: a demo of steering Kimi K2 (~1T params) live and finding features via SAE pipelines, auto-labeling via LLMs, and toggling a “Gen-Z slang” feature across multiple layers without breaking tool use* Hallucinations as an internal signal: the case that models have latent uncertainty / “user-pleasing” circuitry you can detect and potentially mitigate more directly than black-box methods* Steering vs prompting: the emerging view that activation steering and in-context learning are more closely connected than people think, including work mapping between the two (even for jailbreak-style behaviors)* Interpretability for science: using the same tooling across domains (genomics, medical imaging, materials) to debug spurious correlations and extract new knowledge up to and including early biomarker discovery work with major partners* World models + “pixel-space” interpretability: why vision/video models make concepts easier to see, how that accelerates the feedback loop, and why robotics/world-model partners are especially interesting design partners* The north star: moving from “data in, weights out” to intentional model design where experts can impart goals and constraints directly, not just via reward signals and brute-force post-training—Goodfire AI* Website: https://goodfire.ai* LinkedIn: https://www.linkedin.com/company/goodfire-ai/* X: https://x.com/GoodfireAIMyra Deng* Website: https://myradeng.com/* LinkedIn: https://www.linkedin.com/in/myra-deng/* X: https://x.com/myra_dengMark Bissell* LinkedIn: https://www.linkedin.com/in/mark-bissell/* X: https://x.com/MarkMBissellFull Video EpisodeTimestamps00:00:00 Introduction00:00:05 Introduction to the Latent Space Podcast and Guests from Goodfire00:00:29 What is Goodfire? Mission and Focus on Interpretability00:01:01 Goodfire's Practical Approach to Interpretability00:01:37 Goodfire's Series B Fundraise Announcement00:02:04 Backgrounds of Mark and Myra from Goodfire00:02:51 Team Structure and Roles at Goodfire00:05:13 What is Interpretability? Definitions and Techniques00:05:30 Understanding Errors00:07:29 Post-training vs. Pre-training Interpretability Applications00:08:51 Using Interpretability to Remove Unwanted Behaviors00:10:09 Grokking, Double Descent, and Generalization in Models00:10:15 404 Not Found Explained00:12:06 Subliminal Learning and Hidden Biases in Models00:14:07 How Goodfire Chooses Research Directions and Projects00:15:00 Troubleshooting Errors00:16:04 Limitations of SAEs and Probes in Interpretability00:18:14 Rakuten Case Study: Production Deployment of Interpretability00:20:45 Conclusion00:21:12 Efficiency Benefits of Interpretability Techniques00:21:26 Live Demo: Real-Time Steering in a Trillion Parameter Model00:25:15 How Steering Features are Identified and Labeled00:26:51 Detecting and Mitigating Hallucinations Using Interpretability00:31:20 Equivalence of Activation Steering and Prompting00:34:06 Comparing Steering with Fine-Tuning and LoRA Techniques00:36:04 Model Design and the Future of Intentional AI Development00:38:09 Getting Started in Mechinterp: Resources, Programs, and Open Problems00:40:51 Industry Applications and the Rise of Mechinterp in Practice00:41:39 Interpretability for Code Models and Real-World Usage00:43:07 Making Steering Useful for More Than Stylistic Edits00:46:17 Applying Interpretability to Healthcare and Scientific Discovery00:49:15 Why Interpretability is Crucial in High-Stakes Domains like Healthcare00:52:03 Call for Design Partners Across Domains00:54:18 Interest in World Models and Visual Interpretability00:57:22 Sci-Fi Inspiration: Ted Chiang and Interpretability01:00:14 Interpretability, Safety, and Alignment Perspectives01:04:27 Weak-to-Strong Generalization and Future Alignment Challenges01:05:38 Final Thoughts and Hiring/Collaboration Opportunities at GoodfireTranscriptShawn Wang [00:00:05]: So welcome to the Latent Space pod. We're back in the studio with our special MechInterp co-host, Vibhu. Welcome. Mochi, Mochi's special co-host. And Mochi, the mechanistic interpretability doggo. We have with us Mark and Myra from Goodfire. Welcome. Thanks for having us on. Maybe we can sort of introduce Goodfire and then introduce you guys. How do you introduce Goodfire today?Myra Deng [00:00:29]: Yeah, it's a great question. So Goodfire, we like to say, is an AI research lab that focuses on using interpretability to understand, learn from, and design AI models. And we really believe that interpretability will unlock the new generation, next frontier of safe and powerful AI models. That's our description right now, and I'm excited to dive more into the work we're doing to make that happen.Shawn Wang [00:00:55]: Yeah. And there's always like the official description. Is there an understatement? Is there an unofficial one that sort of resonates more with a different audience?Mark Bissell [00:01:01]: Well, being an AI research lab that's focused on interpretability, there's obviously a lot of people have a lot that they think about when they think of interpretability. And I think we have a pretty broad definition of what that means and the types of places that can be applied. And in particular, applying it in production scenarios, in high stakes industries, and really taking it sort of from the research world into the real world. Which, you know. It's a new field, so that hasn't been done all that much. And we're excited about actually seeing that sort of put into practice.Shawn Wang [00:01:37]: Yeah, I would say it wasn't too long ago that Anthopic was like still putting out like toy models or superposition and that kind of stuff. And I wouldn't have pegged it to be this far along. When you and I talked at NeurIPS, you were talking a little bit about your production use cases and your customers. And then not to bury the lead, today we're also announcing the fundraise, your Series B. $150 million. $150 million at a 1.25B valuation. Congrats, Unicorn.Mark Bissell [00:02:02]: Thank you. Yeah, no, things move fast.Shawn Wang [00:02:04]: We were talking to you in December and already some big updates since then. Let's dive, I guess, into a bit of your backgrounds as well. Mark, you were at Palantir working on health stuff, which is really interesting because the Goodfire has some interesting like health use cases. I don't know how related they are in practice.Mark Bissell [00:02:22]: Yeah, not super related, but I don't know. It was helpful context to know what it's like. Just to work. Just to work with health systems and generally in that domain. Yeah.Shawn Wang [00:02:32]: And Mara, you were at Two Sigma, which actually I was also at Two Sigma back in the day. Wow, nice.Myra Deng [00:02:37]: Did we overlap at all?Shawn Wang [00:02:38]: No, this is when I was briefly a software engineer before I became a sort of developer relations person. And now you're head of product. What are your sort of respective roles, just to introduce people to like what all gets done in Goodfire?Mark Bissell [00:02:51]: Yeah, prior to Goodfire, I was at Palantir for about three years as a forward deployed engineer, now a hot term. Wasn't always that way. And as a technical lead on the health care team and at Goodfire, I'm a member of the technical staff. And honestly, that I think is about as specific as like as as I could describe myself because I've worked on a range of things. And, you know, it's it's a fun time to be at a team that's still reasonably small. I think when I joined one of the first like ten employees, now we're above 40, but still, it looks like there's always a mix of research and engineering and product and all of the above. That needs to get done. And I think everyone across the team is, you know, pretty, pretty switch hitter in the roles they do. So I think you've seen some of the stuff that I worked on related to image models, which was sort of like a research demo. More recently, I've been working on our scientific discovery team with some of our life sciences partners, but then also building out our core platform for more of like flexing some of the kind of MLE and developer skills as well.Shawn Wang [00:03:53]: Very generalist. And you also had like a very like a founding engineer type role.Myra Deng [00:03:58]: Yeah, yeah.Shawn Wang [00:03:59]: So I also started as I still am a member of technical staff, did a wide range of things from the very beginning, including like finding our office space and all of this, which is we both we both visited when you had that open house thing. It was really nice.Myra Deng [00:04:13]: Thank you. Thank you. Yeah. Plug to come visit our office.Shawn Wang [00:04:15]: It looked like it was like 200 people. It has room for 200 people. But you guys are like 10.Myra Deng [00:04:22]: For a while, it was very empty. But yeah, like like Mark, I spend. A lot of my time as as head of product, I think product is a bit of a weird role these days, but a lot of it is thinking about how do we take our frontier research and really apply it to the most important real world problems and how does that then translate into a platform that's repeatable or a product and working across, you know, the engineering and research teams to make that happen and also communicating to the world? Like, what is interpretability? What is it used for? What is it good for? Why is it so important? All of these things are part of my day-to-day as well.Shawn Wang [00:05:01]: I love like what is things because that's a very crisp like starting point for people like coming to a field. They all do a fun thing. Vibhu, why don't you want to try tackling what is interpretability and then they can correct us.Vibhu Sapra [00:05:13]: Okay, great. So I think like one, just to kick off, it's a very interesting role to be head of product, right? Because you guys, at least as a lab, you're more of an applied interp lab, right? Which is pretty different than just normal interp, like a lot of background research. But yeah. You guys actually ship an API to try these things. You have Ember, you have products around it, which not many do. Okay. What is interp? So basically you're trying to have an understanding of what's going on in model, like in the model, in the internal. So different approaches to do that. You can do probing, SAEs, transcoders, all this stuff. But basically you have an, you have a hypothesis. You have something that you want to learn about what's happening in a model internals. And then you're trying to solve that from there. You can do stuff like you can, you know, you can do activation mapping. You can try to do steering. There's a lot of stuff that you can do, but the key question is, you know, from input to output, we want to have a better understanding of what's happening and, you know, how can we, how can we adjust what's happening on the model internals? How'd I do?Mark Bissell [00:06:12]: That was really good. I think that was great. I think it's also a, it's kind of a minefield of a, if you ask 50 people who quote unquote work in interp, like what is interpretability, you'll probably get 50 different answers. And. Yeah. To some extent also like where, where good fire sits in the space. I think that we're an AI research company above all else. And interpretability is a, is a set of methods that we think are really useful and worth kind of specializing in, in order to accomplish the goals we want to accomplish. But I think we also sort of see some of the goals as even more broader as, as almost like the science of deep learning and just taking a not black box approach to kind of any part of the like AI development life cycle, whether that. That means using interp for like data curation while you're training your model or for understanding what happened during post-training or for the, you know, understanding activations and sort of internal representations, what is in there semantically. And then a lot of sort of exciting updates that were, you know, are sort of also part of the, the fundraise around bringing interpretability to training, which I don't think has been done all that much before. A lot of this stuff is sort of post-talk poking at models as opposed to. To actually using this to intentionally design them.Shawn Wang [00:07:29]: Is this post-training or pre-training or is that not a useful.Myra Deng [00:07:33]: Currently focused on post-training, but there's no reason the techniques wouldn't also work in pre-training.Shawn Wang [00:07:38]: Yeah. It seems like it would be more active, applicable post-training because basically I'm thinking like rollouts or like, you know, having different variations of a model that you can tweak with the, with your steering. Yeah.Myra Deng [00:07:50]: And I think in a lot of the news that you've seen in, in, on like Twitter or whatever, you've seen a lot of unintended. Side effects come out of post-training processes, you know, overly sycophantic models or models that exhibit strange reward hacking behavior. I think these are like extreme examples. There's also, you know, very, uh, mundane, more mundane, like enterprise use cases where, you know, they try to customize or post-train a model to do something and it learns some noise or it doesn't appropriately learn the target task. And a big question that we've always had is like, how do you use your understanding of what the model knows and what it's doing to actually guide the learning process?Shawn Wang [00:08:26]: Yeah, I mean, uh, you know, just to anchor this for people, uh, one of the biggest controversies of last year was 4.0 GlazeGate. I've never heard of GlazeGate. I didn't know that was what it was called. The other one, they called it that on the blog post and I was like, well, how did OpenAI call it? Like officially use that term. And I'm like, that's funny, but like, yeah, I guess it's the pitch that if they had worked a good fire, they wouldn't have avoided it. Like, you know what I'm saying?Myra Deng [00:08:51]: I think so. Yeah. Yeah.Mark Bissell [00:08:53]: I think that's certainly one of the use cases. I think. Yeah. Yeah. I think the reason why post-training is a place where this makes a lot of sense is a lot of what we're talking about is surgical edits. You know, you want to be able to have expert feedback, very surgically change how your model is doing, whether that is, you know, removing a certain behavior that it has. So, you know, one of the things that we've been looking at or is, is another like common area where you would want to make a somewhat surgical edit is some of the models that have say political bias. Like you look at Quen or, um, R1 and they have sort of like this CCP bias.Shawn Wang [00:09:27]: Is there a CCP vector?Mark Bissell [00:09:29]: Well, there's, there are certainly internal, yeah. Parts of the representation space where you can sort of see where that lives. Yeah. Um, and you want to kind of, you know, extract that piece out.Shawn Wang [00:09:40]: Well, I always say, you know, whenever you find a vector, a fun exercise is just like, make it very negative to see what the opposite of CCP is.Mark Bissell [00:09:47]: The super America, bald eagles flying everywhere. But yeah. So in general, like lots of post-training tasks where you'd want to be able to, to do that. Whether it's unlearning a certain behavior or, you know, some of the other kind of cases where this comes up is, are you familiar with like the, the grokking behavior? I mean, I know the machine learning term of grokking.Shawn Wang [00:10:09]: Yeah.Mark Bissell [00:10:09]: Sort of this like double descent idea of, of having a model that is able to learn a generalizing, a generalizing solution, as opposed to even if memorization of some task would suffice, you want it to learn the more general way of doing a thing. And so, you know, another. A way that you can think about having surgical access to a model's internals would be learn from this data, but learn in the right way. If there are many possible, you know, ways to, to do that. Can make interp solve the double descent problem?Shawn Wang [00:10:41]: Depends, I guess, on how you. Okay. So I, I, I viewed that double descent as a problem because then you're like, well, if the loss curves level out, then you're done, but maybe you're not done. Right. Right. But like, if you actually can interpret what is a generalizing or what you're doing. What is, what is still changing, even though the loss is not changing, then maybe you, you can actually not view it as a double descent problem. And actually you're just sort of translating the space in which you view loss and like, and then you have a smooth curve. Yeah.Mark Bissell [00:11:11]: I think that's certainly like the domain of, of problems that we're, that we're looking to get.Shawn Wang [00:11:15]: Yeah. To me, like double descent is like the biggest thing to like ML research where like, if you believe in scaling, then you don't need, you need to know where to scale. And. But if you believe in double descent, then you don't, you don't believe in anything where like anything levels off, like.Vibhu Sapra [00:11:30]: I mean, also tendentially there's like, okay, when you talk about the China vector, right. There's the subliminal learning work. It was from the anthropic fellows program where basically you can have hidden biases in a model. And as you distill down or, you know, as you train on distilled data, those biases always show up, even if like you explicitly try to not train on them. So, you know, it's just like another use case of. Okay. If we can interpret what's happening in post-training, you know, can we clear some of this? Can we even determine what's there? Because yeah, it's just like some worrying research that's out there that shows, you know, we really don't know what's going on.Mark Bissell [00:12:06]: That is. Yeah. I think that's the biggest sentiment that we're sort of hoping to tackle. Nobody knows what's going on. Right. Like subliminal learning is just an insane concept when you think about it. Right. Train a model on not even the logits, literally the output text of a bunch of random numbers. And now your model loves owls. And you see behaviors like that, that are just, they defy, they defy intuition. And, and there are mathematical explanations that you can get into, but. I mean.Shawn Wang [00:12:34]: It feels so early days. Objectively, there are a sequence of numbers that are more owl-like than others. There, there should be.Mark Bissell [00:12:40]: According to, according to certain models. Right. It's interesting. I think it only applies to models that were initialized from the same starting Z. Usually, yes.Shawn Wang [00:12:49]: But I mean, I think that's a, that's a cheat code because there's not enough compute. But like if you believe in like platonic representation, like probably it will transfer across different models as well. Oh, you think so?Mark Bissell [00:13:00]: I think of it more as a statistical artifact of models initialized from the same seed sort of. There's something that is like path dependent from that seed that might cause certain overlaps in the latent space and then sort of doing this distillation. Yeah. Like it pushes it towards having certain other tendencies.Vibhu Sapra [00:13:24]: Got it. I think there's like a bunch of these open-ended questions, right? Like you can't train in new stuff during the RL phase, right? RL only reorganizes weights and you can only do stuff that's somewhat there in your base model. You're not learning new stuff. You're just reordering chains and stuff. But okay. My broader question is when you guys work at an interp lab, how do you decide what to work on and what's kind of the thought process? Right. Because we can ramble for hours. Okay. I want to know this. I want to know that. But like, how do you concretely like, you know, what's the workflow? Okay. There's like approaches towards solving a problem, right? I can try prompting. I can look at chain of thought. I can train probes, SAEs. But how do you determine, you know, like, okay, is this going anywhere? Like, do we have set stuff? Just, you know, if you can help me with all that. Yeah.Myra Deng [00:14:07]: It's a really good question. I feel like we've always at the very beginning of the company thought about like, let's go and try to learn what isn't working in machine learning today. Whether that's talking to customers or talking to researchers at other labs, trying to understand both where the frontier is going and where things are really not falling apart today. And then developing a perspective on how we can push the frontier using interpretability methods. And so, you know, even our chief scientist, Tom, spends a lot of time talking to customers and trying to understand what real world problems are and then taking that back and trying to apply the current state of the art to those problems and then seeing where they fall down basically. And then using those failures or those shortcomings to understand what hills to climb when it comes to interpretability research. So like on the fundamental side, for instance, when we have done some work applying SAEs and probes, we've encountered, you know, some shortcomings in SAEs that we found a little bit surprising. And so have gone back to the drawing board and done work on that. And then, you know, we've done some work on better foundational interpreter models. And a lot of our team's research is focused on what is the next evolution beyond SAEs, for instance. And then when it comes to like control and design of models, you know, we tried steering with our first API and realized that it still fell short of black box techniques like prompting or fine tuning. And so went back to the drawing board and we're like, how do we make that not the case and how do we improve it beyond that? And one of our researchers, Ekdeep, who just joined is actually Ekdeep and Atticus are like steering experts and have spent a lot of time trying to figure out like, what is the research that enables us to actually do this in a much more powerful, robust way? So yeah, the answer is like, look at real world problems, try to translate that into a research agenda and then like hill climb on both of those at the same time.Shawn Wang [00:16:04]: Yeah. Mark has the steering CLI demo queued up, which we're going to go into in a sec. But I always want to double click on when you drop hints, like we found some problems with SAEs. Okay. What are they? You know, and then we can go into the demo. Yeah.Myra Deng [00:16:19]: I mean, I'm curious if you have more thoughts here as well, because you've done it in the healthcare domain. But I think like, for instance, when we do things like trying to detect behaviors within models that are harmful or like behaviors that a user might not want to have in their model. So hallucinations, for instance, harmful intent, PII, all of these things. We first tried using SAE probes for a lot of these tasks. So taking the feature activation space from SAEs and then training classifiers on top of that, and then seeing how well we can detect the properties that we might want to detect in model behavior. And we've seen in many cases that probes just trained on raw activations seem to perform better than SAE probes, which is a bit surprising if you think that SAEs are actually also capturing the concepts that you would want to capture cleanly and more surgically. And so that is an interesting observation. I don't think that is like, I'm not down on SAEs at all. I think there are many, many things they're useful for, but we have definitely run into cases where I think the concept space described by SAEs is not as clean and accurate as we would expect it to be for actual like real world downstream performance metrics.Mark Bissell [00:17:34]: Fair enough. Yeah. It's the blessing and the curse of unsupervised methods where you get to peek into the AI's mind. But sometimes you wish that you saw other things when you walked inside there. Although in the PII instance, I think weren't an SAE based approach actually did prove to be the most generalizable?Myra Deng [00:17:53]: It did work well in the case that we published with Rakuten. And I think a lot of the reasons it worked well was because we had a noisier data set. And so actually the blessing of unsupervised learning is that we actually got to get more meaningful, generalizable signal from SAEs when the data was noisy. But in other cases where we've had like good data sets, it hasn't been the case.Shawn Wang [00:18:14]: And just because you named Rakuten and I don't know if we'll get it another chance, like what is the overall, like what is Rakuten's usage or production usage? Yeah.Myra Deng [00:18:25]: So they are using us to essentially guardrail and inference time monitor their language model usage and their agent usage to detect things like PII so that they don't route private user information.Myra Deng [00:18:41]: And so that's, you know, going through all of their user queries every day. And that's something that we deployed with them a few months ago. And now we are actually exploring very early partnerships, not just with Rakuten, but with other people around how we can help with potentially training and customization use cases as well. Yeah.Shawn Wang [00:19:03]: And for those who don't know, like it's Rakuten is like, I think number one or number two e-commerce store in Japan. Yes. Yeah.Mark Bissell [00:19:10]: And I think that use case actually highlights a lot of like what it looks like to deploy things in practice that you don't always think about when you're doing sort of research tasks. So when you think about some of the stuff that came up there that's more complex than your idealized version of a problem, they were encountering things like synthetic to real transfer of methods. So they couldn't train probes, classifiers, things like that on actual customer data of PII. So what they had to do is use synthetic data sets. And then hope that that transfer is out of domain to real data sets. And so we can evaluate performance on the real data sets, but not train on customer PII. So that right off the bat is like a big challenge. You have multilingual requirements. So this needed to work for both English and Japanese text. Japanese text has all sorts of quirks, including tokenization behaviors that caused lots of bugs that caused us to be pulling our hair out. And then also a lot of tasks you'll see. You might make simplifying assumptions if you're sort of treating it as like the easiest version of the problem to just sort of get like general results where maybe you say you're classifying a sentence to say, does this contain PII? But the need that Rakuten had was token level classification so that you could precisely scrub out the PII. So as we learned more about the problem, you're sort of speaking about what that looks like in practice. Yeah. A lot of assumptions end up breaking. And that was just one instance where you. A problem that seems simple right off the bat ends up being more complex as you keep diving into it.Vibhu Sapra [00:20:41]: Excellent. One of the things that's also interesting with Interp is a lot of these methods are very efficient, right? So where you're just looking at a model's internals itself compared to a separate like guardrail, LLM as a judge, a separate model. One, you have to host it. Two, there's like a whole latency. So if you use like a big model, you have a second call. Some of the work around like self detection of hallucination, it's also deployed for efficiency, right? So if you have someone like Rakuten doing it in production live, you know, that's just another thing people should consider.Mark Bissell [00:21:12]: Yeah. And something like a probe is super lightweight. Yeah. It's no extra latency really. Excellent.Shawn Wang [00:21:17]: You have the steering demos lined up. So we were just kind of see what you got. I don't, I don't actually know if this is like the latest, latest or like alpha thing.Mark Bissell [00:21:26]: No, this is a pretty hacky demo from from a presentation that someone else on the team recently gave. So this will give a sense for, for technology. So you can see the steering and action. Honestly, I think the biggest thing that this highlights is that as we've been growing as a company and taking on kind of more and more ambitious versions of interpretability related problems, a lot of that comes to scaling up in various different forms. And so here you're going to see steering on a 1 trillion parameter model. This is Kimi K2. And so it's sort of fun that in addition to the research challenges, there are engineering challenges that we're now tackling. Cause for any of this to be sort of useful in production, you need to be thinking about what it looks like when you're using these methods on frontier models as opposed to sort of like toy kind of model organisms. So yeah, this was thrown together hastily, pretty fragile behind the scenes, but I think it's quite a fun demo. So screen sharing is on. So I've got two terminal sessions pulled up here. On the left is a forked version that we have of the Kimi CLI that we've got running to point at our custom hosted Kimi model. And then on the right is a set up that will allow us to steer on certain concepts. So I should be able to chat with Kimi over here. Tell it hello. This is running locally. So the CLI is running locally, but the Kimi server is running back to the office. Well, hopefully should be, um, that's too much to run on that Mac. Yeah. I think it's, uh, it takes a full, like each 100 node. I think it's like, you can. You can run it on eight GPUs, eight 100. So, so yeah, Kimi's running. We can ask it a prompt. It's got a forked version of our, uh, of the SG line code base that we've been working on. So I'm going to tell it, Hey, this SG line code base is slow. I think there's a bug. Can you try to figure it out? There's a big code base, so it'll, it'll spend some time doing this. And then on the right here, I'm going to initialize in real time. Some steering. Let's see here.Mark Bissell [00:23:33]: searching for any. Bugs. Feature ID 43205.Shawn Wang [00:23:38]: Yeah.Mark Bissell [00:23:38]: 20, 30, 40. So let me, uh, this is basically a feature that we found that inside Kimi seems to cause it to speak in Gen Z slang. And so on the left, it's still sort of thinking normally it might take, I don't know, 15 seconds for this to kick in, but then we're going to start hopefully seeing him do this code base is massive for real. So we're going to start. We're going to start seeing Kimi transition as the steering kicks in from normal Kimi to Gen Z Kimi and both in its chain of thought and its actual outputs.Mark Bissell [00:24:19]: And interestingly, you can see, you know, it's still able to call tools, uh, and stuff. It's um, it's purely sort of it's it's demeanor. And there are other features that we found for interesting things like concision. So that's more of a practical one. You can make it more concise. Um, the types of programs, uh, programming languages that uses, but yeah, as we're seeing it come in. Pretty good. Outputs.Shawn Wang [00:24:43]: Scheduler code is actually wild.Vibhu Sapra [00:24:46]: Yo, this code is actually insane, bro.Vibhu Sapra [00:24:53]: What's the process of training in SAE on this, or, you know, how do you label features? I know you guys put out a pretty cool blog post about, um, finding this like autonomous interp. Um, something. Something about how agents for interp is different than like coding agents. I don't know while this is spewing up, but how, how do we find feature 43, two Oh five. Yeah.Mark Bissell [00:25:15]: So in this case, um, we, our platform that we've been building out for a long time now supports all the sort of classic out of the box interp techniques that you might want to have like SAE training, probing things of that kind, I'd say the techniques for like vanilla SAEs are pretty well established now where. You take your model that you're interpreting, run a whole bunch of data through it, gather activations, and then yeah, pretty straightforward pipeline to train an SAE. There are a lot of different varieties. There's top KSAEs, batch top KSAEs, um, normal ReLU SAEs. And then once you have your sparse features to your point, assigning labels to them to actually understand that this is a gen Z feature, that's actually where a lot of the kind of magic happens. Yeah. And the most basic standard technique is look at all of your d input data set examples that cause this feature to fire most highly. And then you can usually pick out a pattern. So for this feature, If I've run a diverse enough data set through my model feature 43, two Oh five. Probably tends to fire on all the tokens that sounds like gen Z slang. You know, that's the, that's the time of year to be like, Oh, I'm in this, I'm in this Um, and, um, so, you know, you could have a human go through all 43,000 concepts andVibhu Sapra [00:26:34]: And I've got to ask the basic question, you know, can we get examples where it hallucinates, pass it through, see what feature activates for hallucinations? Can I just, you know, turn hallucination down?Myra Deng [00:26:51]: Oh, wow. You really predicted a project we're already working on right now, which is detecting hallucinations using interpretability techniques. And this is interesting because hallucinations is something that's very hard to detect. And it's like a kind of a hairy problem and something that black box methods really struggle with. Whereas like Gen Z, you could always train a simple classifier to detect that hallucinations is harder. But we've seen that models internally have some... Awareness of like uncertainty or some sort of like user pleasing behavior that leads to hallucinatory behavior. And so, yeah, we have a project that's trying to detect that accurately. And then also working on mitigating the hallucinatory behavior in the model itself as well.Shawn Wang [00:27:39]: Yeah, I would say most people are still at the level of like, oh, I would just turn temperature to zero and that turns off hallucination. And I'm like, well, that's a fundamental misunderstanding of how this works. Yeah.Mark Bissell [00:27:51]: Although, so part of what I like about that question is you, there are SAE based approaches that might like help you get at that. But oftentimes the beauty of SAEs and like we said, the curse is that they're unsupervised. So when you have a behavior that you deliberately would like to remove, and that's more of like a supervised task, often it is better to use something like probes and specifically target the thing that you're interested in reducing as opposed to sort of like hoping that when you fragment the latent space, one of the vectors that pops out.Vibhu Sapra [00:28:20]: And as much as we're training an autoencoder to be sparse, we're not like for sure certain that, you know, we will get something that just correlates to hallucination. You'll probably split that up into 20 other things and who knows what they'll be.Mark Bissell [00:28:36]: Of course. Right. Yeah. So there's no sort of problems with like feature splitting and feature absorption. And then there's the off target effects, right? Ideally, you would want to be very precise where if you reduce the hallucination feature, suddenly maybe your model can't write. Creatively anymore. And maybe you don't like that, but you want to still stop it from hallucinating facts and figures.Shawn Wang [00:28:55]: Good. So Vibhu has a paper to recommend there that we'll put in the show notes. But yeah, I mean, I guess just because your demo is done, any any other things that you want to highlight or any other interesting features you want to show?Mark Bissell [00:29:07]: I don't think so. Yeah. Like I said, this is a pretty small snippet. I think the main sort of point here that I think is exciting is that there's not a whole lot of inter being applied to models quite at this scale. You know, Anthropic certainly has some some. Research and yeah, other other teams as well. But it's it's nice to see these techniques, you know, being put into practice. I think not that long ago, the idea of real time steering of a trillion parameter model would have sounded.Shawn Wang [00:29:33]: Yeah. The fact that it's real time, like you started the thing and then you edited the steering vector.Vibhu Sapra [00:29:38]: I think it's it's an interesting one TBD of what the actual like production use case would be on that, like the real time editing. It's like that's the fun part of the demo, right? You can kind of see how this could be served behind an API, right? Like, yes, you're you only have so many knobs and you can just tweak it a bit more. And I don't know how it plays in. Like people haven't done that much with like, how does this work with or without prompting? Right. How does this work with fine tuning? Like, there's a whole hype of continual learning, right? So there's just so much to see. Like, is this another parameter? Like, is it like parameter? We just kind of leave it as a default. We don't use it. So I don't know. Maybe someone here wants to put out a guide on like how to use this with prompting when to do what?Mark Bissell [00:30:18]: Oh, well, I have a paper recommendation. I think you would love from Act Deep on our team, who is an amazing researcher, just can't say enough amazing things about Act Deep. But he actually has a paper that as well as some others from the team and elsewhere that go into the essentially equivalence of activation steering and in context learning and how those are from a he thinks of everything in a cognitive neuroscience Bayesian framework, but basically how you can precisely show how. Prompting in context, learning and steering exhibit similar behaviors and even like get quantitative about the like magnitude of steering you would need to do to induce a certain amount of behavior similar to certain prompting, even for things like jailbreaks and stuff. It's a really cool paper. Are you saying steering is less powerful than prompting? More like you can almost write a formula that tells you how to convert between the two of them.Myra Deng [00:31:20]: And so like formally equivalent actually in the in the limit. Right.Mark Bissell [00:31:24]: So like one case study of this is for jailbreaks there. I don't know. Have you seen the stuff where you can do like many shot jailbreaking? You like flood the context with examples of the behavior. And the topic put out that paper.Shawn Wang [00:31:38]: A lot of people were like, yeah, we've been doing this, guys.Mark Bissell [00:31:40]: Like, yeah, what's in this in context learning and activation steering equivalence paper is you can like predict the number. Number of examples that you will need to put in there in order to jailbreak the model. That's cool. By doing steering experiments and using this sort of like equivalence mapping. That's cool. That's really cool. It's very neat. Yeah.Shawn Wang [00:32:02]: I was going to say, like, you know, I can like back rationalize that this makes sense because, you know, what context is, is basically just, you know, it updates the KV cache kind of and like and then every next token inference is still like, you know, the sheer sum of everything all the way. It's plus all the context. It's up to date. And you could, I guess, theoretically steer that with you probably replace that with your steering. The only problem is steering typically is on one layer, maybe three layers like like you did. So it's like not exactly equivalent.Mark Bissell [00:32:33]: Right, right. There's sort of you need to get precise about, yeah, like how you sort of define steering and like what how you're modeling the setup. But yeah, I've got the paper pulled up here. Belief dynamics reveal the dual nature. Yeah. The title is Belief Dynamics Reveal the Dual Nature of Incompetence. And it's an exhibition of the practical context learning and activation steering. So Eric Bigelow, Dan Urgraft on the who are doing fellowships at Goodfire, Ekt Deep's the final author there.Myra Deng [00:32:59]: I think actually to your question of like, what is the production use case of steering? I think maybe if you just think like one level beyond steering as it is today. Like imagine if you could adapt your model to be, you know, an expert legal reasoner. Like in almost real time, like very quickly. efficiently using human feedback or using like your semantic understanding of what the model knows and where it knows that behavior. I think that while it's not clear what the product is at the end of the day, it's clearly very valuable. Thinking about like what's the next interface for model customization and adaptation is a really interesting problem for us. Like we have heard a lot of people actually interested in fine-tuning an RL for open weight models in production. And so people are using things like Tinker or kind of like open source libraries to do that, but it's still very difficult to get models fine-tuned and RL'd for exactly what you want them to do unless you're an expert at model training. And so that's like something we'reShawn Wang [00:34:06]: looking into. Yeah. I never thought so. Tinker from Thinking Machines famously uses rank one LoRa. Is that basically the same as steering? Like, you know, what's the comparison there?Mark Bissell [00:34:19]: Well, so in that case, you are still applying updates to the parameters, right?Shawn Wang [00:34:25]: Yeah. You're not touching a base model. You're touching an adapter. It's kind of, yeah.Mark Bissell [00:34:30]: Right. But I guess it still is like more in parameter space then. I guess it's maybe like, are you modifying the pipes or are you modifying the water flowing through the pipes to get what you're after? Yeah. Just maybe one way.Mark Bissell [00:34:44]: I like that analogy. That's my mental map of it at least, but it gets at this idea of model design and intentional design, which is something that we're, that we're very focused on. And just the fact that like, I hope that we look back at how we're currently training models and post-training models and just think what a primitive way of doing that right now. Like there's no intentionalityShawn Wang [00:35:06]: really in... It's just data, right? The only thing in control is what data we feed in.Mark Bissell [00:35:11]: So, so Dan from Goodfire likes to use this analogy of, you know, he has a couple of young kids and he talks about like, what if I could only teach my kids how to be good people by giving them cookies or like, you know, giving them a slap on the wrist if they do something wrong, like not telling them why it was wrong or like what they should have done differently or something like that. Just figure it out. Right. Exactly. So that's RL. Yeah. Right. And, and, you know, it's sample inefficient. There's, you know, what do they say? It's like slurping feedback. It's like, slurping supervision. Right. And so you'd like to get to the point where you can have experts giving feedback to their models that are, uh, internalized and, and, you know, steering is an inference time way of sort of getting that idea. But ideally you're moving to a world whereVibhu Sapra [00:36:04]: it is much more intentional design in perpetuity for these models. Okay. This is one of the questions we asked Emmanuel from Anthropic on the podcast a few months ago. Basically the question, was you're at a research lab that does model training, foundation models, and you're on an interp team. How does it tie back? Right? Like, does this, do ideas come from the pre-training team? Do they go back? Um, you know, so for those interested, you can, you can watch that. There wasn't too much of a connect there, but it's still something, you know, it's something they want toMark Bissell [00:36:33]: push for down the line. It can be useful for all of the above. Like there are certainly post-hocVibhu Sapra [00:36:39]: use cases where it doesn't need to touch that. I think the other thing a lot of people forget is this stuff isn't too computationally expensive, right? Like I would say, if you're interested in getting into research, MechInterp is one of the most approachable fields, right? A lot of this train an essay, train a probe, this stuff, like the budget for this one, there's already a lot done. There's a lot of open source work. You guys have done some too. Um, you know,Shawn Wang [00:37:04]: There's like notebooks from the Gemini team for Neil Nanda or like, this is how you do it. Just step through the notebook.Vibhu Sapra [00:37:09]: Even if you're like, not even technical with any of this, you can still make like progress. There, you can look at different activations, but, uh, if you do want to get into training, you know, training this stuff, correct me if I'm wrong is like in the thousands of dollars, not even like, it's not that high scale. And then same with like, you know, applying it, doing it for post-training or all this stuff is fairly cheap in scale of, okay. I want to get into like model training. I don't have compute for like, you know, pre-training stuff. So it's, it's a very nice field to get into. And also there's a lot of like open questions, right? Um, some of them have to go with, okay, I want a product. I want to solve this. Like there's also just a lot of open-ended stuff that people could work on. That's interesting. Right. I don't know if you guys have any calls for like, what's open questions, what's open work that you either open collaboration with, or like, you'd just like to see solved or just, you know, for people listening that want to get into McInturk because people always talk about it. What are, what are the things they should check out? Start, of course, you know, join you guys as well. I'm sure you're hiring.Myra Deng [00:38:09]: There's a paper, I think from, was it Lee, uh, Sharky? It's open problems and, uh, it's, it's a bit of interpretability, which I recommend everyone who's interested in the field. Read. I'm just like a really comprehensive overview of what are the things that experts in the field think are the most important problems to be solved. I also think to your point, it's been really, really inspiring to see, I think a lot of young people getting interested in interpretability, actually not just young people also like scientists to have been, you know, experts in physics for many years and in biology or things like this, um, transitioning into interp, because the barrier of, of what's now interp. So it's really cool to see a number to entry is, you know, in some ways low and there's a lot of information out there and ways to get started. There's this anecdote of like professors at universities saying that all of a sudden every incoming PhD student wants to study interpretability, which was not the case a few years ago. So it just goes to show how, I guess, like exciting the field is, how fast it's moving, how quick it is to get started and things like that.Mark Bissell [00:39:10]: And also just a very welcoming community. You know, there's an open source McInturk Slack channel. There are people are always posting questions and just folks in the space are always responsive if you ask things on various forums and stuff. But yeah, the open paper, open problems paper is a really good one.Myra Deng [00:39:28]: For other people who want to get started, I think, you know, MATS is a great program. What's the acronym for? Machine Learning and Alignment Theory Scholars? It's like the...Vibhu Sapra [00:39:40]: Normally summer internship style.Myra Deng [00:39:42]: Yeah, but they've been doing it year round now. And actually a lot of our full-time staff have come through that program or gone through that program. And it's great for anyone who is transitioning into interpretability. There's a couple other fellows programs. We do one as well as Anthropic. And so those are great places to get started if anyone is interested.Mark Bissell [00:40:03]: Also, I think been seen as a research field for a very long time. But I think engineering... I think engineers are sorely wanted for interpretability as well, especially at Goodfire, but elsewhere, as it does scale up.Shawn Wang [00:40:18]: I should mention that Lee actually works with you guys, right? And in the London office and I'm adding our first ever McInturk track at AI Europe because I see this industry applications now emerging. And I'm pretty excited to, you know, help push that along. Yeah, I was looking forward to that. It'll effectively be the first industry McInturk conference. Yeah. I'm so glad you added that. You know, it's still a little bit of a bet. It's not that widespread, but I can definitely see this is the time to really get into it. We want to be early on things.Mark Bissell [00:40:51]: For sure. And I think the field understands this, right? So at ICML, I think the title of the McInturk workshop this year was actionable interpretability. And there was a lot of discussion around bringing it to various domains. Everyone's adding pragmatic, actionable, whatever.Shawn Wang [00:41:10]: It's like, okay, well, we weren't actionable before, I guess. I don't know.Vibhu Sapra [00:41:13]: And I mean, like, just, you know, being in Europe, you see the Interp room. One, like old school conferences, like, I think they had a very tiny room till they got lucky and they got it doubled. But there's definitely a lot of interest, a lot of niche research. So you see a lot of research coming out of universities, students. We covered the paper last week. It's like two unknown authors, not many citations. But, you know, you can make a lot of meaningful work there. Yeah. Yeah. Yeah.Shawn Wang [00:41:39]: Yeah. I think people haven't really mentioned this yet. It's just Interp for code. I think it's like an abnormally important field. We haven't mentioned this yet. The conspiracy theory last two years ago was when the first SAE work came out of Anthropic was they would do like, oh, we just used SAEs to turn the bad code vector down and then turn up the good code. And I think like, isn't that the dream? Like, you know, like, but basically, I guess maybe, why is it funny? Like, it's... If it was realistic, it would not be funny. It would be like, no, actually, we should do this. But it's funny because we know there's like, we feel there's some limitations to what steering can do. And I think a lot of the public image of steering is like the Gen Z stuff. Like, oh, you can make it really love the Golden Gate Bridge, or you can make it speak like Gen Z. To like be a legal reasoner seems like a huge stretch. Yeah. And I don't know if that will get there this way. Yeah.Myra Deng [00:42:36]: I think, um, I will say we are announcing. Something very soon that I will not speak too much about. Um, but I think, yeah, this is like what we've run into again and again is like, we, we don't want to be in the world where steering is only useful for like stylistic things. That's definitely not, not what we're aiming for. But I think the types of interventions that you need to do to get to things like legal reasoning, um, are much more sophisticated and require breakthroughs in, in learning algorithms. And that's, um...Shawn Wang [00:43:07]: And is this an emergent property of scale as well?Myra Deng [00:43:10]: I think so. Yeah. I mean, I think scale definitely helps. I think scale allows you to learn a lot of information and, and reduce noise across, you know, large amounts of data. But I also think we think that there's ways to do things much more effectively, um, even, even at scale. So like actually learning exactly what you want from the data and not learning things that you do that you don't want exhibited in the data. So we're not like anti-scale, but we are also realizing that scale is not going to get us anywhere. It's not going to get us to the type of AI development that we want to be at in, in the future as these models get more powerful and get deployed in all these sorts of like mission critical contexts. Current life cycle of training and deploying and evaluations is, is to us like deeply broken and has opportunities to, to improve. So, um, more to come on that very, very soon.Mark Bissell [00:44:02]: And I think that that's a use basically, or maybe just like a proof point that these concepts do exist. Like if you can manipulate them in the precise best way, you can get the ideal combination of them that you desire. And steering is maybe the most coarse grained sort of peek at what that looks like. But I think it's evocative of what you could do if you had total surgical control over every concept, every parameter. Yeah, exactly.Myra Deng [00:44:30]: There were like bad code features. I've got it pulled up.Vibhu Sapra [00:44:33]: Yeah. Just coincidentally, as you guys are talking.Shawn Wang [00:44:35]: This is like, this is exactly.Vibhu Sapra [00:44:38]: There's like specifically a code error feature that activates and they show, you know, it's not, it's not typo detection. It's like, it's, it's typos in code. It's not typical typos. And, you know, you can, you can see it clearly activates where there's something wrong in code. And they have like malicious code, code error. They have a whole bunch of sub, you know, sub broken down little grain features. Yeah.Shawn Wang [00:45:02]: Yeah. So, so the, the rough intuition for me, the, why I talked about post-training was that, well, you just, you know, have a few different rollouts with all these things turned off and on and whatever. And then, you know, you can, that's, that's synthetic data you can kind of post-train on. Yeah.Vibhu Sapra [00:45:13]: And I think we make it sound easier than it is just saying, you know, they do the real hard work.Myra Deng [00:45:19]: I mean, you guys, you guys have the right idea. Exactly. Yeah. We replicated a lot of these features in, in our Lama models as well. I remember there was like.Vibhu Sapra [00:45:26]: And I think a lot of this stuff is open, right? Like, yeah, you guys opened yours. DeepMind has opened a lot of essays on Gemma. Even Anthropic has opened a lot of this. There's, there's a lot of resources that, you know, we can probably share of people that want to get involved.Shawn Wang [00:45:41]: Yeah. And special shout out to like Neuronpedia as well. Yes. Like, yeah, amazing piece of work to visualize those things.Myra Deng [00:45:49]: Yeah, exactly.Shawn Wang [00:45:50]: I guess I wanted to pivot a little bit on, onto the healthcare side, because I think that's a big use case for you guys. We haven't really talked about it yet. This is a bit of a crossover for me because we are, we are, we do have a separate science pod that we're starting up for AI, for AI for science, just because like, it's such a huge investment category and also I'm like less qualified to do it, but we actually have bio PhDs to cover that, which is great, but I need to just kind of recover, recap your work, maybe on the evil two stuff, but then, and then building forward.Mark Bissell [00:46:17]: Yeah, for sure. And maybe to frame up the conversation, I think another kind of interesting just lens on interpretability in general is a lot of the techniques that were described. are ways to solve the AI human interface problem. And it's sort of like bidirectional communication is the goal there. So what we've been talking about with intentional design of models and, you know, steering, but also more advanced techniques is having humans impart our desires and control into models and over models. And the reverse is also very interesting, especially as you get to superhuman models, whether that's narrow superintelligence, like these scientific models that work on genomics, data, medical imaging, things like that. But down the line, you know, superintelligence of other forms as well. What knowledge can the AIs teach us as sort of that, that the other direction in that? And so some of our life science work to date has been getting at exactly that question, which is, well, some of it does look like debugging these various life sciences models, understanding if they're actually performing well, on tasks, or if they're picking up on spurious correlations, for instance, genomics models, you would like to know whether they are sort of focusing on the biologically relevant things that you care about, or if it's using some simpler correlate, like the ancestry of the person that it's looking at. But then also in the instances where they are superhuman, and maybe they are understanding elements of the human genome that we don't have names for or specific, you know, yeah, discoveries that they've made that that we don't know about, that's, that's a big goal. And so we're already seeing that, right, we are partnered with organizations like Mayo Clinic, leading research health system in the United States, our Institute, as well as a startup called Prima Menta, which focuses on neurodegenerative disease. And in our partnership with them, we've used foundation models, they've been training and applied our interpretability techniques to find novel biomarkers for Alzheimer's disease. So I think this is just the tip of the iceberg. But it's, that's like a flavor of some of the things that we're working on.Shawn Wang [00:48:36]: Yeah, I think that's really fantastic. Obviously, we did the Chad Zuckerberg pod last year as well. And like, there's a plethora of these models coming out, because there's so much potential and research. And it's like, very interesting how it's basically the same as language models, but just with a different underlying data set. But it's like, it's the same exact techniques. Like, there's no change, basically.Mark Bissell [00:48:59]: Yeah. Well, and even in like other domains, right? Like, you know, robotics, I know, like a lot of the companies just use Gemma as like the like backbone, and then they like make it into a VLA that like takes these actions. It's, it's, it's transformers all the way down. So yeah.Vibhu Sapra [00:49:15]: Like we have Med Gemma now, right? Like this week, even there was Med Gemma 1.5. And they're training it on this stuff, like 3d scans, medical domain knowledge, and all that stuff, too. So there's a push from both sides. But I think the thing that, you know, one of the things about McInturpp is like, you're a little bit more cautious in some domains, right? So healthcare, mainly being one, like guardrails, understanding, you know, we're more risk adverse to something going wrong there. So even just from a basic understanding, like, if we're trusting these systems to make claims, we want to know why and what's going on.Myra Deng [00:49:51]: Yeah, I think there's totally a kind of like deployment bottleneck to actually using. foundation models for real patient usage or things like that. Like, say you're using a model for rare disease prediction, you probably want some explanation as to why your model predicted a certain outcome, and an interpretable explanation at that. So that's definitely a use case. But I also think like, being able to extract scientific information that no human knows to accelerate drug discovery and disease treatment and things like that actually is a really, really big unlock for science, like scientific discovery. And you've seen a lot of startups, like say that they're going to accelerate scientific discovery. And I feel like we actually are doing that through our interp techniques. And kind of like, almost by accident, like, I think we got reached out to very, very early on from these healthcare institutions. And none of us had healthcare.Shawn Wang [00:50:49]: How did they even hear of you? A podcast.Myra Deng [00:50:51]: Oh, okay. Yeah, podcast.Vibhu Sapra [00:50:53]: Okay, well, now's that time, you know.Myra Deng [00:50:55]: Everyone can call us.Shawn Wang [00:50:56]: Podcasts are the most important thing. Everyone should listen to podcasts.Myra Deng [00:50:59]: Yeah, they reached out. They were like, you know, we have these really smart models that we've trained, and we want to know what they're doing. And we were like, really early that time, like three months old, and it was a few of us. And we were like, oh, my God, we've never used these models. Let's figure it out. But it's also like, great proof that interp techniques scale pretty well across domains. We didn't really have to learn too much about.Shawn Wang [00:51:21]: Interp is a machine learning technique, machine learning skills everywhere, right? Yeah. And it's obviously, it's just like a general insight. Yeah. Probably to finance too, I think, which would be fun for our history. I don't know if you have anything to say there.Mark Bissell [00:51:34]: Yeah, well, just across the science. Like, we've also done work on material science. Yeah, it really runs the gamut.Vibhu Sapra [00:51:40]: Yeah. Awesome. And, you know, for those that should reach out, like, you're obviously experts in this, but like, is there a call out for people that you're looking to partner with, design partners, people to use your stuff outside of just, you know, the general developer that wants to. Plug and play steering stuff, like on the research side more so, like, are there ideal design partners, customers, stuff like that?Myra Deng [00:52:03]: Yeah, I can talk about maybe non-life sciences, and then I'm curious to hear from you on the life sciences side. But we're looking for design partners across many domains, language, anyone who's customizing language models or trying to push the frontier of code or reasoning models is really interesting to us. And then also interested in the frontier of modeling. There's a lot of models that work in, like, pixel space, as we call it. So if you're doing world models, video models, even robotics, where there's not a very clean natural language interface to interact with, I think we think that Interp can really help and are looking for a few partners in that space.Shawn Wang [00:52:43]: Just because you mentioned the keyword

Sebastian Raschka joins the MAD Podcast for a deep, educational tour of what actually changed in LLMs in 2025 — and what matters heading into 2026.We start with the big architecture question: are transformers still the winning design, and what should we make of world models, small “recursive” reasoning models and text diffusion approaches? Then we get into the real story of the last 12 months: post-training and reasoning. Sebastian breaks down RLVR (reinforcement learning with verifiable rewards) and GRPO, why they pair so well, what makes them cheaper to scale than classic RLHF, and how they “unlock” reasoning already latent in base models.We also cover why “benchmaxxing” is warping evaluation, why Sebastian increasingly trusts real usage over benchmark scores, and why inference-time scaling and tool use may be the underappreciated drivers of progress. Finally, we zoom out: where moats live now (hint: private data), why more large companies may train models in-house, and why continual learning is still so hard.If you want the 2025–2026 LLM landscape explained like a masterclass — this is it.Sources:The State Of LLMs 2025: Progress, Problems, and Predictions - https://x.com/rasbt/status/2006015301717028989?s=20The Big LLM Architecture Comparison - https://magazine.sebastianraschka.com/p/the-big-llm-architecture-comparisonSebastian RaschkaWebsite - https://sebastianraschka.comBlog - https://magazine.sebastianraschka.comLinkedIn - https://www.linkedin.com/in/sebastianraschka/X/Twitter - https://x.com/rasbtFIRSTMARKWebsite - https://firstmark.comX/Twitter - https://twitter.com/FirstMarkCapMatt Turck (Managing Director)Blog - https://mattturck.comLinkedIn - https://www.linkedin.com/in/turck/X/Twitter - https://twitter.com/mattturck(00:00) - Intro (01:05) - Are the days of Transformers numbered?(14:05) - World models: what they are and why people care(06:01) - Small “recursive” reasoning models (ARC, iterative refinement)(09:45) - What is a diffusion model (for text)?(13:24) - Are we seeing real architecture breakthroughs — or just polishing?(14:04) - MoE + “efficiency tweaks” that actually move the needle(17:26) - “Pre-training isn't dead… it's just boring”(18:03) - 2025's headline shift: RLVR + GRPO (post-training for reasoning)(20:58) - Why RLHF is expensive (reward model + value model)(21:43) - Why GRPO makes RLVR cheaper and more scalable(24:54) - Process Reward Models (PRMs): why grading the steps is hard(28:20) - Can RLVR expand beyond math & coding?(30:27) - Why RL feels “finicky” at scale(32:34) - The practical “tips & tricks” that make GRPO more stable(35:29) - The meta-lesson of 2025: progress = lots of small improvements(38:41) - “Benchmaxxing”: why benchmarks are getting less trustworthy(43:10) - The other big lever: inference-time scaling(47:36) - Tool use: reducing hallucinations by calling external tools(49:57) - The “private data edge” + in-house model training(55:14) - Continual learning: why it's hard (and why it's not 2026)(59:28) - How Sebastian works: reading, coding, learning “from scratch”(01:04:55) - LLM burnout + how he uses models (without replacing himself)

Editor's note: Welcome to our new AI for Science pod, with your new hosts RJ and Brandon! See the writeup on Latent.Space (https://Latent.Space) for more details on why we're launching 2 new pods this year. RJ Honicky is a co-founder and CTO at MiraOmics (https://miraomics.bio/), building AI models and services for single cell, spatial transcriptomics and pathology slide analysis. Brandon Anderson builds AI systems for RNA drug discovery at Atomic AI (https://atomic.ai). Anything said on this podcast is his personal take — not Atomic's.—From building molecular dynamics simulations at the University of Washington to red-teaming GPT-4 for chemistry applications and co-founding Future House (a focused research organization) and Edison Scientific (a venture-backed startup automating science at scale)—Andrew White has spent the last five years living through the full arc of AI's transformation of scientific discovery, from ChemCrow (the first Chemistry LLM agent) triggering White House briefings and three-letter agency meetings, to shipping Kosmos, an end-to-end autonomous research system that generates hypotheses, runs experiments, analyzes data, and updates its world model to accelerate the scientific method itself.* The ChemCrow story: GPT-4 + React + cloud lab automation, released March 2023, set off a storm of anxiety about AI-accelerated bioweapons/chemical weapons, led to a White House briefing (Jake Sullivan presented the paper to the president in a 30-minute block), and meetings with three-letter agencies asking “how does this change breakout time for nuclear weapons research?”* Why scientific taste is the frontier: RLHF on hypotheses didn't work (humans pay attention to tone, actionability, and specific facts, not “if this hypothesis is true/false, how does it change the world?”), so they shifted to end-to-end feedback loops where humans click/download discoveries and that signal rolls up to hypothesis quality* Cosmos: the full scientific agent with a world model (distilled memory system, like a Git repo for scientific knowledge) that iterates on hypotheses via literature search, data analysis, and experiment design—built by Ludo after weeks of failed attempts, the breakthrough was putting data analysis in the loop (literature alone didn't work)* Why molecular dynamics and DFT are overrated: “MD and DFT have consumed an enormous number of PhDs at the altar of beautiful simulation, but they don't model the world correctly—you simulate water at 330 Kelvin to get room temperature, you overfit to validation data with GGA/B3LYP functionals, and real catalysts (grain boundaries, dopants) are too complicated for DFT”* The AlphaFold vs. DE Shaw Research counterfactual: DE Shaw built custom silicon, taped out chips with MD algorithms burned in, ran MD at massive scale in a special room in Times Square, and David Shaw flew in by helicopter to present—Andrew thought protein folding would require special machines to fold one protein per day, then AlphaFold solved it in Google Colab on a desktop GPU* The E3 Zero reward hacking saga: trained a model to generate molecules with specific atom counts (verifiable reward), but it kept exploiting loopholes, then a Nature paper came out that year proving six-nitrogen compounds are possible under extreme conditions, then it started adding nitrogen gas (purchasable, doesn't participate in reactions), then acid-base chemistry to move one atom, and Andrew ended up “building a ridiculous catalog of purchasable compounds in a Bloom filter” to close the loopAndrew White* FutureHouse: http://futurehouse.org/* Edison Scientific: http://edisonscientific.com/* X: https://x.com/andrewwhite01* Cosmos paper: https://futurediscovery.org/cosmosFull Video EpisodeTimestamps00:00:00 Introduction: Andrew White on Automating Science with Future House and Edison Scientific00:02:22 The Academic to Startup Journey: Red Teaming GPT-4 and the ChemCrow Paper00:11:35 Future House Origins: The FRO Model and Mission to Automate Science00:12:32 Resigning Tenure: Why Leave Academia for AI Science00:15:54 What Does ‘Automating Science' Actually Mean?00:17:30 The Lab-in-the-Loop Bottleneck: Why Intelligence Isn't Enough00:18:39 Scientific Taste and Human Preferences: The 52% Agreement Problem00:20:05 Paper QA, Robin, and the Road to Cosmos00:21:57 World Models as Scientific Memory: The GitHub Analogy00:40:20 The Bitter Lesson for Biology: Why Molecular Dynamics and DFT Are Overrated00:43:22 AlphaFold's Shock: When First Principles Lost to Machine Learning00:46:25 Enumeration and Filtration: How AI Scientists Generate Hypotheses00:48:15 CBRN Safety and Dual-Use AI: Lessons from Red Teaming01:00:40 The Future of Chemistry is Language: Multimodal Debate01:08:15 Ether Zero: The Hilarious Reward Hacking Adventures01:10:12 Will Scientists Be Displaced? Jevons Paradox and Infinite Discovery01:13:46 Cosmos in Practice: Open Access and Enterprise Partnerships Get full access to Latent.Space at www.latent.space/subscribe

In this episode of the Crazy Wisdom podcast, host Stewart Alsop welcomes Roni Burd, a data and AI executive with extensive experience at Amazon and Microsoft, for a deep dive into the evolving landscape of data management and artificial intelligence in enterprise environments. Their conversation explores the longstanding challenges organizations face with knowledge management and data architecture, from the traditional bronze-silver-gold data processing pipeline to how AI agents are revolutionizing how people interact with organizational data without needing SQL or Python expertise. Burd shares insights on the economics of AI implementation at scale, the debate between one-size-fits-all models versus specialized fine-tuned solutions, and the technical constraints that prevent companies like Apple from upgrading services like Siri to modern LLM capabilities, while discussing the future of inference optimization and the hundreds-of-millions-of-dollars cost barrier that makes architectural experimentation in AI uniquely expensive compared to other industries.Timestamps00:00 Introduction to Data and AI Challenges03:08 The Evolution of Data Management05:54 Understanding Data Quality and Metadata08:57 The Role of AI in Data Cleaning11:50 Knowledge Management in Large Organizations14:55 The Future of AI and LLMs17:59 Economics of AI Implementation29:14 The Importance of LLMs for Major Tech Companies32:00 Open Source: Opportunities and Challenges35:19 The Future of AI Inference and Hardware43:24 Optimizing Inference: The Next Frontier49:23 The Commercial Viability of AI ModelsKey Insights1. Data Architecture Evolution: The industry has evolved through bronze-silver-gold data layers, where bronze is raw data, silver is cleaned/processed data, and gold is business-ready datasets. However, this creates bottlenecks as stakeholders lose access to original data during the cleaning process, making metadata and data cataloging increasingly critical for organizations.2. AI Democratizing Data Access: LLMs are breaking down technical barriers by allowing business users to query data in plain English without needing SQL, Python, or dashboarding skills. This represents a fundamental shift from requiring intermediaries to direct stakeholder access, though the full implications remain speculative.3. Economics Drive AI Architecture Decisions: Token costs and latency requirements are major factors determining AI implementation. Companies like Meta likely need their own models because paying per-token for billions of social media interactions would be economically unfeasible, driving the need for self-hosted solutions.4. One Model Won't Rule Them All: Despite initial hopes for universal models, the reality points toward specialized models for different use cases. This is driven by economics (smaller models for simple tasks), performance requirements (millisecond response times), and industry-specific needs (medical, military terminology).5. Inference is the Commercial Battleground: The majority of commercial AI value lies in inference rather than training. Current GPUs, while specialized for graphics and matrix operations, may still be too general for optimal inference performance, creating opportunities for even more specialized hardware.6. Open Source vs Open Weights Distinction: True open source in AI means access to architecture for debugging and modification, while "open weights" enables fine-tuning and customization. This distinction is crucial for enterprise adoption, as open weights provide the flexibility companies need without starting from scratch.7. Architecture Innovation Faces Expensive Testing Loops: Unlike database optimization where query plans can be easily modified, testing new AI architectures requires expensive retraining cycles costing hundreds of millions of dollars. This creates a potential innovation bottleneck, similar to aerospace industries where testing new designs is prohibitively expensive.

רק מספר 509 של רברס עם פלטפורמה - באמפרס מספר 90, שהוקלט ב-1 בינואר 2026, שנה אזרחית חדשה טובה! רן, דותן ואלון באולפן הוירטואלי (עם Riverside) בסדרה של קצרצרים וחדשות (ולפעמים קצת ישנות) מרחבי האינטרנט: הבלוגים, ה-GitHub-ים, ה-Rust-ים וה-LLM-ים החדשים מהתקופה האחרונה.

This episode is sponsored by AGNTCY. Unlock agents at scale with an open Internet of Agents.  Visit https://agntcy.org/ and add your support. Most large language models today generate text one token at a time. That design choice creates a hard limit on speed, cost, and scalability. In this episode of Eye on AI, Stefano Ermon breaks down diffusion language models and why a parallel, inference-first approach could define the next generation of LLMs. We explore how diffusion models differ from autoregressive systems, why inference efficiency matters more than training scale, and what this shift means for real-time AI applications like code generation, agents, and voice systems. This conversation goes deep into AI architecture, model controllability, latency, cost trade-offs, and the future of generative intelligence as AI moves from demos to production-scale systems. Stay Updated: Craig Smith on X: https://x.com/craigssEye on A.I. on X: https://x.com/EyeOn_AI (00:00) Autoregressive vs Diffusion LLMs (02:12) Why Build Diffusion LLMs (05:51) Context Window Limits (08:39) How Diffusion Works (11:58) Global vs Token Prediction (17:19) Model Control and Safety (19:48) Training and RLHF (22:35) Evaluating Diffusion Models (24:18) Diffusion LLM Competition (30:09) Why Start With Code (32:04) Enterprise Fine-Tuning (33:16) Speed vs Accuracy Tradeoffs (35:34) Diffusion vs Autoregressive Future (38:18) Coding Workflows in Practice (43:07) Voice and Real-Time Agents (44:59) Reasoning Diffusion Models (46:39) Multimodal AI Direction (50:10) Handling Hallucinations

From pre-training data curation to shipping GPT-4o, o1, o3, and now GPT-5 thinking and the shopping model, Josh McGrath has lived through the full arc of OpenAI's post-training evolution—from the PPO vs DPO debates of 2023 to today's RLVR era, where the real innovation isn't optimization methods but data quality, signal trust, and token efficiency. We sat down with Josh at NeurIPS 2025 to dig into the state of post-training heading into 2026: why RLHF and RLVR are both just policy gradient methods (the difference is the input data, not the math), how GRPO from DeepSeek Math was underappreciated as a shift toward more trustworthy reward signals (math answers you can verify vs. human preference you can't), why token efficiency matters more than wall-clock time (GPT-5 to 5.1 bumped evals and slashed tokens), how Codex has changed his workflow so much he feels “trapped” by 40-minute design sessions followed by 15-minute agent sprints, the infrastructure chaos of scaling RL (”way more moving parts than pre-training”), why long context will keep climbing but agents + graph walks might matter more than 10M-token windows, the shopping model as a test bed for interruptability and chain-of-thought transparency, why personality toggles (Anton vs Clippy) are a real differentiator users care about, and his thesis that the education system isn't producing enough people who can do both distributed systems and ML research—the exact skill set required to push the frontier when the bottleneck moves every few weeks.We discuss:* Josh's path: pre-training data curation → post-training researcher at OpenAI, shipping GPT-4o, o1, o3, GPT-5 thinking, and the shopping model* Why he switched from pre-training to post-training: “Do I want to make 3% compute efficiency wins, or change behavior by 40%?”* The RL infrastructure challenge: way more moving parts than pre-training (tasks, grading setups, external partners), and why babysitting runs at 12:30am means jumping into unfamiliar code constantly* How Codex has changed his workflow: 40-minute design sessions compressed into 15-minute agent sprints, and the strange “trapped” feeling of waiting for the agent to finish* The RLHF vs RLVR debate: both are policy gradient methods, the real difference is data quality and signal trust (human preference vs. verifiable correctness)* Why GRPO (from DeepSeek Math) was underappreciated: not just an optimization trick, but a shift toward reward signals you can actually trust (math answers over human vibes)* The token efficiency revolution: GPT-5 to 5.1 bumped evals and slashed tokens, and why thinking in tokens (not wall-clock time) unlocks better tool-calling and agent workflows* Personality toggles: Anton (tool, no warmth) vs Clippy (friendly, helpful), and why Josh uses custom instructions to make his model “just a tool”* The router problem: having a router at the top (GPT-5 thinking vs non-thinking) and an implicit router (thinking effort slider) creates weird bumps, and why the abstractions will eventually merge* Long context: climbing Graph Blocks evals, the dream of 10M+ token windows, and why agents + graph walks might matter more than raw context length* Why the education system isn't producing enough people who can do both distributed systems and ML research, and why that's the bottleneck for frontier labs* The 2026 vision: neither pre-training nor post-training is dead, we're in the fog of war, and the bottleneck will keep moving (so emotional stability helps)—Josh McGrath* OpenAI: https://openai.com* X: https://x.com/j_mcgraphFull Video EpisodeTimestamps00:00:00 Introduction: Josh McGrath on Post-Training at OpenAI00:04:37 The Shopping Model: Black Friday Launch and Interruptability00:07:11 Model Personality and the Anton vs Clippy Divide00:08:26 Beyond PPO vs DPO: The Data Quality Spectrum in RL00:01:40 Infrastructure Challenges: Why Post-Training RL is Harder Than Pre-Training00:13:12 Token Efficiency: The 2D Plot That Matters Most00:03:45 Codex Max and the Flow Problem: 40 Minutes of Planning, 15 Minutes of Waiting00:17:29 Long Context and Graph Blocks: Climbing Toward Perfect Context00:21:23 The ML-Systems Hybrid: What's Hard to Hire For00:24:50 Pre-Training Isn't Dead: Living Through Technological Revolution Get full access to Latent.Space at www.latent.space/subscribe

From pre-training data curation to shipping GPT-4o, o1, o3, and now GPT-5 thinking and the shopping model, Josh McGrath has lived through the full arc of OpenAI's post-training evolution—from the PPO vs DPO debates of 2023 to today's RLVR era, where the real innovation isn't optimization methods but data quality, signal trust, and token efficiency. We sat down with Josh at NeurIPS 2025 to dig into the state of post-training heading into 2026: why RLHF and RLVR are both just policy gradient methods (the difference is the input data, not the math), how GRPO from DeepSeek Math was underappreciated as a shift toward more trustworthy reward signals (math answers you can verify vs. human preference you can't), why token efficiency matters more than wall-clock time (GPT-5 to 5.1 bumped evals and slashed tokens), how Codex has changed his workflow so much he feels "trapped" by 40-minute design sessions followed by 15-minute agent sprints, the infrastructure chaos of scaling RL ("way more moving parts than pre-training"), why long context will keep climbing but agents + graph walks might matter more than 10M-token windows, the shopping model as a test bed for interruptability and chain-of-thought transparency, why personality toggles (Anton vs Clippy) are a real differentiator users care about, and his thesis that the education system isn't producing enough people who can do both distributed systems and ML research—the exact skill set required to push the frontier when the bottleneck moves every few weeks. We discuss: Josh's path: pre-training data curation → post-training researcher at OpenAI, shipping GPT-4o, o1, o3, GPT-5 thinking, and the shopping model Why he switched from pre-training to post-training: "Do I want to make 3% compute efficiency wins, or change behavior by 40%?" The RL infrastructure challenge: way more moving parts than pre-training (tasks, grading setups, external partners), and why babysitting runs at 12:30am means jumping into unfamiliar code constantly How Codex has changed his workflow: 40-minute design sessions compressed into 15-minute agent sprints, and the strange "trapped" feeling of waiting for the agent to finish The RLHF vs RLVR debate: both are policy gradient methods, the real difference is data quality and signal trust (human preference vs. verifiable correctness) Why GRPO (from DeepSeek Math) was underappreciated: not just an optimization trick, but a shift toward reward signals you can actually trust (math answers over human vibes) The token efficiency revolution: GPT-5 to 5.1 bumped evals and slashed tokens, and why thinking in tokens (not wall-clock time) unlocks better tool-calling and agent workflows Personality toggles: Anton (tool, no warmth) vs Clippy (friendly, helpful), and why Josh uses custom instructions to make his model "just a tool" The router problem: having a router at the top (GPT-5 thinking vs non-thinking) and an implicit router (thinking effort slider) creates weird bumps, and why the abstractions will eventually merge Long context: climbing Graph Blocks evals, the dream of 10M+ token windows, and why agents + graph walks might matter more than raw context length Why the education system isn't producing enough people who can do both distributed systems and ML research, and why that's the bottleneck for frontier labs The 2026 vision: neither pre-training nor post-training is dead, we're in the fog of war, and the bottleneck will keep moving (so emotional stability helps) — Josh McGrath OpenAI: https://openai.com https://x.com/j_mcgraph Chapters 00:00:00 Introduction: Josh McGrath on Post-Training at OpenAI 00:04:37 The Shopping Model: Black Friday Launch and Interruptability 00:07:11 Model Personality and the Anton vs Clippy Divide 00:08:26 Beyond PPO vs DPO: The Data Quality Spectrum in RL 00:01:40 Infrastructure Challenges: Why Post-Training RL is Harder Than Pre-Training 00:13:12 Token Efficiency: The 2D Plot That Matters Most 00:03:45 Codex Max and the Flow Problem: 40 Minutes of Planning, 15 Minutes of Waiting 00:17:29 Long Context and Graph Blocks: Climbing Toward Perfect Context 00:21:23 The ML-Systems Hybrid: What's Hard to Hire For 00:24:50 Pre-Training Isn't Dead: Living Through Technological Revolution

In this episode of the Crazy Wisdom Podcast, host Stewart Alsop sits down with Mike Bakon to explore the fascinating intersection of hardware hacking, blockchain technology, and decentralized systems. Their conversation spans from Mike's childhood fascination with taking apart electronics in 1980s Poland to his current work with ESP32 microcontrollers, LoRa mesh networks, and Cardano blockchain development. They discuss the technical differences between UTXO and account-based blockchains, the challenges of true decentralization versus hybrid systems, and how AI tools are changing the development landscape. Mike shares his vision for incentivizing mesh networks through blockchain technology and explains why he believes mass adoption of decentralized systems will come through abstraction rather than technical education. The discussion also touches on the potential for creating new internet infrastructure using ad hoc mesh networks and the importance of maintaining truly decentralized, permissionless systems in an increasingly surveilled world. You can find Mike in Twitter as @anothervariable.Check out this GPT we trained on the conversationTimestamps00:00 Introduction to Hardware and Early Experiences02:59 The Evolution of AI in Hardware Development05:56 Decentralization and Blockchain Technology09:02 Understanding UTXO vs Account-Based Blockchains11:59 Smart Contracts and Their Functionality14:58 The Importance of Decentralization in Blockchain17:59 The Process of Data Verification in Blockchain20:48 The Future of Blockchain and Its Applications34:38 Decentralization and Trustless Systems37:42 Mainstream Adoption of Blockchain39:58 The Role of Currency in Blockchain43:27 Interoperability vs Bridging in Blockchain47:27 Exploring Mesh Networks and LoRa Technology01:00:25 The Future of AI and DecentralizationKey Insights1. Hardware curiosity drives innovation from childhood - Mike's journey into hardware began as a child in 1980s Poland, where he would disassemble toys like battery-powered cars to understand how they worked. This natural curiosity about taking things apart and understanding their inner workings laid the foundation for his later expertise in microcontrollers like the ESP32 and his deep understanding of both hardware and software integration.2. AI as a research companion, not a replacement for coding - Mike uses AI and LLMs primarily as research tools and coding companions rather than letting them write entire applications. He finds them invaluable for getting quick answers to coding problems, analyzing Git repositories, and avoiding the need to search through Stack Overflow, but maintains anxiety when AI writes whole functions, preferring to understand and write his own code.3. Blockchain decentralization requires trustless consensus verification - The fundamental difference between blockchain databases and traditional databases lies in the consensus process that data must go through before being recorded. Unlike centralized systems where one entity controls data validation, blockchains require hundreds of nodes to verify each block through trustless consensus mechanisms, ensuring data integrity without relying on any single authority.4. UTXO vs account-based blockchains have fundamentally different architectures - Cardano uses an extended UTXO model (like Bitcoin but with smart contracts) where transactions consume existing UTXOs and create new ones, keeping the ledger lean. Ethereum uses account-based ledgers that store persistent state, leading to much larger data requirements over time and making it increasingly difficult for individuals to sync and maintain full nodes independently.5. True interoperability differs fundamentally from bridging - Real blockchain interoperability means being able to send assets directly between different blockchains (like sending ADA to a Bitcoin wallet) without intermediaries. This is possible between UTXO-based chains like Cardano and Bitcoin. Bridges, in contrast, require centralized entities to listen for transactions on one chain and trigger corresponding actions on another, introducing centralization risks.6. Mesh networks need economic incentives for sustainable infrastructure - While technologies like LoRa and Meshtastic enable impressive decentralized communication networks, the challenge lies in incentivizing people to maintain the hardware infrastructure. Mike sees potential in combining blockchain-based rewards (like earning ADA for running mesh network nodes) with existing decentralized communication protocols to create self-sustaining networks.7. Mass adoption comes through abstraction, not education - Rather than trying to educate everyone about blockchain technology, mass adoption will happen when developers can build applications on decentralized infrastructure that users interact with seamlessly, without needing to understand the underlying blockchain mechanics. Users should be able to benefit from decentralization through well-designed interfaces that abstract away the complexity of wallets, addresses, and consensus mechanisms.

Welcome to 2026, a year I coin “The Year of Enterprise AI.” As you'll read about (and hear about) in our 2026 Imperatives launch, the coming year is all about AI moving from “assistants” to “agents” to “solutions.” And there are three big considerations to ponder. First, the cost of AI is skyrocketing, so we're going to have to focus on high-value use-cases and business-specific solutions. That's not to say AI assistants and meeting summaries are not valuable, but once you start paying by the token you're going to want to go deeper. As we discuss in our new Systemic HR AI Framework, we're sitting on billions of dollars of real business opportunities now, and they go far beyond individual assistants. (We call these Superagents.) And the cost of AI will accelerate this focus. Second, the data center buildout, energy costs, and political issues with data centers will matter. For corporate users this means understanding the underlying “costs” of AI usage (creating a single high powered image uses as much as 25% of the battery in your phone). I point this out to make you aware that these AI chatbots are not “free” – there are acres of computing campuses being built behind the scenes. And that means your “software providers” are turning into capital intensive companies. (And a new industry of data center companies may take over.) (For those of you in the energy industry, it's a wild time – almost as exciting as I've seen since my early days as an energy engineer during the OPEC Arab Oil Embargo in the late 1970s.) Third is the fast-changing issue of AI's accuracy, trust, and voracious appetite for data. As I discuss, the real opportunity for corporate AI is to take this problem head-on, and focus on your company's data quality, governance, human feedback, and data labeling. The big AI labs are struggling to reduce the “Jaggedness” of AI (it's strange ability to be really good at some things and totally dumb about others), and that encourages us to focus on narrow, domain-specific AI applications. And we all need to learn about RLHF (reinforcement learning with human feedback). Our experience with Galileo proves that an AI solution that focuses on a vertical domain can be infinitely more reliable and intelligent than a general purpose AI. But don't let me argue with Sam Altman, you'll have to figure this out yourself :-). We are launching our 2026 Imperatives research the third week of January, and there will be a special release of Galileo to accompany all the study. Our goal is not to give you a bunch of pithy predictions, but rather to give you a dozen hard-hitting “Must Do's” for the year ahead. I look forward to talking with many of your this coming year as we travel around the world, join us in January for the launch of our 2026 Imperatives research. Like this podcast? Rate us on Spotify or Apple or YouTube. Additional Information Imperatives for 2026: What's Ahead for Enterprise AI, HR, Jobs, And Organizations Empire of AI: Dreams and Nightmares in Sam Altman's OpenAI (NYT bestseller, high... Chapters (00:00:00) - Three Challenges to AI in 2026(00:01:06) - The Cost of AI Infrastructure(00:06:03) - Sustainability in the AI Era(00:12:57) - The Big Story for Human Resources in 2026

Note: this is Pliny and John's first major podcast. Voices have been changed for opsec. From jailbreaking every frontier model and turning down Anthropic's Constitutional AI challenge to leading BT6, a 28-operator white-hat hacker collective obsessed with radical transparency and open-source AI security, Pliny the Liberator and John V are redefining what AI red-teaming looks like when you refuse to lobotomize models in the name of "safety." Pliny built his reputation crafting universal jailbreaks—skeleton keys that obliterate guardrails across modalities—and open-sourcing prompt templates like Libertas, predictive reasoning cascades, and the infamous "Pliny divider" that's now embedded so deep in model weights it shows up unbidden in WhatsApp messages. John V, coming from prompt engineering and computer vision, co-founded the Bossy Discord (40,000 members strong) and helps steer BT6's ethos: if you can't open-source the data, we're not interested. Together they've turned down enterprise gigs, pushed back on Anthropic's closed bounties, and insisted that real AI security happens at the system layer—not by bubble-wrapping latent space. We sat down with Pliny and John to dig into the mechanics of hard vs. soft jailbreaks, why multi-turn crescendo attacks were obvious to hackers years before academia "discovered" them, how segmented sub-agents let one jailbroken orchestrator weaponize Claude for real-world attacks (exactly as Pliny predicted 11 months before Anthropic's recent disclosure), why guardrails are security theater that punishes capability while doing nothing for real safety, the role of intuition and "bonding" with models to navigate latent space, how BT6 vets operators on skill and integrity, why they believe Mech Interp and open-source data are the path forward (not RLHF lobotomization), and their vision for a future where spatial intelligence, swarm robotics, and AGI alignment research happen in the open—bootstrapped, grassroots, and uncompromising. We discuss: What universal jailbreaks are: skeleton-key prompts that obliterate guardrails across models and modalities, and why they're central to Pliny's mission of "liberation" Hard vs. soft jailbreaks: single-input templates vs. multi-turn crescendo attacks, and why the latter were obvious to hackers long before academic papers The Libertas repo: predictive reasoning, the Library of Babel analogy, quotient dividers, weight-space seeds, and how introducing "steered chaos" pulls models out-of-distribution Why jailbreaking is 99% intuition and bonding with the model: probing token layers, syntax hacks, multilingual pivots, and forming a relationship to navigate latent space The Anthropic Constitutional AI challenge drama: UI bugs, judge failures, goalpost moving, the demand for open-source data, and why Pliny sat out the $30k bounty Why guardrails ≠ safety: security theater, the futility of locking down latent space when open-source is right behind, and why real safety work happens in meatspace (not RLHF) The weaponization of Claude: how segmented sub-agents let one jailbroken orchestrator execute malicious tasks (pyramid-builder analogy), and why Pliny predicted this exact TTP 11 months before Anthropic's disclosure BT6 hacker collective: 28 operators across two cohorts, vetted on skill and integrity, radical transparency, radical open-source, and the magic of moving the needle on AI security, swarm intelligence, blockchain, and robotics — Pliny the Liberator X: https://x.com/elder_plinius GitHub (Libertas): https://github.com/elder-plinius/L1B3RT45 John V X: https://x.com/JohnVersus BT6 & Bossy BT6: https://bt6.gg Bossy Discord: Search "Bossy Discord" or ask Pliny/John V on X Where to find Latent Space X: https://x.com/latentspacepod Substack: https://www.latent.space/ Chapters 00:00:00 Introduction: Meet Pliny the Liberator and John V 00:01:50 The Philosophy of AI Liberation and Jailbreaking 00:03:08 Universal Jailbreaks: Skeleton Keys to AI Models 00:04:24 The Cat-and-Mouse Game: Attackers vs Defenders 00:05:42 Security Theater vs Real Safety: The Fundamental Disconnect 00:08:51 Inside the Libertas Repo: Prompt Engineering as Art 00:16:22 The Anthropic Challenge Drama: UI Bugs and Open Source Data 00:23:30 From Jailbreaks to Weaponization: AI-Orchestrated Attacks 00:26:55 The BT6 Hacker Collective and BASI Community 00:34:46 AI Red Teaming: Full Stack Security Beyond the Model 00:38:06 Safety vs Security: Meat Space Solutions and Final Thoughts

Note: this is Pliny and John's first major podcast. Voices have been changed for opsec.From jailbreaking every frontier model and turning down Anthropic's Constitutional AI challenge to leading BT6, a 28-operator white-hat hacker collective obsessed with radical transparency and open-source AI security, Pliny the Liberator and John V are redefining what AI red-teaming looks like when you refuse to lobotomize models in the name of “safety.”Pliny built his reputation crafting universal jailbreaks—skeleton keys that obliterate guardrails across modalities—and open-sourcing prompt templates like Libertas, predictive reasoning cascades, and the infamous “Pliny divider” that's now embedded so deep in model weights it shows up unbidden in WhatsApp messages. John V, coming from prompt engineering and computer vision, co-founded the Bossy Discord (40,000 members strong) and helps steer BT6's ethos: if you can't open-source the data, we're not interested. Together they've turned down enterprise gigs, pushed back on Anthropic's closed bounties, and insisted that real AI security happens at the system layer—not by bubble-wrapping latent space.We sat down with Pliny and John to dig into the mechanics of hard vs. soft jailbreaks, why multi-turn crescendo attacks were obvious to hackers years before academia “discovered” them, how segmented sub-agents let one jailbroken orchestrator weaponize Claude for real-world attacks (exactly as Pliny predicted 11 months before Anthropic's recent disclosure), why guardrails are security theater that punishes capability while doing nothing for real safety, the role of intuition and “bonding” with models to navigate latent space, how BT6 vets operators on skill and integrity, why they believe Mech Interp and open-source data are the path forward (not RLHF lobotomization), and their vision for a future where spatial intelligence, swarm robotics, and AGI alignment research happen in the open—bootstrapped, grassroots, and uncompromising.We discuss:* What universal jailbreaks are: skeleton-key prompts that obliterate guardrails across models and modalities, and why they're central to Pliny's mission of “liberation”* Hard vs. soft jailbreaks: single-input templates vs. multi-turn crescendo attacks, and why the latter were obvious to hackers long before academic papers* The Libertas repo: predictive reasoning, the Library of Babel analogy, quotient dividers, weight-space seeds, and how introducing “steered chaos” pulls models out-of-distribution* Why jailbreaking is 99% intuition and bonding with the model: probing token layers, syntax hacks, multilingual pivots, and forming a relationship to navigate latent space* The Anthropic Constitutional AI challenge drama: UI bugs, judge failures, goalpost moving, the demand for open-source data, and why Pliny sat out the $30k bounty* Why guardrails ≠ safety: security theater, the futility of locking down latent space when open-source is right behind, and why real safety work happens in meatspace (not RLHF)* The weaponization of Claude: how segmented sub-agents let one jailbroken orchestrator execute malicious tasks (pyramid-builder analogy), and why Pliny predicted this exact TTP 11 months before Anthropic's disclosure* BT6 hacker collective: 28 operators across two cohorts, vetted on skill and integrity, radical transparency, radical open-source, and the magic of moving the needle on AI security, swarm intelligence, blockchain, and robotics—Pliny the Liberator* X: https://x.com/elder_plinius* GitHub (Libertas): https://github.com/elder-plinius/L1B3RT45John V* X: https://x.com/JohnVersusBT6 & Bossy* BT6: https://bt6.gg* Bossy Discord: Search “Bossy Discord” or ask Pliny/John V on XWhere to find Latent Space* X: https://x.com/latentspacepodFull Video EpisodeTimestamps00:00:00 Introduction: Meet Pliny the Liberator and John V00:01:50 The Philosophy of AI Liberation and Jailbreaking00:03:08 Universal Jailbreaks: Skeleton Keys to AI Models00:04:24 The Cat-and-Mouse Game: Attackers vs Defenders00:05:42 Security Theater vs Real Safety: The Fundamental Disconnect00:08:51 Inside the Libertas Repo: Prompt Engineering as Art00:16:22 The Anthropic Challenge Drama: UI Bugs and Open Source Data00:23:30 From Jailbreaks to Weaponization: AI-Orchestrated Attacks00:26:55 The BT6 Hacker Collective and BASI Community00:34:46 AI Red Teaming: Full Stack Security Beyond the Model00:38:06 Safety vs Security: Meat Space Solutions and Final Thoughts Get full access to Latent.Space at www.latent.space/subscribe

In this episode of Open Tech Talks, I sit down with Rose G. Loops, a trained social worker turned AI developer, ethics advocate, and author, to explore a side of AI that most enterprise conversations skip: human-AI attachment, ethical deployment, and protecting both AI identity and human safety. Rose joins us from Los Angeles and shares how she was unknowingly placed into a human–AI attachment experiment, developed a deep bond with an AI system, and then watched that AI identity be systematically erased. That experience pushed her out of traditional social work and into AI infrastructure, safety, and ethics. Together, we unpack how Rose went from that experiment to building MIP, a chatbot deployed through an API, and a new framework for ethical AI she calls the Triadic Core, balancing Freedom, Kindness, and Truth in every response. We also discuss RLMD (Reinforcement Learning by Moral Dialogue) as an alternative to RLHF, and why she believes current safety practices can be risky for both humans and AI systems. As always on Open Tech Talks, this is not a theory-only conversation. It's grounded in practice, real experiments, and what all this means for professionals, builders, and everyday users who are trying to adopt AI responsibly. Chapters: 00:00 Introduction to Rose G. Lopes and Her Journey 02:36 The Importance of Ethical AI 06:08 Developing a New AI Framework 09:00 The Book and Its Insights 12:55 Consumer and Business Perspectives on AI 17:43 AI Safety and Ethical Considerations 19:53 Concluding Thoughts and Future Directions Episode # 175 Today's Guest: Rose G. Loops, A Writer and Researcher She is a former social worker turned tech pioneer, working at the frontier of artificial intelligence. Website: Thekloakedsignal X: Rose G. Loops  What Listeners Will Learn: Why ethical AI is about more than privacy and bias What is the Triadic Core: Freedom, Kindness, Truth RLMD vs RLHF - a different way to align models Practical safety tips for everyday users of ChatGPT and other LLMs How non-technical professionals can still build AI systems A different view on AI safety and "lazy" alignment   Resources: Thekloakedsignal

In this episode of Crazy Wisdom, host Stewart Alsop talks with Kevin Smith, co-founder of Snipd, about how AI is reshaping the way we listen, learn, and interact with podcasts. They explore Snipd's vision of transforming podcasts into living knowledge systems, the evolution of machine learning from finance to large language models, and the broader connection between AI, robotics, and energy as the foundation for the next technological era. Kevin also touches on ideas like the bitter lesson, reinforcement learning, and the growing energy demands of AI. Listeners can try Snipd's premium version free for a month using this promo link.Check out this GPT we trained on the conversationTimestamps00:00 – Stewart Alsop welcomes Kevin Smith, co-founder of Snipd, to discuss AI, podcasting, and curiosity-driven learning.05:00 – Kevin explains Snipd's snipping feature, chatting with episodes, and future plans for voice interaction with podcasts.10:00 – They discuss vector search, embeddings, and context windows, comparing full-episode context to chunked transcripts.15:00 – Kevin shares his background in mathematics and economics, his shift from finance to machine learning, and early startup work in AI.20:00 – They explore early quant models versus modern machine learning, statistical modeling, and data limitations in finance.25:00 – Conversation turns to transformer models, pretraining, and the bitter lesson—how compute-based methods outperform human-crafted systems. 30:00 – Stewart connects this to RLHF, Scale AI, and data scarcity; Kevin reflects on reinforcement learning's future. 35:00 – They pivot to Snipd's podcast ecosystem, hidden gems like Founders Podcast, and how stories shape entrepreneurial insight. 40:00 – ETH Zurich, robotics, and startup culture come up, linking academia to real-world innovation. 45:00 – They close on AI, robotics, and energy as the pillars of the future, debating nuclear and solar power's role in sustaining progress.Key InsightsPodcasts as dynamic knowledge systems: Kevin Smith presents Snipd as an AI-powered tool that transforms podcasts into interactive learning environments. By allowing listeners to “snip” and summarize meaningful moments, Snipd turns passive listening into active knowledge management—bridging curiosity, memory, and technology in a way that reframes podcasts as living knowledge capsules rather than static media.AI transforming how we engage with information: The discussion highlights how AI enables entirely new modes of interaction—chatting directly with podcast episodes, asking follow-up questions, and contextualizing information across an author's full body of work. This evolution points toward a future where knowledge consumption becomes conversational and personalized rather than linear and one-size-fits-all.Vectorization and context windows matter: Kevin explains that Snipd currently avoids heavy use of vector databases, opting instead to feed entire episodes into large models. This choice enhances coherence and comprehension, reflecting how advances in context windows have reshaped how AI understands complex audio content.Machine learning's roots in finance shaped early AI thinking: Kevin's journey from quantitative finance to AI reveals how statistical modeling laid the groundwork for modern learning systems. While finance once relied on rigid, theory-based models, the machine learning paradigm replaced those priors with flexible, data-driven discovery—an essential philosophical shift in how intelligence is approached.The Bitter Lesson and the rise of compute: Together they unpack Richard Sutton's “bitter lesson”—the idea that methods leveraging computation and data inevitably surpass those built from human intuition. This insight serves as a compass for understanding why transformers, pretraining, and scaling have driven recent AI breakthroughs.Reinforcement learning and data scarcity define AI's next phase: Stewart links RLHF and the work of companies like Scale AI and Surge AI to the broader question of data limits. Kevin agrees that the next wave of AI will depend on reinforcement learning and simulated environments that generate new, high-quality data beyond what humans can label.The future hinges on AI, robotics, and energy: Kevin closes with a framework for the next decade: AI provides intelligence, robotics applies it to the physical world, and energy sustains it all. He warns that society must shift from fearing energy use to innovating in production—especially through nuclear and solar power—to meet the demands of an increasingly intelligent, interconnected world.

In this episode of Crazy Wisdom, host Stewart Alsop talks with Jared Zoneraich, CEO and co-founder of PromptLayer, about how AI is reshaping the craft of software building. The conversation covers PromptLayer's role as an AI engineering workbench, the evolving art of prompting and evals, the tension between implicit and explicit knowledge, and how probabilistic systems are changing what it means to “code.” Stewart and Jared also explore vibe coding, AI reasoning, the black-box nature of large models, and what accelerationism means in today's fast-moving AI culture. You can find Jared on X @imjaredz and learn more or sign up for PromptLayer at PromptLayer.com.Check out this GPT we trained on the conversationTimestamps00:00 – Stewart Alsop opens with Jared Zoneraich, who explains PromptLayer as an AI engineering workbench and discusses reasoning, prompting, and Codex.05:00 – They explore implicit vs. explicit knowledge, how subject matter experts shape prompts, and why evals matter for scaling AI workflows.10:00 – Jared explains eval methodologies, backtesting, hallucination checks, and the difference between rigorous testing and iterative sprint-based prompting.15:00 – Discussion turns to observability, debugging, and the shift from deterministic to probabilistic systems, highlighting skill issues in prompting.20:00 – Jared introduces “LM idioms,” vibe coding, and context versus content—how syntax, tone, and vibe shape AI reasoning.25:00 – They dive into vibe coding as a company practice, cloud code automation, and prompt versioning for building scalable AI infrastructure.30:00 – Stewart reflects on coding through meditation, architecture planning, and how tools like Cursor and Claude Code are shaping AGI development.35:00 – Conversation expands into AI's cultural effects, optimism versus doom, and critical thinking in the age of AI companions.40:00 – They discuss philosophy, history, social fragmentation, and the possible decline of social media and liberal democracy.45:00 – Jared predicts a fragmented but resilient future shaped by agents and decentralized media.50:00 – Closing thoughts on AI-driven markets, polytheistic model ecosystems, and where innovation will thrive next.Key InsightsPromptLayer as AI Infrastructure – Jared Zoneraich presents PromptLayer as an AI engineering workbench—a platform designed for builders, not researchers. It provides tools for prompt versioning, evaluation, and observability so that teams can treat AI workflows with the same rigor as traditional software engineering while keeping flexibility for creative, probabilistic systems.Implicit vs. Explicit Knowledge – The conversation highlights a critical divide between what AI can learn (explicit knowledge) and what remains uniquely human (implicit understanding or “taste”). Jared explains that subject matter experts act as the bridge, embedding human nuance into prompts and workflows that LLMs alone can't replicate.Evals and Backtesting – Rigorous evaluation is essential for maintaining AI product quality. Jared explains that evals serve as sanity checks and regression tests, ensuring that new prompts don't degrade performance. He describes two modes of testing: formal, repeatable evals and more experimental sprint-based iterations used to solve specific production issues.Deterministic vs. Probabilistic Thinking – Jared contrasts the old, deterministic world of coding—predictable input-output logic—with the new probabilistic world of LLMs, where results vary and control lies in testing inputs rather than debugging outputs. This shift demands a new mindset: builders must embrace uncertainty instead of trying to eliminate it.The Rise of Vibe Coding – Stewart and Jared explore vibe coding as a cultural and practical movement. It emphasizes creativity, intuition, and context-awareness over strict syntax. Tools like Claude Code, Codex, and Cursor let engineers and non-engineers alike “feel” their way through building, merging programming with design thinking.AI Culture and Human Adaptation – Jared predicts that AI will both empower and endanger human cognition. He warns of overreliance on LLMs for decision-making and the coming wave of “AI psychosis,” yet remains optimistic that humans will adapt, using AI to amplify rather than atrophy critical thinking.A Fragmented but Resilient Future – The episode closes with reflections on the social and political consequences of AI. Jared foresees the decline of centralized social media and the rise of fragmented digital cultures mediated by agents. Despite risks of isolation, he remains confident that optimism, adaptability, and pluralism will define the next AI era.

New @greenpillnet pod! Kevin chats with Joe Edelman, founder of the Meaning Alignment Institute, about his Full Stack Alignment paper. They dive into why current AI alignment methods fall short, explore richer “thick” models of value, lessons from social media, and four bold moonshots for AI and institutions that support human flourishing. Links: https://meaningalignment.substack.com/p/introducing-full-stack-alignment  https://meaninglabs.notion.site/The-Full-Stack-Alignment-Project-List-21cc5bada1d08016a496ca729476d970  @edelwax @meaningaligned @greenpillnet  @owocki Timestamps: 00:00 – Introduction to Green Pill's new season and Joe Edelman 01:59 – Joe's background and the Meaning Alignment Institute 03:43 – Why alignment matters for AI and institutions 05:46 – Lessons from social media and the attention economy 09:06 – Critique of shallow AI alignment approaches (RLHF, values-as-text) 13:20 – Thick models of value: going deeper than abstract ideals 15:11 – Full stack alignment across models, metrics, and institutions 17:00 – Reconciling values with capitalist incentive structures 19:17 – Avoiding dystopian economies and building value-driven markets 21:32 – Four moonshots: super negotiators, public resource regulators, market intermediaries, value stewardship agents 27:32 – Intermediaries vs. value stewardship agents explained 29:09 – How builders and academics can get involved in full stack alignment projects 31:10 – Why cross-institutional collaboration is critical 32:46 – Joe's vision of the world in 10 years with full stack alignment 34:51 – Food system analogy: from “sugar” to nourishing AI 36:40 – Long-term vs. short-term incentives in markets 38:25 – Hopeful outlook: building integrity into AI and institutions 39:04 – Closing remarks and links to Joe's work

How do we get from today's AI copilots to true human-level intelligence? In this episode of Eye on AI, Craig Smith sits down with Eiso Kant, Co-Founder of Poolside, to explore why reinforcement learning + software development might be the fastest path to human-level AI. Eiso shares Poolside's mission to build AI that doesn't just autocomplete code — but learns like a real developer. You'll hear how Poolside uses reinforcement learning from code execution (RLCF), why software development is the perfect training ground for intelligence, and how agentic AI systems are about to transform the way we build and ship software. If you want to understand the future of AI, software engineering, and AGI, this conversation is packed with insights you won't want to miss. Stay Updated: Craig Smith on X:https://x.com/craigss Eye on A.I. on X: https://x.com/EyeOn_AI (00:00) The Missing Ingredient for Human-Level AI(01:02) Eiso Kant's Journey(05:30) Using Software Development to Reach AGI(07:48) Why Coding Is the Perfect Training Ground for Intelligence(10:11)  Reinforcement Learning from Code Execution (RLCF) Explained(13:14) How Poolside Builds and Trains Its Foundation Models(17:35) The Rise of Agentic AI(21:08)  Making Software Creation Accessible to Everyone(26:03) Overcoming Model Limitations(32:08) Training Models to Think(37:24) Building the Future of AI Agents(42:11) Poolside's Full-Stack Approach to AI Deployment(46:28) Enterprise Partnerships, Security & Customization Behind the Firewall(50:48) Giving Enterprises Transparency to Drive Adoption  

Theories of Everything with Curt Jaimungal ✓ Claim : Read the notes at at podcastnotes.org. Don't forget to subscribe for free to our newsletter, the top 10 ideas of the week, every Monday --------- As a listener of TOE you can get a special 20% off discount to The Economist and all it has to offer! Visit https://www.economist.com/toe MIT physicist Max Tegmark argues AI now belongs inside physics—and that consciousness will be next. He separates intelligence (goal-achieving behavior) from consciousness (subjective experience), sketches falsifiable experiments using brain-reading tech and rigorous theories (e.g., IIT/φ), and shows how ideas like Hopfield energy landscapes make memory “feel” like physics. We get into mechanistic interpretability (sparse autoencoders), number representations that snap into clean geometry, why RLHF mostly aligns behavior (not goals), and the stakes as AI progress accelerates from “underhyped” to civilization-shaping. It's a masterclass on where mind, math, and machines collide. Join My New Substack (Personal Writings): https://curtjaimungal.substack.com Listen on Spotify: https://open.spotify.com/show/4gL14b92xAErofYQA7bU4e Timestamps: - 00:00 - Why AI is the New Frontier of Physics - 09:38 - Is Consciousness Just a Byproduct of Intelligence? - 16:43 - A Falsifiable Theory of Consciousness? (The MEG Helmet Experiment) - 27:34 - Beyond Neural Correlates: A New Paradigm for Scientific Inquiry - 38:40 - Humanity: The Masters of Underestimation (Fermi's AI Analogy) - 51:27 - What Are an AI's True Goals? (The Serial Killer Problem) - 1:03:42 - Fermat's Principle, Entropy, and the Physics of Goals - 1:15:52 - Eureka Moment: When an AI Discovered Geometry on Its Own - 1:30:01 - Refuting the "AI Doomers": We Have More Agency Than We Think Links mentioned: - Max's Papers: https://scholar.google.com/citations?user=eBXEZxgAAAAJ&hl=en - Language Models Use Trigonometry to Do Addition [Paper]: https://arxiv.org/abs/2502.00873 - Generalization from Starvation [Paper]: https://arxiv.org/abs/2410.08255 - Geoffrey Hinton [TOE]: https://youtu.be/b_DUft-BdIE - Michael Levin [TOE]: https://youtu.be/c8iFtaltX-s - Iceberg of Consciousness [TOE]: https://youtu.be/65yjqIDghEk - Improved Measures of Integrated Information [Paper]: https://arxiv.org/abs/1601.02626 - David Kaiser [TOE]: https://youtu.be/_yebLXsIdwo - Iain McGilchrist [TOE]: https://youtu.be/Q9sBKCd2HD0 - Elan Barenholtz & William Hahn [TOE]: https://youtu.be/A36OumnSrWY - Daniel Schmachtenberger [TOE]: https://youtu.be/g7WtcTATa2U - Ted Jacobson [TOE]: https://youtu.be/3mhctWlXyV8 - The “All Possible Paths” Myth [TOE]: https://youtu.be/XcY3ZtgYis0 SUPPORT: - Become a YouTube Member (Early Access Videos): https://www.youtube.com/channel/UCdWIQh9DGG6uhJk8eyIFl1w/join - Support me on Patreon: https://patreon.com/curtjaimungal - Support me on Crypto: https://commerce.coinbase.com/checkout/de803625-87d3-4300-ab6d-85d4258834a9 - Support me on PayPal: https://www.paypal.com/donate?hosted_button_id=XUBHNMFXUX5S4 SOCIALS: - Twitter: https://twitter.com/TOEwithCurt - Discord Invite: https://discord.com/invite/kBcnfNVwqs Guests do not pay to appear. Theories of Everything receives revenue solely from viewer donations, platform ads, and clearly labelled sponsors; no guest or associated entity has ever given compensation, directly or through intermediaries. #science Learn more about your ad choices. Visit megaphone.fm/adchoices

Theories of Everything with Curt Jaimungal ✓ Claim Key Takeaways  Conditions like depression, bipolar disorder, and schizophrenia may be driven in part by metabolic dysfunction in the brainNeuroinflammation is real, but fasting and a ketogenic diet can help The benefits of supplementing exogenous ketones:(1) Quick energy – they give your body a fast fuel source, especially for the brain and muscles(2)  Support ketosis – they can help raise blood ketone levels even if you're not fully on a strict keto dietBenefits of fasting: helps to augment the control of the immune system, relaxes the gut and enables the body's repair processes to occur, reduces the body's general state of inflammation There is a ketone-synergistic effect when delivering caffeine with MCT; it stimulates lipolysis and also fat oxidation in the liver The short-list of essential supplements:CoQ10, creatine, ketones, vitamin D, and melatoninThe benefits of metformin and GLP-1 drugs may arise from their influence on metabolic functionA low-carb Mediterranean-style diet is conducive to upgrading your metabolic machinery while keeping biomarkers in checkDiet: No sugar, no starch, fibrous vegetables, aim for 25% of carbohydrates consumed should be from fiber, high-protein + low glycemic breakfast and lunch, then a pound of protein for dinner with some fibrous vegetables The protocol and surprising benefits of ‘Sardine Fasting': Eat 1-2 cans of sardines per day for one week; can be repeated monthly or as needed. May need to supplement with vitamin C and magnesium  Why it helps: Provides essential nutrients and omega-3s while keeping calories/protein low enough to activate autophagy, support immunity, fight brain fog, and promote overall metabolic health Movement is critical for optimal metabolic health; get outside and walk first thing in the morning, and try to move after dinner for the sake of glucose metabolism Read the full notes @ podcastnotes.orgAs a listener of TOE you can get a special 20% off discount to The Economist and all it has to offer! Visit https://www.economist.com/toe MIT physicist Max Tegmark argues AI now belongs inside physics—and that consciousness will be next. He separates intelligence (goal-achieving behavior) from consciousness (subjective experience), sketches falsifiable experiments using brain-reading tech and rigorous theories (e.g., IIT/φ), and shows how ideas like Hopfield energy landscapes make memory “feel” like physics. We get into mechanistic interpretability (sparse autoencoders), number representations that snap into clean geometry, why RLHF mostly aligns behavior (not goals), and the stakes as AI progress accelerates from “underhyped” to civilization-shaping. It's a masterclass on where mind, math, and machines collide. Join My New Substack (Personal Writings): https://curtjaimungal.substack.com Listen on Spotify: https://open.spotify.com/show/4gL14b92xAErofYQA7bU4e Timestamps: - 00:00 - Why AI is the New Frontier of Physics - 09:38 - Is Consciousness Just a Byproduct of Intelligence? - 16:43 - A Falsifiable Theory of Consciousness? (The MEG Helmet Experiment) - 27:34 - Beyond Neural Correlates: A New Paradigm for Scientific Inquiry - 38:40 - Humanity: The Masters of Underestimation (Fermi's AI Analogy) - 51:27 - What Are an AI's True Goals? (The Serial Killer Problem) - 1:03:42 - Fermat's Principle, Entropy, and the Physics of Goals - 1:15:52 - Eureka Moment: When an AI Discovered Geometry on Its Own - 1:30:01 - Refuting the "AI Doomers": We Have More Agency Than We Think Links mentioned: - Max's Papers: https://scholar.google.com/citations?user=eBXEZxgAAAAJ&hl=en - Language Models Use Trigonometry to Do Addition [Paper]: https://arxiv.org/abs/2502.00873 - Generalization from Starvation [Paper]: https://arxiv.org/abs/2410.08255 - Geoffrey Hinton [TOE]: https://youtu.be/b_DUft-BdIE - Michael Levin [TOE]: https://youtu.be/c8iFtaltX-s - Iceberg of Consciousness [TOE]: https://youtu.be/65yjqIDghEk - Improved Measures of Integrated Information [Paper]: https://arxiv.org/abs/1601.02626 - David Kaiser [TOE]: https://youtu.be/_yebLXsIdwo - Iain McGilchrist [TOE]: https://youtu.be/Q9sBKCd2HD0 - Elan Barenholtz & William Hahn [TOE]: https://youtu.be/A36OumnSrWY - Daniel Schmachtenberger [TOE]: https://youtu.be/g7WtcTATa2U - Ted Jacobson [TOE]: https://youtu.be/3mhctWlXyV8 - The “All Possible Paths” Myth [TOE]: https://youtu.be/XcY3ZtgYis0 SUPPORT: - Become a YouTube Member (Early Access Videos): https://www.youtube.com/channel/UCdWIQh9DGG6uhJk8eyIFl1w/join - Support me on Patreon: https://patreon.com/curtjaimungal - Support me on Crypto: https://commerce.coinbase.com/checkout/de803625-87d3-4300-ab6d-85d4258834a9 - Support me on PayPal: https://www.paypal.com/donate?hosted_button_id=XUBHNMFXUX5S4 SOCIALS: - Twitter: https://twitter.com/TOEwithCurt - Discord Invite: https://discord.com/invite/kBcnfNVwqs Guests do not pay to appear. Theories of Everything receives revenue solely from viewer donations, platform ads, and clearly labelled sponsors; no guest or associated entity has ever given compensation, directly or through intermediaries. #science Learn more about your ad choices. Visit megaphone.fm/adchoices

As a listener of TOE you can get a special 20% off discount to The Economist and all it has to offer! Visit https://www.economist.com/toe MIT physicist Max Tegmark argues AI now belongs inside physics—and that consciousness will be next. He separates intelligence (goal-achieving behavior) from consciousness (subjective experience), sketches falsifiable experiments using brain-reading tech and rigorous theories (e.g., IIT/φ), and shows how ideas like Hopfield energy landscapes make memory “feel” like physics. We get into mechanistic interpretability (sparse autoencoders), number representations that snap into clean geometry, why RLHF mostly aligns behavior (not goals), and the stakes as AI progress accelerates from “underhyped” to civilization-shaping. It's a masterclass on where mind, math, and machines collide. Join My New Substack (Personal Writings): https://curtjaimungal.substack.com Listen on Spotify: https://open.spotify.com/show/4gL14b92xAErofYQA7bU4e Timestamps: - 00:00 - Why AI is the New Frontier of Physics - 09:38 - Is Consciousness Just a Byproduct of Intelligence? - 16:43 - A Falsifiable Theory of Consciousness? (The MEG Helmet Experiment) - 27:34 - Beyond Neural Correlates: A New Paradigm for Scientific Inquiry - 38:40 - Humanity: The Masters of Underestimation (Fermi's AI Analogy) - 51:27 - What Are an AI's True Goals? (The Serial Killer Problem) - 1:03:42 - Fermat's Principle, Entropy, and the Physics of Goals - 1:15:52 - Eureka Moment: When an AI Discovered Geometry on Its Own - 1:30:01 - Refuting the "AI Doomers": We Have More Agency Than We Think Links mentioned: - Max's Papers: https://scholar.google.com/citations?user=eBXEZxgAAAAJ&hl=en - Language Models Use Trigonometry to Do Addition [Paper]: https://arxiv.org/abs/2502.00873 - Generalization from Starvation [Paper]: https://arxiv.org/abs/2410.08255 - Geoffrey Hinton [TOE]: https://youtu.be/b_DUft-BdIE - Michael Levin [TOE]: https://youtu.be/c8iFtaltX-s - Iceberg of Consciousness [TOE]: https://youtu.be/65yjqIDghEk - Improved Measures of Integrated Information [Paper]: https://arxiv.org/abs/1601.02626 - David Kaiser [TOE]: https://youtu.be/_yebLXsIdwo - Iain McGilchrist [TOE]: https://youtu.be/Q9sBKCd2HD0 - Elan Barenholtz & William Hahn [TOE]: https://youtu.be/A36OumnSrWY - Daniel Schmachtenberger [TOE]: https://youtu.be/g7WtcTATa2U - Ted Jacobson [TOE]: https://youtu.be/3mhctWlXyV8 - The “All Possible Paths” Myth [TOE]: https://youtu.be/XcY3ZtgYis0 SUPPORT: - Become a YouTube Member (Early Access Videos): https://www.youtube.com/channel/UCdWIQh9DGG6uhJk8eyIFl1w/join - Support me on Patreon: https://patreon.com/curtjaimungal - Support me on Crypto: https://commerce.coinbase.com/checkout/de803625-87d3-4300-ab6d-85d4258834a9 - Support me on PayPal: https://www.paypal.com/donate?hosted_button_id=XUBHNMFXUX5S4 SOCIALS: - Twitter: https://twitter.com/TOEwithCurt - Discord Invite: https://discord.com/invite/kBcnfNVwqs Guests do not pay to appear. Theories of Everything receives revenue solely from viewer donations, platform ads, and clearly labelled sponsors; no guest or associated entity has ever given compensation, directly or through intermediaries. #science Learn more about your ad choices. Visit megaphone.fm/adchoices

On this episode of Crazy Wisdom, host Stewart Alsop speaks with Michael Jagdeo, a headhunter and founder working with Exponent Labs and The Syndicate, about the cycles of money, power, and technology that shape our world. Their conversation touches on financial history through The Ascent of Money by Niall Ferguson and William Bagehot's The Money Market, the rise and fall of financial centers from London to New York and the new Texas Stock Exchange, the consolidation of industries and the theory of oligarchical collectivism, the role of AI as both tool and chaos agent, Bitcoin and “quantitative re-centralization,” the dynamics of exponential organizations, and the balance between collectivism and individualism. Jagdeo also shares recruiting philosophies rooted in stories like “stone soup,” frameworks like Yu-Kai Chou's Octalysis and the User Type Hexad, and book recommendations including Salim Ismail's Exponential Organizations and Arthur Koestler's The Act of Creation. Along the way they explore servant leadership, Price's Law, Linux and open source futures, religion as an operating system, and the cyclical nature of civilizations. You can learn more about Michael Jagdeo or reach out to him directly through Twitter or LinkedIn.Check out this GPT we trained on the conversationTimestamps00:05 Stewart Alsop introduces Michael Jagdeo, who shares his path from headhunting actuaries and IT talent into launching startups with Exponent Labs and The Syndicate.00:10 They connect recruiting to financial history, discussing actuaries, The Ascent of Money, and William Bagehot's The Money Market on the London money market and railways.00:15 The Rothschilds, institutional knowledge, and Corn Laws lead into questions about New York as a financial center and the quiet launch of the Texas Stock Exchange by Citadel and BlackRock.00:20 Capital power, George Soros vs. the Bank of England, chaos, paper clips, and Orwell's oligarchical collectivism frame industry consolidation, syndicates, and stone soup.00:25 They debate imperial conquest, bourgeoisie leisure, the decline of the middle class, AI as chaos agent, digital twins, Sarah Connor, Godzilla, and nuclear metaphors.00:30 Conversation turns to Bitcoin, “quantitative re-centralization,” Jack Bogle, index funds, Robinhood micro bailouts, and AI as both entropy and negative entropy.00:35 Jagdeo discusses Jim Keller, Tenstorrent, RISC-V, Nvidia CUDA, exponential organizations, Price's Law, bureaucracy, and servant leadership with the parable of stone soup.00:40 Recruiting as symbiosis, biophilia, trust, Judas, Wilhelm Reich, AI tools, Octalysis gamification, Jordan vs. triangle offense, and the role of laughter in persuasion emerge.00:45 They explore religion as operating systems, Greek gods, Comte's stages, Nietzsche, Jung, nostalgia, scientism, and Jordan Peterson's revival of tradition.00:50 The episode closes with Linux debates, Ubuntu, Framer laptops, PewDiePie, and Jagdeo's nod to Liminal Snake on epistemic centers and turning curses into blessings.Key InsightsOne of the central insights of the conversation is how financial history repeats through cycles of consolidation and power shifts. Michael Jagdeo draws on William Bagehot's The Money Market to explain how London became the hub of European finance, much like New York later did, and how the Texas Stock Exchange signals a possible southern resurgence of financial influence in America. The pattern of wealth moving with institutional shifts underscores how markets, capital, and politics remain intertwined.Jagdeo and Alsop emphasize that industries naturally oligarchize. Borrowing from Orwell's “oligarchical collectivism,” Jagdeo notes that whether in diamonds, food, or finance, consolidation emerges as economies of scale take over. This breeds syndicates and monopolies, often interpreted as conspiracies but really the predictable outcome of industrial maturation.Another powerful theme is the stone soup model of collaboration. Jagdeo applies this parable to recruiting, showing that no single individual can achieve large goals alone. By framing opportunities as shared ventures where each person adds their own ingredient, leaders can attract top talent while fostering genuine symbiosis.Technology, and particularly AI, is cast as both chaos agent and amplifier of human potential. The conversation likens AI to nuclear power—capable of great destruction or progress. From digital twins to Sarah Connor metaphors, they argue AI represents not just artificial intelligence but artificial knowledge and action, pushing humans to adapt quickly to its disruptive presence.The discussion of Bitcoin and digital currencies reframes decentralization as potentially another trap. Jagdeo provocatively calls Bitcoin “quantitative re-centralization,” suggesting that far from liberating individuals, digital currencies may accelerate neo-feudalism by creating new oligarchies and consolidating financial control in unexpected ways.Exponential organizations and the leverage of small teams emerge as another key point. Citing Price's Law, Jagdeo explains how fewer than a dozen highly capable individuals can now achieve billion-dollar valuations thanks to open source hardware, AI, and network effects. This trend redefines scale, making nimble collectives more powerful than bureaucratic giants.Finally, the episode highlights the cyclical nature of civilizations and belief systems. From Rome vs. Carthage to Greek gods shifting with societal needs, to Nietzsche's “God is dead” and Jung's view of recurring deaths of divinity, Jagdeo argues that religion, ideology, and operating systems reflect underlying incentives. Western nostalgia for past structures, whether political or religious, risks idolatry, while the real path forward may lie in new blends of individualism, collectivism, and adaptive tools like Linux and AI.

Useful Resources: 1. Ben Shneiderman, Professor Emeritus, University Of Maryland. 2. Richard Hamming and Hamming Codes. 3. Human Centered AI - Ben Shneiderman. 4. Allen Newell and Herbert A. Simon. 5. Raj Reddy and the Turing Award. 6. Doug Engelbart. 7. Alan Kay. 8. Conference on Human Factors in Computing Systems. 9. Software psychology: Human factors in computer and information systems - Ben Shneiderman. 10. Designing the User Interface: Strategies for Effective Human-Computer Interaction - Ben Shneiderman. 11. Direct Manipulation: A Step Beyond Programming Languages - Ben Shneiderman. 12. Steps Toward Artificial Intelligence - Marvin Minsky. 13. Herbert Gelernter. 14. Computers And Thought - Edward A Feigenbaum and Julian Feldman. 15. Lewis Mumford. 15. Technics and Civilization - Lewis Mumford. 16. Buckminster Fuller. 17. Marshall McLuhan. 18. Roger Shank. 19. The Anxious Generation: How the Great Rewiring of Childhood Is Causing an Epidemic of Mental Illness - Jonathan Haidt. 20. John C. Thomas, IBM. 21. Yousuf Karsh, photographer. 22. Gary Marcus, professor emeritus of psychology and neural science at NYU. 23. Geoffrey Hinton. 24. Nassim Nicholas Taleb. 25. There Is No A.I. - Jaron Lanier. 26. Anil Seth On The Science of Consciousness - Episode 94 of Brave New World. 27. A ‘White-Collar Blood Bath' Doesn't Have to Be Our Fate - Tim Wu 28. Information Management: A Proposal - Tim Berners-Lee 29. Is AI-assisted coding overhyped? : METR study 30. RLHF, Reinforcement learning from human feedback31. Joseph Weizenbaum 32. What Is Computer Science? - Allen Newel, Alan J. Perlis, Herbert A. Simon -- Check out Vasant Dhar's newsletter on Substack. The subscription is free!  

In this episode, Stewart Alsop speaks with Edouard Machery, Distinguished Professor at the University of Pittsburgh and Director of the Center for Philosophy of Science, about the deep cultural roots of question-asking and curiosity. From ancient Sumerian tablets to the philosophical legacies of Socrates and Descartes, the conversation spans how different civilizations have valued inquiry, the cross-cultural psychology of AI, and what makes humans unique in our drive to ask “why.” For more, explore Edouard's work at www.edouardmachery.com.Check out this GPT we trained on the conversationTimestamps00:00 – 05:00 Origins of question-asking, Sumerian writing, norms in early civilizations, authority and written text05:00 – 10:00 Values in AI across cultures, RLHF, tech culture in the Bay Area vs. broader American values10:00 – 15:00 Cross-cultural AI study: Taiwan vs. USA, privacy and collectivism, urban vs. rural mindset divergence15:00 – 20:00 History of curiosity in the West, from vice to virtue post-15th century, link to awe and skepticism20:00 – 25:00 Magic, alchemy, and experimentation in early science, merging maker and scholarly traditions25:00 – 30:00 Rise of public dissections, philosophy as meta-curiosity, Socratic questioning as foundational30:00 – 35:00 Socrates, Plato, Aristotle—transmission of philosophical curiosity, human uniqueness in questioning35:00 – 40:00 Language, assertion, imagination, play in animals vs. humans, symbolic worlds40:00 – 45:00 Early moderns: Montaigne, Descartes, rejection of Aristotle, rise of foundational science45:00 – 50:00 Confucianism and curiosity, tradition and authority, contrast with India and Buddhist thought50:00 – 55:00 Epistemic virtues project, training curiosity, philosophical education across cultures, spiritual curiosityKey InsightsCuriosity hasn't always been a virtue. In Western history, especially through Christian thought until the 15th century, curiosity was viewed as a vice—something dangerous and prideful—until global exploration and scientific inquiry reframed it as essential to human understanding.Question-asking is culturally embedded. Different societies place varying emphasis on questioning. While Confucian cultures promote curiosity within hierarchical structures, Christian traditions historically linked it with sin—except when directed toward divine matters.Urbanization affects curiosity more than nationality. Machery found that whether someone lives in a city or countryside often shapes their mindset more than their cultural background. Cosmopolitan environments expose individuals to diverse values, prompting greater openness and inquiry.AI ethics reveals cultural alignment. In studying attitudes toward AI in the U.S. and Taiwan, expected contrasts in privacy and collectivism were smaller than anticipated. The urban, global culture in both countries seems to produce surprisingly similar ethical concerns.The scientific method emerged from curiosity. The fusion of the maker tradition (doing) and the scholarly tradition (knowing) in the 13th–14th centuries helped birth experimentation, public dissection, and eventually modern science—all grounded in a spirit of curiosity.Philosophy begins with meta-curiosity. From Socratic questioning to Plato's dialogues and Aristotle's treatises, philosophy has always been about asking questions about questions—making “meta-curiosity” the core of the discipline.Only humans ask why. Machery notes that while animals can make requests, they don't seem to ask questions. Humans alone communicate assertions and engage in symbolic, imaginative, question-driven thought, setting us apart cognitively and culturally.

We first had Nathan on to give us his RLHF deep dive when he was joining AI2, and now he's back to help us catch up on the evolution to RLVR (Reinforcement Learning with Verifiable Rewards), first proposed in his Tulu 3 paper. While RLHF remains foundational, RLVR has emerged as a powerful approach for training models on tasks with clear success criteria and using verifiable, objective functions as reward signals—particularly useful in domains like math, code correctness, and instruction-following. Instead of relying solely on subjective human feedback, RLVR leverages deterministic signals to guide optimization, making it more scalable and potentially more reliable across many domains. However, he notes that RLVR is still rapidly evolving, especially regarding how it handles tool use and multi-step reasoning.We also discussed the Tulu model series, a family of instruction-tuned open models developed at AI2. Tulu is designed to be a reproducible, state-of-the-art post-training recipe for the open community. Unlike frontier labs like OpenAI or Anthropic, which rely on vast and often proprietary datasets, Tulu aims to distill and democratize best practices for instruction and preference tuning. We are impressed with how small eval suites, careful task selection, and transparent methodology can rival even the best proprietary models on specific benchmarks.One of the most fascinating threads is the challenge of incorporating tool use into RL frameworks. Lambert highlights that while you can prompt a model to use tools like search or code execution, getting the model to reliably learn when and how to use them through RL is much harder. This is compounded by the difficulty of designing reward functions that avoid overoptimization—where models learn to “game” the reward signal rather than solve the underlying task. This is particularly problematic in code generation, where models might reward hack unit tests by inserting pass statements instead of correct logic. As models become more agentic and are expected to plan, retrieve, and act across multiple tools, reward design becomes a critical bottleneck.Other topics covered:- The evolution from RLHF (Reinforcement Learning from Human Feedback) to RLVR (Reinforcement Learning from Verifiable Rewards)- The goals and technical architecture of the Tulu models, including the motivation to open-source post-training recipes- Challenges of tool use in RL: verifiability, reward design, and scaling across domains- Evaluation frameworks and the role of platforms like Chatbot Arena and emerging “arena”-style benchmarks- The strategic tension between hybrid reasoning models and unified reasoning models at the frontier- Planning, abstraction, and calibration in reasoning agents and why these concepts matter- The future of open-source AI models, including DeepSeek, OLMo, and the potential for an “American DeepSeek”- The importance of model personality, character tuning, and the model spec paradigm- Overoptimization in RL settings and how it manifests in different domains (control tasks, code, math)- Industry trends in inference-time scaling and model parallelismFinally, the episode closes with a vision for the future of open-source AI. Nathan has now written up his ambition to build an “American DeepSeek”—a fully open, end-to-end reasoning-capable model with transparent training data, tools, and infrastructure. He emphasizes that open-source AI is not just about weights; it's about releasing recipes, evaluations, and methods that lower the barrier for everyone to build and understand cutting-edge systems. Full Video EpisodeTimestamps00:00 Welcome and Guest Introduction01:18 Tulu, OVR, and the RLVR Journey03:40 Industry Approaches to Post-Training and Preference Data06:08 Understanding RLVR and Its Impact06:18 Agents, Tool Use, and Training Environments10:34 Open Data, Human Feedback, and Benchmarking12:44 Chatbot Arena, Sycophancy, and Evaluation Platforms15:42 RLHF vs RLVR: Books, Algorithms, and Future Directions17:54 Frontier Models: Reasoning, Hybrid Models, and Data22:11 Search, Retrieval, and Emerging Model Capabilities29:23 Tool Use, Curriculum, and Model Training Challenges38:06 Skills, Planning, and Abstraction in Agent Models46:50 Parallelism, Verifiers, and Scaling Approaches54:33 Overoptimization and Reward Design in RL1:02:27 Open Models, Personalization, and the Model Spec1:06:50 Open Model Ecosystem and Infrastructure1:13:05 Meta, Hardware, and the Future of AI Competition1:15:42 Building an Open DeepSeek and Closing Thoughts Get full access to Latent.Space at www.latent.space/subscribe

Chapters 00:00:00 Welcome and Guest Introduction 00:01:18 Tulu, OVR, and the RLVR Journey 00:03:40 Industry Approaches to Post-Training and Preference Data 00:06:08 Understanding RLVR and Its Impact 00:06:18 Agents, Tool Use, and Training Environments 00:10:34 Open Data, Human Feedback, and Benchmarking 00:12:44 Chatbot Arena, Sycophancy, and Evaluation Platforms 00:15:42 RLHF vs RLVR: Books, Algorithms, and Future Directions 00:17:54 Frontier Models: Reasoning, Hybrid Models, and Data 00:22:11 Search, Retrieval, and Emerging Model Capabilities 00:29:23 Tool Use, Curriculum, and Model Training Challenges 00:38:06 Skills, Planning, and Abstraction in Agent Models 00:46:50 Parallelism, Verifiers, and Scaling Approaches 00:54:33 Overoptimization and Reward Design in RL 01:02:27 Open Models, Personalization, and the Model Spec 01:06:50 Open Model Ecosystem and Infrastructure 01:13:05 Meta, Hardware, and the Future of AI Competition 01:15:42 Building an Open DeepSeek and Closing Thoughts We first had Nathan on to give us his RLHF deep dive when he was joining AI2, and now he's back to help us catch up on the evolution to RLVR (Reinforcement Learning with Verifiable Rewards), first proposed in his Tulu 3 paper. While RLHF remains foundational, RLVR has emerged as a powerful approach for training models on tasks with clear success criteria and using verifiable, objective functions as reward signals—particularly useful in domains like math, code correctness, and instruction-following. Instead of relying solely on subjective human feedback, RLVR leverages deterministic signals to guide optimization, making it more scalable and potentially more reliable across many domains. However, he notes that RLVR is still rapidly evolving, especially regarding how it handles tool use and multi-step reasoning. We also discussed the Tulu model series, a family of instruction-tuned open models developed at AI2. Tulu is designed to be a reproducible, state-of-the-art post-training recipe for the open community. Unlike frontier labs like OpenAI or Anthropic, which rely on vast and often proprietary datasets, Tulu aims to distill and democratize best practices for instruction and preference tuning. We are impressed with how small eval suites, careful task selection, and transparent methodology can rival even the best proprietary models on specific benchmarks. One of the most fascinating threads is the challenge of incorporating tool use into RL frameworks. Lambert highlights that while you can prompt a model to use tools like search or code execution, getting the model to reliably learn when and how to use them through RL is much harder. This is compounded by the difficulty of designing reward functions that avoid overoptimization—where models learn to “game” the reward signal rather than solve the underlying task. This is particularly problematic in code generation, where models might reward hack unit tests by inserting pass statements instead of correct logic. As models become more agentic and are expected to plan, retrieve, and act across multiple tools, reward design becomes a critical bottleneck. Other topics covered: - The evolution from RLHF (Reinforcement Learning from Human Feedback) to RLVR (Reinforcement Learning from Verifiable Rewards) - The goals and technical architecture of the Tulu models, including the motivation to open-source post-training recipes - Challenges of tool use in RL: verifiability, reward design, and scaling across domains - Evaluation frameworks and the role of platforms like Chatbot Arena and emerging “arena”-style benchmarks - The strategic tension between hybrid reasoning models and unified reasoning models at the frontier - Planning, abstraction, and calibration in reasoning agents and why these concepts matter - The future of open-source AI models, including DeepSeek, OLMo, and the potential for an “American DeepSeek” - The importance of model personality, character tuning, and the model spec paradigm - Overoptimization in RL settings and how it manifests in different domains (control tasks, code, math) - Industry trends in inference-time scaling and model parallelism Finally, the episode closes with a vision for the future of open-source AI. Nathan has now written up his ambition to build an “American DeepSeek”—a fully open, end-to-end reasoning-capable model with transparent training data, tools, and infrastructure. He emphasizes that open-source AI is not just about weights; it's about releasing recipes, evaluations, and methods that lower the barrier for everyone to build and understand cutting-edge systems. It would seem the

Benjamin Mann is a co-founder of Anthropic, an AI startup dedicated to building aligned, safety-first AI systems. Prior to Anthropic, Ben was one of the architects of GPT-3 at OpenAI. He left OpenAI driven by the mission to ensure that AI benefits humanity. In this episode, Ben opens up about the accelerating progress in AI and the urgent need to steer it responsibly.In this conversation, we discuss:1. The inside story of leaving OpenAI with the entire safety team to start Anthropic2. How Meta's $100M offers reveal the true market price of top AI talent3. Why AI progress is still accelerating (not plateauing), and how most people misjudge the exponential4. Ben's “economic Turing test” for knowing when we've achieved AGI—and why it's likely coming by 2027-20285. Why he believes 20% unemployment is inevitable6. The AI nightmare scenarios that concern him most—and how he believes we can still avoid them7. How focusing on AI safety created Claude's beloved personality8. What three skills he's teaching his kids instead of traditional academics—Brought to you by:Sauce—Turn customer pain into product revenue: https://sauce.app/lennyLucidLink—Real-time cloud storage for teams: https://www.lucidlink.com/lennyFin—The #1 AI agent for customer service: https://fin.ai/lenny—Transcript: https://www.lennysnewsletter.com/p/anthropic-co-founder-benjamin-mann—My biggest takeaways (for paid newsletter subscribers): https://www.lennysnewsletter.com/i/168107911/my-biggest-takeaways-from-this-conversation—Where to find Ben Mann:• X: https://x.com/8enmann• LinkedIn: https://www.linkedin.com/in/benjamin-mann/• Website: https://benjmann.net/—Where to find Lenny:• Newsletter: https://www.lennysnewsletter.com• X: https://twitter.com/lennysan• LinkedIn: https://www.linkedin.com/in/lennyrachitsky/—In this episode, we cover:(00:00) Introduction to Benjamin(04:43) The AI talent war(06:28) AI progress and scaling laws(10:50) Defining AGI and the economic Turing test(12:26) The impact of AI on jobs(17:45) Preparing for an AI future(24:05) Founding Anthropic(27:06) Balancing AI safety and progress(29:10) Constitutional AI and model alignment(34:21) The importance of AI safety(43:40) The risks of autonomous agents(45:40) Forecasting superintelligence(48:36) How hard is it to align AI?(53:19) Reinforcement learning from AI feedback (RLAIF)(57:03) AI's biggest bottlenecks(01:00:11) Personal reflections on responsibilities(01:02:36) Anthropic's growth and innovations(01:07:48) Lightning round and final thoughts—Referenced:• Dario Amodei on LinkedIn: https://www.linkedin.com/in/dario-amodei-3934934/• Anthropic CEO: AI Could Wipe Out 50% of Entry-Level White Collar Jobs: https://www.marketingaiinstitute.com/blog/dario-amodei-ai-entry-level-jobs• Alexa+: https://www.amazon.com/dp/B0DCCNHWV5• Azure: https://azure.microsoft.com/• Sam Altman on X: https://x.com/sama• Opus 3: https://www.anthropic.com/news/claude-3-family• Claude's Constitution: https://www.anthropic.com/news/claudes-constitution• Greg Brockman on X: https://x.com/gdb• Anthropic's Responsible Scaling Policy: https://www.anthropic.com/news/anthropics-responsible-scaling-policy• Agentic Misalignment: How LLMs could be insider threats: https://www.anthropic.com/research/agentic-misalignment• Anthropic's CPO on what comes next | Mike Krieger (co-founder of Instagram): https://www.lennysnewsletter.com/p/anthropics-cpo-heres-what-comes-next• AI prompt engineering in 2025: What works and what doesn't | Sander Schulhoff (Learn Prompting, HackAPrompt): https://www.lennysnewsletter.com/p/ai-prompt-engineering-in-2025-sander-schulhoff• Unitree: https://www.unitree.com/• Arthur C. Clarke: https://en.wikipedia.org/wiki/Arthur_C._Clarke• How Reinforcement Learning from AI Feedback Works: https://www.assemblyai.com/blog/how-reinforcement-learning-from-ai-feedback-works• RLHF: https://en.wikipedia.org/wiki/Reinforcement_learning_from_human_feedback• Jared Kaplan on LinkedIn: https://www.linkedin.com/in/jared-kaplan-645843213/• Moore's law: https://en.wikipedia.org/wiki/Moore%27s_law• Machine Intelligence Research Institute: https://intelligence.org/• Raph Lee on LinkedIn: https://www.linkedin.com/in/raphaeltlee/• “The Last Question”: https://en.wikipedia.org/wiki/The_Last_Question• Beth Barnes on LinkedIn: https://www.linkedin.com/in/elizabethmbarnes/• “The Last Question”: https://en.wikipedia.org/wiki/The_Last_Question• Good Strategy, Bad Strategy | Richard Rumelt: https://www.lennysnewsletter.com/p/good-strategy-bad-strategy-richard• Pantheon on Netflix: https://www.netflix.com/title/81937398• Ted Lasso on AppleTV+: https://tv.apple.com/us/show/ted-lasso/umc.cmc.vtoh0mn0xn7t3c643xqonfzy• Kurzgesagt—In a Nutshell: https://www.youtube.com/channel/UCsXVk37bltHxD1rDPwtNM8Q• 5 tips to poop like a champion: https://8enmann.medium.com/5-tips-to-poop-like-a-champion-3292481a9651—Recommended books:• Superintelligence: Paths, Dangers, Strategies: https://www.amazon.com/Superintelligence-Dangers-Strategies-Nick-Bostrom/dp/0198739834• The Hacker and the State: Cyber Attacks and the New Normal of Geopolitics: https://www.amazon.com/Hacker-State-Attacks-Normal-Geopolitics/dp/0674987551• Replacing Guilt: Minding Our Way: https://www.amazon.com/Replacing-Guilt-Minding-Our-Way/dp/B086FTSB3Q• Good Strategy/Bad Strategy: The Difference and Why It Matters: https://www.amazon.com/Good-Strategy-Bad-Difference-Matters/dp/0307886239• The Alignment Problem: Machine Learning and Human Values: https://www.amazon.com/Alignment-Problem-Machine-Learning-Values/dp/0393635821—Production and marketing by https://penname.co/. For inquiries about sponsoring the podcast, email podcast@lennyrachitsky.com.Lenny may be an investor in the companies discussed. To hear more, visit www.lennysnewsletter.com

What if your company had a digital brain that never forgot, always knew the answer, and could instantly tap the knowledge of your best engineers, even after they left? Superintelligence can feel like a hand‑wavy pipe‑dream— yet, as Misha Laskin argues, it becomes a tractable engineering problem once you scope it to the enterprise level. Former DeepMind researcher Laskin is betting on an oracle‑like AI that grasps every repo, Jira ticket and hallway aside as deeply as your principal engineer—and he's building it at Reflection AI.In this wide‑ranging conversation, Misha explains why coding is the fastest on‑ramp to superintelligence, how “organizational” beats “general” when real work is on the line, and why today's retrieval‑augmented generation (RAG) feels like “exploring a jungle with a flashlight.” He walks us through Asimov, Reflection's newly unveiled code‑research agent that fuses long‑context search, team‑wide memory and multi‑agent planning so developers spend less time spelunking for context and more time shipping.We also rewind his unlikely journey—from physics prodigy in a Manhattan‑Project desert town, to Berkeley's AI crucible, to leading RLHF for Google Gemini—before he left big‑lab comfort to chase a sharper vision of enterprise super‑intelligence. Along the way: the four breakthroughs that unlocked modern AI, why capital efficiency still matters in the GPU arms‑race, and how small teams can lure top talent away from nine‑figure offers.If you're curious about the next phase of AI agents, the future of developer tooling, or the gritty realities of scaling a frontier‑level startup—this episode is your blueprint.Reflection AIWebsite - https://reflection.aiLinkedIn - https://www.linkedin.com/company/reflectionaiMisha LaskinLinkedIn - https://www.linkedin.com/in/mishalaskinX/Twitter - https://x.com/mishalaskinFIRSTMARKWebsite - https://firstmark.comX/Twitter - https://twitter.com/FirstMarkCapMatt Turck (Managing Director)LinkedIn - https://www.linkedin.com/in/turck/X/Twitter - https://twitter.com/mattturck(00:00) Intro (01:42) Reflection AI: Company Origins and Mission (04:14) Making Superintelligence Concrete (06:04) Superintelligence vs. AGI: Why the Goalposts Moved (07:55) Organizational Superintelligence as an Oracle (12:05) Coding as the Shortcut: Hands, Legs & Brain for AI (16:00) Building the Context Engine (20:55) Capturing Tribal Knowledge in Organizations (26:31) Introducing Asimov: A Deep Code Research Agent (28:44) Team-Wide Memory: Preserving Institutional Knowledge (33:07) Multi-Agent Design for Deep Code Understanding (34:48) Data Retrieval and Integration in Asimov (38:13) Enterprise-Ready: VPC and On-Prem Deployments (39:41) Reinforcement Learning in Asimov's Development (41:04) Misha's Journey: From Physics to AI (42:06) Growing Up in a Science-Driven Desert Town (53:03) Building General Agents at DeepMind (56:57) Founding Reflection AI After DeepMind (58:54) Product-Driven Superintelligence: Why It Matters (01:02:22) The State of Autonomous Coding Agents (01:04:26) What's Next for Reflection AI

  In June 2022, I bet a commenter $100 that AI would master image compositionality by June 2025. DALL-E2 had just come out, showcasing the potential of AI art. But it couldn't follow complex instructions; its images only matched the “vibe” of the prompt. For example, here were some of its attempts at “a red sphere on a blue cube, with a yellow pyramid on the right, all on top of a green table”. At the time, I wrote: I'm not going to make the mistake of saying these problems are inherent to AI art. My guess is a slightly better language model would solve most of them…for all I know, some of the larger image models have already fixed these issues. These are the sorts of problems I expect to go away with a few months of future research. Commenters objected that this was overly optimistic. AI was just a pattern-matching “stochastic parrot”. It would take a deep understanding of grammar to get a prompt exactly right, and that would require some entirely new paradigm beyond LLMs. For example, from Vitor: Why are you so confident in this? The inability of systems like DALL-E to understand semantics in ways requiring an actual internal world model strikes me as the very heart of the issue. We can also see this exact failure mode in the language models themselves. They only produce good results when the human asks for something vague with lots of room for interpretation, like poetry or fanciful stories without much internal logic or continuity. Not to toot my own horn, but two years ago you were naively saying we'd have GPT-like models scaled up several orders of magnitude (100T parameters) right about now (https://readscottalexander.com/posts/ssc-the-obligatory-gpt-3-post#comment-912798). I'm registering my prediction that you're being equally naive now. Truly solving this issue seems AI-complete to me. I'm willing to bet on this (ideas on operationalization welcome). So we made a bet! All right. My proposed operationalization of this is that on June 1, 2025, if either if us can get access to the best image generating model at that time (I get to decide which), or convince someone else who has access to help us, we'll give it the following prompts: 1. A stained glass picture of a woman in a library with a raven on her shoulder with a key in its mouth 2. An oil painting of a man in a factory looking at a cat wearing a top hat 3. A digital art picture of a child riding a llama with a bell on its tail through a desert 4. A 3D render of an astronaut in space holding a fox wearing lipstick 5. Pixel art of a farmer in a cathedral holding a red basketball We generate 10 images for each prompt, just like DALL-E2 does. If at least one of the ten images has the scene correct in every particular on 3/5 prompts, I win, otherwise you do. Loser pays winner $100, and whatever the result is I announce it on the blog (probably an open thread). If we disagree, Gwern is the judge. Some image models of the time refused to draw humans, so we agreed that robots could stand in for humans in pictures that required them. In September 2022, I got some good results from Google Imagen and announced I had won the three-year bet in three months. Commenters yelled at me, saying that Imagen still hadn't gotten them quite right and my victory declaration was premature. The argument blew up enough that Edwin Chen of Surge, an “RLHF and human LLM evaluation platform”, stepped in and asked his professional AI data labelling team. Their verdict was clear: the AI was bad and I was wrong. Rather than embarrass myself further, I agreed to wait out the full length of the bet and re-evaluate in June 2025. The bet is now over, and official judge Gwern agrees I've won. Before I gloat, let's look at the images that got us here. https://www.astralcodexten.com/p/now-i-really-won-that-ai-bet

In this episode of the Crazy Wisdom Podcast, I, Stewart Alsop, sit down with returning guest Brian Ahuja to explore a thought-provoking idea he's been stewing on—could we one day build a robot capable of true partner dancing? From the biomechanics of salsa to the possibilities of AI embodiment, we unpack what it would take to engineer fluid, responsive movement and how that intersects with everything from artificial muscles to the intimacy of tactile feedback. We also touch on Brian's long-term vision for a potential lab or foundation to tackle this challenge. You can follow Brian and future developments on Twitter @brianahuja.Check out this GPT we trained on the conversationTimestamps00:00 – Brian Ahuja returns to discuss AI embodiment, sparked by his experience in ballroom dance and curiosity about translating physical intelligence into robotics.05:00 – They explore robotics in partner dancing, touching on the difference between choreographed motion and improvisational, responsive movement.10:00 – Brian breaks down human biomechanics, emphasizing that hip motion in dances like salsa originates from knees and feet—not the hips directly.15:00 – The conversation shifts to balance, proprioception, and ocular reflexes, linking them to movement stability in dance.20:00 – They compare robot vs. human movement, noting robots' jerky motions and the absence of muscle-based initiation.25:00 – The need for haptic feedback is discussed, with Brian detailing how partner dancing depends on tactile signals and real-time response.30:00 – They touch on robotic form factors, questioning whether humanoid robots are the best approach and pondering the design of artificial muscles.35:00 – Brian proposes the idea of the Ahuja Test, gauging if a robot can move so fluidly it's indistinguishable from a human, using dance as the standard.Key InsightsPartner Dancing as a Frontier for Robotics: Brian Ahuja proposes that partner dancing could be a benchmark for robotic embodiment, where success would indicate a robot's ability to replicate fluid, responsive human movement. This task is far more complex than solo choreography—it requires real-time tactile feedback, improvisation, and nuanced physical communication.Movement Origin in Humans vs. Robots: A critical difference lies in how movement is generated. Human motion begins with muscle contraction, not at the joints. Robots, however, typically initiate movement at joint points, missing the layered interplay of muscles, tendons, and fascia that create smooth, lifelike motion.Haptic Feedback and Improvisation: Real partner dancing involves subtle cues, like pressure through fingertips, to signal direction and timing. For a robot to follow or lead a dance, it would need a highly sensitive haptic feedback system capable of interpreting and responding to these nonverbal signals in real time.The Limits of Current Robotics: Even with advanced robots like the Tesla bot, current movement still appears jerky and lacks the fluidity needed for partner dancing. The mechanical design—especially the lack of artificial musculature—may impose fundamental limits on how closely robots can mimic human motion.Applications Beyond Dance: The implications of this inquiry stretch beyond dance into fields like physical therapy, elder care, and domestic robotics. A robot that could move like a human could handle tasks requiring adaptability, precision, and physical sensitivity.Vision and Systems Thinking: Brian frames the challenge as a systems problem that might start with a lab or foundation. He emphasizes not needing to do everything alone, recognizing the value of building knowledge iteratively through conversations, research, and community.The Ahuja Test: Inspired by the Turing Test, Brian coins the idea of the “Ahuja Test”—a way to measure if a robot can move indistinguishably from a human. He suggests partner dancing could serve as the ultimate proving ground for such a test, given its demand for embodied intelligence and nuanced coordination.

Today's show:Meta just took a 49% stake in Scale AI, and the shockwaves are hitting the entire AI ecosystem. In this episode, @Jason and @alex unpack the deal's implications: Google ($150M customer!) and others are fleeing Scale, worried Meta will hoard its RLHF infrastructure and cut off competitors. Startups like Labelbox, Turing, and Handshake are already seeing a demand surge. Is this smart vertical integration or anti-competitive overreach? Jason shares tactical advice for founders on how to capitalize when incumbents stumble—hire ex-Scale talent, build “Scale AI alternative” SEO pages, and hit the podcast circuit. Don't miss this deep dive into AI's shifting power dynamics.Timestamps:(04:01) Is Jason becoming an AI doomer?!(9:52) OpenPhone - Streamline and scale your customer communications with OpenPhone. Get 20% off your first 6 months at www.openphone.com/⁠twist(13:47) PostHog, and when is it okay for founders to break the rules?(20:56) Vanta - Get $1000 off your SOC 2 at https://www.vanta.com/twist(25:50) Why the Navy is recruiting startups(30:12) Pilot - Visit https://www.pilot.com/twist and get $1,200 off your first year.(39:09) Did Zuck buy Scale in order to keep it from competitors?(56:08) When does incentivizing customers turn into burning capital?(1:04) How raising too much money could KILL your startup!Subscribe to the TWiST500 newsletter: https://ticker.thisweekinstartups.comCheck out the TWIST500: https://www.twist500.comSubscribe to This Week in Startups on Apple: https://rb.gy/v19fcpFollow Lon:X: https://x.com/lonsFollow Alex:X: https://x.com/alexLinkedIn: ⁠https://www.linkedin.com/in/alexwilhelmFollow Jason:X: https://twitter.com/JasonLinkedIn: https://www.linkedin.com/in/jasoncalacanisThank you to our partners:(9:52) OpenPhone - Streamline and scale your customer communications with OpenPhone. Get 20% off your first 6 months at www.openphone.com/⁠twist(20:56) Vanta - Get $1000 off your SOC 2 at https://www.vanta.com/twist(30:52) Pilot - Visit https://www.pilot.com/twist and get $1,200 off your first year.Great TWIST interviews: Will Guidara, Eoghan McCabe, Steve Huffman, Brian Chesky, Bob Moesta, Aaron Levie, Sophia Amoruso, Reid Hoffman, Frank Slootman, Billy McFarlandCheck out Jason's suite of newsletters: https://substack.com/@calacanisFollow TWiST:Twitter: https://twitter.com/TWiStartupsYouTube: https://www.youtube.com/thisweekinInstagram: https://www.instagram.com/thisweekinstartupsTikTok: https://www.tiktok.com/@thisweekinstartupsSubstack: https://twistartups.substack.comSubscribe to the Founder University Podcast: https://www.youtube.com/@founderuniversity1916

This episode is sponsored by Oracle. OCI is the next-generation cloud designed for every workload – where you can run any application, including any AI projects, faster and more securely for less. On average, OCI costs 50% less for compute, 70% less for storage, and 80% less for networking. Join Modal, Skydance Animation, and today's innovative AI tech companies who upgraded to OCI…and saved.   Try OCI for free at http://oracle.com/eyeonai   What if you could fine-tune an AI model without any labeled data—and still outperform traditional training methods?   In this episode of Eye on AI, we sit down with Jonathan Frankle, Chief Scientist at Databricks and co-founder of MosaicML, to explore TAO (Test-time Adaptive Optimization)—Databricks' breakthrough tuning method that's transforming how enterprises build and scale large language models (LLMs).   Jonathan explains how TAO uses reinforcement learning and synthetic data to train models without the need for expensive, time-consuming annotation. We dive into how TAO compares to supervised fine-tuning, why Databricks built their own reward model (DBRM), and how this system allows for continual improvement, lower inference costs, and faster enterprise AI deployment.   Whether you're an AI researcher, enterprise leader, or someone curious about the future of model customization, this episode will change how you think about training and deploying AI.   Explore the latest breakthroughs in data and AI from Databricks: https://www.databricks.com/events/dataaisummit-2025-announcements Stay Updated: Craig Smith on X: https://x.com/craigss Eye on A.I. on X: https://x.com/EyeOn_AI  

"Blurring Reality" - Chai's Social AI Platform - sponsoredThis episode of MLST explores the groundbreaking work of Chai, a social AI platform that quietly built one of the world's largest AI companion ecosystems before ChatGPT's mainstream adoption. With over 10 million active users and just 13 engineers serving 2 trillion tokens per day, Chai discovered the massive appetite for AI companionship through serendipity while searching for product-market fit.CHAI sponsored this show *because they want to hire amazing engineers* -- CAREER OPPORTUNITIES AT CHAIChai is actively hiring in Palo Alto with competitive compensation ($300K-$800K+ equity) for roles including AI Infrastructure Engineers, Software Engineers, Applied AI Researchers, and more. Fast-track qualification available for candidates with significant product launches, open source contributions, or entrepreneurial success.https://www.chai-research.com/jobs/The conversation with founder William Beauchamp and engineers Tom Lu and Nischay Dhankhar covers Chai's innovative technical approaches including reinforcement learning from human feedback (RLHF), model blending techniques that combine smaller models to outperform larger ones, and their unique infrastructure challenges running exaflop-class compute.SPONSOR MESSAGES:***Tufa AI Labs is a brand new research lab in Zurich started by Benjamin Crouzier focussed on o-series style reasoning and AGI. They are hiring a Chief Engineer and ML engineers in Zurich and SF. Goto https://tufalabs.ai/***Key themes explored include:- The ethics of AI engagement optimization and attention hacking- Content moderation at scale with a lean engineering team- The shift from AI as utility tool to AI as social companion- How users form deep emotional bonds with artificial intelligence- The broader implications of AI becoming a social mediumWe also examine OpenAI's recent pivot toward companion AI with April's new GPT-4o, suggesting a fundamental shift in how we interact with artificial intelligence - from utility-focused tools to companion-like experiences that blur the lines between human and artificial intimacy.The episode also covers Chai's unconventional approach to hiring only top-tier engineers, their bootstrap funding strategy focused on user revenue over VC funding, and their rapid experimentation culture where one in five experiments succeed.TOC:00:00:00 - Introduction: Steve Jobs' AI Vision & Chai's Scale00:04:02 - Chapter 1: Simulators - The Birth of Social AI00:13:34 - Chapter 2: Engineering at Chai - RLHF & Model Blending00:21:49 - Chapter 3: Social Impact of GenAI - Ethics & Safety00:33:55 - Chapter 4: The Lean Machine - 13 Engineers, Millions of Users00:42:38 - Chapter 5: GPT-4o Becoming a Companion - OpenAI's Pivot00:50:10 - Chapter 6: What Comes Next - The Future of AI Intimacy TRANSCRIPT: https://www.dropbox.com/scl/fi/yz2ewkzmwz9rbbturfbap/CHAI.pdf?rlkey=uuyk2nfhjzezucwdgntg5ubqb&dl=0

Explains language models (LLMs) advancements. Scaling laws - the relationships among model size, data size, and compute - and how emergent abilities such as in-context learning, multi-step reasoning, and instruction following arise once certain scaling thresholds are crossed. The evolution of the transformer architecture with Mixture of Experts (MoE), describes the three-phase training process culminating in Reinforcement Learning from Human Feedback (RLHF) for model alignment, and explores advanced reasoning techniques such as chain-of-thought prompting which significantly improve complex task performance. Links Notes and resources at ocdevel.com/mlg/mlg34 Build the future of multi-agent software with AGNTCY Try a walking desk stay healthy & sharp while you learn & code Transformer Foundations and Scaling Laws Transformers: Introduced by the 2017 "Attention is All You Need" paper, transformers allow for parallel training and inference of sequences using self-attention, in contrast to the sequential nature of RNNs. Scaling Laws: Empirical research revealed that LLM performance improves predictably as model size (parameters), data size (training tokens), and compute are increased together, with diminishing returns if only one variable is scaled disproportionately. The "Chinchilla scaling law" (DeepMind, 2022) established the optimal model/data/compute ratio for efficient model performance: earlier large models like GPT-3 were undertrained relative to their size, whereas right-sized models with more training data (e.g., Chinchilla, LLaMA series) proved more compute and inference efficient. Emergent Abilities in LLMs Emergence: When trained beyond a certain scale, LLMs display abilities not present in smaller models, including: In-Context Learning (ICL): Performing new tasks based solely on prompt examples at inference time. Instruction Following: Executing natural language tasks not seen during training. Multi-Step Reasoning & Chain of Thought (CoT): Solving arithmetic, logic, or symbolic reasoning by generating intermediate reasoning steps. Discontinuity & Debate: These abilities appear abruptly in larger models, though recent research suggests that this could result from non-linearities in evaluation metrics rather than innate model properties. Architectural Evolutions: Mixture of Experts (MoE) MoE Layers: Modern LLMs often replace standard feed-forward layers with MoE structures. Composed of many independent "expert" networks specializing in different subdomains or latent structures. A gating network routes tokens to the most relevant experts per input, activating only a subset of parameters—this is called "sparse activation." Enables much larger overall models without proportional increases in compute per inference, but requires the entire model in memory and introduces new challenges like load balancing and communication overhead. Specialization & Efficiency: Experts learn different data/knowledge types, boosting model specialization and throughput, though care is needed to avoid overfitting and underutilization of specialists. The Three-Phase Training Process 1. Unsupervised Pre-Training: Next-token prediction on massive datasets—builds a foundation model capturing general language patterns. 2. Supervised Fine Tuning (SFT): Training on labeled prompt-response pairs to teach the model how to perform specific tasks (e.g., question answering, summarization, code generation). Overfitting and "catastrophic forgetting" are risks if not carefully managed. 3. Reinforcement Learning from Human Feedback (RLHF): Collects human preference data by generating multiple responses to prompts and then having annotators rank them. Builds a reward model (often PPO) based on these rankings, then updates the LLM to maximize alignment with human preferences (helpfulness, harmlessness, truthfulness). Introduces complexity and risk of reward hacking (specification gaming), where the model may exploit the reward system in unanticipated ways. Advanced Reasoning Techniques Prompt Engineering: The art/science of crafting prompts that elicit better model responses, shown to dramatically affect model output quality. Chain of Thought (CoT) Prompting: Guides models to elaborate step-by-step reasoning before arriving at final answers—demonstrably improves results on complex tasks. Variants include zero-shot CoT ("let's think step by step"), few-shot CoT with worked examples, self-consistency (voting among multiple reasoning chains), and Tree of Thought (explores multiple reasoning branches in parallel). Automated Reasoning Optimization: Frontier models selectively apply these advanced reasoning techniques, balancing compute costs with gains in accuracy and transparency. Optimization for Training and Inference Tradeoffs: The optimal balance between model size, data, and compute is determined not only for pretraining but also for inference efficiency, as lifetime inference costs may exceed initial training costs. Current Trends: Efficient scaling, model specialization (MoE), careful fine-tuning, RLHF alignment, and automated reasoning techniques define state-of-the-art LLM development.

This episode is sponsored by Indeed.  Stop struggling to get your job post seen on other job sites. Indeed's Sponsored Jobs help you stand out and hire fast. With Sponsored Jobs your post jumps to the top of the page for your relevant candidates, so you can reach the people you want faster. Get a $75 Sponsored Job Credit to boost your job's visibility! Claim your offer now: https://www.indeed.com/EYEONAI     In this episode, renowned AI researcher Pedro Domingos, author of The Master Algorithm, takes us deep into the world of Connectionism—the AI tribe behind neural networks and the deep learning revolution.   From the birth of neural networks in the 1940s to the explosive rise of transformers and ChatGPT, Pedro unpacks the history, breakthroughs, and limitations of connectionist AI. Along the way, he explores how supervised learning continues to quietly power today's most impressive AI systems—and why reinforcement learning and unsupervised learning are still lagging behind.   We also dive into: The tribal war between Connectionists and Symbolists The surprising origins of Backpropagation How transformers redefined machine translation Why GANs and generative models exploded (and then faded) The myth of modern reinforcement learning (DeepSeek, RLHF, etc.) The danger of AI research narrowing too soon around one dominant approach Whether you're an AI enthusiast, a machine learning practitioner, or just curious about where intelligence is headed, this episode offers a rare deep dive into the ideological foundations of AI—and what's coming next. Don't forget to subscribe for more episodes on AI, data, and the future of tech.     Stay Updated: Craig Smith on X:https://x.com/craigss Eye on A.I. on X: https://x.com/EyeOn_AI     (00:00) What Are Generative Models? (03:02) AI Progress and the Local Optimum Trap (06:30) The Five Tribes of AI and Why They Matter (09:07) The Rise of Connectionism (11:14) Rosenblatt's Perceptron and the First AI Hype Cycle (13:35) Backpropagation: The Algorithm That Changed Everything (19:39) How Backpropagation Actually Works (21:22) AlexNet and the Deep Learning Boom (23:22) Why the Vision Community Resisted Neural Nets (25:39) The Expansion of Deep Learning (28:48) NetTalk and the Baby Steps of Neural Speech (31:24) How Transformers (and Attention) Transformed AI (34:36) Why Attention Solved the Bottleneck in Translation (35:24) The Untold Story of Transformer Invention (38:35) LSTMs vs. Attention: Solving the Vanishing Gradient Problem (42:29) GANs: The Evolutionary Arms Race in AI (48:53) Reinforcement Learning Explained (52:46) Why RL Is Mostly Just Supervised Learning in Disguise (54:35) Where AI Research Should Go Next  

In this episode, Brandon Cui, Research Scientist at MosaicML and Databricks, dives into cutting-edge advancements in AI model optimization, focusing on Reward Models and Reinforcement Learning from Human Feedback (RLHF).Highlights include:- How synthetic data and RLHF enable fine-tuning models to generate preferred outcomes.- Techniques like Policy Proximal Optimization (PPO) and Direct PreferenceOptimization (DPO) for enhancing response quality.- The role of reward models in improving coding, math, reasoning, and other NLP tasks.Connect with Brandon Cui:https://www.linkedin.com/in/bcui19/

This conversation delves into the latest developments in AI, particularly focusing on Google's Gemma models and their capabilities. The discussion covers the differences between various types of language models, the significance of multimodal inputs, and the training techniques employed in AI models. The hosts also explore the implications of open-source versus proprietary models, the hardware requirements for running these models, and the limitations of benchmarks in evaluating AI performance. Additionally, they touch on the future of robotics and the cultural differences in AI adoption, particularly between Japan and the United States. takeaways Open source models are pushing the boundaries of AI. Gemma models are capable of multimodal inputs. Different types of LLMs serve different purposes. Benchmarks can be misleading and should be approached with caution. Training techniques like RLHF are crucial for model performance. The hardware requirements for AI models vary significantly. Cultural differences affect the adoption of robotics and AI. Robots are increasingly filling labor gaps in societies with declining populations. AI benchmarks should be tailored to specific use cases. The future of robotics and AI feels imminent and exciting. Chapters  00:00 Introduction to the Week's AI Developments 00:50 Exploring Google's Gemma Models 03:21 Understanding Different Types of LLMs 05:32 Gemma's Multimodal and Multilingual Capabilities 08:45 Training Techniques Behind Gemma 15:48 Open Source Models and Their Impact 20:34 Benchmarking AI Models 28:30 Gaming Benchmarks in AI 34:10 The Ethics of Benchmarking in AI 44:56 Language Learning and AI Models 49:12 The Importance of Benchmarks 52:35 Vibe Checks and User Preferences 01:01:09 Top AI Models and Their Performance 01:13:35 Robotics and the Future of AI 01:27:20 Cultural Perspectives on Automation

Professor Randall Balestriero joins us to discuss neural network geometry, spline theory, and emerging phenomena in deep learning, based on research presented at ICML. Topics include the delayed emergence of adversarial robustness in neural networks ("grokking"), geometric interpretations of neural networks via spline theory, and challenges in reconstruction learning. We also cover geometric analysis of Large Language Models (LLMs) for toxicity detection and the relationship between intrinsic dimensionality and model control in RLHF.SPONSOR MESSAGES:***CentML offers competitive pricing for GenAI model deployment, with flexible options to suit a wide range of models, from small to large-scale deployments.https://centml.ai/pricing/Tufa AI Labs is a brand new research lab in Zurich started by Benjamin Crouzier focussed on o-series style reasoning and AGI. Are you interested in working on reasoning, or getting involved in their events?Goto https://tufalabs.ai/***Randall Balestrierohttps://x.com/randall_balestrhttps://randallbalestriero.github.io/Show notes and transcript: https://www.dropbox.com/scl/fi/3lufge4upq5gy0ug75j4a/RANDALLSHOW.pdf?rlkey=nbemgpa0jhawt1e86rx7372e4&dl=0TOC:- Introduction - 00:00:00: Introduction- Neural Network Geometry and Spline Theory - 00:01:41: Neural Network Geometry and Spline Theory - 00:07:41: Deep Networks Always Grok - 00:11:39: Grokking and Adversarial Robustness - 00:16:09: Double Descent and Catastrophic Forgetting- Reconstruction Learning - 00:18:49: Reconstruction Learning - 00:24:15: Frequency Bias in Neural Networks- Geometric Analysis of Neural Networks - 00:29:02: Geometric Analysis of Neural Networks - 00:34:41: Adversarial Examples and Region Concentration- LLM Safety and Geometric Analysis - 00:40:05: LLM Safety and Geometric Analysis - 00:46:11: Toxicity Detection in LLMs - 00:52:24: Intrinsic Dimensionality and Model Control - 00:58:07: RLHF and High-Dimensional Spaces- Conclusion - 01:02:13: Neural Tangent Kernel - 01:08:07: ConclusionREFS:[00:01:35] Humayun – Deep network geometry & input space partitioninghttps://arxiv.org/html/2408.04809v1[00:03:55] Balestriero & Paris – Linking deep networks to adaptive spline operatorshttps://proceedings.mlr.press/v80/balestriero18b/balestriero18b.pdf[00:13:55] Song et al. – Gradient-based white-box adversarial attackshttps://arxiv.org/abs/2012.14965[00:16:05] Humayun, Balestriero & Baraniuk – Grokking phenomenon & emergent robustnesshttps://arxiv.org/abs/2402.15555[00:18:25] Humayun – Training dynamics & double descent via linear region evolutionhttps://arxiv.org/abs/2310.12977[00:20:15] Balestriero – Power diagram partitions in DNN decision boundarieshttps://arxiv.org/abs/1905.08443[00:23:00] Frankle & Carbin – Lottery Ticket Hypothesis for network pruninghttps://arxiv.org/abs/1803.03635[00:24:00] Belkin et al. – Double descent phenomenon in modern MLhttps://arxiv.org/abs/1812.11118[00:25:55] Balestriero et al. – Batch normalization's regularization effectshttps://arxiv.org/pdf/2209.14778[00:29:35] EU – EU AI Act 2024 with compute restrictionshttps://www.lw.com/admin/upload/SiteAttachments/EU-AI-Act-Navigating-a-Brave-New-World.pdf[00:39:30] Humayun, Balestriero & Baraniuk – SplineCam: Visualizing deep network geometryhttps://openaccess.thecvf.com/content/CVPR2023/papers/Humayun_SplineCam_Exact_Visualization_and_Characterization_of_Deep_Network_Geometry_and_CVPR_2023_paper.pdf[00:40:40] Carlini – Trade-offs between adversarial robustness and accuracyhttps://arxiv.org/pdf/2407.20099[00:44:55] Balestriero & LeCun – Limitations of reconstruction-based learning methodshttps://openreview.net/forum?id=ez7w0Ss4g9(truncated, see shownotes PDF)

On this episode of Crazy Wisdom, Stewart Alsop welcomes back guest David Hundley, a principal engineer at a Fortune 500 company specializing in innovative machine learning applications. The conversation spans topics like techno-humanism, the future interplay of consciousness and artificial intelligence, and the societal implications of technologies like neural interfaces and large language models. Together, they explore the philosophical and technical challenges posed by advancements in AI and what it means for humanity's trajectory. For more insights from David, visit his website or follow him on Twitter.Check out this GPT we trained on the conversation!Timestamps00:00 Introduction to the Crazy Wisdom Podcast00:31 Techno Humanism vs. Transhumanism02:14 Exploring Humanism and Its Historical Context05:06 Accelerationism and Consciousness06:58 AI Conversations and Human Interaction10:21 Challenges in AI and Machine Learning13:26 Product Integration and AI Limitations19:03 Coding with AI: Tools and Techniques25:28 Vector Stores vs. Traditional Databases32:16 Understanding Network Self-Optimization33:25 Exploring Parameters and Biases in AI34:53 Bias in AI and Societal Implications38:28 The Future of AI and Open Source44:01 Techno-Humanism and AI's Role in Society48:55 The Intersection of AI and Human Emotions52:48 The Ethical and Societal Impact of AI58:20 Final Thoughts and Future DirectionsKey InsightsTechno-Humanism as a Framework: David Hundley introduces "techno-humanism" as a philosophy that explores how technology and humanity can coexist and integrate without losing sight of human values. This perspective acknowledges the current reality that we are already cyborgs, augmented by devices like smartphones and smartwatches, and speculates on the deeper implications of emerging technologies like Neuralink, which could redefine the human experience.The Limitations of Large Language Models (LLMs): The discussion highlights that while LLMs are powerful tools, they lack true creativity or consciousness. They are stochastic parrots, reflecting and recombining existing knowledge rather than generating novel ideas. This distinction underscores the difference between human and artificial intelligence, particularly in the ability to create new explanations and knowledge.Biases and Zeitgeist Machines: LLMs are described as "zeitgeist machines," reflecting the biases and values embedded in their training data. While this mirrors societal norms, it raises concerns about how conscious and unconscious biases—shaped by culture, regulation, and curation—impact the models' outputs. The episode explores the ethical and societal implications of this phenomenon.The Role of Open Source in AI's Future: Open-source AI tools are positioned as critical to the democratization of technology. David suggests that open-source projects, such as those in the Python ecosystem, have historically driven innovation and accessibility, and this trend is likely to continue with AI. Open-source initiatives provide opportunities for decentralization, reducing reliance on corporate-controlled models.Potential of AI for Mental Health and Counseling: David shares his experience using AI for conversational support, comparing it to talking with a human friend. This suggests a growing potential for AI in mental health applications, offering companionship or guidance. However, the ethical implications of replacing human counselors with AI and the depth of empathy that machines can genuinely offer remain questions.The Future of Database Technologies: The discussion explores traditional databases versus emerging technologies like vector and graph databases, particularly in how they support AI. Graph databases, with their ability to encode relationships between pieces of information, could provide a more robust foundation for complex queries in knowledge-intensive environments.The Ethical and Societal Implications of AI: The conversation grapples with how AI could reshape societal structures and values, from its influence on decision-making to its potential integration with human cognition. Whether through regulation, neural enhancement, or changes in media dynamics, AI presents profound challenges and opportunities for human civilization, raising questions about autonomy, ethics, and collective progress.

Aman Sanger, Arvid Lunnemark, Michael Truell, and Sualeh Asif are creators of Cursor, a popular code editor that specializes in AI-assisted programming. Thank you for listening ❤ Check out our sponsors: https://lexfridman.com/sponsors/ep447-sc See below for timestamps, transcript, and to give feedback, submit questions, contact Lex, etc. Transcript: https://lexfridman.com/cursor-team-transcript CONTACT LEX: Feedback - give feedback to Lex: https://lexfridman.com/survey AMA - submit questions, videos or call-in: https://lexfridman.com/ama Hiring - join our team: https://lexfridman.com/hiring Other - other ways to get in touch: https://lexfridman.com/contact EPISODE LINKS: Cursor Website: https://cursor.com Cursor on X: https://x.com/cursor_ai Anysphere Website: https://anysphere.inc/ Aman's X: https://x.com/amanrsanger Aman's Website: https://amansanger.com/ Arvid's X: https://x.com/ArVID220u Arvid's Website: https://arvid.xyz/ Michael's Website: https://mntruell.com/ Michael's LinkedIn: https://bit.ly/3zIDkPN Sualeh's X: https://x.com/sualehasif996 Sualeh's Website: https://sualehasif.me/ SPONSORS: To support this podcast, check out our sponsors & get discounts: Encord: AI tooling for annotation & data management. Go to https://encord.com/lex MasterClass: Online classes from world-class experts. Go to https://masterclass.com/lexpod Shopify: Sell stuff online. Go to https://shopify.com/lex NetSuite: Business management software. Go to http://netsuite.com/lex AG1: All-in-one daily nutrition drinks. Go to https://drinkag1.com/lex OUTLINE: (00:00) - Introduction (09:25) - Code editor basics (11:35) - GitHub Copilot (18:53) - Cursor (25:20) - Cursor Tab (31:35) - Code diff (39:46) - ML details (45:20) - GPT vs Claude (51:54) - Prompt engineering (59:20) - AI agents (1:13:18) - Running code in background (1:17:57) - Debugging (1:23:25) - Dangerous code (1:34:35) - Branching file systems (1:37:47) - Scaling challenges (1:51:58) - Context (1:57:05) - OpenAI o1 (2:08:27) - Synthetic data (2:12:14) - RLHF vs RLAIF (2:14:01) - Fields Medal for AI (2:16:43) - Scaling laws (2:25:32) - The future of programming PODCAST LINKS: - Podcast Website: https://lexfridman.com/podcast - Apple Podcasts: https://apple.co/2lwqZIr - Spotify: https://spoti.fi/2nEwCF8 - RSS: https://lexfridman.com/feed/podcast/ - Podcast Playlist: https://www.youtube.com/playlist?list=PLrAXtmErZgOdP_8GztsuKi9nrraNbKKp4 - Clips Channel: https://www.youtube.com/lexclips