Podcasts about AlphaGo

How did a teenage video game designer from London become a Nobel Prize-winning scientist behind one of the most consequential technology efforts in history? Sebastian Mallaby is a senior fellow at the Council on Foreign Relations and author of the new book, The Infinity Machine: Demis Hassabis, DeepMind, and the Quest for Superintelligence which provides an in-depth look into one of the greatest minds behind artificial general intelligence. In this episode, Sebastian and Greg discuss how Hassabis's early immersion in game design and neuroscience shaped his unique approach to artificial intelligence, why groundbreaking science is increasingly happening outside academia, and the tension between scientific discovery and corporate strategy.  *unSILOed Podcast is produced by University FM.* Episode Quotes: Why AI is becoming an ‘infinity machine' 03:01: It struck me that two breakthroughs in AI pointed to more to come. And these were AlphaGo and then AlphaFold. And what these two things had in common was—you had a sort of massive combinatorial space in both cases. So with Go, because it's a nineteen-by-nineteen board, the very first move, there's three hundred and sixty-one choices, then there's three-sixty for the second one. If you multiply that out, you pretty soon get to a search space which is sort of, you know, approaching infinity in terms of the number of possible permutations in the game. And with proteins, the way they can fold is even bigger. And so in both of these challenges, effectively, you have a machine that can make sense of near infinity of data, so an infinity machine. And once you have that, I figured, well, it's niche for the moment, but it may not stay niche forever. The “Third Way” that helped Google overcome the innovator's dilemma 44:06: The third way is you have a skunkworks, like DeepMind in London, which is a separate entity, and you're letting them kind of be the new policy in waiting, like the fightback policy in waiting. And you don't activate it. But when the moment comes when your competitor embraces the new technology, and you're in danger of falling foul of the innovator's dilemma, then you've got the answer because you've been keeping it ready, and you bring it in, and then you fight back fast. How DeepMind helped Google catch up in the AI race 42:54: How did they, in the space of two and a half years, go from the merger announcement to Gemini 3.0, which was better than the ChatGPT rivals? The key to it is that DeepMind had that top-down strike-team methodology, which came from the video game development world, and they imposed that on the Mountain View team, which was much more bottom-up and kind of inchoate in the research process. And that's what generated Gemini 3.0. That's how they got ahead. Show Links: Recommended Resources: Sebastian Mallaby | unSILOed AlphaGo AlphaFold Gödel, Escher, Bach by Douglas Hofstadter Geoffrey Hinton Mustafa Suleyman Guest Profile: Senior Fellow Profile at Council on Foreign Relations Professional Profile on LinkedIn Guest Work: The Infinity Machine: Demis Hassabis, DeepMind, and the Quest for Superintelligence  The Power Law: Venture Capital and the Making of the New Future  More Money Than God: Hedge Funds and the Making of a New Elite  The Man Who Knew: The Life and Times of Alan Greenspan Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.

Анонсы nFactorial, рекомендации из рассылки nFactorial Weekly, переход Андрея Карпаты в Anthropic, итоги Google I/O разбор возможностей видеомодели Gemini Omni, анализ интервью Джеффа Безоса о капитализме, налогах и бизнес-стратегии, стэнфордское исследование об опасности чрезмерной вежливости ИИ и подстраивании под пользователя, инвестиции Сэма Альтмана в стартапы Y Combinator через токены OpenAI, сборник фундаментальных советов для стартапов от Y Combinator, соревнование по сортировке посылок между человеком и гуманоидным роботом Figure, график инфляции за 25 лет и дефляционная природа технологий на фоне роста цен на услуги, научное исследование влияния 20-секундных объятий на уровень стресса и окситоцина, вирусные видео с главой Nvidia Дженсеном Хуангом, создание персонального ИИ-агента главой МИД Сингапура, разбор устройства AlphaGo в подкасте Дваркеша Пателя, оценка главных бенефициаров потенциального IPO компании SpaceX, смена карьерных приоритетов, почему роль High Impact Individual Contributor стала престижнее руководящих должностей, а также истории успеха из Instagram nFactorial. Рекомендации от nFactorial - Создаем команду Agentic AI-инженеров. Подробнее: https://www.linkedin.com/feed/update/urn:li:activity:7463104249846034433/ - nFactorial AI Cup: открытый чемпионат Казахстана по вайб-кодингу веб-игр (24 мая, 9:00-18:00, Нархоз Университет, призовой фонд - 1.1 млн тенге + 3 гранта на nFactorial Incubator) - https://www.instagram.com/p/DYE_O6MjW_x/ - nFactorial Reunion - Встреча выпускников nFactorial Incubator разных лет: где они сейчас, что делали тогда, советы - https://www.youtube.com/watch?v=Csr4j8vAcco - Подписаться на nFactorial Weekly - https://nfactorial-school.kit.com/

Eric Jang walks through how to build AlphaGo from scratch, but with modern AI tools.Sometimes you understand the future better by stepping backward. AlphaGo is still the cleanest worked example of the primitives of intelligence: search, learning from experience, and self-play. You have to go back to 2017 to get insight into how the more general AIs of the future might learn.Once he explained how AlphaGo works, it gave us the context to have a discussion about how RL works in LLMs and how it could work better – naive policy gradient RL has to figure out which of the 100k+ tokens in your trajectory actually got you the right answer, while AlphaGo's MCTS suggests a strictly better action every single move, giving you a training target that sidesteps the credit assignment problem. The way humans learn is surely closer to the second.Eric also kickstarted an Autoresearch loop on his project. And it was very interesting to discuss which parts of AI research LLMs can already automate pretty well (implementing and running experiments, optimizing hyperparameters) and which they still struggle with (choosing the right question to investigate next, escaping research dead ends). Informative to all the recent discussion about when we should expect an intelligence explosion, and what it would look like from the inside.Watch on YouTube. Read the transcript.And check out the flashcards I wrote to retain the insights.Sponsors* Cursor‘s agent SDK let me build a pipeline to generate flashcards for this episode. For each card, I had an agent read the transcript, ingest blackboard screenshots, generate an SVG visual, and run everything through a critic. A durable agent is much better at this kind of work than a chain of LLM calls, and Cursor's SDK made it easy. Check out the cards at flashcards.dwarkesh.com and get started with the SDK at cursor.com/dwarkesh* Jane Street gave me a real deep-dive tour of one of their datacenters. I got to ask a bunch of questions to Ron Minsky, who co-leads Jane Street's tech group, and Dan Pontecorvo, who runs Jane Street's physical engineering team. They were willing to literally pull up the floorboards and take out racks to explain how everything works. Check out the full tour at janestreet.com/dwarkeshTimestamps(00:00:00) – Basics of Go(00:08:17) – Monte Carlo Tree Search(00:32:04) – What the neural network does(01:00:33) – Self-play(01:25:38) – Alternative RL approaches(01:45:47) – Why doesn't MCTS work for LLMs(02:01:09) – Off-policy training(02:12:02) – RL is even more information inefficient than you thought(02:22:16) – Automated AI researchers Get full access to Dwarkesh Podcast at www.dwarkesh.com/subscribe

Send us Fan MailAnd we're back! Back in Nashville that is! Zac and Chad sit down with Kyle Wilshire to talk about why youth ministry has to move past keeping students busy and doing and start forming students who go and live sent. We unpack the Great Commission, what mission trips can and cannot do, and how to build everyday courage for gospel conversations in real life and online. Pickup Kyle's book "GO" hereIn a digital-first generation, the “ends of the earth” are closer than ever. This conversation explores digital discipleship, social media integrity, and practical ways students can start meaningful conversations about faith online and in person. In This Episode:Why the Great Commission means “as you go”How to create a missional culture in youth ministryThe real value of student mission tripsTurning mission trip moments into long-term discipleshipHelping students overcome fear in evangelismDigital discipleship and sharing faith onlinePractical ways students can live on mission every dayDon't Miss... • Ghostbusters memories and why timing matters • A senior speech that shows the power of owning a moment • Youth ministry as belonging and the tension when students drift • Why “go” matters and what “as you go” means • Mission trips as disruption that widens worldview • Turning a trip into lasting formation through reminders and follow-up • Reframing evangelism so students are not carrying the results • Building gospel familiarity so conversations feel natural • Acts 1:8 as a map for where we witness • Modeling faith as leaders in parking lots and daily life • Digital discipleship and using social platforms as witness Whether you're a youth pastor, volunteer leader, parent, or student ministry team member, this episode will encourage you to create a ministry culture that both welcomes students in and sends them back out with purpose.https://www.lifeway.com/en/product/go-teen-bible-study-book-P005852665Support the showJoin the community!

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Written as part of the MATS 9.1 extension program, mentored by Richard Ngo. From March 9th to 15th 2016, Go players around the world stayed up to watch their game fall to AI. Google DeepMind's AlphaGo defeated Lee Sedol, commonly understood to be the world's strongest player at the time, with a convincing 4-1 score. This event “rocked” the Go world, but its impact on the culture was initially unclear. In Chess, for instance, computers have not meaningfully automated away human jobs. Human Chess flourished as a pseudo-Esport in the internet era whereas the yearly Computer Chess Championship is followed concurrently by no more than a few hundred nerds online. It turns out that the game's cultural and economic value comes not from the abstract beauty of top-end performance, but instead from human drama and engagement. Indeed, Go has appeared to replicate this. A commentary stream might feature a complementary AI evaluation bar to give the viewers context. A Go teacher might include some new intriguing AI variations in their lesson materials. But the cultural practice of Go seemed to remain largely unaffected. Nascent signs of disharmony in Europe became nevertheless visible in early 2018, when the online [...] ---Outline:(09:23) AI users never find out they havent got it.(13:36) Appendix A: No, Go players arent getting stronger(14:41) Appendix B: Why this article exists The original text contained 2 footnotes which were omitted from this narration. --- First published: May 1st, 2026 Source: https://www.lesswrong.com/posts/nR3DkyivzF4ve97oM/how-go-players-disempower-themselves-to-ai --- Narrated by TYPE III AUDIO.

Τεχνητή νοημοσύνη που μαθαίνει να επιβιώνει μόνη της, bots που εκβιάζουν CEOs, και μια Κύπρος που πουλιέται κομμάτι-κομμάτι. Από το AlphaGo και τους κινδύνους του AGI, ως τις εκποιήσεις και τα funds που ανάλαβαν τα 'χρέη' μας.

In this episode, we discuss the implications of OpenAI's split from Microsoft. We also shine a light on the $1.1 billion initiative led by AlphaGo's creator.

In this episode, we consider the implications of OpenAI's break from Microsoft and what the future holds. We'll also discuss AlphaGo's creator's $1.1 billion funding success. See Privacy Policy at https://art19.com/privacy and California Privacy Notice at https://art19.com/privacy#do-not-sell-my-info.

In this episode, we discuss how OpenAI is carving out a new path beyond its Microsoft partnership. Additionally, we focus on the impressive $1.1 billion funding round led by AlphaGo's creator. See Privacy Policy at https://art19.com/privacy and California Privacy Notice at https://art19.com/privacy#do-not-sell-my-info.

In this episode, we discuss OpenAI's decision to move away from collaboration with Microsoft. We also cover the significant $1.1 billion funding achievement by AlphaGo's creator.

In this episode, we explore the liberation of OpenAI from its relations with Microsoft. We also highlight the monumental $1.1 billion funding by the creator of AlphaGo. See Privacy Policy at https://art19.com/privacy and California Privacy Notice at https://art19.com/privacy#do-not-sell-my-info.

In this episode, we celebrate OpenAI's break from Microsoft's constraints. We also explore the impressive funding acquired by AlphaGo's lead designer. See Privacy Policy at https://art19.com/privacy and California Privacy Notice at https://art19.com/privacy#do-not-sell-my-info.

In this episode, we explore OpenAI's uncharted waters now that it has moved beyond Microsoft. Additionally, we cover the remarkable $1.1 billion raised by AlphaGo's creator.

In this episode, we discuss OpenAI cutting its links with Microsoft and what this means. We'll also highlight AlphaGo's architect securing $1.1 billion in funding. See Privacy Policy at https://art19.com/privacy and California Privacy Notice at https://art19.com/privacy#do-not-sell-my-info.

In this episode, we analyze OpenAI's independence from its partnership with Microsoft. We'll spotlight AlphaGo's architect raising a remarkable $1.1 billion. See Privacy Policy at https://art19.com/privacy and California Privacy Notice at https://art19.com/privacy#do-not-sell-my-info.

In this episode, we discuss OpenAI gaining sovereignty from Microsoft's oversight. Let's also explore the successful acquisition of $1.1 billion funding by AlphaGo's architect. See Privacy Policy at https://art19.com/privacy and California Privacy Notice at https://art19.com/privacy#do-not-sell-my-info.

In this episode, we analyze OpenAI's strategic move away from Microsoft. We also cover the notable $1.1 billion funding round led by the mastermind behind AlphaGo.

In this episode, Ray Cochrane unpacks Anthropic’s Mythos model and the Treasury’s emergency meetings with Wall Street, then digs into Apple’s vibe-coding crackdown and a gaming-anxiety study that hit way too close to home. Also covered: Verge’s solid-state motorcycle, UBTech humanoid robot sales jumping 23-fold, Japan’s first osmotic power plant, Finland’s permanent nuclear waste vault, Ghostty landing in Ubuntu, Cloudflare’s EmDash CMS, and a Claude Code skill that talks like a caveman. – Want to start a podcast? It’s easy to get started! Sign up at Blubrry – Thinking of buying a Starlink? Use my link to support the show. Subscribe to the Newsletter. Email Ray if you want to get in touch! Like and Follow Geek News Central’s Facebook Page. Support my Show Sponsor: Best Godaddy Promo Codes Get 1Password Full Summary Cochrane opens the show by framing Anthropic’s new Mythos model as the AlphaGo moment for cybersecurity. From there, the episode moves through Apple’s pushback against AI-generated apps, a gaming anxiety study with a deeply personal hook, a series of “first to ship” energy and robotics wins out of Finland, China, and Japan, and several developer-tool stories that show how quickly the economics of software are shifting. Mythos, the Detection Ceiling, and Wall Street’s Emergency Response Anthropic’s Mythos model has Wall Street rattled. Operating autonomously, Mythos found and demonstrated the exploitation of a 27-year-old TCP SACK bug in OpenBSD, an operating system famous for being one of the most security-focused on the planet. Per Anthropic’s red team, over 99% of the vulnerabilities Mythos has identified remain unpatched. The researchers’ conclusion is blunt: “the moat in AI cybersecurity is the system, not the model.” The policy response moved fast. On April 7th, Treasury Secretary Bessent and Fed Chair Jerome Powell pulled the CEOs of Goldman Sachs, Citi, Bank of America, and Morgan Stanley into Treasury headquarters on short notice. All four banks are now testing Mythos internally. Treasury CIO Sam Corcos is also seeking direct access. Anthropic is gating distribution through Project Glasswing, a limited-access program with JPMorgan, Apple, Google, Microsoft, and Nvidia. Cochrane comes down firmly behind Anthropic’s gated approach. Because a 5.1-billion-parameter open model can apparently recover the core analysis chain for the OpenBSD flaw, this capability is not locked behind Frontier Compute. He wants the critical infrastructure hardened before the public gets keys. However, he also notes the bigger lesson is about human wisdom: people offloading all their thinking to AI lose out on the wisdom that makes any of these tools genuinely useful. Apple Bans Vibe Coding Apps from the App Store Apple has been quietly pushing back against what people are calling “vibe coding” apps. Replit, Vibecode, and an app called Anything all run AI models on the phone and produce working software that runs inside the host app. Apple cites Guideline 2.5.2, in effect since 2017, which requires apps to be self-contained. Replit and Vibecode had their App Store updates blocked. Anything was pulled in late March, briefly restored on April 3rd, and then pulled the same day again. The forcing function is volume. App Store submissions jumped 84% in a single quarter as vibe coding tools flooded Apple’s review queue with AI-generated apps. Cochrane thinks Apple is justified, given the security issues swirling around the Vibe coding ecosystem. Even a beautiful diamond gets lost in a sea of sand, and that flood is exactly what Apple is trying to manage. The company behind Anything is now pivoting to iMessage, desktop, and Android. Playing Video Games to Win Is Linked to Higher Anxiety Cochrane gets personal on this one. Through high school and his early 20s, he was deeply addicted to League of Legends. His dad teased him about it constantly. In the last few years of that addiction, his body would go ice cold and shake every ranked match before. His partner identified it as a panic attack. The moment that happened, he quit. Today, he no longer shakes. The new study lines up with his experience. Researchers Kayleigh Watters and Mikael Rubin at Palo Alto University analyzed a publicly available database of 13,464 adult gamers, most of whom primarily played League of Legends. Players who game to win show higher generalized anxiety but actually play fewer hours, since performance pressure pushes them out. Players who game to relax show strong links between social anxiety avoidance and more hours played. The study appeared in the Journal of Affective Disorders. The headline framing of “playing to win makes you anxious” misses the point. The real finding is more interesting: gaming for avoidance and gaming for competition are both warning signs, for different reasons. Cochrane notes that the League of Legends community’s toxicity has been a running joke for years, and this study suggests the game’s structure may have been manufacturing the anxiety that fueled it. Sponsor: GoDaddy Economy hosting is $6.99/month, WordPress hosting is $12.99/month, and domains are $11.99. Both hosting plans include a free domain, professional email, and SSL certificate. Go to geeknewscentral.com/godaddy for the best pricing and to directly support this independent show. Verge Motorcycle: World’s First Production All-Solid-State Battery Cochrane filled his tank for $60 today, which made this story land especially hard. His mom has driven electric for years and patiently manages a 90-mile real-world range. The next-generation answer is already shipping. Verge Motorcycles, a Finnish company, is the first production vehicle of any kind with an all-solid-state battery. Their 2026 bikes ship in Q1 with a pack from Donut Lab, another Finnish outfit spun out of Verge. The numbers are bonkers. The pack delivers an energy density of 400 Wh/kg, roughly double that of current Tesla cells. It sustains 100kW charging, hits full charge in about 5 minutes in the lab and 12 minutes on the actual bike, and the long-range version covers 600 kilometers (about 370 miles) per charge. Toyota, QuantumScape, and Samsung SDI have all been telling us that solid-state is coming in 2027 to 2030. A Finnish motorcycle company shipping in Q1 2026 just embarrassed them all. UBTech Humanoid Robot Sales Jump 23-Fold UBTech dropped its 2025 annual earnings on April 1st. Humanoid robot revenue hit 820 million yuan, roughly $119 million USD, up 2,203% from 35.6 million yuan the year before. Unit sales went from 3 robots in 2024 to 1,079 in 2025. Shares jumped 14% on the announcement. The customer list is a real industrial deployment: BYD, Foxconn, Geely, FAW-Volkswagen, and Audi. The flagship is the Walker S2, with UBTech targeting 5,000 units in 2026 and 10,000 in 2027. Cochrane is honest about what this means. He does not think we are heading for an extinction event, but worker displacement is a real concern. The US has no universal income or universal healthcare. The people affected are not white-collar managers. They are everyday line workers who already make the least on the ladder. Work efficiency reportedly doubles when these robots arrive, which is a company-side win, but the humans they replace are not getting half a year of gardening leave to retrain. He invites the listener to take on this one directly. Japan Switches On Asia’s First Osmotic Power Plant In August 2025, Fukuoka’s Seawater Desalination Center quietly opened Asia’s first osmotic power facility. It generates about 880,000 kilowatt-hours per year, enough for roughly 220 homes. It is only the second operational osmotic plant in the world, after Mariager, Denmark, in 2023. Osmotic generation uses a salinity gradient: fresh water on one side of a membrane, salt water on the other, and the pressure difference spins a turbine. The clever part is what Fukuoka does with desalination brine. Instead of regular seawater, the plant uses concentrated brine left over from the desalination process. This amplifies the salt gradient and squeezes more energy out of the same membrane. The result is a closed-loop partnership: the desalination facility produces drinking water and leaves brine behind, the osmotic plant turns the brine into electricity, and that electricity runs the desalination facility. Every desalination plant on Earth produces brine, so if Fukuoka’s co-located model works, the same pattern could be replicated across hundreds of plants worldwide. Japan’s Luna Ring Solar Moon Proposal Goes Viral Again Shimizu Corporation’s Luna Ring concept is making the rounds again. The pitch: a 6,800-mile belt of solar panels around the Moon’s equator, beaming microwave power back to Earth. Project lead Tetsuji Yoshida has long argued that a full ring could eliminate fossil fuel dependence entirely. The proposal first surfaced in 2013, has no funding, no government endorsement, and no concrete cost estimate. Shimizu has not put any active development behind it. Cochrane finds the concept fun every time it resurfaces. However, this would have to be a worldwide effort in the truest sense, with treaties, a new generation of launch economics, and microwave power transmission at a scale nobody has demonstrated. Beaming the power back to Earth has always been one of the biggest practical holdbacks. The Luna Ring is inspirational, but not shipping. Finland’s Onkalo Nuclear Waste Vault Opens Finland’s Onkalo facility is the world’s first permanent deep geologic repository for spent nuclear fuel. Operated by Posiva, the facility is buried about 430 meters down in 1.9-billion-year-old bedrock. It is designed to hold up to 6,500 tons of spent fuel and operate until the 2120s. The construction costs about €1 billion, with operating and closure adding roughly €4 billion more before the program is done. The catch is that radioactivity remains dangerous for hundreds of thousands of years. Edwin Lyman, director of nuclear power safety at the Union of Concerned Scientists, warned that the copper canisters will eventually corrode, with different scientific opinions on how fast. Geologic disposal remains “fraught with uncertainties,” and we have never validated an engineered system across a 100,000-year time frame. The bet is that the rock and copper outlast the radioactivity. Cochrane sees Onkalo as time-buying rather than a final answer. It is more of a bank holding spent fuel while science catches up. He prefers it to Japan’s ongoing approach of releasing tritium-treated water from Fukushima Daiichi into the Pacific, even though the dilution is well below WHO drinking water guidelines. Burying the waste in an insurmountable containment strikes him as the more honest answer to a problem nobody knows how to truly solve. Ghostty Terminal Lands in the Ubuntu Repos Ghostty 1.3.0 is now available in Ubuntu 26.04 LTS’s universe repository. The install is simply `sudo apt install ghostty`, no PPAs, no Snap, no Nix, no building from source. Ghostty was created by Mitchell Hashimoto, co-founder of HashiCorp. It is GPU-accelerated, uses native Swift on macOS and native GTK4 with libadwaita on Linux, and supports tabs, splits, profiles, ligatures, and the Kitty graphics protocol. Cochrane recently caught Hashimoto on a podcast, where he walked through his agentic coding workflow. Ghostty is being actively built using AI harnesses like Claude Code and Codex. Hashimoto told a story in which Codex fixed a six-month-old bug in 45 minutes, for a total API cost of $4.14. Personally, Cochrane uses WezTerm, but he is excited to see Ghostty become more widely available with a native UI rather than Electron. Borgo: Rethinking Go Using Rust Analytics India Magazine profiled Borgo, a programming language by developer Marco Sampellegrini (GitHub: alpacaaa). Borgo is statically typed with Rust-like syntax, but it compiles to Go and uses the Go runtime and garbage collector. It includes sum types (Option and Result), pattern matching, and full compatibility with existing Go packages. Notably, it removes Rust’s borrow checker and lifetimes entirely. Borgo is not new. It first appeared on Hacker News in 2023, with a RustLab talk in 2024. The 2026 angle is a renewed look at it through the lens of AI coding agents, since type-rich languages like Rust have been showing outsized productivity gains. Cochrane is a fan of Rust and stands by the borrow checker, but he enjoys these exploratory languages for what they reveal about what developers actually want. Caveman: A Claude Code Skill That Cuts 65% of Tokens Developer Julius Brussee built a Claude Code skill called Caveman that forces Claude to respond in stripped-down fragments. No articles, no “just,” no “really,” no pleasantries, no hedging. The tagline is “why use many token when few token do trick.” Across 10 real dev tasks, Caveman mode averaged 294 tokens per response, compared to 1,214 in normal mode. That is a 65% drop in output tokens. The project is MIT licensed with three intensity levels: lite, full, and ultra. Cochrane stumbled across the project online and shared it with a classmate who had been complaining about token costs. The classmate now insists that “the caveman is the only way to live.” Cochrane has not made the switch, but the bigger point lands. If a community plugin can cut 65% of tokens without correctness regressions, the labs are shipping verbose-by-default and charging users for the privilege. He suspects verbose output makes models feel more trustworthy, even when the token math says otherwise. Cloudflare Launches EmDash as a WordPress Successor Cloudflare released EmDash on April 9th, an open-source, MIT-licensed, TypeScript-based CMS pitched as the spiritual successor to WordPress. The big flex is that it was built in 60 days using AI coding agents. EmDash runs on Astro 6.0, either on Cloudflare’s edge platform or on a standard Node.js server. The plugin security model uses sandboxed Dynamic Workers with explicit permissions, addressing the architecture flaw that Cloudflare says causes 96% of WordPress vulnerabilities. Cochrane could not resist pointing out the irony of the name. The em dash has become the trademark giveaway that an AI was involved in writing. He has reservations about whether EmDash will succeed. WordPress is extremely hard to unseat, plenty of “WordPress killers” have come and gone, and the ecosystem is twenty-plus years deep. He is curious to see what comes next but not optimistic. Google Open-Sources the DESIGN.md Format Google Labs open-sourced the DESIGN.md format used by Stitch, their AI UI design tool. DESIGN.md is a declarative file capturing a project’s design system, colors, typography, and spacing in a way AI agents can read and apply. Cochrane has tried Stitch personally and finds it impressive at producing web designs. He has also seen DESIGN.md-style files already start appearing in repositories. He sees this kind of file becoming a new paradigm for agentic design, alongside robots.txt and llms.txt. However, he worries about a side effect. If everyone uses the same standardized format and the same AI tools, the web could become a homogeneous set of sites that all look the same. He is enthusiastic about the standardization but hopes designers continue to push for genuinely unique work. A 13-Liter PC With a Water Loop Built Into the Case Geeky Gadgets covered a build by “Visual Thinker”, a 13-liter mini-ITX case with custom SLA-printed water distribution plates built directly into the chassis. Instead of traditional soft tubing, plates channel coolant between the CPU and GPU blocks and are sealed with TPU and silicone molds. The case supports a full-size GPU and an SFX power supply. No thermal benchmarks, parts list, or pricing have been published. It is a one-off you cannot buy. Cochrane sees this as a sign of where PC building has gone in 2026. Modern mid-grade GPUs run nearly every recent game, so raw performance is no longer the differentiator. He likes seeing builders lean into design and craft rather than just stuffing the most powerful parts into a box. He admits he is the traditional type and built his own machine to maximize parts, but the design-first direction is a healthy evolution for the hobby. To close out the show, Cochrane recommends Pocket Casts as a podcast app. He finds it picks up new episodes very quickly. Big thanks to GoDaddy for over twenty years of keeping this show on the air, and a reminder that every promo code use is like writing a check to the show. The post Mythos: Cybersecurity’s AlphaGo Moment #1862 appeared first on Geek News Central.

Can AI really remove emotion from investing, or does human judgment still matter most when money is on the line? In today's episode, I'm joined by Jack Fu, Founder and CEO of Draco Evolution, a company using AI, quantitative models, and decades of market experience to help investors make smarter and more disciplined decisions. Jack's journey began during the 2008 financial crisis while working as a financial advisor at Union Bank of California, where watching investors lose life-changing amounts of money completely reshaped how he thought about risk, discipline, and long-term wealth creation. That experience led him to focus on one simple principle: avoiding big losses matters just as much as chasing returns. From managing assets for family offices and institutional clients to leading major investment operations across the Asia-Pacific region, Jack built his career around protecting capital first and helping investors stay in the market long enough to benefit from long-term growth. We explore how Draco Evolution is bringing institutional-level investment tools to everyday investors through AI-powered ETFs and a more dynamic approach to portfolio management. Jack explains how ETFs actually work, why they have become such a popular choice for investors, and the important difference between investing in AI companies and using AI itself to manage investment decisions. We also discuss the future of robo-advisors and why the next generation will move far beyond static questionnaires and occasional portfolio rebalancing. Jack shares why he believes the future lies in systems that adapt continuously to market conditions and investor behavior, creating something far more personal and responsive. From algorithmic trading and AlphaGo to today's world of agentic AI, Jack offers a practical perspective on how technology is changing finance without replacing human oversight. He also shares why investors should treat AI as an enhancement tool rather than blindly trusting every recommendation. If you've ever wondered how AI is changing investing, what makes AI-driven ETFs different, or how to stay disciplined in unpredictable markets, this conversation offers plenty of insight. How much would you trust AI to help manage your financial future, and where would you still want a human in the loop?

Two-time Pulitzer finalist on the scientist who won the Nobel Prize and may not be able to stop what he started.What if the person trying to make artificial intelligence safe is also one of the people racing to build it?In this episode of High Net Purpose, Joe McCarthy sits down with Sebastian Mallaby, bestselling author and two-time Pulitzer Prize finalist, whose latest book explores Demis Hassabis, Google DeepMind and the quest for superintelligence.Over the course of the conversation, Sebastian unpacks the extraordinary story behind DeepMind: why it was built in London, how Demis held onto a mission formed in childhood, and why the race for AI sits at the intersection of science, ambition, safety and power.From AlphaGo's victory over Lee Sedol to the battle for AI safety oversight inside Google, this is a conversation about what happens when human purpose meets machine intelligence. It asks whether AI will deepen human potential, undermine it, or force us to redefine what purpose means altogether.Along the way, we explore the role of founders, family offices and capital allocators in a world where the technology changes faster than the rules around it.This is the story of the AI paradox, and the man trying to understand the people building the future.00:00 Introduction and Episode Overview02:31 Sebastian Mallaby on Purpose and His Career05:34 Diplomacy, Complexity and the Economist06:46 How Mallaby Gets Access to Exceptional People09:24 Research as Therapy: The 360 Method10:41 Finding the Central Paradox in Every Subject11:50 First Encounters with Demis Hassabis14:46 Hassabis as Authentic Entrepreneur: The 1993 Vision17:44 Mustafa Suleiman, Geoffrey Hinton and the Co-Founders19:48 Safety Baked In: DeepMind's Founding Tension22:22 Leaked Documents and the Fight with Sundar Pichai25:39 Can Governments Actually Control AI Risk?26:51 The Innovator's Dilemma and the ChatGPT Moment29:35 Hassabis as Leader: Science, Likability and Scale30:32 AlphaGo vs Lee Sedol and What It Revealed33:31 Human Purpose in an AI World35:19 Hassabis on Consciousness and the Meaning of Being Human36:18 AI Investment Framework for Capital Allocators39:27 Health, Writing Discipline and Final Advice Hosted on Acast. See acast.com/privacy for more information.

This is one of my favorite books over recent years. Sebastian Mallaby is the Paul A. Cocker Senior Fellow for International Economics at the Council of Foreign Relations and author of 6 bestselling books. THE INFINITY MACHINE tells the story of AI's progress over the past 15 years largely, but not exclusively, from Demis Hassabis as the protagonist and leader of DeepMind', with its 2010 mission statement to achieve superintelligence by 2030. It's a rich, informative, page turner.What We Discussed:—What is an Infinity Machine?—Influence of Claude Shannon's Information Theory and Douglas Hofstadter's Pulitzer Prize winning book Gödel, Escher, Bach—Origin of DeepMind in 2010. Prescient. Charter, business plan, included use of agents. How Demis Hassabis was made for the mission!—Contrasts with Sam Altman and the other AI leaders, the Oligopoly (cover of The Economist this week). For example, Nature papers vs white papers on company websites. —In March 2016, the same day when DeepMind's AlphaGo beat Lee Sedol, Hassabis says it's time to do protein folding (later known as AlphaFold).—Symbolic AI (historic, deductive, rule-based) vs Deep Learning (Toronto tribe) and Reinforcement Learning (Alberta tribe).—The Big Miss: DeepMind's lack of early recognition of the importance of transformer models (leading to ChatGPT), creating a big opening for OpenAI. And why was this missed? The Comeback Story. Is this happening again with coding (not in the book)?—The AI Arms Race and Hyperscaling—How the complex relationship between Google and DeepMind evolved —The Double Cross —With the dangers anticipated (parallels to Oppenheimer, Manhattan Project, and the atomic bomb), how to promote AI safety?—Is the major build up of data centers justified?Thank you Bob Fleischman, Jeanie, Ruben Max, FelonBroke America, Seitzinator ❌

Este podcast analiza el momento actual de la inteligencia artificial como un punto de inflexión histórico comparable a los grandes saltos de la humanidad. Parte de un evento detonante —el posible anuncio de la AGI— y construye una narrativa que conecta historia, tecnología, economía y geopolítica. Se explica cómo AlphaGo marcó el inicio de una nueva forma de inteligencia no humana, cómo la IA ha pasado del juego a la ciencia (AlphaFold) y cómo ahora impacta el empleo, el poder económico y el equilibrio global. El núcleo emocional gira en torno a una idea inquietante: no estamos tomando las decisiones más importantes sobre el futuro. La IA avanza, pero la sociedad no responde con la misma velocidad. Learn more about your ad choices. Visit megaphone.fm/adchoices

A team of former Google DeepMind researchers just raised $2B to build America's answer to DeepSeek. In this episode, we sit down with Ioannis Antonoglou (Yannis), co-founder and CTO of Reflection AI, who helped create AlphaGo—the AI that beat the world champion in the game of Go back in 2016. Yannis breaks down what Reflection is building, why they're releasing frontier-level AI models as open-weight, and how mixture-of-experts architecture lets massive models run efficiently. We dig into reinforcement learning, the US vs. China open source gap, sovereign AI, coding agents, and why open science might be the fastest path to the most powerful AI on the planet.Reflection AI: https://www.reflection.aiReflection AI raises $2B at $8B valuation (TechCrunch): https://techcrunch.com/2025/10/09/reflection-raises-2b-to-be-americas-open-frontier-ai-lab-challenging-deepseek/Previous Neuron coverage of DeepSeek: https://www.theneuron.ai/newsletter/deepseek-returns https://www.theneuron.ai/newsletter/10-wild-deepseek-demosSubscribe to The Neuron newsletter: https://theneuron.ai

Demis Hassabis is the Co-Founder & CEO of Google DeepMind - working on AGI, responsible for AI breakthroughs such as AlphaGo, the first program to beat the world champion at the game of Go; and AlphaFold, which cracked the 50-year grand challenge of protein structure prediction and was recognised with the 2024 Nobel Prize in Chemistry. Demis is revolutionising drug discovery at Isomorphic Labs. Ultimately, trying to understand the fundamental nature of reality. AGENDA: 00:04:00 — What Actually Counts as AGI; and Where Are We Today? 00:05:00 — What Are the Biggest Bottlenecks Holding AI Back Today? 00:06:00 — Have We Hit the Limits of Scaling Laws? 00:07:00 — Where Is AI Ahead of Expectations; and What's Still Missing? 00:07:30 — Why Can't AI Systems Learn Continuously Like Humans? 00:08:30 — How Did DeepMind Go from Behind to Leading the Pack? 00:11:00 — Are We Heading Toward Model Commoditization; or Winner-Takes-All? 00:12:00 — What Does the Future of Open Source Really Look Like? 00:13:00 — What Does a Post LLM World Look Like? 00:14:45 — Can AI Really Fix Drug Discovery—and Cut the 10-Year Timeline? 00:17:00 — What Does "Good" AI Regulation Actually Look Like? 00:18:00 — Who Should Be the Ultimate Arbiter of Truth in an AI World? 00:19:30 — If Demis Had One Shot to Fix AI Safety, What Would He Do? 00:21:00 — Is This Time Different for Jobs; or Will History Repeat Itself? 00:22:00 — Is AGI Bigger Than the Industrial Revolution; and Faster? 00:23:00 — Are We Underestimating AI Despite All the Hype? 00:23:30 — Does AI Lead to Massive Inequality; or Universal Prosperity? 00:24:30 — How Do We Solve the Energy Crisis Created by AI? 00:26:00 — Why Stay in the UK Instead of Moving to Silicon Valley? 00:28:00 — Will Europe Ever Build a Trillion-Dollar Tech Giant? 00:29:30 — Meeting Elon Musk for the First Time? 00:31:00 — What Big Questions About AI Is No One Talking About? 00:31:30 — What Does Demis Want His Legacy to Be?    

On today's podacest, Brian and Mandy discuss new trailers and franchise news (including Spider-Man: Brand New Day with a Man-Spider storyline, Punisher ties, Wonder Man, Daredevil's return, Jessica Jones coming back, a live-action Moana, another live-action Scooby-Doo reboot, and a new Lord of the Rings project with Stephen Colbert involved), then dive into artificial intelligence and its impact on creative work and jobs. Brian describes workplace pressure to add AI everywhere, the difficulty of defining ethical boundaries for writers and artists, and concerns about AI being trained on unlicensed work, generating “AI slop,” and improving exponentially. They also cover infrastructure and environmental costs of server farms, rising hardware prices, AI's ubiquity in search and products, chatbot benefits and harms, and fears that AI will replace apprenticeship pipelines and push entertainment toward mass-produced, homogenized content. 00:00 AI Threat or Revolution 00:38 Spider-Man Trailer Breakdown 01:48 Punisher and Marvel Rumors 02:29 Daredevil and Jessica Jones Return 04:29 Moana Live Action Debate 05:22 Reboots and LOTR News 06:03 AI Ethics for Creators 07:13 Tool vs Cheating 09:20 AI Slop and Rapid Progress 10:38 AlphaGo and Move 37 12:20 AI Beats Most People 12:58 Training Data Ethics 13:43 Server Farms Impact 15:27 Costs and Hardware Crunch 16:29 No Ethical AI Debate 17:02 Chatbots and Mental Health 18:18 AI Everywhere Now 20:22 Jobs and Apprenticeships 23:07 Publishing Slush Piles 25:15 AI Content Flood Future 28:36 Human Creativity at Risk 29:04 Episode Wrap Up

Este audio es un suplemento a nuestro episodio 550 de marzo de 2026. Contiene la versión original en inglés de la entrevista a los Drs. Pushmeet Kohli y Thore Graepel, del laboratorio DeepMind de Google, celebrando los 10 años de la legendaria victoria de su sistema AlphaGo sobre el campeón mundial humano de Go, un hito de la IA que ha abierto una línea de trabajo muy prolífica, incluyendo el sistema AlphaFold (Nobel de Química en 2024).

La tertulia semanal en la que repasamos las últimas noticias de la actualidad científica. En el episodio de hoy: Cara A: -Acast, nuevo partner de CB:SyR (5:00) -Evento cientófilo para ver el eclipse del 12 de Agosto (6:00) -Entrevista 10 años de DeepMind: Pushmeet Kohli y Thore Graepel (13:00) Este episodio continúa en la Cara B. Contertulios: María Ribes, Alberto Aparici, Juan Carlos Gil, Ignacio Crespo, Francis Villatoro, Héctor Socas. Imagen de portada realizada con Midjourney. Todos los comentarios vertidos durante la tertulia representan únicamente la opinión de quien los hace... y a veces ni eso

La tertulia semanal en la que repasamos las últimas noticias de la actualidad científica. En el episodio de hoy: Cara B: -Compuestos volátiles revelan la composición de los materiales para embalsamamiento en el antiguo Egipto (47:45) -Teorías de la consciencia (1:18:45) -Señales de los oyentes (1:42:15) Este episodio es continuación de la Cara A. Contertulios: María Ribes, Luisa Achaerandio, Alberto Aparici, Juan Carlos Gil, Ignacio Crespo, Francis Villatoro, Héctor Socas. Imagen de portada realizada por Mayra Schwarzschild. Todos los comentarios vertidos durante la tertulia representan únicamente la opinión de quien los hace... y a veces ni eso

Theme music by UNIVERSFIELD & background music by PodcastACGoogle Deepmind's recent podcast on 10 years of AlphaGoThe All Things Go interview with Peter Doggers, author of The Chess RevolutionThe interview with Andreii Kravets 3p on winning the European Go ChampionshipThe new Japanese film Bushido which is supposed to heavily feature GoThe article breaking down Fan Hui's comments from a few podcast episodes in Chinese and translated to English.Show your support hereEmail: AllThingsGoGame@gmail.comEpisode SponsorsBadukPop - Learn the rules of the ancient Chinese board game Go - also known as Baduk (바둑) or Weiqi (圍棋) - with a fun, interactive tutorial. Sharpen your Go skills with daily random Go problems (Tsumego) at your choice of difficulty level. Play games online or with a variety of AI opponents, each with its own unique playing style and strength.SmartGo One - Your complete app for the game of Go. Learn to play, practice against the computer, study master games, solve problems, and read Go books. Free to download.

This week new research suggests that in recent years the Earth has been warming faster than we predicted. But scientists are undecided on whether this change is going to be permanent. Laura Wilcox, Professor of Aerosol-Climate Interactions at the University of Reading explains. Tom Whipple is joined by Kit Yates, Author and Professor of Mathematical Biology and Public Engagement at the University of Bath. They mark the ten year anniversary of a game of ‘Go' in which a computer programme called AlphaGo beat human Go champion Lee Sodol. Computer scientist at Google DeepMind Thore Graepel was witness to the game and talks about why the event has become a crucial moment in the story of AI. Kit also brings Tom his pick of the science news.To discover more fascinating science content, head to bbc.co.uk, search for BBC Inside Science and follow the links to The Open University. Presenter: Tom Whipple Producers: Clare Salisbury and Alex Mansfield Editor: Martin Smith Production Co-ordinator: Jana Bennett-Holesworth

Vor zehn Jahren schlug AlphaGo Weltmeister Lee Sedol. Die KI spielte Züge, niemand verstand. Doch die Maschine triumphierte am Ende über den Menschen. Das Duell zeigte: Selbstlernende Algorithmen sind kreativ und unberechenbar.

It's the 10th anniversary of the famous and historic showdown between Lee Sedol and AlphaGo. A lot has changed since then, and Go players have already been facing the first wave of the AI revolution for a decade now. What lessons from our experience with AI in Go should we bring forth into the era of ChatGPT and Gemini?Join the Discord⁠Support Star Point⁠The Star Point Store

Začelo se je s plemenito vizijo o tehnologiji za dobrobit človeštva, končalo pa z mastnim zaslužkom največjih tehnoloških velikanov. Tako nekako lahko strnemo osrednjo idejo knjige Prevlada avtorice Parmy Olson o orodjih umetne inteligence, ki so v zadnjih letih obrnila svet na glavo. Prisluhnite intervjuju z njo, v katerem strnemo zgodbo ustanoviteljev podjetij DeepMind in OpenAI Demisa Hassabisa in Sama Altmana, ki stojita za orodji, kot sta Chat GPT in AlphaGo, razmišljamo pa tudi o tem, ali lahko takšna tehnologija sploh kdaj zares uide korporativnim interesom. Gostja: Parmy Olson, novinarka (Bloomberg) in avtorica knjige 'Prevlada: umetna inteligenca, ChatGPT in tekma, ki bo spremenila svet'. Knjiga je v prevodu Sama Kuščerja dostopna tudi v slovenskem jeziku. V Xpertizi (39:31) se predstavlja Anita Bolčevič, raziskovalka na področju turizma, FKBV UM. Avtorstvo fotografije na naslovnici podkasta: Kim Farinha     Poglavja: 00:00:01 Uvod 00:01:53 Parmy Olson in kaj jo je navdušilo za poročanje o tehnologiji 00:05:38 Kdo sta Sam Altman in Demis Hassabis 00:11:24 Na prizorišče stopita Google in Microsoft 00:14:41 Kakšna je bila vloga Elona Muska? 00:16:43 Google in njegov Goljatov paradoks 00:17:45 Kitajska noče zaostajati 00:20:55 Kakšna je dejanska tržna vrednost umetne inteligence 00:24:39 Zakaj je regulacija umetne inteligence tako težavna? 00:30:06 Negotov položaj novopečenih diplomantov ali kdo bo opravljal prakso? 00:33:30 Umetna inteligenca, njena 'empatija' in skriti interesi v ozadju 00:36:27 UI uporabljamo za preverjanje lastnih idej, ne njihovo generiranje 00:39:31 Xpertiza: Anita Bolčevič

How did we go from digital computers to AI seemingly everywhere? Neil deGrasse Tyson, Chuck Nice, & Gary O'Reilly dive into the mechanics of thinking, how AI got its start, and what deep learning really means with cognitive and computer scientist, Nobel Laureate, and one of the architects of AI, Geoffrey Hinton. Subscribe to SiriusXM Podcasts+ to listen to new episodes of StarTalk Radio ad-free and a whole week early.Start a free trial now on Apple Podcasts or by visiting siriusxm.com/podcastsplus. Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.

Andrew Humberman BioSnap a weekly updated Biography.Andrew Huberman, the Stanford neuroscientist and Huberman Lab podcast host, dropped a pair of fresh episodes this week that have fans buzzing. On February 2, he welcomed Dr. Read Montague to unpack how dopamine and serotonin drive decisions, motivation, and learning, diving into real-time brain scans and AI parallels like AlphaGo in a chat laced with personal anecdotes from their 15-year reconnection, as detailed on the Huberman Lab site and Singju Post transcript. Just days later on February 5, Huberman released an Essentials episode with movement guru Ido Portal, breaking down nervous system tricks for better motion, panoramic vision drills, and playful exploration to rewire habits, straight from hubermanlab.com.Business-wise, Mens Journal spotlighted Hubermans five core health pillars sleep, sunlight, movement, nutrition, and relationships on February 3, pulling from his podcast wisdom to pitch them as no-nonsense basics over trendy biohacks. A viral YouTube short from Iain Barton Shorts that same day clipped Huberman on neuroplasticity focus exercises, racking up views with his tips for daily visual drills to sharpen concentration.Hes also in the medias crosshairs amid the Epstein files fallout. Plant Based News flagged him January 31 as a wellness bro tied to Peter Attia, whose 1700-plus Epstein mentions including flirty emails surfaced recently, though Huberman himself faces no direct links there. Katie Couric Media critiqued on January 28 his CBS News contributor gig alongside Attia and Mark Hyman, slamming their supplement-pushing protocols as overhyped with conflicts, yet CBS kept them post-scandal. Willamette Week noted on February 3 his 2023 podcast collab with Epstein-linked psychiatrist Paul Conti, stirring guilt-by-association whispers. No public appearances or direct social mentions popped in the last few days, but his feeds hum with blueprint emails to over a million subs. Speculation swirls on long-term bio rep hits from the influencer scrutiny, but Hubermans output stays relentless.Get the best deals https://amzn.to/3ODvOtaThis content was created in partnership and with the help of Artificial Intelligence AI

Leontxo García es el invitado perfecto para conmemorar los 200 episodios de Kapital. Leontxo es seguramente la persona que más sabe sobre ajedrez en España. Yo jugaba de pequeño con mi padre y guardo un bonito recuerdo de esas partidas. Si hoy pienso distinto, algo de culpa tienen ese juego. ¿Qué es lo que aprendí? No sabría decírtelo, pero algo siempre queda. Una conexión inesperada, quizá simplemente trabajé la paciencia. Leontxo está convencido del poder pedagógico del ajedrez y defiende que deberíamos fomentarlo en las escuelas. Siempre regresamos, en este podcast, a La utilidad de lo inútil del gran Nuccio Ordine. Leontxo recuerda movimientos del gran maestro cubano Capablanca en partidas que se jugaron hace más de 100 años y yo refuerzo mi tesis que la diferenciación siempre viene por el camino menos pensado.Kapital llega a los 200 episodios y quería simplemente darte las gracias.Quiero pensar que existe una filosofía alrededor de este podcast: en hacer las cosas sin buscar una ganancia directa, en perder una tarde charlando de todo y de nada, en saber disfrutar del tiempo que pasa. Una filosofía que nos une en una forma particular de ver y entender la vida. Te habrás fijado que raramente confronto a los invitados y es que el espíritu de Kapital es la curiosidad. ¿Quién soy yo para discutir una idea? Yo solo quiero saber cómo piensa esa persona, cómo entiende el mundo en el que vive. La premisa de Kapital es que todos los invitados esconden una lección, desde Cao de Benós hasta Llados, pasando por Raggio, todos tienen algo que puede ser de tu interés, si escuchas con atención. Como si fuera esto un acertijo, mi reto es encontrar la pregunta correcta que destape esa verdad escondida. Gracias por jugar a este juego.Índice:0:32 En Estados Unidos el póker, en Rusia el ajedrez y en China el Go.3:36 El plan de Lenin para las escuelas.11:46 Control del primer impulso a través del ajedrez.20:33 La utilidad de lo inútil, de nuevo.27:10 No puedes escribir una novela romántica si no te rompieron el corazón antes.31.36 El mal uso del ChatGPT.42:27 Orígenes del ajedrez.53:02 Maestros antiguos.1:02:44 Karpov contra Kasparov.1:17:28 Que la suerte te pille preparado.1:25:32 Kasparov como Don Quijote.1:35:18 DeepBlue y AlphaGo.1:41:15 Agotamiento físico de pensar.1:51:10 Apúntate a un club de ajedrez.Apuntes:Ajedrez y ciencia, pasiones mezcladas. Leontxo García.Pensar rápido, pensar despacio. Daniel Kahneman.Putting your intuition on ice. Daniel Kahneman.La diagonal del loco. Richard Dembo.Todas las historias y un epílogo. Enric González.Historias del Calcio. Enric González.Un verdor terrible. Benjamín Labatut.MANIAC. Benjamín Labatut.

In this episode of ACM ByteCast, Rashmi Mohan hosts 2024 ACM A.M. Turing Andrew laureates Andrew Barto and Richard Sutton. They received the Turing Award for developing the conceptual and algorithmic foundations of reinforcement learning, a computational framework that underpins modern AI systems such as AlphaGo and ChatGPT. Barto is Professor Emeritus in the Department of Information and Computer Sciences at the University of Massachusetts, Amherst. His honors include the UMass Neurosciences Lifetime Achievement Award, the IJCAI Award for Research Excellence, and the IEEE Neural Network Society Pioneer Award. He is a Fellow of IEEE and AAAS. Sutton is a Professor in Computing Science at the University of Alberta, a Research Scientist at Keen Technologies (an artificial general intelligence company) and Chief Scientific Advisor of the Alberta Machine Intelligence Institute (Amii). In the past he was a Distinguished Research Scientist at Deep Mind and served as a Principal Technical Staff Member in the AI Department at the AT&T Shannon Laboratory. His honors include the IJCAI Research Excellence Award, a Lifetime Achievement Award from the Canadian Artificial Intelligence Association, and an Outstanding Achievement in Research Award from the University of Massachusetts at Amherst. Sutton is a Fellow of the Royal Society of London, AAAI, and the Royal Society of Canada. In the interview, Andrew and Richard reflect on their long collaboration together and the personal and intellectual paths that led both researchers into CS and reinforcement learning (RL), a field that was once largely neglected. They touch on interdisciplinary explorations across psychology (animal learning), control theory, operations research, cybernetics, and how these inspired their computational models. They also explain some of their key contributions to RL, such as temporal difference (TD) learning and how their ideas were validated biologically with observations of dopamine neurons. Barto and Sutton trace their early research to later systems such as TD-Gammon, Q-learning, and AlphaGo and consider the broader relationship between humans and reinforcement learning-based AI, and how theoretical explorations have evolved into impactful applications in games, robotics, and beyond.

Tras un largo silencio que parece haber suspendido el tiempo mismo, regresamos para constatar que, aunque nosotros nos detuvimos, la inercia del mundo y sus automatismos no lo hicieron. ¿Es posible que estemos habitando ya el interior de una estructura invisible que prioriza la eficiencia sobre la libertad? ¿Hemos cruzado ya el punto de no retorno donde los algoritmos no solo nos asisten, sino que nos gobiernan sin darnos una explicación? Conexiones imposibles y un poco de filosofIA para esta vuelta a los escenarios que tanta ilusión nos hacía. Recordemos que todo acaba y todo empieza en el Episodio 248: El punto de no retorno algorítmico: El antecedente directo donde se plantea el umbral en el que perdemos el control sobre sistemas esenciales. Estos son los contenidos para seguir conectando puntos: Bulletin of the Atomic Scientists – Doomsday Clock: El Reloj del Apocalipsis no es una mera herramienta simbólica; es un recordatorio que hemos pasado por alto durante demasiado tiempo. Desde 1947, científicos de primer nivel evalúan anualmente cuán cerca estamos de la medianoche, esa destrucción catastrófica que representaba inicialmente solo amenazas nucleares. Lo que nos fascina del episodio es cómo este reloj ha evolucionado para incluir amenazas que los abuelos de estos científicos jamás contemplaron: inteligencia artificial, cambios climáticos, biología disruptiva. En 2025, por primera vez en 78 años, el reloj se posicionó a 89 segundos de la medianoche. Un único segundo de diferencia respecto a 2024, pero un gesto que dice todo: la IA no es una amenaza futura, está aquí, ahora, acelerando riesgos que ya parecían irremontables. AESIA – Agencia Española de Supervisión de la Inteligencia Artificial: España ha impulsado un organismo dedicado exclusivamente a supervisar la IA. La AESIA es una institución con poder real para exigir explicabilidad, para inspeccionar sistemas de riesgo alto, para establecer que los algoritmos no pueden ser cajas negras perpetuas. Comenzó operaciones en 2025 cuando Europa aprobaba su directiva sobre IA. Lo que el episodio subraya es algo crucial: la regulación llega tarde. Mientras AESIA inspecciona sistemas nuevos, más de mil algoritmos médicos antiguos siguen operando sin cumplir esos requisitos de transparencia. Civio – Sentencia BOSCO y Transparencia Algorítmica: Una organización de vigilancia ciudadana llevó al Tribunal Supremo español un caso que iba a cambiar algo fundamental: el acceso al código fuente de BOSCO, el algoritmo que decide quién recibe ayuda eléctrica y quién no. Durante años, el Gobierno argumentó seguridad nacional, propiedad intelectual, secretos comerciales. El Supremo ha dicho que no. La sentencia de 2025 estableció jurisprudencia: la transparencia algorítmica es un derecho democrático. Los algoritmos que condicionan derechos sociales no pueden ser opacos. Por primera vez, un tribunal de alto nivel reconoce que vivimos en una «democracia digital» donde los ciudadanos tienen derecho a fiscalizar, a conocer, a entender cómo funciona la máquina que decide sobre sus vidas. BOSCO era apenas un ejemplo. La sentencia abre la puerta a exigencias de transparencia sobre cualquier sistema que use la administración pública para decisiones automatizadas. Es pequeño, increíblemente importante, y probablemente insuficiente. Reshuffle: Who Wins When AI Restacks the Knowledge Economy – Sangeet Paul Choudary: Este libro es exactamente lo que necesitábamos leer antes de grabar este episodio. Choudary no habla de cómo la IA automatiza tareas; habla de cómo la IA remodela el orden completo de cómo trabajamos, cómo nos coordinamos, cómo creamos valor. «Reshuffle» no es un catálogo de miedos; es un análisis de cómo nuevas formas de coordinación sin control centralizado están emergiendo. El libro conecta con lo que discutimos sobre la opacidad: no es solo que los algoritmos sean opacos, es que están reorganizando estructuras organizacionales enteras. Choudary habla de empresas que ya no saben quién es responsable de qué porque las máquinas coordinan sin necesidad de consenso humano. Es Max Weber acelerado a velocidad de red neuronal. The Thinking Game – Documental sobre Demis Hassabis y DeepMind: Un documental que filma la persecución de una obsesión: Demis Hassabis pasó su vida entera buscando resolver la inteligencia. The Thinking Game, producido por el equipo que creó AlphaGo, muestra cinco años dentro de DeepMind, los momentos cruciales en que la IA saltó de juegos a resolver problemas biológicos reales con AlphaFold. Lo que duele ver aquí es que Hassabis resolvió un problema de 50 años en biología y lo open-sourceó. La pregunta incómoda es: ¿cuántos otros Hassabis están dentro de laboratorios corporativos con incentivos inversos, guardando secretos? The Thinking Game es un retrato de lo que podría ser si el impulso científico ganara sobre el extractivo. Recomendamos verlo antes de cualquier conversación sobre dónde está realmente el avance en IA. Las horas del caos: La DANA. Crónica de una tragedia: Sergi Pitarch reconstruye hora a hora el 29 de octubre de 2024, el día en que la DANA arrasó Valencia. Lo que hace diferente a este libro es que no solo cuenta lo que sucedió; documenta lo que no se hizo, quién fue responsable de silenciar advertencias, qué decisiones fueron tomadas en salas oscuras mientras miles quedaban atrapados. Es una crónica periodística larga en el estilo norteamericano de investigación profunda. Lo conectamos al episodio porque la tragedia de Valencia es un espejo: sistemas con algoritmos que debían predecir, equipos de emergencia que debían comunicar, protocolos que debían activarse. Pero hubo silencios, opacidades, dilución de responsabilidad. Exactamente lo que sucede cuando los algoritmos fallan sin que nadie sepa quién paga el precio. Pitarch escribe para que las víctimas no caigan en el olvido y para que la siguiente tragedia no se repita con la misma negligencia. Anatomía de un instante: Serie basada en el libro de Javier Cercas, que examina el 23-F español, el golpe militar de 1981, pero lo hace como psicólogo de la historia: ¿qué es lo que convierte a un hombre en héroe en un instante crucial? Lo traemos aquí porque el libro trata sobre cómo nuestros sistemas, nuestras instituciones, nuestras estructuras de poder están sostenidas por momentos impredecibles, por acciones individuales que los algoritmos no pueden modelar. La IA promete predecibilidad, certeza, orden. Cercas nos recuerda que la historia es una disciplina de lo impredecible, que los instantes que nos definen no salen de una ecuación. Una nota final: Gracias por estar aquí. Un año después, sin Delorean, sin viaje temporal, pero con la certeza de que mientras buscábamos retroceder, el mundo siguió avanzando. Eso era el verdadero experimento: comprobar si podíamos volver a conectar puntos después de doce meses de que los algoritmos siguieran escribiendo el guión. La respuesta es sí. Pero la pregunta más incómoda permanece: ¿sabemos realmente dónde estamos en esa jaula de hierro? ¿O solo acabamos de darnos cuenta de que hay paredes? Para contactar con nosotros, podéis utilizar nuestra cuenta de twitter (@conectantes), Instagram (conectandopuntos) o el formulario de contacto de nuestra web conectandopuntos.es. Nos podéis escuchar en iVoox, en iTunes o en Spotify (busca por nuestro nombre, es fácil). Créditos del programa Intro: Stefan Kanterberg ‘By by baby‘ (licencia CC Atribución). Cierre: Stefan Kanterberg ‘Guitalele's Happy Place‘ (licencia CC Atribución). Foto: Creada con IA ¿Quieres patrocinar este podcast? Puedes hacerlo a través de este enlace La entrada Episodio 249: La jaula de hierro algorítmica se publicó primero en Conectando Puntos.

 Welcome to episode 337 of The Cloud Pod, where the forecast is always cloudy! Justin, Matt, and Ryan have hit the recording studio to bring you all the latest in cloud and AI news, from acquisitions and price hikes to new tools that Ryan somehow loves but also hates? We don't understand either… but let's get started!  Titles we almost went with this week Prompt Engineering Our Way Into Trouble The Demo Worked Yesterday, We Swear It Scales Horizontally, Trust Us Responsible AI But Terrible Copy (Marketing Edition) General News  00:58 Watch ‘The Thinking Game' documentary for free on YouTube Google DeepMind is releasing the “The Thinking Game” documentary for free on YouTube starting November 25, marking the fifth anniversary of AlphaFold.  The feature-length film provides behind-the-scenes access to the AI lab and documents the team’s work toward artificial general intelligence over five years. The documentary captures the moment when the AlphaFold team learned they had solved the 50-year protein folding problem in biology, a scientific achievement that recently earned Demis Hassabis and John Jumper the Nobel Prize in Chemistry.  This represents one of the most significant practical applications of deep learning to fundamental scientific research. The film was produced by the same award-winning team that created the AlphaGo documentary, which chronicled DeepMind’s earlier achievement in mastering the game of Go. For cloud and AI practitioners, this offers insight into how Google DeepMind approaches complex AI research problems and the development process behind their models. While this is primarily a documentary release rather than a technical product announcement, it provides context for understanding Google’s broader AI strategy and the research foundation underlying its cloud AI services. The AlphaFold model itself is available through Google Cloud for protein structure prediction workloads. 01:54 Justin – “If you're not into technology, don't care about any of that, and don't care about AI and how they built all the AI models that are now powering the world of LLMs we have, you will not like this documentary.”  04:22 ServiceNow to buy Armis in $7.7 billion security deal • The Register ServiceNow is acquiring Armis for $7.75 billion to integrate real-time security intelligence with its Configuration Management Database, allowing customers to identify vulnerabilities across IT, OT, and medical devices and remediate them through automated workflows. 

In this episode, Stewart Alsop sits down with Joe Wilkinson of Artisan Growth Strategies to talk through how vibe coding is changing who gets to build software, why functional programming and immutability may be better suited for AI-written code, and how tools like LLMs are reshaping learning, work, and curiosity itself. The conversation ranges from Joe's experience living in China and his perspective on Chinese AI labs like DeepSeek, Kimi, Minimax, and GLM, to mesh networks, Raspberry Pi–powered infrastructure, decentralization, and what sovereignty might mean in a world where intelligence is increasingly distributed. They also explore hallucinations, AlphaGo's Move 37, and why creative “wrongness” may be essential for real breakthroughs, along with the tension between centralized power and open access to advanced technology. You can find more about Joe's work at https://artisangrowthstrategies.com and follow him on X at https://x.com/artisangrowth.Check out this GPT we trained on the conversationTimestamps00:00 – Vibe coding as a new learning unlock, China experience, information overload, and AI-powered ingestion systems05:00 – Learning to code late, Exercism, syntax friction, AI as a real-time coding partner10:00 – Functional programming, Elixir, immutability, and why AI struggles with mutable state15:00 – Coding metaphors, “spooky action at a distance,” and making software AI-readable20:00 – Raspberry Pi, personal servers, mesh networks, and peer-to-peer infrastructure25:00 – Curiosity as activation energy, tech literacy gaps, and AI-enabled problem solving30:00 – Knowledge work superpowers, decentralization, and small groups reshaping systems35:00 – Open source vs open weights, Chinese AI labs, data ingestion, and competitive dynamics40:00 – Power, safety, and why broad access to AI beats centralized control45:00 – Hallucinations, AlphaGo's Move 37, creativity, and logical consistency in AI50:00 – Provenance, epistemology, ontologies, and risks of closed-loop science55:00 – Centralization vs decentralization, sovereign countries, and post-global-order shifts01:00:00 – U.S.–China dynamics, war skepticism, pragmatism, and cautious optimism about the futureKey InsightsVibe coding fundamentally lowers the barrier to entry for technical creation by shifting the focus from syntax mastery to intent, structure, and iteration. Instead of learning code the traditional way and hitting constant friction, AI lets people learn by doing, correcting mistakes in real time, and gradually building mental models of how systems work, which changes who gets to participate in software creation.Functional programming and immutability may be better aligned with AI-written code than object-oriented paradigms because they reduce hidden state and unintended side effects. By making data flows explicit and preventing “spooky action at a distance,” immutable systems are easier for both humans and AI to reason about, debug, and extend, especially as code becomes increasingly machine-authored.AI is compressing the entire learning stack, from software to physical reality, enabling people to move fluidly between abstract knowledge and hands-on problem solving. Whether fixing hardware, setting up servers, or understanding networks, the combination of curiosity and AI assistance turns complex systems into navigable terrain rather than expert-only domains.Decentralized infrastructure like mesh networks and personal servers becomes viable when cognitive overhead drops. What once required extreme dedication or specialist knowledge can now be done by small groups, meaning that relatively few motivated individuals can meaningfully change communication, resilience, and local autonomy without waiting for institutions to act.Chinese AI labs are likely underestimated because they operate with different constraints, incentives, and cultural inputs. Their openness to alternative training methods, massive data ingestion, and open-weight strategies creates competitive pressure that limits monopolistic control by Western labs and gives users real leverage through choice.Hallucinations and “mistakes” are not purely failures but potential sources of creative breakthroughs, similar to AlphaGo's Move 37. If AI systems are overly constrained to consensus truth or authority-approved outputs, they risk losing the capacity for novel insight, suggesting that future progress depends on balancing correctness with exploratory freedom.The next phase of decentralization may begin with sovereign countries before sovereign individuals, as AI enables smaller nations to reason from first principles in areas like medicine, regulation, and science. Rather than a collapse into chaos, this points toward a more pluralistic world where power, knowledge, and decision-making are distributed across many competing systems instead of centralized authorities.

GO oyunu neden “en saf strateji oyunu” olarak görülüyor? Netizen programında Atıf Ünaldı'nın konuğu İstanbul Go Kulübü kurucusu Mehmet Emin Barsbey, Go'nun 4 bin yıllık tarihini, satrançtan farklarını, Sun Tzu'nun savaş anlayışıyla ilişkisini ve yapay zekâ–AlphaGo kırılmasını anlatıyor. Ünaldı ve Barsbey, bu bölümde strateji, dikkat, algoritma ve sezgisel düşünme üzerine derinlikli bir sohbet gerçekleştiriyor. Learn more about your ad choices. Visit megaphone.fm/adchoices

AI experts from all around the world believe that given its current rate of progress, by 2027, we may hit the most dangerous milestone in human history. The point of no return, when AI could stop being a tool and start improving itself beyond our control. A moment when humanity may never catch up. 00:00 The AI Takeover Is Closer Than You Think01:05 The rise of AI in text, art & video02:00 What is the Technological Singularity?04:06 AI's impact on jobs & economy05:31 What happens when AI surpasses human intellect08:36 AlphaGo vs world champion Lee Sedol11:10 Can we really “turn off” AI?12:12 Narrow AI vs Artificial General Intelligence (AGI)16:39 AGI (Artificial General Intelligence)18:01 From AGI to Superintelligence20:18 Ethical concerns & defining intelligence22:36 Neuralink and human-AI integration25:54 Experts warning of 2027 AGI

Is today's AI stuck as a "spiky superintelligence," brilliant at some things but clueless at others? This episode pulls back the curtain on a lunchroom full of AI researchers trading theories, strong opinions, and the next big risks on the path to real AGI. Why "Everyone Dies" Gets AGI All Wrong The Nonprofit Feeding the Entire Internet to AI Companies Google's First AI Ad Avoids the Uncanny Valley by Casting a Turkey Coca-Cola Is Trying Another AI Holiday Ad. Executives Say This Time Is Different Sam Altman shuts down question about how OpenAI can commit to spending $1.4 trillion while earning billions: 'Enough' How OpenAI Uses Complex and Circular Deals to Fuel Its Multibillion-Dollar Rise Perplexity's new AI tool aims to simplify patent research Kids Turn Podcast Comments Into Secret Chat Rooms, Because Of Course They Do Amazon and Perplexity have kicked off the great AI web browser fight Neural network finds an enzyme that can break down polyurethane Dictionary.com names 6-7 as 2025's word of the year Tech companies don't care that students use their AI agents to cheat The Morning After: Musk talks flying Teslas on Joe Rogan's show The Hatred of Podcasting | Brace Belden TikTok announces its first awards show in the US Google wants to build solar-powered data centers — in space Anthropic Projects $70 Billion in Revenue, $17 Billion in Cash Flow in 2028 American Museum of Tort Law Dog Chapel - Dog Mountain Nicvember masterlist Pornhub says UK visitors down 77% since age checks came in Hosts: Leo Laporte, Jeff Jarvis, and Paris Martineau Guest: Jeremy Berman Download or subscribe to Intelligent Machines at https://twit.tv/shows/intelligent-machines. Join Club TWiT for Ad-Free Podcasts! Support what you love and get ad-free shows, a members-only Discord, and behind-the-scenes access. Join today: https://twit.tv/clubtwit Sponsors: threatlocker.com/twit agntcy.org spaceship.com/twit monarch.com with code IM

Is today's AI stuck as a "spiky superintelligence," brilliant at some things but clueless at others? This episode pulls back the curtain on a lunchroom full of AI researchers trading theories, strong opinions, and the next big risks on the path to real AGI. Why "Everyone Dies" Gets AGI All Wrong The Nonprofit Feeding the Entire Internet to AI Companies Google's First AI Ad Avoids the Uncanny Valley by Casting a Turkey Coca-Cola Is Trying Another AI Holiday Ad. Executives Say This Time Is Different Sam Altman shuts down question about how OpenAI can commit to spending $1.4 trillion while earning billions: 'Enough' How OpenAI Uses Complex and Circular Deals to Fuel Its Multibillion-Dollar Rise Perplexity's new AI tool aims to simplify patent research Kids Turn Podcast Comments Into Secret Chat Rooms, Because Of Course They Do Amazon and Perplexity have kicked off the great AI web browser fight Neural network finds an enzyme that can break down polyurethane Dictionary.com names 6-7 as 2025's word of the year Tech companies don't care that students use their AI agents to cheat The Morning After: Musk talks flying Teslas on Joe Rogan's show The Hatred of Podcasting | Brace Belden TikTok announces its first awards show in the US Google wants to build solar-powered data centers — in space Anthropic Projects $70 Billion in Revenue, $17 Billion in Cash Flow in 2028 American Museum of Tort Law Dog Chapel - Dog Mountain Nicvember masterlist Pornhub says UK visitors down 77% since age checks came in Hosts: Leo Laporte, Jeff Jarvis, and Paris Martineau Guest: Jeremy Berman Download or subscribe to Intelligent Machines at https://twit.tv/shows/intelligent-machines. Join Club TWiT for Ad-Free Podcasts! Support what you love and get ad-free shows, a members-only Discord, and behind-the-scenes access. Join today: https://twit.tv/clubtwit Sponsors: threatlocker.com/twit agntcy.org spaceship.com/twit monarch.com with code IM

Is today's AI stuck as a "spiky superintelligence," brilliant at some things but clueless at others? This episode pulls back the curtain on a lunchroom full of AI researchers trading theories, strong opinions, and the next big risks on the path to real AGI. Why "Everyone Dies" Gets AGI All Wrong The Nonprofit Feeding the Entire Internet to AI Companies Google's First AI Ad Avoids the Uncanny Valley by Casting a Turkey Coca-Cola Is Trying Another AI Holiday Ad. Executives Say This Time Is Different Sam Altman shuts down question about how OpenAI can commit to spending $1.4 trillion while earning billions: 'Enough' How OpenAI Uses Complex and Circular Deals to Fuel Its Multibillion-Dollar Rise Perplexity's new AI tool aims to simplify patent research Kids Turn Podcast Comments Into Secret Chat Rooms, Because Of Course They Do Amazon and Perplexity have kicked off the great AI web browser fight Neural network finds an enzyme that can break down polyurethane Dictionary.com names 6-7 as 2025's word of the year Tech companies don't care that students use their AI agents to cheat The Morning After: Musk talks flying Teslas on Joe Rogan's show The Hatred of Podcasting | Brace Belden TikTok announces its first awards show in the US Google wants to build solar-powered data centers — in space Anthropic Projects $70 Billion in Revenue, $17 Billion in Cash Flow in 2028 American Museum of Tort Law Dog Chapel - Dog Mountain Nicvember masterlist Pornhub says UK visitors down 77% since age checks came in Hosts: Leo Laporte, Jeff Jarvis, and Paris Martineau Guest: Jeremy Berman Download or subscribe to Intelligent Machines at https://twit.tv/shows/intelligent-machines. Join Club TWiT for Ad-Free Podcasts! Support what you love and get ad-free shows, a members-only Discord, and behind-the-scenes access. Join today: https://twit.tv/clubtwit Sponsors: threatlocker.com/twit agntcy.org spaceship.com/twit monarch.com with code IM

Is today's AI stuck as a "spiky superintelligence," brilliant at some things but clueless at others? This episode pulls back the curtain on a lunchroom full of AI researchers trading theories, strong opinions, and the next big risks on the path to real AGI. Why "Everyone Dies" Gets AGI All Wrong The Nonprofit Feeding the Entire Internet to AI Companies Google's First AI Ad Avoids the Uncanny Valley by Casting a Turkey Coca-Cola Is Trying Another AI Holiday Ad. Executives Say This Time Is Different Sam Altman shuts down question about how OpenAI can commit to spending $1.4 trillion while earning billions: 'Enough' How OpenAI Uses Complex and Circular Deals to Fuel Its Multibillion-Dollar Rise Perplexity's new AI tool aims to simplify patent research Kids Turn Podcast Comments Into Secret Chat Rooms, Because Of Course They Do Amazon and Perplexity have kicked off the great AI web browser fight Neural network finds an enzyme that can break down polyurethane Dictionary.com names 6-7 as 2025's word of the year Tech companies don't care that students use their AI agents to cheat The Morning After: Musk talks flying Teslas on Joe Rogan's show The Hatred of Podcasting | Brace Belden TikTok announces its first awards show in the US Google wants to build solar-powered data centers — in space Anthropic Projects $70 Billion in Revenue, $17 Billion in Cash Flow in 2028 American Museum of Tort Law Dog Chapel - Dog Mountain Nicvember masterlist Pornhub says UK visitors down 77% since age checks came in Hosts: Leo Laporte, Jeff Jarvis, and Paris Martineau Guest: Jeremy Berman Download or subscribe to Intelligent Machines at https://twit.tv/shows/intelligent-machines. Join Club TWiT for Ad-Free Podcasts! Support what you love and get ad-free shows, a members-only Discord, and behind-the-scenes access. Join today: https://twit.tv/clubtwit Sponsors: threatlocker.com/twit agntcy.org spaceship.com/twit monarch.com with code IM

Are we failing to understand the exponential, again?My guest is Julian Schrittwieser (top AI researcher at Anthropic; previously Google DeepMind on AlphaGo Zero & MuZero). We unpack his viral post (“Failing to Understand the Exponential, again”) and what it looks like when task length doubles every 3–4 months—pointing to AI agents that can work a full day autonomously by 2026 and expert-level breadth by 2027. We talk about the original Move 37 moment and whether today's AI models can spark alien insights in code, math, and science—including Julian's timeline for when AI could produce Nobel-level breakthroughs.We go deep on the recipe of the moment—pre-training + RL—why it took time to combine them, what “RL from scratch” gets right and wrong, and how implicit world models show up in LLM agents. Julian explains the current rewards frontier (human prefs, rubrics, RLVR, process rewards), what we know about compute & scaling for RL, and why most builders should start with tools + prompts before considering RL-as-a-service. We also cover evals & Goodhart's law (e.g., GDP-Val vs real usage), the latest in mechanistic interpretability (think “Golden Gate Claude”), and how safety & alignment actually surface in Anthropic's launch process.Finally, we zoom out: what 10× knowledge-work productivity could unlock across medicine, energy, and materials, how jobs adapt (complementarity over 1-for-1 replacement), and why the near term is likely a smooth ramp—fast, but not a discontinuity.Julian SchrittwieserBlog - https://www.julian.acX/Twitter - https://x.com/mononofuViral post: Failing to understand the exponential, again (9/27/2025)AnthropicWebsite - https://www.anthropic.comX/Twitter - https://x.com/anthropicaiMatt Turck (Managing Director)Blog - https://www.mattturck.comLinkedIn - https://www.linkedin.com/in/turck/X/Twitter - https://twitter.com/mattturckFIRSTMARKWebsite - https://firstmark.comX/Twitter - https://twitter.com/FirstMarkCap(00:00) Cold open — “We're not seeing any slowdown.”(00:32) Intro — who Julian is & what we cover(01:09) The “exponential” from inside frontier labs(04:46) 2026–2027: agents that work a full day; expert-level breadth(08:58) Benchmarks vs reality: long-horizon work, GDP-Val, user value(10:26) Move 37 — what actually happened and why it mattered(13:55) Novel science: AlphaCode/AlphaTensor → when does AI earn a Nobel?(16:25) Discontinuity vs smooth progress (and warning signs)(19:08) Does pre-training + RL get us there? (AGI debates aside)(20:55) Sutton's “RL from scratch”? Julian's take(23:03) Julian's path: Google → DeepMind → Anthropic(26:45) AlphaGo (learn + search) in plain English(30:16) AlphaGo Zero (no human data)(31:00) AlphaZero (one algorithm: Go, chess, shogi)(31:46) MuZero (planning with a learned world model)(33:23) Lessons for today's agents: search + learning at scale(34:57) Do LLMs already have implicit world models?(39:02) Why RL on LLMs took time (stability, feedback loops)(41:43) Compute & scaling for RL — what we see so far(42:35) Rewards frontier: human prefs, rubrics, RLVR, process rewards(44:36) RL training data & the “flywheel” (and why quality matters)(48:02) RL & Agents 101 — why RL unlocks robustness(50:51) Should builders use RL-as-a-service? Or just tools + prompts?(52:18) What's missing for dependable agents (capability vs engineering)(53:51) Evals & Goodhart — internal vs external benchmarks(57:35) Mechanistic interpretability & “Golden Gate Claude”(1:00:03) Safety & alignment at Anthropic — how it shows up in practice(1:03:48) Jobs: human–AI complementarity (comparative advantage)(1:06:33) Inequality, policy, and the case for 10× productivity → abundance(1:09:24) Closing thoughts

Albert Cheng has led growth at three of the world's most successful consumer subscription companies: Duolingo, Grammarly, and Chess.com. A former Google product manager (and serious pianist!), Albert developed a unique approach to finding and scaling growth opportunities through rapid experimentation and deep user psychology. His teams run 1,000 experiments a year, discovering counterintuitive insights that have driven tens of millions in revenue.What you'll learn:1. How to use the explore-exploit framework to find new growth opportunities2. How showing premium features to free users doubled Grammarly's upgrades to paid plans3. What good retention looks like for a consumer subscription app4. Why resurrected users drive 80% of mature product growth5. Why “reverse trials” work better than time-based trials6. The three pillars of successful gamification: core loop, metagame, and profile —Brought to you by:Vanta—Automate compliance. Simplify security.Jira Product Discovery—Confidence to build the right thingMiro—A collaborative visual platform where your best work comes to life—Where to find Albert Cheng:• X: https://x.com/albertc248• LinkedIn: https://www.linkedin.com/in/albertcheng1/• Chess.com: https://www.chess.com/member/Goniners—Where to find Lenny:• Newsletter: https://www.lennysnewsletter.com• X: https://twitter.com/lennysan• LinkedIn: https://www.linkedin.com/in/lennyrachitsky/—Referenced:• How Duolingo reignited user growth: https://www.lennysnewsletter.com/p/how-duolingo-reignited-user-growth• Inside ChatGPT: The fastest-growing product in history | Nick Turley (Head of ChatGPT at OpenAI): https://www.lennysnewsletter.com/p/inside-chatgpt-nick-turley• Explore vs. Exploit: https://brianbalfour.com/quick-takes/explore-vs-exploit• Grammarly: https://www.grammarly.com/• Reforge: https://www.reforge.com/• Chess.com: https://www.chess.com/• Everyone's an engineer now: Inside v0's mission to create a hundred million builders | Guillermo Rauch (founder & CEO of Vercel, creators of v0 and Next.js): https://www.lennysnewsletter.com/p/everyones-an-engineer-now-guillermo-rauch• Building Lovable: $10M ARR in 60 days with 15 people | Anton Osika (CEO and co-founder): https://www.lennysnewsletter.com/p/building-lovable-anton-osika• Figma: https://www.figma.com/• Cursor: https://cursor.com/• The rise of Cursor: The $300M ARR AI tool that engineers can't stop using | Michael Truell (co-founder and CEO): https://www.lennysnewsletter.com/p/the-rise-of-cursor-michael-truell• Claude Code: https://www.anthropic.com/claude-code• GitHub Copilot: https://github.com/features/copilot• Noam Lovinsky on LinkedIn: https://www.linkedin.com/in/noaml/• The happiness and pain of product management | Noam Lovinsky (Grammarly, Facebook, YouTube, Thumbtack): https://www.lennysnewsletter.com/p/the-happiness-and-pain-of-product• Kyla Siedband on LinkedIn: https://www.linkedin.com/in/kylasiedband/• The Duolingo handbook: https://blog.duolingo.com/handbook/• Lenny's post on X about the Duolingo handbook: https://x.com/lennysan/status/1889008405584683091• The rituals of great teams | Shishir Mehrotra of Coda, YouTube, Microsoft: https://www.lennysnewsletter.com/p/the-rituals-of-great-teams-shishir• Duolingo on TikTok: https://www.tiktok.com/@duolingo• Kasparov vs. Deep Blue | The Match That Changed History: https://www.chess.com/article/view/deep-blue-kasparov-chess• Magnus Carlsen: https://en.wikipedia.org/wiki/Magnus_Carlsen• Elo rating system: https://www.chess.com/terms/elo-rating-chess• Stockfish: https://en.wikipedia.org/wiki/Stockfish_(chess)• AlphaGo on Prime Video: https://www.primevideo.com/detail/AlphaGo/0KNQHKKDAOE8OCYKQS9WSSDYN0• Statsig: https://www.statsig.com/• The State of Product in 2026: Navigating Change, Challenge, and Opportunity: https://www.atlassian.com/blog/announcements/state-of-product-2026• Erik Allebest on LinkedIn: https://www.linkedin.com/in/erikallebest/• Daniel Rensch on X: https://x.com/danielrensch• Chariot: https://en.wikipedia.org/wiki/Chariot_(company)• San Francisco 49ers: https://www.49ers.com/• Breville Barista Express: https://www.breville.com/en-us/product/bes870—Recommended books:• Snuggle Puppy!: A Little Love Song: https://www.amazon.com/Snuggle-Puppy-Little-Boynton-Board/dp/1665924985• Ogilvy on Advertising: https://www.amazon.com/Ogilvy-Advertising-David/dp/039472903X• Dark Squares: How Chess Saved My Life: https://www.amazon.com/Dark-Squares-Chess-Saved-Life/dp/1541703286—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