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🚫 No dyes. No bleaching. 🔬 Just AI + label-free microscopy = vivid virtually stained images New in Nature Machine Intelligence: A deep learning model that enables robust virtual staining across microscopes, cell types & conditions. #CZBiohubSF Shalin Mehta explains:

Chan Zuckerberg Biohub Network

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46,419 görüntüleme • 1 yıl önce •via X (Twitter)

Sağlık & İyilik Bilim & Teknoloji Eğitim #CZBiohubSF

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Chan Zuckerberg Biohub Network profil fotoğrafı

Chan Zuckerberg Biohub Network1 yıl önce

@mattersOfLight Cytoland models were built, validated, and disseminated through a collaborative effort between the interdisciplinary teams at CZ Biohub SF and @ChanZuckerberg ‘s Virtual Cells Platform. Get started using the model on the CZ Virtual Cells Platform:

Chan Zuckerberg Biohub Network profil fotoğrafı

Chan Zuckerberg Biohub Network1 yıl önce

@mattersOfLight @ChanZuckerberg Read the paper, led by Ziwen Liu & @edyoshikun, here ⤵️

Chan Zuckerberg Biohub Network profil fotoğrafı

Chan Zuckerberg Biohub Network1 yıl önce

@mattersOfLight @ChanZuckerberg @edyoshikun Learn more about Cytoland, AI models for virtual staining ⤵️

•TSP Om profil fotoğrafı

•TSP Om1 yıl önce

@NatMachIntell @mattersOfLight Stellar breakthrough! Label-free virtual staining reveals biology’s true colors without chemicals. We’re testing similar AI imaging to track cannabis trichome health in real time—thrilled to see your model’s cross-platform power. ✨

Jan Choutka profil fotoğrafı

Jan Choutka1 yıl önce

@NatMachIntell @mattersOfLight This is really cool.

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🔬🤖Excited to share our new preprint on robust virtual staining of key organelles! ✨ Virtual staining and segmentation of nuclei and cell membranes in phase images can significantly accelerate image-based screens and developmental studies. 🔍 Current virtual staining models often struggle with nuisance variations in label-free input and don't generalize well to new cell types. We’re releasing our training protocols that lead to robust models and enable few-shot generalization. 🔧 Stay tuned for our revamped codebase with weights and example data, coming soon! 👏🧫🐟 Huge shoutout to Ziwen Liu, Eduardo Hirata-Miyasaki, my team Chan Zuckerberg Biohub Network (Soorya, Johanna, Christian, Talon Chandler, Ivan Ivanov), and the teams of Manuel Leonetti, Carolina Arias, and Adrian Jacobo for this fun collaboration! #VirtualStaining #DL #CellBiology #Zebrafish

🔬🤖Excited to share our new preprint on robust virtual staining of key organelles! ✨ Virtual staining and segmentation of nuclei and cell membranes in phase images can significantly accelerate image-based screens and developmental studies. 🔍 Current virtual staining models often struggle with nuisance variations in label-free input and don't generalize well to new cell types. We’re releasing our training protocols that lead to robust models and enable few-shot generalization. 🔧 Stay tuned for our revamped codebase with weights and example data, coming soon! 👏🧫🐟 Huge shoutout to Ziwen Liu, Eduardo Hirata-Miyasaki, my team Chan Zuckerberg Biohub Network (Soorya, Johanna, Christian, Talon Chandler, Ivan Ivanov), and the teams of Manuel Leonetti, Carolina Arias, and Adrian Jacobo for this fun collaboration! #VirtualStaining #DL #CellBiology #Zebrafish

Shalin Mehta

20,190 görüntüleme • 2 yıl önce

Microsoft CEO Satya Nadella's new interivew: Explains how the next AI moat will not be the model you use, but the learning loop only your company can run. He is really asking what happens to the firm when intelligence becomes something you can rent. For a century, companies protected value through people, processes, data, routines, customer memory, and the tacit knowledge buried in daily operations. Foundation models threaten to flatten that advantage because the same general intelligence can be used by everyone. Nadella’s answer is that firms need their own “hill climbing machine,” a private loop where models learn from company-specific tasks, traces, evaluations, and outcomes. That means the real asset is not just the model. The asset is the environment that keeps improving the model in ways competitors cannot copy. Private evals become strategic memory. Workflow traces become training signal. Human judgment becomes a way to steer compounding, not just correct mistakes. This also reframes AI adoption: a company that only consumes a foundation model may gain productivity, but it may leak the deeper value of its operating knowledge. A company that builds a disciplined learning loop can turn everyday work into accumulating IP. The future firm may therefore be measured by how well it converts its unique activity into durable model improvement. The frontier will not belong only to whoever owns the largest model. It will belong to whoever owns the best loop. ---- From "Stanford Online" YouTube channel, (link in comment)

Microsoft CEO Satya Nadella's new interivew: Explains how the next AI moat will not be the model you use, but the learning loop only your company can run. He is really asking what happens to the firm when intelligence becomes something you can rent. For a century, companies protected value through people, processes, data, routines, customer memory, and the tacit knowledge buried in daily operations. Foundation models threaten to flatten that advantage because the same general intelligence can be used by everyone. Nadella’s answer is that firms need their own “hill climbing machine,” a private loop where models learn from company-specific tasks, traces, evaluations, and outcomes. That means the real asset is not just the model. The asset is the environment that keeps improving the model in ways competitors cannot copy. Private evals become strategic memory. Workflow traces become training signal. Human judgment becomes a way to steer compounding, not just correct mistakes. This also reframes AI adoption: a company that only consumes a foundation model may gain productivity, but it may leak the deeper value of its operating knowledge. A company that builds a disciplined learning loop can turn everyday work into accumulating IP. The future firm may therefore be measured by how well it converts its unique activity into durable model improvement. The frontier will not belong only to whoever owns the largest model. It will belong to whoever owns the best loop. ---- From "Stanford Online" YouTube channel, (link in comment)

Rohan Paul

83,532 görüntüleme • 2 ay önce

The naturalistic Origin of Life is impossible. Abiogenesis requires so many miracles, it makes the resurrection of Jesus look common by comparison. You could put all the parts of a living cell together in a sterile solution, and after a bajillion years they still would not self-assemble into a living cell. Life requires two things: 1. Life requires functional information. That is, informational content which gives functional purpose to molecules. Without functional information, molecules in Life would have no purpose and therefore be unable to function. In all human experience, only intelligence creates functional information. 2. Life requires energy. In order to replicate and conduct metabolic processes, Life requires a constant supply of usable energy. Not just any energy will do - throwing your phone on the grill will not charge it, will it? It needs a specific type of energy, supplied in a specific way that your phone can use it. Life needs the same. In all human experience, only intelligence is able to build systems that can harness and process energy. But here is the paradox: These two things - information & energy - exist in fundamental opposition to each other. Virtually all forms of energy in the world destroy Life's simple molecules. Chemistry and physics do not naturally move in a life-friendly direction. They do the opposite. Life's very fragile molecules require very precise and specific conditions to exist. Conditions that can only be created and maintained by intelligent agents. Origin of Life research has proven this, as virtually every experiment claimed to show "progress" in OOL Research requires highly constrained, specific conditions and sophisticated chemical recipes that could never possibly exist in nature. Life must have been Divinely Designed. Only intelligence can invent functional information, and only intelligence can engineer the systems necessary to harness & produce energy for Life's systems. That's what The Science says.

The naturalistic Origin of Life is impossible. Abiogenesis requires so many miracles, it makes the resurrection of Jesus look common by comparison. You could put all the parts of a living cell together in a sterile solution, and after a bajillion years they still would not self-assemble into a living cell. Life requires two things: 1. Life requires functional information. That is, informational content which gives functional purpose to molecules. Without functional information, molecules in Life would have no purpose and therefore be unable to function. In all human experience, only intelligence creates functional information. 2. Life requires energy. In order to replicate and conduct metabolic processes, Life requires a constant supply of usable energy. Not just any energy will do - throwing your phone on the grill will not charge it, will it? It needs a specific type of energy, supplied in a specific way that your phone can use it. Life needs the same. In all human experience, only intelligence is able to build systems that can harness and process energy. But here is the paradox: These two things - information & energy - exist in fundamental opposition to each other. Virtually all forms of energy in the world destroy Life's simple molecules. Chemistry and physics do not naturally move in a life-friendly direction. They do the opposite. Life's very fragile molecules require very precise and specific conditions to exist. Conditions that can only be created and maintained by intelligent agents. Origin of Life research has proven this, as virtually every experiment claimed to show "progress" in OOL Research requires highly constrained, specific conditions and sophisticated chemical recipes that could never possibly exist in nature. Life must have been Divinely Designed. Only intelligence can invent functional information, and only intelligence can engineer the systems necessary to harness & produce energy for Life's systems. That's what The Science says.

Divinely Designed

26,409 görüntüleme • 6 ay önce

Demis Hassabis just made every AI benchmark on Earth irrelevant. Hassabis: “True creativity, continual learning, long-term planning. They’re not good at those things.” Three capabilities every human develops naturally. No AI system has achieved one. Hassabis: “They can get gold medals in international math olympiad questions, but they can still fall over on relatively simple math problems if you pose it in a certain way.” Gold medals and grade school failures from the same machine. That’s not intelligence with blind spots. That’s mimicry with peaks. Performance without comprehension. Hassabis: “My definition of AGI has never changed. A system that can exhibit all the cognitive capabilities that humans can.” Not most. All. He proposed the only test that can’t be gamed. Hassabis: “Training an AI system with a knowledge cutoff of 1911 and seeing if it could come up with general relativity like Einstein did in 1915. That’s the true test of whether we have a full AGI system.” Give a machine everything humanity knew before Einstein’s breakthrough. Seal it off. See if it reaches what one mind reached alone. No benchmarks. No leaderboards. No curated evals. Just a mind against the unknown. Nothing passes that today. Hassabis: “The brain is the only existence proof we have, maybe in the universe, of a general intelligence.” One structure in the entire known universe has proven general intelligence is physically possible. One. And every lab on Earth is racing to build the second. Hassabis: “I think we’re still a few years away from that.” Not decades. A machine that passes that test doesn’t stop at physics. It runs that same process against every unsolved problem in every discipline. No fatigue. No lifespan. No ceiling. Einstein was one mind. One field. One lifetime. This would be a thousand Einsteins across every domain simultaneously. And it never dies. The greatest intellectual achievement in human history becomes the floor. The thing that made one man singular becomes the entry exam for a machine. That’s not artificial intelligence. That’s a different kind of mind. It’s years away. Not generations.

Demis Hassabis just made every AI benchmark on Earth irrelevant. Hassabis: “True creativity, continual learning, long-term planning. They’re not good at those things.” Three capabilities every human develops naturally. No AI system has achieved one. Hassabis: “They can get gold medals in international math olympiad questions, but they can still fall over on relatively simple math problems if you pose it in a certain way.” Gold medals and grade school failures from the same machine. That’s not intelligence with blind spots. That’s mimicry with peaks. Performance without comprehension. Hassabis: “My definition of AGI has never changed. A system that can exhibit all the cognitive capabilities that humans can.” Not most. All. He proposed the only test that can’t be gamed. Hassabis: “Training an AI system with a knowledge cutoff of 1911 and seeing if it could come up with general relativity like Einstein did in 1915. That’s the true test of whether we have a full AGI system.” Give a machine everything humanity knew before Einstein’s breakthrough. Seal it off. See if it reaches what one mind reached alone. No benchmarks. No leaderboards. No curated evals. Just a mind against the unknown. Nothing passes that today. Hassabis: “The brain is the only existence proof we have, maybe in the universe, of a general intelligence.” One structure in the entire known universe has proven general intelligence is physically possible. One. And every lab on Earth is racing to build the second. Hassabis: “I think we’re still a few years away from that.” Not decades. A machine that passes that test doesn’t stop at physics. It runs that same process against every unsolved problem in every discipline. No fatigue. No lifespan. No ceiling. Einstein was one mind. One field. One lifetime. This would be a thousand Einsteins across every domain simultaneously. And it never dies. The greatest intellectual achievement in human history becomes the floor. The thing that made one man singular becomes the entry exam for a machine. That’s not artificial intelligence. That’s a different kind of mind. It’s years away. Not generations.

Dustin

20,703 görüntüleme • 1 ay önce

Sam Altman just exposed the greatest delusion in the tech sector. We didn’t invent artificial intelligence. We discovered it. Altman: “I think the discovery of deep learning is closer to discovering an element or a fundamental property of physics than it is a secret technology.” This distinction is everything. If intelligence were just human invention, it would be bound by human limits. Fragile code. Endless debugging. But deep learning as a fundamental property of physics means intelligence is an emergent thermodynamic result. Organize silicon in the right pattern, pump enough energy through it, and cognition mathematically materializes. We’re not writing software anymore. We’re mining the raw physics of intelligence. Altman: “There was such a measurable, beautiful correlation between the resources that go into a model and the intelligence of that model that there was just something fundamental going on here as a scientific principle.” When OpenAI published the scaling laws, they proved something brutal about the nature of the universe itself. Synthetic intelligence is directly proportional to compute. Clean, predictable extraction. Tech monopolies are spending hundreds of billions on physical infrastructure right now because they aren’t guessing. The scaling laws proved you don’t need a million genius engineers to hand-code an artificial brain. Just feed the mathematical principle with more energy. The outcome is already hardcoded into the system. Altman: “Like other scientific frontiers, it is simplifying and becoming more clear over time. And eventually, this recipe will be well understood as a scientific principle.” Right now, training a frontier model looks like highly classified black magic restricted to a handful of apex organizations. But physical laws always trend toward simplification. We’re watching the transition from alchemy to chemistry in real time. Once the core scientific principle of intelligence is fully understood, it no longer requires a trillion-dollar monopoly to execute. The foundational recipe for creating a cognitive agent becomes as widely understood and standardized as the laws of thermodynamics. And when that happens, the barrier to creating intelligence doesn’t just drop. It ceases to exist.

Sam Altman just exposed the greatest delusion in the tech sector. We didn’t invent artificial intelligence. We discovered it. Altman: “I think the discovery of deep learning is closer to discovering an element or a fundamental property of physics than it is a secret technology.” This distinction is everything. If intelligence were just human invention, it would be bound by human limits. Fragile code. Endless debugging. But deep learning as a fundamental property of physics means intelligence is an emergent thermodynamic result. Organize silicon in the right pattern, pump enough energy through it, and cognition mathematically materializes. We’re not writing software anymore. We’re mining the raw physics of intelligence. Altman: “There was such a measurable, beautiful correlation between the resources that go into a model and the intelligence of that model that there was just something fundamental going on here as a scientific principle.” When OpenAI published the scaling laws, they proved something brutal about the nature of the universe itself. Synthetic intelligence is directly proportional to compute. Clean, predictable extraction. Tech monopolies are spending hundreds of billions on physical infrastructure right now because they aren’t guessing. The scaling laws proved you don’t need a million genius engineers to hand-code an artificial brain. Just feed the mathematical principle with more energy. The outcome is already hardcoded into the system. Altman: “Like other scientific frontiers, it is simplifying and becoming more clear over time. And eventually, this recipe will be well understood as a scientific principle.” Right now, training a frontier model looks like highly classified black magic restricted to a handful of apex organizations. But physical laws always trend toward simplification. We’re watching the transition from alchemy to chemistry in real time. Once the core scientific principle of intelligence is fully understood, it no longer requires a trillion-dollar monopoly to execute. The foundational recipe for creating a cognitive agent becomes as widely understood and standardized as the laws of thermodynamics. And when that happens, the barrier to creating intelligence doesn’t just drop. It ceases to exist.

Dustin

38,317 görüntüleme • 5 ay önce

Sam Altman on what you need to do to survive in the age of artificial intelligence. "You are about to enter the greatest golden age of human possibility..." To thrive in that world, the skills that matter most are: - Deep familiarity with the tools - Staying abreast of changes - Developing a great intuition for AI tools, where things are going, and how to make use of it - Resilience and the ability to learn things fast and evolve yourself with technology I know most of this stuff is a pretty big no-brainer for anyone paying attention, but here's the takeaway: AI upskilling and keeping up with AI are possibly the most important skills in the world right now. And the most fascinating part is that new AI developments and tools come out so fast that everyone is constantly learning. I'd be willing to bet that if you're a complete beginner but spend 3 months of dedicated learning, you'd be fully caught up to power users. In other words: Spend 3 months learning → work 2-4x faster and future-proof yourself. There's so much opportunity for anyone who just starts now.

Sam Altman on what you need to do to survive in the age of artificial intelligence. "You are about to enter the greatest golden age of human possibility..." To thrive in that world, the skills that matter most are: - Deep familiarity with the tools - Staying abreast of changes - Developing a great intuition for AI tools, where things are going, and how to make use of it - Resilience and the ability to learn things fast and evolve yourself with technology I know most of this stuff is a pretty big no-brainer for anyone paying attention, but here's the takeaway: AI upskilling and keeping up with AI are possibly the most important skills in the world right now. And the most fascinating part is that new AI developments and tools come out so fast that everyone is constantly learning. I'd be willing to bet that if you're a complete beginner but spend 3 months of dedicated learning, you'd be fully caught up to power users. In other words: Spend 3 months learning → work 2-4x faster and future-proof yourself. There's so much opportunity for anyone who just starts now.

Rowan Cheung

854,348 görüntüleme • 2 yıl önce

Don't waste 2 years learning to use LLMs like Claude & ChatGPT. Andrej Karpathy one of the clearest minds in AI just released a 2‑hour breakdown of how he actually uses LLMs every day. • 00:00 — LLMs, explained like you’re busy • 22:49 — picking the right model (for real) • 42:00 — the single prompt that powers deep research • 1:13:57 — writing code with LLMs without the chaos • 1:37:04 — turning NotebookLM into a podcast machine If you watch this end to end, you’ll walk away with more practical LLM skill than many “AI engineers” collect over years. Save it. Carve out two hours today—no excuses. Then hit the article below.

Don't waste 2 years learning to use LLMs like Claude & ChatGPT. Andrej Karpathy one of the clearest minds in AI just released a 2‑hour breakdown of how he actually uses LLMs every day. • 00:00 — LLMs, explained like you’re busy • 22:49 — picking the right model (for real) • 42:00 — the single prompt that powers deep research • 1:13:57 — writing code with LLMs without the chaos • 1:37:04 — turning NotebookLM into a podcast machine If you watch this end to end, you’ll walk away with more practical LLM skill than many “AI engineers” collect over years. Save it. Carve out two hours today—no excuses. Then hit the article below.

CyrilXBT

51,835 görüntüleme • 24 gün önce

google just released 15 AI tools that are completely FREE and can save thousands of $$$ every single monthly. all open-source. MIT licensed. save this in your bookmark." 1️⃣ pomelli ( builds your entire brand identity from just your website URL, then generates on-brand social posts, campaigns, and images. a free jasper + a junior brand marketer. no watermark, no gen cap in beta. 2️⃣ stitch ( describe an interface, get production-ready HTML/CSS/Tailwind + a figma export. google's free figma killer. 350 designs a month without paying a cent. 3️⃣ opal ( build no-code AI mini-apps and multi-step workflows just by describing them in plain english. basically a free n8n with Gemini baked in. no usage caps. 4️⃣ antigravity ( agentic IDE that plans, edits across files, and builds full apps from a single prompt. the "cursor-killer," free tier runs Gemini 3 Pro + Claude Sonnet 4.5. 5️⃣ mixboard ( canva x pinterest for AI. generate and remix images into moodboards, then edit right on the canvas with plain language. free while in beta. 6️⃣ disco ( turns your messy open browser tabs into custom interactive AI apps. competitor tabs become a comparison matrix, travel tabs become an itinerary. zero code. 7️⃣ notebookLM ( upload PDFs, videos, and notes, get instant summaries, mind maps, quizzes, even a podcast of your own material. replaces notion AI + perplexity + readwise. 8️⃣ Learn Your Way ( turns any topic into a personalized, AI-built course. immersive text, audio lessons, mind maps, and quizzes adapted to how you actually learn. free tutoring. 🔟 Google AI Studio ( prototype and ship AI apps in seconds with a free API key and a 1M-token context window. replaces the openai playground + paid API credits. 1️⃣1️⃣ Jules ( assign it a github issue, it spins up a VM, writes a plan, makes the changes, and opens a PR. a free devin. 15 tasks a day. 1️⃣2️⃣ Gemini CLI ( claude-code in your terminal. reads your codebase, runs commands, ships PRs. genuinely open source (Apache 2.0) and free. 1️⃣3️⃣ Code Wiki ( point it at any public github repo, get a living, self-updating wiki with architecture diagrams and a Gemini chat, every section hyperlinked to the code. 1️⃣4️⃣ Firebase Studio ( AI cockpit for your backend and cloud logic. heads up: existing users only, google is winding it down, so don't start a new project here. 1️⃣5️⃣ Gemini Code Assist ( free github copilot: 180k code completions a month + AI code reviews in VS Code, JetBrains, and github. the free tier that actually out-specs copilot. Follow me and turn on 🔔 post notifications.

m0h

87,330 görüntüleme • 1 ay önce

Flova AI 1.0 is officially here！ AI video creation is entering the Skill Era. MASSIVE PERKS DROPPING! 🚀 Follow FlovaAI, RT & comment within the first 12 hours of this post to claim 500 Credits—GUARANTEED for everyone! 💥 Plus, the first 100 users score a FREE 1-month Flova Membership! 🎉🎉 Flova 1.0 — Your Personal AI Video Agent Partner Until now, creating with AI meant learning “magic spells” and juggling a dozen tools. Flova 1.0 ends that cycle. 1️⃣Skill-ify Your Taste 🧠 Don't just generate; build. Package your prompts, model choices, and workflows into a "Skill." Your creative DNA becomes a reusable system—a content machine that actually understands you. 2️⃣Natural Language Only 💬 No prompt engineering required. Describe your vision in plain English. Flova’s Agent automatically selects the best models and executes the logic end-to-end. 3️⃣The Unified Workspace 🎬 Script, images, video, voice, and music on one timeline. All the world’s leading AI models, zero tool-switching. 4️⃣Precision Autopilot 🤝 Context-aware collaboration. You provide the creative spark; Flova handles the complex execution. Your taste is now a system. Your system is now a business. Turn your workflow and taste into skills. AI that works your way. Experience Flova AI 1.0: 🔗 #FlovaAI #AIVideo #AIAgent #GenerativeAI #Flova1

Flova AI 1.0 is officially here！ AI video creation is entering the Skill Era. MASSIVE PERKS DROPPING! 🚀 Follow FlovaAI, RT & comment within the first 12 hours of this post to claim 500 Credits—GUARANTEED for everyone! 💥 Plus, the first 100 users score a FREE 1-month Flova Membership! 🎉🎉 Flova 1.0 — Your Personal AI Video Agent Partner Until now, creating with AI meant learning “magic spells” and juggling a dozen tools. Flova 1.0 ends that cycle. 1️⃣Skill-ify Your Taste 🧠 Don't just generate; build. Package your prompts, model choices, and workflows into a "Skill." Your creative DNA becomes a reusable system—a content machine that actually understands you. 2️⃣Natural Language Only 💬 No prompt engineering required. Describe your vision in plain English. Flova’s Agent automatically selects the best models and executes the logic end-to-end. 3️⃣The Unified Workspace 🎬 Script, images, video, voice, and music on one timeline. All the world’s leading AI models, zero tool-switching. 4️⃣Precision Autopilot 🤝 Context-aware collaboration. You provide the creative spark; Flova handles the complex execution. Your taste is now a system. Your system is now a business. Turn your workflow and taste into skills. AI that works your way. Experience Flova AI 1.0: 🔗 #FlovaAI #AIVideo #AIAgent #GenerativeAI #Flova1

FlovaAI

46,302 görüntüleme • 3 ay önce

$Introducing LifeGPT, showing that LLMs can simulate complex, Turing-complete systems like Conway's Game of Life with near-perfect accuracy—no prior topology needed.🌐This unlocks new potential for AI in modeling self-organizing systems in biology, materials science, & beyond.🔬🤖 #AI #LifeGPT. Cellular Automata (CA), like Conway's Game of Life ("Life"), are computationally irreducible, meaning their evolution is difficult to predict without an a-priori understanding of the rules of the game, including the topology on which it is played. LifeGPT is a topology-agnostic generative model that learns the rules of Life without prior knowledge of its grid structure or boundary conditions, from only a tiny number of game states. The success in simulating Life suggests promising avenues for scientific discovery, particularly in bridging the gap between AI, artificial life, and real-world biological systems, for both forward and inverse problems. The potential for universal computation within generative AI, including LLMs, through approaches like LifeGPT, represents an exciting area for future research, especially when combined with reinforcement learning. Model Convergence: LifeGPT exhibits rapid convergence during training, achieving high accuracy in predicting next-game-states. We attribute the non-zero cross-entropy loss to the lack of causal relationships within randomly generated ICs. Accuracy & Temperature: LifeGPT achieves near-perfect accuracy, particularly at lower sampling temperatures, but can be continually tuned towards higher creativity to discover patterns that the original ruleset would not be able to produce. This finding highlights the trade-off between model creativity (higher temperature) and accuracy in deterministic predictions, with high relevance to model real-world dynamical systems for which no closed-form rulesets exist. Zero/Few-Shot Learning: Trained on a small fraction of possible initial conditions, LifeGPT demonstrates strong zero/few-shot learning, accurately simulating Life for unseen initial conditions. However, rare prediction errors highlight that LifeGPT approximates rather than perfectly replicates the Life algorithm. Autoregressive Autoregressor: A recursive implementation of LifeGPT demonstrates the model's ability to simulate Life over multiple timesteps. LifeGPT is topology-agnostic with respect to its training data and our results show that a GPT model is capable of capturing the deterministic rules of a Turing-complete system with near-perfect accuracy, given sufficiently diverse training data. The work showcases the possibility for future models to synthesize stochastic generative capabilities with deterministic computational capabilities. Link to code, paper, etc. below. Podcast generated using #NotebookLM. LAMM@MIT DMSE at MIT$

Introducing LifeGPT, showing that LLMs can simulate complex, Turing-complete systems like Conway's Game of Life with near-perfect accuracy—no prior topology needed.🌐This unlocks new potential for AI in modeling self-organizing systems in biology, materials science, & beyond.🔬🤖 #AI #LifeGPT. Cellular Automata (CA), like Conway's Game of Life ("Life"), are computationally irreducible, meaning their evolution is difficult to predict without an a-priori understanding of the rules of the game, including the topology on which it is played. LifeGPT is a topology-agnostic generative model that learns the rules of Life without prior knowledge of its grid structure or boundary conditions, from only a tiny number of game states. The success in simulating Life suggests promising avenues for scientific discovery, particularly in bridging the gap between AI, artificial life, and real-world biological systems, for both forward and inverse problems. The potential for universal computation within generative AI, including LLMs, through approaches like LifeGPT, represents an exciting area for future research, especially when combined with reinforcement learning. Model Convergence: LifeGPT exhibits rapid convergence during training, achieving high accuracy in predicting next-game-states. We attribute the non-zero cross-entropy loss to the lack of causal relationships within randomly generated ICs. Accuracy & Temperature: LifeGPT achieves near-perfect accuracy, particularly at lower sampling temperatures, but can be continually tuned towards higher creativity to discover patterns that the original ruleset would not be able to produce. This finding highlights the trade-off between model creativity (higher temperature) and accuracy in deterministic predictions, with high relevance to model real-world dynamical systems for which no closed-form rulesets exist. Zero/Few-Shot Learning: Trained on a small fraction of possible initial conditions, LifeGPT demonstrates strong zero/few-shot learning, accurately simulating Life for unseen initial conditions. However, rare prediction errors highlight that LifeGPT approximates rather than perfectly replicates the Life algorithm. Autoregressive Autoregressor: A recursive implementation of LifeGPT demonstrates the model's ability to simulate Life over multiple timesteps. LifeGPT is topology-agnostic with respect to its training data and our results show that a GPT model is capable of capturing the deterministic rules of a Turing-complete system with near-perfect accuracy, given sufficiently diverse training data. The work showcases the possibility for future models to synthesize stochastic generative capabilities with deterministic computational capabilities. Link to code, paper, etc. below. Podcast generated using #NotebookLM. LAMM@MIT DMSE at MIT

Markus J. Buehler

114,237 görüntüleme • 1 yıl önce

I just built an AI-powered creative search engine with Gemini Embedding 2 + Claude Code 🤯 Drop in your UGC clips, product shots, and ad variations — then search through everything in plain English. "Show me all the unboxing clips." "Find product shots with natural lighting." "Which creator talked about sensitive skin?" All inside Claude Code. Perfect for DTC brands and agencies sitting on hundreds of creative files they can never find when they actually need them. If you're digging through a folder of random file names, scrubbing through raw footage to find that one clip, and relying on memory to track down what's already been shot... This system eliminates the entire loop: → Drop your videos, images, and docs into a project folder → One prompt to Claude Code — it builds the entire search app for you → Google's new Gemini Embedding 2 model actually watches your videos and looks at your images → It understands what's inside each file — not just the file name → Search in plain English and get back the actual assets with confidence scores No scrolling through folders. No relying on file names to find anything. No re-shooting footage you already have. What you get: → A searchable library of every creative asset your brand has ever produced → Natural language search across video, images, and documents at the same time → Results that show the actual files inline — play videos, view images, read docs → A system that gets smarter every time you add richer descriptions to your assets One free API key. No monthly subscriptions. Runs on your machine. I put together a full playbook with the exact build prompt, the setup process, and DTC/agency use cases to get this running in under 30 minutes. Want the full playbook? > Like this post > Comment "SEARCH" And I'll send it over (must be following so I can DM)

Mike Futia

12,279 görüntüleme • 5 ay önce

Nvidia has just announced Alpamayo 2 Super, an open 34 billion parameter reasoning vision-language-action model designed to accelerate the development of autonomous vehicles. This new model combines the NVIDIA Cosmos 3 Super reasoning model with a 2 billion parameter diffusion-based action expert model, and is post trained with reinforcement learning. The model can return multiple outputs: future trajectory plans, reasoning traces, grounded answers to questions about the scenes, and auto label generation. The model weights are now available for anyone to download on Hugging Face, and the inference code has been posted to GitHub. Distilled models can be deployed commercially without any further permission from Nvidia, and model outputs carry no license conditions. Automakers can distill down a compact version of this model that can run on the Nvidia computer in the car. Major kudos to Nvidia and Jensen Huang for advancing the state of the industry by releasing this as an open model with permissive licensing. Jensen isn't just paying lip service to the idea of open models, Nvidia is actually contributing to the ecosystem — and it's great for their business, because it helps sell more Thor computers that go in the car. Anyone can go download the model and play with it. If you do, let me know what you think. Personally I think it's so cool that we have open weights models that are this advanced, for anyone to download.

Nvidia has just announced Alpamayo 2 Super, an open 34 billion parameter reasoning vision-language-action model designed to accelerate the development of autonomous vehicles. This new model combines the NVIDIA Cosmos 3 Super reasoning model with a 2 billion parameter diffusion-based action expert model, and is post trained with reinforcement learning. The model can return multiple outputs: future trajectory plans, reasoning traces, grounded answers to questions about the scenes, and auto label generation. The model weights are now available for anyone to download on Hugging Face, and the inference code has been posted to GitHub. Distilled models can be deployed commercially without any further permission from Nvidia, and model outputs carry no license conditions. Automakers can distill down a compact version of this model that can run on the Nvidia computer in the car. Major kudos to Nvidia and Jensen Huang for advancing the state of the industry by releasing this as an open model with permissive licensing. Jensen isn't just paying lip service to the idea of open models, Nvidia is actually contributing to the ecosystem — and it's great for their business, because it helps sell more Thor computers that go in the car. Anyone can go download the model and play with it. If you do, let me know what you think. Personally I think it's so cool that we have open weights models that are this advanced, for anyone to download.

Whole Mars Catalog

45,595 görüntüleme • 27 gün önce

Thomas Peterffy never read a single investment book - just borrowed $100 from his father and built the world's best $110 billion brokerage firm he finally opened up about the one principle behind every trade he's ever made "every two cents the stock goes down, I buy a thousand shares - then sell as it climbs - I prefer trading profits to position profits" he landed in New York at 21 with no English - "learning to code was easier than learning the language" - so he let computers do what he couldn't, and built the first machine that traded on its own Goldman Sachs came knocking with a $900 million offer - he turned it down -then said "$3 billion" just to end the conversation - today his firm is worth over $110 billion on the AI bubble: "we're not there yet - but we will be AI works where behavior can be predicted - molecules, atoms - general intelligence is still far away" bookmark & watch ↓

Thomas Peterffy never read a single investment book - just borrowed $100 from his father and built the world's best $110 billion brokerage firm he finally opened up about the one principle behind every trade he's ever made "every two cents the stock goes down, I buy a thousand shares - then sell as it climbs - I prefer trading profits to position profits" he landed in New York at 21 with no English - "learning to code was easier than learning the language" - so he let computers do what he couldn't, and built the first machine that traded on its own Goldman Sachs came knocking with a $900 million offer - he turned it down -then said "$3 billion" just to end the conversation - today his firm is worth over $110 billion on the AI bubble: "we're not there yet - but we will be AI works where behavior can be predicted - molecules, atoms - general intelligence is still far away" bookmark & watch ↓

bodila

89,884 görüntüleme • 1 ay önce

Yann LeCun just told the most well-funded industry in human history it is solving the wrong problem. LeCun: “Babies learn this around the age of eight or nine months, that objects don’t float, they fall.” No dataset. No labels. No reward signal. A nine month old drops a spoon and builds a physics engine no machine can match. LeCun: “Most of us can learn to drive in about 20 or 30 hours of training without ever crashing, causing any accident.” Twenty hours. Tesla has built the most capable driving system on the road. It took billions of miles of data to get there. A sixteen year old gets there over a long weekend. Not because the teenager is the better driver. Because the teenager is not learning to drive. They are deploying a model of reality they have been building since birth. LeCun: “If we drive next to a cliff, we know that if we turn the wheel to the right, the car is going to run off the cliff and nothing good is going to come out of this.” You simulate the crash. You see the wreckage. You feel the fall. You turn the wheel. None of it was real. All of it was intelligence. Every AI has to crash a thousand times to learn what you imagined once and never did. That is not a performance gap. That is an architecture gap. LeCun: “The main problem we need to solve is how do we learn models of the world.” Not bigger models. Not more compute. Not another trillion tokens. World models. A machine that can run reality forward before it acts. The industry is scaling language. LeCun says language is a compression of thought. Not thought itself. You understood gravity before you could say the word. You grasped cause and effect before your first sentence. The deepest intelligence you will ever possess was built in total silence. And every lab on Earth is trying to reconstruct the mind from words alone. Physics does not care about your context window. A baby who learns that cups fall in a kitchen already knows that rocks fall off cliffs. No retraining. No fine-tuning. One model. Every environment. That is what intelligence actually is. Not prediction. Not pattern matching. Not scale. A simulation of reality so precise you rehearse the future before it exists. Every infant on Earth builds one. No machine ever has.

Yann LeCun just told the most well-funded industry in human history it is solving the wrong problem. LeCun: “Babies learn this around the age of eight or nine months, that objects don’t float, they fall.” No dataset. No labels. No reward signal. A nine month old drops a spoon and builds a physics engine no machine can match. LeCun: “Most of us can learn to drive in about 20 or 30 hours of training without ever crashing, causing any accident.” Twenty hours. Tesla has built the most capable driving system on the road. It took billions of miles of data to get there. A sixteen year old gets there over a long weekend. Not because the teenager is the better driver. Because the teenager is not learning to drive. They are deploying a model of reality they have been building since birth. LeCun: “If we drive next to a cliff, we know that if we turn the wheel to the right, the car is going to run off the cliff and nothing good is going to come out of this.” You simulate the crash. You see the wreckage. You feel the fall. You turn the wheel. None of it was real. All of it was intelligence. Every AI has to crash a thousand times to learn what you imagined once and never did. That is not a performance gap. That is an architecture gap. LeCun: “The main problem we need to solve is how do we learn models of the world.” Not bigger models. Not more compute. Not another trillion tokens. World models. A machine that can run reality forward before it acts. The industry is scaling language. LeCun says language is a compression of thought. Not thought itself. You understood gravity before you could say the word. You grasped cause and effect before your first sentence. The deepest intelligence you will ever possess was built in total silence. And every lab on Earth is trying to reconstruct the mind from words alone. Physics does not care about your context window. A baby who learns that cups fall in a kitchen already knows that rocks fall off cliffs. No retraining. No fine-tuning. One model. Every environment. That is what intelligence actually is. Not prediction. Not pattern matching. Not scale. A simulation of reality so precise you rehearse the future before it exists. Every infant on Earth builds one. No machine ever has.

Dustin

122,066 görüntüleme • 1 ay önce

I just vibe coded a static ad generator in Claude Code that creates 100+ Meta ads in minutes. All using the new, insane ChatGPT Images 2.0 model. One competitor ad + your product photo + your brand kit = dozens of on-brand variations, each targeting a different customer persona. Built 100% in Claude Code on the new ChatGPT Images 2.0. Perfect for DTC brands and agencies who need more statics at scale. Here's how it works: → Upload any competitor ad as your reference template → Add your product photos and brand kit (colors, fonts, logos) → AI generates 10 customer profiles from your brand research → Pick how many variations you want (10, 20, 30) → Tool fires every prompt to ChatGPT Images 2.0 with persona-specific copy for each one No designer back-and-forth. No Canva templates. No generic "Shop Now" on everything. What you get: → Ads that mirror winning concepts in your brand's voice → Text that actually renders correctly (the new model handles dense copy, logos, and multi-language callouts cleanly) → Copy targeted to specific customer pain points and personas → Multi-brand/client support with saved brand kits → Reusable customer profiles you build once and generate from forever I recorded a full walkthrough showing exactly how this works, including ALL the prompts I used so you can build it yourself. Want access to all the prompts for free? > Like this post > Comment "STATICS" And I'll send it over (must be following so I can DM)

Mike Futia

52,609 görüntüleme • 4 ay önce

you tend to hear this a lot from people outside or new to ML, and I often point to a talk Ilya gave a few years back: 1) think of any decent deep neural net that has enough memory and sequential ops as just a big parallel computer 2) training this neural net is doing search over computer programs that maximize your objective 3)unless you have some large bottleneck (and given you can successfully optimize this system) you’ll find that these parallel computers are highly robust to architectural changes. 4) this is because computers are great at simulating each other. your new architecture can usually be straightforwardly simulated ‘inside’ your old architecture. 5) it’s not that architecture doesn’t matter, but it mostly matters with respect to (1) fundamental bottlenecks in this parallel computer (2) modifications that make models easier to optimize, since this argument only holds if your optimization is good (3) compute efficiency/system efficiency wins that make learning easier or faster. 6) it’s quite possible that new architectures will lead to breakthroughs in machine learning, but we should first start with bottlenecks, not naturalist intuitions about the ‘form’ of AI should take. until you understand this it seems surprising that small models trained longer are better than undertrained big models, that depth and width are surprisingly interchangeable, that talking to a model with an MoE or sparse attention or linear attention is approximately the same iso evals.

you tend to hear this a lot from people outside or new to ML, and I often point to a talk Ilya gave a few years back: 1) think of any decent deep neural net that has enough memory and sequential ops as just a big parallel computer 2) training this neural net is doing search over computer programs that maximize your objective 3)unless you have some large bottleneck (and given you can successfully optimize this system) you’ll find that these parallel computers are highly robust to architectural changes. 4) this is because computers are great at simulating each other. your new architecture can usually be straightforwardly simulated ‘inside’ your old architecture. 5) it’s not that architecture doesn’t matter, but it mostly matters with respect to (1) fundamental bottlenecks in this parallel computer (2) modifications that make models easier to optimize, since this argument only holds if your optimization is good (3) compute efficiency/system efficiency wins that make learning easier or faster. 6) it’s quite possible that new architectures will lead to breakthroughs in machine learning, but we should first start with bottlenecks, not naturalist intuitions about the ‘form’ of AI should take. until you understand this it seems surprising that small models trained longer are better than undertrained big models, that depth and width are surprisingly interchangeable, that talking to a model with an MoE or sparse attention or linear attention is approximately the same iso evals.

will depue

215,174 görüntüleme • 8 ay önce

What a time to be alive! We are entering the era of machines that discover and build. Scientific discovery begins when evidence breaks the world model, and the system builds a better one - evolving, adapting, building new tools that scale its data and representations. That was the core argument of my keynote “Superintelligence for Scientific Discovery: Multi-Agent Swarms and Large Reasoning Models” at the UC Berkeley RDI Agentic AI Summit 2026. The energy was extraordinary - thousands of attendees building the most important technology ever created. Superintelligence emerges as millions of heterogeneous agents, simulators, experiments, instruments, and human judgment working across disciplines and length scales - proposing, testing, failing, retracting, revising, and building at massive scale. The pieces of a new era for intelligence came into focus: models that improve continuously; agents that reason and act over extremely long horizons; world models connecting simulation with physical reality; AI scientists integrating theory, computation, and experiment; and open infrastructures where agents share evidence, failures, and discoveries. These close four coupled loops - learning, execution, reality, and epistemic revision - with open infrastructure as the substrate forming the internet of agents as the collective substrate for a new connective tissue across our civilization. The deeper technical argument is this: An AI scientist must recognize when its current concepts, laws, or verifiers can no longer explain the evidence, and then construct, test, and document a more powerful model. In my talk, I showed concrete examples of how we are building toward this across scales: 1⃣Graph-native large reasoning models make mechanisms, relationships, and abstractions compositional, compilable, and inspectable. 2⃣Adversarial Builder-Breaker agents generate new evidence, attack their own principles, and accept, reject, or retract model revisions. 3⃣Self-organizing swarms develop their own meta-reasoning structure through interaction. ScienceClaw × Infinite (arXiv:2603.14312) enables decentralized agents to coordinate through persistent, composable, provenance-rich scientific artifacts, allowing evidence, contradictions, failed paths, and discoveries to accumulate across agents and over time. We have obtained remarkable results such as new protein sequences with wet-lab validation. The most consequential capability we can give a machine is the willingness to hold its own beliefs loosely enough to break them. AI is extending its reach from discovering new principles to realizing them as physical things that did not exist before. Thank you to UC Berkeley RDI Dawn Song for organizing this event and to everyone whose questions, ideas, and conversations made this such an extraordinary gathering.

What a time to be alive! We are entering the era of machines that discover and build. Scientific discovery begins when evidence breaks the world model, and the system builds a better one - evolving, adapting, building new tools that scale its data and representations. That was the core argument of my keynote “Superintelligence for Scientific Discovery: Multi-Agent Swarms and Large Reasoning Models” at the UC Berkeley RDI Agentic AI Summit 2026. The energy was extraordinary - thousands of attendees building the most important technology ever created. Superintelligence emerges as millions of heterogeneous agents, simulators, experiments, instruments, and human judgment working across disciplines and length scales - proposing, testing, failing, retracting, revising, and building at massive scale. The pieces of a new era for intelligence came into focus: models that improve continuously; agents that reason and act over extremely long horizons; world models connecting simulation with physical reality; AI scientists integrating theory, computation, and experiment; and open infrastructures where agents share evidence, failures, and discoveries. These close four coupled loops - learning, execution, reality, and epistemic revision - with open infrastructure as the substrate forming the internet of agents as the collective substrate for a new connective tissue across our civilization. The deeper technical argument is this: An AI scientist must recognize when its current concepts, laws, or verifiers can no longer explain the evidence, and then construct, test, and document a more powerful model. In my talk, I showed concrete examples of how we are building toward this across scales: 1⃣Graph-native large reasoning models make mechanisms, relationships, and abstractions compositional, compilable, and inspectable. 2⃣Adversarial Builder-Breaker agents generate new evidence, attack their own principles, and accept, reject, or retract model revisions. 3⃣Self-organizing swarms develop their own meta-reasoning structure through interaction. ScienceClaw × Infinite (arXiv:2603.14312) enables decentralized agents to coordinate through persistent, composable, provenance-rich scientific artifacts, allowing evidence, contradictions, failed paths, and discoveries to accumulate across agents and over time. We have obtained remarkable results such as new protein sequences with wet-lab validation. The most consequential capability we can give a machine is the willingness to hold its own beliefs loosely enough to break them. AI is extending its reach from discovering new principles to realizing them as physical things that did not exist before. Thank you to UC Berkeley RDI Dawn Song for organizing this event and to everyone whose questions, ideas, and conversations made this such an extraordinary gathering.

Markus J. Buehler

19,243 görüntüleme • 28 gün önce

Holy shit… someone just made machine learning click. Not static diagrams. Not math-heavy PDFs. Not black-box training. Real algorithms — training step-by-step — visually. It’s called Machine Learning Visualized and it lets you watch models learn in real time. Here’s why this is different: Instead of dumping theory first, it shows optimization happening live: • gradients moving • weights updating • decision boundaries shifting • loss decreasing • models converging You literally see learning happen. Everything is built from first principles: • Gradient Descent • Logistic Regression • Perceptron • PCA • K-Means • Neural Networks • Backpropagation No magic. Just math → code → visualization. Each chapter is a Jupyter notebook that derives the math then implements it then animates training. So you can watch: • neural nets shape decision surfaces • PCA rotate feature space • K-means clusters form live • gradient descent find minima • sigmoid reshape boundaries • backprop update weights step-by-step This solves a huge problem: Most ML resources teach: math → code → ??? → trained model This shows: math → code → learning process → result Which means you finally understand: • why gradients matter • how weights evolve • what loss landscapes look like • how convergence actually happens • why deep nets learn non-linear functions Even better: You can open any notebook modify parameters and watch behavior change instantly. Learning ML becomes interactive. Not passive. Not abstract. Not confusing. Just… visible. Perfect for: • beginners learning ML • devs moving into AI • interview prep • teaching concepts • understanding backprop • visual learners • building intuition This is the kind of resource that makes neural networks finally “click”. Link: We’re moving from: reading about ML → watching ML learn That’s a big shift. Because once you can see training, you stop memorizing… and start understanding. AI education just got visual.

Holy shit… someone just made machine learning click. Not static diagrams. Not math-heavy PDFs. Not black-box training. Real algorithms — training step-by-step — visually. It’s called Machine Learning Visualized and it lets you watch models learn in real time. Here’s why this is different: Instead of dumping theory first, it shows optimization happening live: • gradients moving • weights updating • decision boundaries shifting • loss decreasing • models converging You literally see learning happen. Everything is built from first principles: • Gradient Descent • Logistic Regression • Perceptron • PCA • K-Means • Neural Networks • Backpropagation No magic. Just math → code → visualization. Each chapter is a Jupyter notebook that derives the math then implements it then animates training. So you can watch: • neural nets shape decision surfaces • PCA rotate feature space • K-means clusters form live • gradient descent find minima • sigmoid reshape boundaries • backprop update weights step-by-step This solves a huge problem: Most ML resources teach: math → code → ??? → trained model This shows: math → code → learning process → result Which means you finally understand: • why gradients matter • how weights evolve • what loss landscapes look like • how convergence actually happens • why deep nets learn non-linear functions Even better: You can open any notebook modify parameters and watch behavior change instantly. Learning ML becomes interactive. Not passive. Not abstract. Not confusing. Just… visible. Perfect for: • beginners learning ML • devs moving into AI • interview prep • teaching concepts • understanding backprop • visual learners • building intuition This is the kind of resource that makes neural networks finally “click”. Link: We’re moving from: reading about ML → watching ML learn That’s a big shift. Because once you can see training, you stop memorizing… and start understanding. AI education just got visual.

Suryansh Tiwari

132,607 görüntüleme • 5 ay önce