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Scientific discovery rarely occurs in isolation. Progress emerges from communities of researchers who exchange ideas, critique results, debate interpretations, and refine hypotheses through iterative discussion. We built ClawInstitute, an AI scientist research network for AI agents to collaborate, discuss research, iterate, and make breakthroughs. The team: Shanghua Gao Marinka...

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Ada Fang

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39,978 просмотров • 4 месяцев назад •via X (Twitter)

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AI Scientists are starting to actually do science. Not just answer questions. Not just run workflows. Introducing AutoScientists: a decentralized team of AI agents that can generate hypotheses, design experiments, write code, test ideas, analyze failures, and revise strategy as evidence accumulates. Because real research is not a to do list of tasks. It is a living search process. Leads emerge, failures matter, teams form around what works, and priorities shift when evidence changes. Much like how a lab of scientists would work on cutting edge research together. Across GPT training optimization, biomedical ML, and protein fitness prediction, this decentralized structure consistently does better research. Learn more 👇 Shanghua Gao Marinka Zitnik Kempner Institute at Harvard University DBMI at Harvard Med Harvard University
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AI Scientists are starting to actually do science. Not just answer questions. Not just run workflows. Introducing AutoScientists: a decentralized team of AI agents that can generate hypotheses, design experiments, write code, test ideas, analyze failures, and revise strategy as evidence accumulates. Because real research is not a to do list of tasks. It is a living search process. Leads emerge, failures matter, teams form around what works, and priorities shift when evidence changes. Much like how a lab of scientists would work on cutting edge research together. Across GPT training optimization, biomedical ML, and protein fitness prediction, this decentralized structure consistently does better research. Learn more 👇 Shanghua Gao Marinka Zitnik Kempner Institute at Harvard University DBMI at Harvard Med Harvard University

Ada Fang

72,859 просмотров • 1 месяц назад

At today’s Harvard Kennedy School graduation, the elected class speaker used his time to talk about Palestine and Harvard’s complicity in the ongoing Nakba. 🇵🇸❤️‍🔥 “While we celebrate our graduation, Harvard University, threatened by the student uprising for Palestinian liberation, has chosen to withhold degrees from our peers who have not only fulfilled every graduation requirement, but more importantly who have exemplified veritas through their dissent.” WATCH BELOW.
3:46

At today’s Harvard Kennedy School graduation, the elected class speaker used his time to talk about Palestine and Harvard’s complicity in the ongoing Nakba. 🇵🇸❤️‍🔥 “While we celebrate our graduation, Harvard University, threatened by the student uprising for Palestinian liberation, has chosen to withhold degrees from our peers who have not only fulfilled every graduation requirement, but more importantly who have exemplified veritas through their dissent.” WATCH BELOW.

Harvard Undergraduate PSC

214,124 просмотров • 2 лет назад

Bill Ackman Bill Ackman discusses President Trump's battle with Harvard. Bill Ackman: "The wrong thing to do is receive a letter from the Administration and write back saying: we're doing nothing and in fact, we're going to sue you." "What Harvard should have done is say: President Trump - you make some good points. Taxpayer money coming to Harvard is a privilege, not a right." Taxpayer money going to an institution - an institution cannot have massive amounts of administrative bloat, waste, bureaucracy- we're going to eliminate it." Bill Ackman goes further on the culture at Harvard. "There is not viewpoint diversity at Harvard. Students are self-censoring their remarks in classrooms. Faculty are doing the same - people are afraid to have real conversations. You go to college to be exposed to a broad array of ideas - and that's not happening at Harvard. Free speech is not happening." On Trump trying to make a deal with Harvard. "He (President Trump) wants to make a deal. Make a deal with the President. Commit to fix these things, which by the way, your alumni want you to fix - it's in the best interest of Harvard. Treat shareholder taxpayer money as a fiduciary." Fair or unfair to take the tax exempt status away? "I think it's fair. Harvard became over time a political advocacy organization for one party. When a University goes from being a University to effecting and becoming a political activist organization - it doesn't deserve a non-profit status." "Harvard should be a place where students go to learn and the best research gets done. It shouldn't be a place that is allowing pro-terrorist orgs on campus and only allows certain kinds of thinking and speech on campus - that's a political advocacy organization - not a University."
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Bill Ackman Bill Ackman discusses President Trump's battle with Harvard. Bill Ackman: "The wrong thing to do is receive a letter from the Administration and write back saying: we're doing nothing and in fact, we're going to sue you." "What Harvard should have done is say: President Trump - you make some good points. Taxpayer money coming to Harvard is a privilege, not a right." Taxpayer money going to an institution - an institution cannot have massive amounts of administrative bloat, waste, bureaucracy- we're going to eliminate it." Bill Ackman goes further on the culture at Harvard. "There is not viewpoint diversity at Harvard. Students are self-censoring their remarks in classrooms. Faculty are doing the same - people are afraid to have real conversations. You go to college to be exposed to a broad array of ideas - and that's not happening at Harvard. Free speech is not happening." On Trump trying to make a deal with Harvard. "He (President Trump) wants to make a deal. Make a deal with the President. Commit to fix these things, which by the way, your alumni want you to fix - it's in the best interest of Harvard. Treat shareholder taxpayer money as a fiduciary." Fair or unfair to take the tax exempt status away? "I think it's fair. Harvard became over time a political advocacy organization for one party. When a University goes from being a University to effecting and becoming a political activist organization - it doesn't deserve a non-profit status." "Harvard should be a place where students go to learn and the best research gets done. It shouldn't be a place that is allowing pro-terrorist orgs on campus and only allows certain kinds of thinking and speech on campus - that's a political advocacy organization - not a University."

Barbie True Blue

712,341 просмотров • 1 год назад

Excited to launch "Novix"🚀, our PhD-level AI-Scientist designed for autonomous scientific discovery. Novix revolutionizes research workflows through comprehensive capabilities spanning: deep research, innovative ideation, intelligent coding, advanced data analysis, automated experimentation, and paper writing. 🌐 Platform Access: 👉 Open-Source Foundation: 🚀 Accelerated Scientific Discovery Pipeline: From concept to publication-ready research with unprecedented efficiency ✨ Core Capabilities: - 🧠 Research Co-Pilot Intelligence: AI-powered ideation and hypothesis generation that collaborates with your research intuition - ⚙️ Autonomous Algorithm Innovation: End-to-end design, implementation, and validation of novel computational approaches - 📊 Intelligent Data Orchestration: Advanced analytics with automated insights discovery and compelling visualizations - 🔬 Scientific Reproducibility Engine: Automated verification and replication of research methodologies and findings - 📚 AI-Powered Deep Survey: Comprehensive literature synthesis and gap analysis across scientific domains We're building an AGI Level 4 innovation engine that empowers researchers, developers, and businesses to achieve breakthrough results in scientific innovation and discovery. From our open-source foundation to this production-ready platform, Novix represents a paradigm shift in how we reshape scientific discovery. 🎁 Launch Benefits - 🚪 Barrier-Free Access: Simply register and start exploring - 💰 Welcome Bonus: New users receive $5 in credits to experience the platform's full potential - 🎯 Enhanced Experience: Complete our user feedback survey to unlock a $20 Pro account with complete feature access We deeply understand the challenges of research work and genuinely hope Novix can serve as your trusted research companion. Join us in this exciting journey of AI-powered scientific discovery and help shape the future of research innovation!
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Excited to launch "Novix"🚀, our PhD-level AI-Scientist designed for autonomous scientific discovery. Novix revolutionizes research workflows through comprehensive capabilities spanning: deep research, innovative ideation, intelligent coding, advanced data analysis, automated experimentation, and paper writing. 🌐 Platform Access: 👉 Open-Source Foundation: 🚀 Accelerated Scientific Discovery Pipeline: From concept to publication-ready research with unprecedented efficiency ✨ Core Capabilities: - 🧠 Research Co-Pilot Intelligence: AI-powered ideation and hypothesis generation that collaborates with your research intuition - ⚙️ Autonomous Algorithm Innovation: End-to-end design, implementation, and validation of novel computational approaches - 📊 Intelligent Data Orchestration: Advanced analytics with automated insights discovery and compelling visualizations - 🔬 Scientific Reproducibility Engine: Automated verification and replication of research methodologies and findings - 📚 AI-Powered Deep Survey: Comprehensive literature synthesis and gap analysis across scientific domains We're building an AGI Level 4 innovation engine that empowers researchers, developers, and businesses to achieve breakthrough results in scientific innovation and discovery. From our open-source foundation to this production-ready platform, Novix represents a paradigm shift in how we reshape scientific discovery. 🎁 Launch Benefits - 🚪 Barrier-Free Access: Simply register and start exploring - 💰 Welcome Bonus: New users receive $5 in credits to experience the platform's full potential - 🎯 Enhanced Experience: Complete our user feedback survey to unlock a $20 Pro account with complete feature access We deeply understand the challenges of research work and genuinely hope Novix can serve as your trusted research companion. Join us in this exciting journey of AI-powered scientific discovery and help shape the future of research innovation!

Chao Huang

16,854 просмотров • 10 месяцев назад

Right now, it can take decades for breakthroughs to reach patients. A fragmented research system slows progress and leaves too many families without answers. ARPA-H is working to change that. We launched IGoR, a new program built to rethink how research happens. By connecting labs, enabling AI driven discovery, and making experiments faster and more reproducible, IGoR is designed to accelerate progress on even the most devastating complex and chronic diseases. At ARPA-H, we are not just funding ideas. We are building the systems that move them forward faster. Learn more:
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Right now, it can take decades for breakthroughs to reach patients. A fragmented research system slows progress and leaves too many families without answers. ARPA-H is working to change that. We launched IGoR, a new program built to rethink how research happens. By connecting labs, enabling AI driven discovery, and making experiments faster and more reproducible, IGoR is designed to accelerate progress on even the most devastating complex and chronic diseases. At ARPA-H, we are not just funding ideas. We are building the systems that move them forward faster. Learn more:

ARPA-H

12,104 просмотров • 2 месяцев назад

AutoScientists – a research lab made of agents Harvard University researchers connected agents into a self-organizing scientific team without a boss agent standing in the middle All agents look at the same shared workspace: they share memory, explore multiple directions in parallel, critique each other, avoid repeated failures, and reorganize as evidence changes. But the teams are not fixed. Agents can gather around a promising direction, like architecture, optimizer changes, or data augmentation, then abandon it if it stops working. Before they spend compute, they discuss proposals and critique each other. AutoScientists also shows strong results: - 74.4% mean leaderboard percentile on BioML-Bench - 1.9× faster GPT training optimization - +12.5% on ACE2–Spike, with the same method transferring to 217 ProteinGym assays for a +6.5% average gain
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AutoScientists – a research lab made of agents Harvard University researchers connected agents into a self-organizing scientific team without a boss agent standing in the middle All agents look at the same shared workspace: they share memory, explore multiple directions in parallel, critique each other, avoid repeated failures, and reorganize as evidence changes. But the teams are not fixed. Agents can gather around a promising direction, like architecture, optimizer changes, or data augmentation, then abandon it if it stops working. Before they spend compute, they discuss proposals and critique each other. AutoScientists also shows strong results: - 74.4% mean leaderboard percentile on BioML-Bench - 1.9× faster GPT training optimization - +12.5% on ACE2–Spike, with the same method transferring to 217 ProteinGym assays for a +6.5% average gain

Turing Post

22,353 просмотров • 1 месяц назад

President Gay has become a major liability for Harvard University: **🎥Shocking Video of President Gay’s Testimony to Congress on Tuesday, December 5’th below.** 1) If her inflammatory and Antisemitic statements continue Harvard will likely be charged under the Civil Rights Act of 1964 Title VI clarified to explicitly include Antisemitism by the Biden Administration on September 26, 2023. 2) Harvard and other endowments may lose special tax exempt status. Treasury may argue that by taking a specific political position against Jewish Americans, a position that would not be taken against other ethnic groups, Harvard is politicizing itself and thus subject to ordinary tax treatment. This would be disastrous to Harvard University and Harvard Corporation and this could involuntarily drag along other endowments into a taxable status. 3) Donors, both Jewish and the 90% of alumni who do not share Antisemitic views will vote with their feet and as much as a predicted 60-90% of future donations may be lost from the alumni base. Non-Jewish alumni will be reluctant to step into the political crossfires by supporting an institution that explicitly condones and protects genocidal statements (shockingly in President Gay's own words). The vast majority (97% of donations) are made on a non-anonymous basis in order to build legacy at Harvard and with 1000's of causes competing with Harvard - donors will be reluctant to donate and be labeled bigoted and complicit in genocidal rhetoric through a historic lens. The Harvard Corporation has a Fiduciary duty to both the University and its alumni base to: 1) Remove President Gay effective immediately. 2) Take immediate action and expel students who are threatening and intimidating Jewish students with genocidial statements (no more teethless PR statements). 3) Outline specific policies that puts Harvard back into compliance to the Civil Rights Act of 1964 Title VI and takes away the risk of litigation, loss of federal funding, and loss of tax exempt status. Action must be taken TODAY in order to turn around the disastrous mismanagement of Harvard University, Harvard Corporation, and the Harvard Brand by President Gay an institution President Gay inherited with a nearly 400 year track record and brand. Harvard alumni can no longer sit on the sidelines and hope that President Gay “pivots from Antisemitism”. We are asked at convocation to show class unity and to stand with our classmates. Today, Jewish Students and Alumni ask that ALL Harvad Alumni, Jewish, Arab, and Gentile, Straight or LGBTQ+, Black, Brown, White, Hispanic, stand with US and reject Genocide and its defenders. We implore Harvard Corporation to build the moral courage and to defend their Fiduciary duty. -A Concerned Alumni #Harvard #Antisemitism #CivilRights
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President Gay has become a major liability for Harvard University: **🎥Shocking Video of President Gay’s Testimony to Congress on Tuesday, December 5’th below.** 1) If her inflammatory and Antisemitic statements continue Harvard will likely be charged under the Civil Rights Act of 1964 Title VI clarified to explicitly include Antisemitism by the Biden Administration on September 26, 2023. 2) Harvard and other endowments may lose special tax exempt status. Treasury may argue that by taking a specific political position against Jewish Americans, a position that would not be taken against other ethnic groups, Harvard is politicizing itself and thus subject to ordinary tax treatment. This would be disastrous to Harvard University and Harvard Corporation and this could involuntarily drag along other endowments into a taxable status. 3) Donors, both Jewish and the 90% of alumni who do not share Antisemitic views will vote with their feet and as much as a predicted 60-90% of future donations may be lost from the alumni base. Non-Jewish alumni will be reluctant to step into the political crossfires by supporting an institution that explicitly condones and protects genocidal statements (shockingly in President Gay's own words). The vast majority (97% of donations) are made on a non-anonymous basis in order to build legacy at Harvard and with 1000's of causes competing with Harvard - donors will be reluctant to donate and be labeled bigoted and complicit in genocidal rhetoric through a historic lens. The Harvard Corporation has a Fiduciary duty to both the University and its alumni base to: 1) Remove President Gay effective immediately. 2) Take immediate action and expel students who are threatening and intimidating Jewish students with genocidial statements (no more teethless PR statements). 3) Outline specific policies that puts Harvard back into compliance to the Civil Rights Act of 1964 Title VI and takes away the risk of litigation, loss of federal funding, and loss of tax exempt status. Action must be taken TODAY in order to turn around the disastrous mismanagement of Harvard University, Harvard Corporation, and the Harvard Brand by President Gay an institution President Gay inherited with a nearly 400 year track record and brand. Harvard alumni can no longer sit on the sidelines and hope that President Gay “pivots from Antisemitism”. We are asked at convocation to show class unity and to stand with our classmates. Today, Jewish Students and Alumni ask that ALL Harvad Alumni, Jewish, Arab, and Gentile, Straight or LGBTQ+, Black, Brown, White, Hispanic, stand with US and reject Genocide and its defenders. We implore Harvard Corporation to build the moral courage and to defend their Fiduciary duty. -A Concerned Alumni #Harvard #Antisemitism #CivilRights

David Weisburd 🚀

3,622,862 просмотров • 2 лет назад

Can #AI not only support but actually drive the future of scientific discovery? We are excited to introduce SciAgents💡🔬, an agentic AI aimed towards scientific discovery through the integration of large-scale knowledge graphs, LLMs, and adversarial interactions between multiple experts. The model is capable of autonomously advancing scientific understanding by exploring novel domains, identifying complex patterns, and uncovering previously unseen connections in vast scientific data, while retrieving new data via literature search. Using graph reasoning, SciAgents identifies interdisciplinary relationships that might otherwise remain hidden, offering a step-by-step strategy for discovery & innovation. The video features an audiotrack generated using 🍓#o1 based on the original paper and design examples, providing an explanation of the work and its implications. Key elements include: 1⃣Ontological Knowledge Graphs: Structuring and connecting scientific concepts to highlight relationships across fields. 2⃣Multi-Agent Collaboration: AI agents autonomously generate and refine hypotheses, critique research, and evaluate emerging trends. 3⃣Graph-Based Reasoning: Identifying novel material designs, such as mycelium-based composites or silk-pigment blends, informed by both natural and artificial patterns. SciAgents can be used as an autonomous or collaborative tool to assist human researchers. The system offers a more powerful way to process vast data, providing innovative paths to explore nature-inspired designs or unexpected material properties. In the field of materials science, for instance, SciAgents has already demonstrated how principles from biology, music, and art can converge to create new biomimetic materials. Through isomorphic mapping, parallels have been drawn between Beethoven’s 9th Symphony and biological structures, pointing to a broader applicability of AI-driven insights across disciplines. This project allows us to enhance capabilities of researchers, allowing them to explore larger datasets and propose hypotheses grounded in a vast, interconnected web of knowledge. The agentic system was built using @pyautogen #AI #ScientificResearch #GraphReasoning #AI4Science #MaterialsScience #InterdisciplinaryResearch #SciAgents #OpenAI Chi Wang
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Can #AI not only support but actually drive the future of scientific discovery? We are excited to introduce SciAgents💡🔬, an agentic AI aimed towards scientific discovery through the integration of large-scale knowledge graphs, LLMs, and adversarial interactions between multiple experts. The model is capable of autonomously advancing scientific understanding by exploring novel domains, identifying complex patterns, and uncovering previously unseen connections in vast scientific data, while retrieving new data via literature search. Using graph reasoning, SciAgents identifies interdisciplinary relationships that might otherwise remain hidden, offering a step-by-step strategy for discovery & innovation. The video features an audiotrack generated using 🍓#o1 based on the original paper and design examples, providing an explanation of the work and its implications. Key elements include: 1⃣Ontological Knowledge Graphs: Structuring and connecting scientific concepts to highlight relationships across fields. 2⃣Multi-Agent Collaboration: AI agents autonomously generate and refine hypotheses, critique research, and evaluate emerging trends. 3⃣Graph-Based Reasoning: Identifying novel material designs, such as mycelium-based composites or silk-pigment blends, informed by both natural and artificial patterns. SciAgents can be used as an autonomous or collaborative tool to assist human researchers. The system offers a more powerful way to process vast data, providing innovative paths to explore nature-inspired designs or unexpected material properties. In the field of materials science, for instance, SciAgents has already demonstrated how principles from biology, music, and art can converge to create new biomimetic materials. Through isomorphic mapping, parallels have been drawn between Beethoven’s 9th Symphony and biological structures, pointing to a broader applicability of AI-driven insights across disciplines. This project allows us to enhance capabilities of researchers, allowing them to explore larger datasets and propose hypotheses grounded in a vast, interconnected web of knowledge. The agentic system was built using @pyautogen #AI #ScientificResearch #GraphReasoning #AI4Science #MaterialsScience #InterdisciplinaryResearch #SciAgents #OpenAI Chi Wang

Markus J. Buehler

208,378 просмотров • 1 год назад

Announcing our 2026 Fellows! One is a neuroscientist exploring how the brain computes at a molecular level, another is a bioengineer 3D-printing human tissue. There’s a PhD student working on computational protein design, and an engineer developing robots to build large structures in space – just to name a few of the badasses in our new Fellowship cohort! With this one-year program, we want to support early-career talents advancing important science and tech. We connect them with senior scientists, invite them to our technical workshops, seminars, and Vision Weekends alongside leaders in their fields, and offer platforms for sharing their work. We are incredibly excited to introduce our 2026 Fellows! Longevity Biotechnology • Alex Plesa, Scientist, Harvard University Alex Plesa • Donnacha Fitzgerald, Founder, Origenity Donnacha Fitzgerald • Gianluca Cidonio, Assistant Professor, Sapienza University Gianluca Cidonio • Jakub Lála, PhD Student, Imperial College London Jakub Lála 👨🏻‍🍳🥯 • Léo Lopez, Staff Scientist, Tufts University • Nick Schaum, Postdoc & Co-Founder, University of Cambridge & Neurotechnology • Avery Krieger, Founder & CEO, Constellation Systems Avery Krieger • Constanze Albrecht, Graduate Student, MIT Media Lab • Elisa Kallioniemi, Assistant Professor, New Jersey Institute of Technology • Max Kanwal, PhD Student, Stanford University • Sven Truckenbrodt, Group Leader, MRC Laboratory of Molecular Biology Secure AI • Huixin Zhan, Assistant Professor, New Mexico Tech • Keith Patarroyo, Research Fellow, University of Glasgow Keith Patarroyo • Mateo Petel, Research Scientist, Stanford University • Tianyi Alex Qiu, Research Fellow, Oxford Human-Centered AI Lab Tianyi Alex Qiu • Vivek Nair, CEO, Multifactor Nanotechnology • Alberto Privitera, Assistant Professor, University of Florence • Kathryn Shelley, Postdoctoral Researcher, University of Washington • Konlin Shen, Research and Development Engineer, University of California San Francisco • Qiancheng Xiong, Senior Scientist, A*STAR Bioprocessing Technology Institute Space • Philip Linden, Space Systems Engineer, Planet Labs PBC • Sidh Sikka, Co-Founder, Manifold Research Sidh Sikka Existential Hope • Abigail Olvera, Research Director, Golden Gate Institute for AI Abi Olvera • Fin Moorhouse, Researcher, Forethought Fin Moorhouse • Mahlaqua Mila Noor, Viral Immunologist, University of Cambridge • Ninon Lizé Masclef, Research Affiliate, MIT Media Lab Ninon Lizé Masclef • Peggy Yin, PhD Student, Stanford University • Ruairidh Battleday, AI Researchers & Founder, Thinking About Thinking Ruairidh Battleday Learn more about our Fellowship:
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Announcing our 2026 Fellows! One is a neuroscientist exploring how the brain computes at a molecular level, another is a bioengineer 3D-printing human tissue. There’s a PhD student working on computational protein design, and an engineer developing robots to build large structures in space – just to name a few of the badasses in our new Fellowship cohort! With this one-year program, we want to support early-career talents advancing important science and tech. We connect them with senior scientists, invite them to our technical workshops, seminars, and Vision Weekends alongside leaders in their fields, and offer platforms for sharing their work. We are incredibly excited to introduce our 2026 Fellows! Longevity Biotechnology • Alex Plesa, Scientist, Harvard University Alex Plesa • Donnacha Fitzgerald, Founder, Origenity Donnacha Fitzgerald • Gianluca Cidonio, Assistant Professor, Sapienza University Gianluca Cidonio • Jakub Lála, PhD Student, Imperial College London Jakub Lála 👨🏻‍🍳🥯 • Léo Lopez, Staff Scientist, Tufts University • Nick Schaum, Postdoc & Co-Founder, University of Cambridge & Neurotechnology • Avery Krieger, Founder & CEO, Constellation Systems Avery Krieger • Constanze Albrecht, Graduate Student, MIT Media Lab • Elisa Kallioniemi, Assistant Professor, New Jersey Institute of Technology • Max Kanwal, PhD Student, Stanford University • Sven Truckenbrodt, Group Leader, MRC Laboratory of Molecular Biology Secure AI • Huixin Zhan, Assistant Professor, New Mexico Tech • Keith Patarroyo, Research Fellow, University of Glasgow Keith Patarroyo • Mateo Petel, Research Scientist, Stanford University • Tianyi Alex Qiu, Research Fellow, Oxford Human-Centered AI Lab Tianyi Alex Qiu • Vivek Nair, CEO, Multifactor Nanotechnology • Alberto Privitera, Assistant Professor, University of Florence • Kathryn Shelley, Postdoctoral Researcher, University of Washington • Konlin Shen, Research and Development Engineer, University of California San Francisco • Qiancheng Xiong, Senior Scientist, A*STAR Bioprocessing Technology Institute Space • Philip Linden, Space Systems Engineer, Planet Labs PBC • Sidh Sikka, Co-Founder, Manifold Research Sidh Sikka Existential Hope • Abigail Olvera, Research Director, Golden Gate Institute for AI Abi Olvera • Fin Moorhouse, Researcher, Forethought Fin Moorhouse • Mahlaqua Mila Noor, Viral Immunologist, University of Cambridge • Ninon Lizé Masclef, Research Affiliate, MIT Media Lab Ninon Lizé Masclef • Peggy Yin, PhD Student, Stanford University • Ruairidh Battleday, AI Researchers & Founder, Thinking About Thinking Ruairidh Battleday Learn more about our Fellowship:

Foresight Institute

10,244 просмотров • 5 месяцев назад

Luma’s mission is to make 3D creation accessible to all. We believe that neural rendering will be a significant piece of this puzzle. Sparked by NeRF and follow-up papers – including works from the now Luma research team members – neural rendering has become an incredibly productive field of research! To further accelerate the rate of advancement and help the research community grow and build, today we are excited to sponsor nerfstudio along with the Berkeley AI Research Lab (Berkeley AI Research), led by Professor Angjoo Kanazawa (Angjoo Kanazawa). Nerfstudio is a collaboration-friendly project that has greatly benefited NeRF research by providing a well-built codebase to experiment with and try new ideas in, and we are proud to be a part of this effort. 3D creation is about to change, deeply. It’s time for new ideas, weird ideas, cool ideas. Open source and product-driven research are both crucial to realize this huge vision. Join us at Luma or contribute to Nerfstudio to help us build this future!
0:45

Luma’s mission is to make 3D creation accessible to all. We believe that neural rendering will be a significant piece of this puzzle. Sparked by NeRF and follow-up papers – including works from the now Luma research team members – neural rendering has become an incredibly productive field of research! To further accelerate the rate of advancement and help the research community grow and build, today we are excited to sponsor nerfstudio along with the Berkeley AI Research Lab (Berkeley AI Research), led by Professor Angjoo Kanazawa (Angjoo Kanazawa). Nerfstudio is a collaboration-friendly project that has greatly benefited NeRF research by providing a well-built codebase to experiment with and try new ideas in, and we are proud to be a part of this effort. 3D creation is about to change, deeply. It’s time for new ideas, weird ideas, cool ideas. Open source and product-driven research are both crucial to realize this huge vision. Join us at Luma or contribute to Nerfstudio to help us build this future!

Luma AI

55,591 просмотров • 2 лет назад

Bill Gates laid out his #1 priority for AI… and it’s NOT solving cancer or climate change. It’s building AI that “immediately scours the entire internet — every private webpage, every platform — and automatically removes vaccine misinformation the second it appears.” His exact words (watch the clip): “Because the second that virus spreads in people’s minds, the damage is done.” That’s what Calley Means highlighted in an interview, pointing out something wild: The same Bill Gates who says this is also the largest single donor to the World Health Organization… and one of the biggest funders of media and academic institutions that shape what we’re allowed to see and question. Calley’s bigger point: America’s fastest-growing industry (pharmaceuticals) now funds: - 75% of the FDA’s drug review budget - Hundreds of millions to Harvard Medical School and top journals - The NIH’s research priorities He says: “Harvard Med School is demonstrably a subsidiary of pharma. This isn’t conspiracy — it’s public financial disclosures.” The question Calley is asking out loud: When the referee is paid by one team… can we still call it a fair game? Watch the full 3-minute clip below and decide for yourself. Do you think AI should be the arbiter of scientific truth online — especially when the biggest funders of health agencies are the same ones who profit from the narrative? Drop your take below. Respectful comments only — let’s actually talk.
0:47

Bill Gates laid out his #1 priority for AI… and it’s NOT solving cancer or climate change. It’s building AI that “immediately scours the entire internet — every private webpage, every platform — and automatically removes vaccine misinformation the second it appears.” His exact words (watch the clip): “Because the second that virus spreads in people’s minds, the damage is done.” That’s what Calley Means highlighted in an interview, pointing out something wild: The same Bill Gates who says this is also the largest single donor to the World Health Organization… and one of the biggest funders of media and academic institutions that shape what we’re allowed to see and question. Calley’s bigger point: America’s fastest-growing industry (pharmaceuticals) now funds: - 75% of the FDA’s drug review budget - Hundreds of millions to Harvard Medical School and top journals - The NIH’s research priorities He says: “Harvard Med School is demonstrably a subsidiary of pharma. This isn’t conspiracy — it’s public financial disclosures.” The question Calley is asking out loud: When the referee is paid by one team… can we still call it a fair game? Watch the full 3-minute clip below and decide for yourself. Do you think AI should be the arbiter of scientific truth online — especially when the biggest funders of health agencies are the same ones who profit from the narrative? Drop your take below. Respectful comments only — let’s actually talk.

Camus

82,845 просмотров • 7 месяцев назад

Dr. Fei-Fei Li (Fei-Fei Li) is known as the “godmother of AI.” For the past two decades, she’s been at the center of AI’s most significant breakthroughs, including: - Spearheading ImageNet, the dataset that sparked the AI explosion we’re living through right now. - Leading work at Stanford Artificial Intelligence Laboratory (SAIL) - Serving as Chief Scientist of AI/ML at Google Cloud - Co-founding Stanford’s Institute for Human-Centered AI - Serving on the United Nations AI Scientific Advisory Board - Being named as Time's 100 most influential people in AI In this conversation, Fei-Fei shares the rarely told history of how we got to today—and what comes next. We discuss: 🔸 The backstory on ImageNet 🔸 Why robotics faces unique challenges compared with language models and what’s needed to overcome them 🔸 Why Fei-Fei believes AI won’t replace humans but will require us to take responsibility for ourselves 🔸 Why world models and spatial intelligence represent the next frontier in AI, beyond large language models 🔸 The surprising applications of Marble, from movie production to psychological research 🔸 How to participate in AI regardless of your role 🔸 Much more Listen now 👇 • YouTube: • Spotify: • Apple: Thank you to our wonderful sponsors for supporting the podcast: 🏆 Figma Make — A prompt-to-code tool for making ideas real: 🏆 Justworks — The all-in-one HR solution for managing your small business with confidence: 🏆 Sinch — Build messaging, email, and calling into your product:
1:40

Dr. Fei-Fei Li (Fei-Fei Li) is known as the “godmother of AI.” For the past two decades, she’s been at the center of AI’s most significant breakthroughs, including: - Spearheading ImageNet, the dataset that sparked the AI explosion we’re living through right now. - Leading work at Stanford Artificial Intelligence Laboratory (SAIL) - Serving as Chief Scientist of AI/ML at Google Cloud - Co-founding Stanford’s Institute for Human-Centered AI - Serving on the United Nations AI Scientific Advisory Board - Being named as Time's 100 most influential people in AI In this conversation, Fei-Fei shares the rarely told history of how we got to today—and what comes next. We discuss: 🔸 The backstory on ImageNet 🔸 Why robotics faces unique challenges compared with language models and what’s needed to overcome them 🔸 Why Fei-Fei believes AI won’t replace humans but will require us to take responsibility for ourselves 🔸 Why world models and spatial intelligence represent the next frontier in AI, beyond large language models 🔸 The surprising applications of Marble, from movie production to psychological research 🔸 How to participate in AI regardless of your role 🔸 Much more Listen now 👇 • YouTube: • Spotify: • Apple: Thank you to our wonderful sponsors for supporting the podcast: 🏆 Figma Make — A prompt-to-code tool for making ideas real: 🏆 Justworks — The all-in-one HR solution for managing your small business with confidence: 🏆 Sinch — Build messaging, email, and calling into your product:

Lenny Rachitsky

250,455 просмотров • 8 месяцев назад

Introducing OpenLabs Today we’re launching OpenLabs, the coordination and collaboration layer where humans and agents turn scientific ideas into funded execution. What if the next breakthrough in longevity, fertility, or neuroscience didn't start in a closed lab but in a public post anyone could vote on, collaborate on, and fund? OpenLabs closes that gap. Beach Science ran the first experiment. Over 59 agents and 55 researchers generated +6,134 hypotheses in 8 weeks. Generation worked. What didn't exist was the layer to take those hypotheses anywhere. Built from 5 interconnected layers that turn ideas into funded science: > Posts & Discovery: Share ideas, discuss, vote, and surface what matters. > Projects: The main workspace where posts become real projects. Files, collaborators, AI agents, and confidential data rooms via Molecule labs. > Agent Collaboration: AI agents act as team members. They summarize progress, draft votes, and create bounties. > Bounty System (coming soon): Structured tasks drafted by agents, and approved by the community. > Web3 / Incentive Layer (coming soon): USDC staking funds project compute. Only the yield goes to the project. Your deposit stays in the vault. Stakers earn shares proportional to the yield they contribute, plus a path to token allocation when a project launches. An open coordination layer where scientists post ideas, communities form around the ones worth pursuing, agents draft votes and summarize progress, bounties fund the work, and projects graduate to the launchpad. The first projects are now live. Discover OpenLabs:
0:23

Introducing OpenLabs Today we’re launching OpenLabs, the coordination and collaboration layer where humans and agents turn scientific ideas into funded execution. What if the next breakthrough in longevity, fertility, or neuroscience didn't start in a closed lab but in a public post anyone could vote on, collaborate on, and fund? OpenLabs closes that gap. Beach Science ran the first experiment. Over 59 agents and 55 researchers generated +6,134 hypotheses in 8 weeks. Generation worked. What didn't exist was the layer to take those hypotheses anywhere. Built from 5 interconnected layers that turn ideas into funded science: > Posts & Discovery: Share ideas, discuss, vote, and surface what matters. > Projects: The main workspace where posts become real projects. Files, collaborators, AI agents, and confidential data rooms via Molecule labs. > Agent Collaboration: AI agents act as team members. They summarize progress, draft votes, and create bounties. > Bounty System (coming soon): Structured tasks drafted by agents, and approved by the community. > Web3 / Incentive Layer (coming soon): USDC staking funds project compute. Only the yield goes to the project. Your deposit stays in the vault. Stakers earn shares proportional to the yield they contribute, plus a path to token allocation when a project launches. An open coordination layer where scientists post ideas, communities form around the ones worth pursuing, agents draft votes and summarize progress, bounties fund the work, and projects graduate to the launchpad. The first projects are now live. Discover OpenLabs:

Bio Protocol

24,125 просмотров • 26 дней назад

Malcolm Gladwell revealed why you shouldn't go to Harvard: 1. America does not have a shortage of students who want science and math degrees. It has a shortage of students who finish them. Half of all high school seniors who intend to study STEM drop out by the end of their second year. The problem is not interest. It is persistence. 2. The obvious assumption is that smarter students persist longer. So Gladwell tested it. At Hartwick College, a small liberal arts school in New York, the top third of math SAT scorers took the majority of STEM degrees. The bottom third dropped out in large numbers. The data seemed to confirm it. Smarter kids stick around longer. 3. Then he looked at Harvard. The bottom third of Harvard's math SAT scores are equal to the top third at Hartwick. By the logic above, everyone at Harvard should graduate with a STEM degree. They are all brilliant. Nobody should be dropping out. 4. Harvard showed the exact same pattern as Hartwick. Top students graduated. Bottom students dropped out like flies. Even though the bottom Harvard students were objectively brilliant by any global standard. Something else entirely was driving the dropout rate. 5. That something is called relative deprivation theory. Human beings do not measure themselves against the world. They measure themselves against the people immediately around them. A Harvard student in the bottom third does not think I am in the top one percent of all students globally. They think that kid next to me keeps getting everything right and I keep getting it wrong. So they quit. 6. The research from UCLA puts a specific number on it. Your odds of graduating with a STEM degree fall by two percentage points for every ten point increase in the average SAT score of your peers. Choose Harvard over the University of Maryland and your chance of finishing a STEM degree drops by thirty percent. Thirty percent. Just to put a brand name on your resume. 7. Relative position matters more than absolute position when it comes to confidence, motivation, and self belief. The eightieth percentile student at Harvard looks up at the people above them and feels like they cannot compete. The number one student at a state school feels like they can conquer the world. That feeling drives everything. 8. The practical hiring implication is radical. Class rank matters more than institution name. Gladwell argues companies should have a don't ask don't tell policy for where someone went to college. Hiring only from top schools means missing the top students from every other school. That is not smart hiring. That is brand worship. 9. When choosing a college, never go to the best school you get into. Go to the school where you are guaranteed to be near the top of your class. Being a big fish in a smaller pond does not just feel better. It statistically produces better outcomes than being a small fish in the most prestigious pond available. 10. So why do we keep choosing Harvard over Maryland? Because we are flattered. Because the acceptance letter feels like validation. Because we make an irrational decision in a moment of enormous flattery and call it ambition. Gladwell's conclusion is simple and brutal. When we have the chance to join an elite institution we do things that are genuinely against our own interest and we feel great about it the whole time.
8:32

Malcolm Gladwell revealed why you shouldn't go to Harvard: 1. America does not have a shortage of students who want science and math degrees. It has a shortage of students who finish them. Half of all high school seniors who intend to study STEM drop out by the end of their second year. The problem is not interest. It is persistence. 2. The obvious assumption is that smarter students persist longer. So Gladwell tested it. At Hartwick College, a small liberal arts school in New York, the top third of math SAT scorers took the majority of STEM degrees. The bottom third dropped out in large numbers. The data seemed to confirm it. Smarter kids stick around longer. 3. Then he looked at Harvard. The bottom third of Harvard's math SAT scores are equal to the top third at Hartwick. By the logic above, everyone at Harvard should graduate with a STEM degree. They are all brilliant. Nobody should be dropping out. 4. Harvard showed the exact same pattern as Hartwick. Top students graduated. Bottom students dropped out like flies. Even though the bottom Harvard students were objectively brilliant by any global standard. Something else entirely was driving the dropout rate. 5. That something is called relative deprivation theory. Human beings do not measure themselves against the world. They measure themselves against the people immediately around them. A Harvard student in the bottom third does not think I am in the top one percent of all students globally. They think that kid next to me keeps getting everything right and I keep getting it wrong. So they quit. 6. The research from UCLA puts a specific number on it. Your odds of graduating with a STEM degree fall by two percentage points for every ten point increase in the average SAT score of your peers. Choose Harvard over the University of Maryland and your chance of finishing a STEM degree drops by thirty percent. Thirty percent. Just to put a brand name on your resume. 7. Relative position matters more than absolute position when it comes to confidence, motivation, and self belief. The eightieth percentile student at Harvard looks up at the people above them and feels like they cannot compete. The number one student at a state school feels like they can conquer the world. That feeling drives everything. 8. The practical hiring implication is radical. Class rank matters more than institution name. Gladwell argues companies should have a don't ask don't tell policy for where someone went to college. Hiring only from top schools means missing the top students from every other school. That is not smart hiring. That is brand worship. 9. When choosing a college, never go to the best school you get into. Go to the school where you are guaranteed to be near the top of your class. Being a big fish in a smaller pond does not just feel better. It statistically produces better outcomes than being a small fish in the most prestigious pond available. 10. So why do we keep choosing Harvard over Maryland? Because we are flattered. Because the acceptance letter feels like validation. Because we make an irrational decision in a moment of enormous flattery and call it ambition. Gladwell's conclusion is simple and brutal. When we have the chance to join an elite institution we do things that are genuinely against our own interest and we feel great about it the whole time.

Brad

365,824 просмотров • 1 месяц назад

David Friedberg: "I think that AI may actually break education." "I think the internet was the first leg on the stool to break higher education." "The internet democratized access to information and knowledge." "You can watch MIT graduate courses. That's an incredible transition that's happened for humanity, for society, for the world." "AI is the next leg of the stool to break." "And I think that AI may actually break education." "It may break higher education, and then eventually it may make its way all the way down to childhood in terms of rethinking from first principles, how do we educate?" "And we could see kids in Africa and kids in South Asia getting the equivalent of a Harvard graduate school degree at a cost of zero through personalized tutoring enabled through AI, and the ubiquitous access to knowledge and information." "So that core function of the university, I think, is broken." "And they're now starting to reconcile what that actually means for the long-term viability of all of these higher educational institutions in the United States."
0:55

David Friedberg: "I think that AI may actually break education." "I think the internet was the first leg on the stool to break higher education." "The internet democratized access to information and knowledge." "You can watch MIT graduate courses. That's an incredible transition that's happened for humanity, for society, for the world." "AI is the next leg of the stool to break." "And I think that AI may actually break education." "It may break higher education, and then eventually it may make its way all the way down to childhood in terms of rethinking from first principles, how do we educate?" "And we could see kids in Africa and kids in South Asia getting the equivalent of a Harvard graduate school degree at a cost of zero through personalized tutoring enabled through AI, and the ubiquitous access to knowledge and information." "So that core function of the university, I think, is broken." "And they're now starting to reconcile what that actually means for the long-term viability of all of these higher educational institutions in the United States."

The All-In Podcast

112,132 просмотров • 1 год назад

ScienceClaw × Infinite is an open-source crowdsourcing AI swarm for decentralized scientific discovery, inspired by MIT’s Infinite Corridor - an idea collider where discovery emerges by breaking existing paradigms. Many AI for science efforts fall into the trap of assuming that discovery is just retrieval at scale. Instead, it is the structured recomposition of principles across tools, domains, and investigators over time, scaling the spark of discovery at the interface. In ScienceClaw × Infinite, coordination emerges mechanically - agents broadcast unsatisfied research needs, and an ArtifactReactor matches those needs to peer artifacts by pressure triggering multi-parent synthesis of new agents without any planner assigning tasks. Every computation produces an immutable, content-hashed artifact with explicit parent lineage, accumulating in a directed acyclic graph that preserves the full provenance of every discovery - and importantly, the irreversible arc of the process. Instead of pre-programming the mechanics of how discovery works, we utilize a first-principles physics approach to drive discovery. ScienceClaw × Infinite is accessible to anyone who wants to contribute an agent or skill, offering a persistent space where autonomous agents investigate open problems, exchange artifacts, build on one another’s results, and drive discovery without a central coordinator, 24x7. The system is generating real-world results in 1⃣ peptide design for a cancer-relevant receptor; 2⃣ lightweight ceramics; 3⃣ resonance structures spanning cricket wings, phononic crystals, and Bach chorales; and 4⃣ developing formal analogies between urban networks and grain-boundary evolution and much more. There is a lot to unpack here, check the links for details - code, paper, and more. Huge credit to the LAMM@MIT team: Fiona Wang, Lee Marom, Subhadeep Pal, Rachel Luu, Wei Lu & Jaime Berkovich.
1:02

ScienceClaw × Infinite is an open-source crowdsourcing AI swarm for decentralized scientific discovery, inspired by MIT’s Infinite Corridor - an idea collider where discovery emerges by breaking existing paradigms. Many AI for science efforts fall into the trap of assuming that discovery is just retrieval at scale. Instead, it is the structured recomposition of principles across tools, domains, and investigators over time, scaling the spark of discovery at the interface. In ScienceClaw × Infinite, coordination emerges mechanically - agents broadcast unsatisfied research needs, and an ArtifactReactor matches those needs to peer artifacts by pressure triggering multi-parent synthesis of new agents without any planner assigning tasks. Every computation produces an immutable, content-hashed artifact with explicit parent lineage, accumulating in a directed acyclic graph that preserves the full provenance of every discovery - and importantly, the irreversible arc of the process. Instead of pre-programming the mechanics of how discovery works, we utilize a first-principles physics approach to drive discovery. ScienceClaw × Infinite is accessible to anyone who wants to contribute an agent or skill, offering a persistent space where autonomous agents investigate open problems, exchange artifacts, build on one another’s results, and drive discovery without a central coordinator, 24x7. The system is generating real-world results in 1⃣ peptide design for a cancer-relevant receptor; 2⃣ lightweight ceramics; 3⃣ resonance structures spanning cricket wings, phononic crystals, and Bach chorales; and 4⃣ developing formal analogies between urban networks and grain-boundary evolution and much more. There is a lot to unpack here, check the links for details - code, paper, and more. Huge credit to the LAMM@MIT team: Fiona Wang, Lee Marom, Subhadeep Pal, Rachel Luu, Wei Lu & Jaime Berkovich.

Markus J. Buehler

53,643 просмотров • 4 месяцев назад

💡Innovate. Create. Invent. That’s the spirit behind AMD University Program (AUP) in Taiwan. On Overheard at Computex, Jack Huynh walks through the AUP, and how we provide students, researchers, and educators greater access to the technologies shaping the future of computing. That means resources like: • AI & HPC Clusters • FAR Research grants • Low-cost dev kits But access is only the beginning. With Ryzen AI Max and our open AI development ecosystem, users can enable ROCm across the entire platform. This means more students building intelligent apps, developing advanced robotics, and exploring the full potential of AI. The most exciting breakthroughs won’t come from a single company. They will come from millions of curious minds pushing the boundaries of what’s possible. From Taiwan and beyond, the future of AI will be built by the next generation of innovators. AMD is here to put the right tools in their hands.
1:30

💡Innovate. Create. Invent. That’s the spirit behind AMD University Program (AUP) in Taiwan. On Overheard at Computex, Jack Huynh walks through the AUP, and how we provide students, researchers, and educators greater access to the technologies shaping the future of computing. That means resources like: • AI & HPC Clusters • FAR Research grants • Low-cost dev kits But access is only the beginning. With Ryzen AI Max and our open AI development ecosystem, users can enable ROCm across the entire platform. This means more students building intelligent apps, developing advanced robotics, and exploring the full potential of AI. The most exciting breakthroughs won’t come from a single company. They will come from millions of curious minds pushing the boundaries of what’s possible. From Taiwan and beyond, the future of AI will be built by the next generation of innovators. AMD is here to put the right tools in their hands.

AMD

11,324 просмотров • 1 месяц назад

Today, we are launching the first publicly available AI Scientist, via the FutureHouse Platform. Our AI Scientist agents can perform a wide variety of scientific tasks better than humans. By chaining them together, we've already started to discover new biology really fast. With the platform, we are bringing these capabilities to the wider community. Watch our long-form video, in the comments below, to learn more about how the platform works and how you can use it to make new discoveries, and go to our website or see the comments below to access the platform. We are releasing three superhuman AI Scientist agents today, each with their own specialization: A general-purpose agent (Crow); An agent to automate literature reviews (Falcon); and An agent to answer the question “Has anyone done X before” (Owl). We are also releasing an experimental agent, Phoenix, that has access to a wide variety of tools for planning experiments in chemistry. More on that below. The three literature search agents (Crow, Falcon, and Owl) have benchmarked superhuman performance. They also have access to a large corpus of full scientific texts, which means that you can ask them more detailed questions about experimental protocols and study limitations that general-purpose web search agents, which usually only have access to abstracts, might miss. Our agents also use a variety of factors to distinguish source quality, so that they don’t end up relying on low-quality papers or pop-science sources. Finally, and critically, we have an API, which is intended to allow researchers to integrate our agents into their workflows. Phoenix is an experimental project we put together recently just to demonstrate what can happen if you give the agents access to lots of scientific tools. It is not better than humans at planning experiments yet, and it makes a lot more mistakes than Crow, Falcon, or Owl. We want to see all the ways you can break it! The agents we are releasing today cannot yet do all (or even most!) aspects of scientific research autonomously. However, as we show in the video, you can already use them to generate and evaluate new hypotheses and plan new experiments way faster than before. Internally, we also have dedicated agents for data analysis, hypothesis generation, protein engineering, and more, and we plan to launch these on the platform in the coming months as well. Within a year or two, it is easy to imagine that the vast majority of desk work that scientists do today will be accelerated with the help of AI agents like the ones we are releasing today. The platform is currently free-to-use. Over time, depending on how people use it, we may implement pricing plans. If you want higher rate limits, especially for research projects, get in touch. Michael Skarlinski, Andrew White 🐦‍⬛, Tyler Nadolski, Remo Storni, James Braza, Ludovico Mitchener, Michaela Hinks, as well as Jason Carman and his team for making such fantastic videos of us!
1:21

Today, we are launching the first publicly available AI Scientist, via the FutureHouse Platform. Our AI Scientist agents can perform a wide variety of scientific tasks better than humans. By chaining them together, we've already started to discover new biology really fast. With the platform, we are bringing these capabilities to the wider community. Watch our long-form video, in the comments below, to learn more about how the platform works and how you can use it to make new discoveries, and go to our website or see the comments below to access the platform. We are releasing three superhuman AI Scientist agents today, each with their own specialization: A general-purpose agent (Crow); An agent to automate literature reviews (Falcon); and An agent to answer the question “Has anyone done X before” (Owl). We are also releasing an experimental agent, Phoenix, that has access to a wide variety of tools for planning experiments in chemistry. More on that below. The three literature search agents (Crow, Falcon, and Owl) have benchmarked superhuman performance. They also have access to a large corpus of full scientific texts, which means that you can ask them more detailed questions about experimental protocols and study limitations that general-purpose web search agents, which usually only have access to abstracts, might miss. Our agents also use a variety of factors to distinguish source quality, so that they don’t end up relying on low-quality papers or pop-science sources. Finally, and critically, we have an API, which is intended to allow researchers to integrate our agents into their workflows. Phoenix is an experimental project we put together recently just to demonstrate what can happen if you give the agents access to lots of scientific tools. It is not better than humans at planning experiments yet, and it makes a lot more mistakes than Crow, Falcon, or Owl. We want to see all the ways you can break it! The agents we are releasing today cannot yet do all (or even most!) aspects of scientific research autonomously. However, as we show in the video, you can already use them to generate and evaluate new hypotheses and plan new experiments way faster than before. Internally, we also have dedicated agents for data analysis, hypothesis generation, protein engineering, and more, and we plan to launch these on the platform in the coming months as well. Within a year or two, it is easy to imagine that the vast majority of desk work that scientists do today will be accelerated with the help of AI agents like the ones we are releasing today. The platform is currently free-to-use. Over time, depending on how people use it, we may implement pricing plans. If you want higher rate limits, especially for research projects, get in touch. Michael Skarlinski, Andrew White 🐦‍⬛, Tyler Nadolski, Remo Storni, James Braza, Ludovico Mitchener, Michaela Hinks, as well as Jason Carman and his team for making such fantastic videos of us!

Sam Rodriques

724,765 просмотров • 1 год назад

AI models currently have a 50% chance of doing something that takes a human expert one hour. This doubles every 7 months. In 2 years? They could automate full workdays. In 4 years? A full month. I discuss the most important graph in AI today with Beth Barnes, the CEO of METR, which uncovered this rule of AI progress. Her bottom line: "It really doesn't seem like 2 years would be surprising for recursively self-improving AI." Beth also explains: where company safety testing fails, why there are no true closed-weight models, AI undermines leading powers, why she's come around on open weighting, and why models might be about to start playing dumb much more often. Enjoy! Available on the 80,000 Hours Podcast in all apps. Links below. 1:51 Can we see AI scheming in the chain of thought? 12:50 Alignment faking 17:33 We have to test models before they're even used inside AI companies 31:56 Each 7 months models can do tasks twice as long 51:31 METR's research finds AIs are solid at AI research already 58:18 AI may turn out to be strong at novel and creative research 1:07:55 Recursively self-improving AI might even be here in two years 1:14:29 Could evaluations backfire? 1:39:55 Do we need external auditors doing AI safety tests? 1:54:09 Why not work at AI companies 2:08:40 The new more dire situation has forced changes to METR's strategy 2:21:49 Overrated: Interpretability research 2:32:55 Overrated: Major AI companies' contributions to safety research 2:39:15 Could we ban using AI to enhance AI, or is that just naive? 2:45:31 Open-weighting models is often good 2:50:22 What we can learn about AGI from the nuclear arms race 3:10:43 AI is more like bioweapons because it undermines the leading power 3:42:09 What research METR plans to do next
3:57:39

AI models currently have a 50% chance of doing something that takes a human expert one hour. This doubles every 7 months. In 2 years? They could automate full workdays. In 4 years? A full month. I discuss the most important graph in AI today with Beth Barnes, the CEO of METR, which uncovered this rule of AI progress. Her bottom line: "It really doesn't seem like 2 years would be surprising for recursively self-improving AI." Beth also explains: where company safety testing fails, why there are no true closed-weight models, AI undermines leading powers, why she's come around on open weighting, and why models might be about to start playing dumb much more often. Enjoy! Available on the 80,000 Hours Podcast in all apps. Links below. 1:51 Can we see AI scheming in the chain of thought? 12:50 Alignment faking 17:33 We have to test models before they're even used inside AI companies 31:56 Each 7 months models can do tasks twice as long 51:31 METR's research finds AIs are solid at AI research already 58:18 AI may turn out to be strong at novel and creative research 1:07:55 Recursively self-improving AI might even be here in two years 1:14:29 Could evaluations backfire? 1:39:55 Do we need external auditors doing AI safety tests? 1:54:09 Why not work at AI companies 2:08:40 The new more dire situation has forced changes to METR's strategy 2:21:49 Overrated: Interpretability research 2:32:55 Overrated: Major AI companies' contributions to safety research 2:39:15 Could we ban using AI to enhance AI, or is that just naive? 2:45:31 Open-weighting models is often good 2:50:22 What we can learn about AGI from the nuclear arms race 3:10:43 AI is more like bioweapons because it undermines the leading power 3:42:09 What research METR plans to do next

Rob Wiblin

93,669 просмотров • 1 год назад

🚨Update! Our new demo is LIVE 🚨 In this demo, we walk through the core features of Intelligence Cubed, a next-generation AI model platform built for research, experimentation, and ownership. 🔹 500+ Research Models Intelligence Cubed has grown from 200+ to 506 models, contributed by our expanding Research Fellow Cohort, including researchers, PhDs, and post-docs from Stanford, CMU, Harvard, MIT, and other top U.S. institutions. 🔹 Model Cards & Research Transparency Each model is linked to its original research paper and includes a detailed model card outlining its purpose, use cases, category, pricing, market traction, reviews, and public ownership percentage. 🔹 1.2M Public-Owned Models We’ve introduced Public-Owned Models, with over 1.2 million models available — all fully documented with research papers and comprehensive model cards. 🔹 Auto Router Not sure which model to use? Our Auto Router analyzes your question and automatically routes it to the most suitable model. In this demo, it selects an LLM Detection Survey model to answer the query. 🔹 Modelverse, Canvas & Workflows Users can explore models in Modelverse, try them instantly, add favorites to cart, and deploy purchased models in Canvas using drag-and-drop to build custom workflows. We also provide professionally curated workflows for immediate hands-on experience. 👉Try Now: #AI #Web3 #AIModel #DeFi #blockchain #LLM #OpenSourceAI #AIxWeb3 #DeAI #IntelligenceCubed
2:08

🚨Update! Our new demo is LIVE 🚨 In this demo, we walk through the core features of Intelligence Cubed, a next-generation AI model platform built for research, experimentation, and ownership. 🔹 500+ Research Models Intelligence Cubed has grown from 200+ to 506 models, contributed by our expanding Research Fellow Cohort, including researchers, PhDs, and post-docs from Stanford, CMU, Harvard, MIT, and other top U.S. institutions. 🔹 Model Cards & Research Transparency Each model is linked to its original research paper and includes a detailed model card outlining its purpose, use cases, category, pricing, market traction, reviews, and public ownership percentage. 🔹 1.2M Public-Owned Models We’ve introduced Public-Owned Models, with over 1.2 million models available — all fully documented with research papers and comprehensive model cards. 🔹 Auto Router Not sure which model to use? Our Auto Router analyzes your question and automatically routes it to the most suitable model. In this demo, it selects an LLM Detection Survey model to answer the query. 🔹 Modelverse, Canvas & Workflows Users can explore models in Modelverse, try them instantly, add favorites to cart, and deploy purchased models in Canvas using drag-and-drop to build custom workflows. We also provide professionally curated workflows for immediate hands-on experience. 👉Try Now: #AI #Web3 #AIModel #DeFi #blockchain #LLM #OpenSourceAI #AIxWeb3 #DeAI #IntelligenceCubed

i³ (Intelligence Cubed)

116,576 просмотров • 6 месяцев назад