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We've made a breakthrough in self-evolving AI scientists moving from "search" to "principled discovery": Scientific discovery requires that the search space itself changes, and an AI scientist must perceive this shift without intervention. We built an AI that achieves this for the first time with the ability to discover...

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Markus J. Buehler

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785,881 просмотров • 25 дней назад •via X (Twitter)

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🔬 New Science pod with CuspAI! We are entering a new era where materials science and discovery is transitioning from slow, manual experimentation, to a high-speed search problem powered by generative AI and "physics processing units." Max Welling argues that the foundation of all modern technology—from GPUs to climate solutions—is a materials problem, and that unifying the mathematics of stochastic thermodynamics with generative AI will unlock a new paradigm of automated scientific discovery.
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🔬 New Science pod with CuspAI! We are entering a new era where materials science and discovery is transitioning from slow, manual experimentation, to a high-speed search problem powered by generative AI and "physics processing units." Max Welling argues that the foundation of all modern technology—from GPUs to climate solutions—is a materials problem, and that unifying the mathematics of stochastic thermodynamics with generative AI will unlock a new paradigm of automated scientific discovery.

Latent.Space

20,343 просмотров • 4 месяцев 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.
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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

53,290 просмотров • 3 месяцев назад

In the coming weeks, Pennie Li and I are sharing lively discussions with founders we love, who are building in AI. First, I spoke with my friend Andrew Yan 🏛️ from AthenaHQ (Y Combinator W25), who helps brands shine in generative AI search results. We explored a striking shift in customer discovery. For the first time, a customer found A1Base through an AI chat, not a traditional search engine. This trend is gaining momentum. Andrew revealed that late last year, when ChatGPT tapped fresher data, product discovery transformed. People now ask AI systems, "Which corporate card should I choose?" instead of turning to Google. We faced a tough truth: earning a spot in AI search results isn't easy. To stand out, brands must craft precise content that answers specific prompts and publish it where AI systems look. After years of mastering Google's algorithms, we now confront a fresh challenge: staying discoverable. Those who embrace this shift and adapt will keep connecting with customers as behaviours change. How has AI search reshaped the way people find your products?
2:15

In the coming weeks, Pennie Li and I are sharing lively discussions with founders we love, who are building in AI. First, I spoke with my friend Andrew Yan 🏛️ from AthenaHQ (Y Combinator W25), who helps brands shine in generative AI search results. We explored a striking shift in customer discovery. For the first time, a customer found A1Base through an AI chat, not a traditional search engine. This trend is gaining momentum. Andrew revealed that late last year, when ChatGPT tapped fresher data, product discovery transformed. People now ask AI systems, "Which corporate card should I choose?" instead of turning to Google. We faced a tough truth: earning a spot in AI search results isn't easy. To stand out, brands must craft precise content that answers specific prompts and publish it where AI systems look. After years of mastering Google's algorithms, we now confront a fresh challenge: staying discoverable. Those who embrace this shift and adapt will keep connecting with customers as behaviours change. How has AI search reshaped the way people find your products?

Pasha Rayan

86,963 просмотров • 1 год назад

Scientific discovery is reaching the limits of human capacity: too much data, too many disconnected fields, and too few ways to connect ideas fast enough to matter. The next breakthroughs in materials, medicine, energy, and beyond will not come from scaling today’s AI paradigm alone or from relying on serendipity alone. They will require a new kind of AI for knowledge discovery that not only models the world but shapes what it could become. At Unreasonable Labs, we are building superintelligence for knowledge discovery: systems that reason across disciplines, generate novel hypotheses, test them through simulation and experimentation, and help guide real-world discovery. Our AI engine is not confined to what it has seen in training. It creates new data, builds new tools, and maintains a persistent world model that grows more powerful as it reasons. Why now? Even today's most powerful AI models face a core limitation: they are trained on what we already know. True discovery begins when a system encounters something its current model cannot explain. This is why you cannot train your way to a discovery - a system has to reason through new problems, update its beliefs, and revise its understanding of the world as it thinks. Another critical insight is that rich knowledge already exists, but is not yet applied to solve pressing problems. It sits in millions of papers, patents, and datasets, trapped in isolated silos, often in legacy data vaults. What's missing is a way to connect it, scale it, unlock the potential, and synthesize genuine novel predictions. The time is now to build a system that enables practitioners to design, explore, and direct discovery, whether through human guidance or full automation, while capturing the tacit insight that domain experts bring. Steerable reasoning That is why we built an operating system for scientific discovery - one that replaces chance with steerable reasoning. Rather than retrieving static facts, our AI builds and continuously updates a living world model - a representation of knowledge the system can actively reason over, question, and revise. A concrete example: say you want to create "smart concrete" that can flex - a concept that doesn't exist yet. Our AI maps relationships across domains, finds a path from morphable smart materials to concrete, and identifies the most efficient way to bridge those concepts. It then autonomously writes simulations, tests the hypothesis, and refines the idea. Then it interacts with hardware to produce a physical artifact, and the loop expands into the real-world, where the machine becomes world-shaping. Our AI gives users full visibility into how the system arrived at a conclusion. It delineates which existing patents and papers it drew upon versus what is genuinely new - protecting IP and competitive concerns from the start, and offering deep compositional insights into technology advances. It takes unreasonable people to make progress Our team reflects the interdisciplinary expertise required to build this next breakthrough - my co-founder Yuan Cao Yuan Cao (formerly DeepMind) and Andrew Lew, Haiqian Yang, Matt Insler, Jennifer Kang and Julia McLaughlin. We are backed by $13.5M in seed funding led by Playground Global with participation from AIX, E14 Fund, and MS&AD. We are guided by advisors including Robert Langer (1,000+ patents), Kostya Novoselov (Nobel Prize in Physics), and Thomas Wolf (Co-founder of Hugging Face). We already have multiple pilot programs underway with leading industrial partners in materials science and engineering, with additional engagements developing across energy, logistics, bioengineering, and other strategic domains. The biggest challenges of our time - fusion energy, sustainable materials, new medicines - demand exponentially more innovation than humans alone can produce. We are not replacing scientists, and instead are making every scientist capable of leading their own team of AI-powered researchers. Abundant innovation leads to abundant prosperity. Watch our launch video below to see what we're building Unreasonable Labs 👇
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Scientific discovery is reaching the limits of human capacity: too much data, too many disconnected fields, and too few ways to connect ideas fast enough to matter. The next breakthroughs in materials, medicine, energy, and beyond will not come from scaling today’s AI paradigm alone or from relying on serendipity alone. They will require a new kind of AI for knowledge discovery that not only models the world but shapes what it could become. At Unreasonable Labs, we are building superintelligence for knowledge discovery: systems that reason across disciplines, generate novel hypotheses, test them through simulation and experimentation, and help guide real-world discovery. Our AI engine is not confined to what it has seen in training. It creates new data, builds new tools, and maintains a persistent world model that grows more powerful as it reasons. Why now? Even today's most powerful AI models face a core limitation: they are trained on what we already know. True discovery begins when a system encounters something its current model cannot explain. This is why you cannot train your way to a discovery - a system has to reason through new problems, update its beliefs, and revise its understanding of the world as it thinks. Another critical insight is that rich knowledge already exists, but is not yet applied to solve pressing problems. It sits in millions of papers, patents, and datasets, trapped in isolated silos, often in legacy data vaults. What's missing is a way to connect it, scale it, unlock the potential, and synthesize genuine novel predictions. The time is now to build a system that enables practitioners to design, explore, and direct discovery, whether through human guidance or full automation, while capturing the tacit insight that domain experts bring. Steerable reasoning That is why we built an operating system for scientific discovery - one that replaces chance with steerable reasoning. Rather than retrieving static facts, our AI builds and continuously updates a living world model - a representation of knowledge the system can actively reason over, question, and revise. A concrete example: say you want to create "smart concrete" that can flex - a concept that doesn't exist yet. Our AI maps relationships across domains, finds a path from morphable smart materials to concrete, and identifies the most efficient way to bridge those concepts. It then autonomously writes simulations, tests the hypothesis, and refines the idea. Then it interacts with hardware to produce a physical artifact, and the loop expands into the real-world, where the machine becomes world-shaping. Our AI gives users full visibility into how the system arrived at a conclusion. It delineates which existing patents and papers it drew upon versus what is genuinely new - protecting IP and competitive concerns from the start, and offering deep compositional insights into technology advances. It takes unreasonable people to make progress Our team reflects the interdisciplinary expertise required to build this next breakthrough - my co-founder Yuan Cao Yuan Cao (formerly DeepMind) and Andrew Lew, Haiqian Yang, Matt Insler, Jennifer Kang and Julia McLaughlin. We are backed by $13.5M in seed funding led by Playground Global with participation from AIX, E14 Fund, and MS&AD. We are guided by advisors including Robert Langer (1,000+ patents), Kostya Novoselov (Nobel Prize in Physics), and Thomas Wolf (Co-founder of Hugging Face). We already have multiple pilot programs underway with leading industrial partners in materials science and engineering, with additional engagements developing across energy, logistics, bioengineering, and other strategic domains. The biggest challenges of our time - fusion energy, sustainable materials, new medicines - demand exponentially more innovation than humans alone can produce. We are not replacing scientists, and instead are making every scientist capable of leading their own team of AI-powered researchers. Abundant innovation leads to abundant prosperity. Watch our launch video below to see what we're building Unreasonable Labs 👇

Markus J. Buehler

55,012 просмотров • 3 месяцев назад

Today, we’re announcing the first major discovery made by our AI Scientist with the lab in the loop: a promising new treatment for dry AMD, a major cause of blindness. Our agents generated the hypotheses, designed the experiments, analyzed the data, iterated, even made figures for the paper. The resulting manuscript is a first-of-a-kind in the natural sciences, in which everything that needed to be done to write the paper was done by AI agents, apart from actually conducting the physical experiments in the lab and writing the final manuscript. We are also introducing Robin, the first multi-agent system that fully automates the in-silico components of scientific discovery, which made this discovery. This is the first time that we are aware of that hypothesis generation, experimentation, and data analysis have been joined up in closed loop, and is the beginning of a massive acceleration in the pace of scientific discovery that will be driven by these agents. We will be open-sourcing the code and data next week. Robin is a multi-agent system that uses Crow, Falcon, and Finch, the agents on our platform, to generate novel hypotheses, plan experiments, and analyze data. We asked Robin to find a new treatment for dry age-related macular degeneration. Robin considered the disease mechanisms associated with dry AMD, proposed a specific experimental assay that could be used to evaluate hypotheses in the wet lab, and proposed specific molecules we could test in that assay. We tested the molecules and gave it the resulting data, which it analyzed before proposing more experiments. In the end, it identified Ripasudil, a Rho Kinase inhibitor (ROCK inhibitor) that is approved in Japan for several other diseases, which seems very promising as potential treatment for dry AMD. It also identified specific molecular mechanisms that might underlie the effects of Ripasudil in RPE cells, from an RNA sequencing experiment it proposed. To be clear, no one has proposed using ROCK inhibitors to treat dry AMD in the literature before, as far as we can find, and I think it would have been very difficult for us to come up with this hypothesis without the agents. We have also run the proposed treatment by several experts in AMD, who confirm that it is interesting and novel. Moreover, this project was fast: with Robin in hand, the entire project took about 10 weeks, which is way shorter than it would have taken if we had been doing all of the in-silico components ourselves. Important caveats: We are real biologists at FutureHouse, so I want to be clear that although the discovery here is exciting, we are not claiming that we have cured dry AMD. Fully validating this hypothesis as a treatment for dry AMD will take human trials, which will take much longer. Also, this discovery is cool, but it is not yet a "move 37"-style discovery. At the current rate of progress, I'm sure we will get to that level soon. Congratulations to the team. Congratulations in particular to Robin, which generated the hypotheses, proposed the experiments, analyzed the data and generated the figures. And major congratulations also to the human team, which built Robin: Michaela Hinks, Ali Ghareeb, Benjamin Chang, Ludovico Mitchener, Mo Razzak, Kiki Szostkiewicz, and Angela Yiu.
5:57

Today, we’re announcing the first major discovery made by our AI Scientist with the lab in the loop: a promising new treatment for dry AMD, a major cause of blindness. Our agents generated the hypotheses, designed the experiments, analyzed the data, iterated, even made figures for the paper. The resulting manuscript is a first-of-a-kind in the natural sciences, in which everything that needed to be done to write the paper was done by AI agents, apart from actually conducting the physical experiments in the lab and writing the final manuscript. We are also introducing Robin, the first multi-agent system that fully automates the in-silico components of scientific discovery, which made this discovery. This is the first time that we are aware of that hypothesis generation, experimentation, and data analysis have been joined up in closed loop, and is the beginning of a massive acceleration in the pace of scientific discovery that will be driven by these agents. We will be open-sourcing the code and data next week. Robin is a multi-agent system that uses Crow, Falcon, and Finch, the agents on our platform, to generate novel hypotheses, plan experiments, and analyze data. We asked Robin to find a new treatment for dry age-related macular degeneration. Robin considered the disease mechanisms associated with dry AMD, proposed a specific experimental assay that could be used to evaluate hypotheses in the wet lab, and proposed specific molecules we could test in that assay. We tested the molecules and gave it the resulting data, which it analyzed before proposing more experiments. In the end, it identified Ripasudil, a Rho Kinase inhibitor (ROCK inhibitor) that is approved in Japan for several other diseases, which seems very promising as potential treatment for dry AMD. It also identified specific molecular mechanisms that might underlie the effects of Ripasudil in RPE cells, from an RNA sequencing experiment it proposed. To be clear, no one has proposed using ROCK inhibitors to treat dry AMD in the literature before, as far as we can find, and I think it would have been very difficult for us to come up with this hypothesis without the agents. We have also run the proposed treatment by several experts in AMD, who confirm that it is interesting and novel. Moreover, this project was fast: with Robin in hand, the entire project took about 10 weeks, which is way shorter than it would have taken if we had been doing all of the in-silico components ourselves. Important caveats: We are real biologists at FutureHouse, so I want to be clear that although the discovery here is exciting, we are not claiming that we have cured dry AMD. Fully validating this hypothesis as a treatment for dry AMD will take human trials, which will take much longer. Also, this discovery is cool, but it is not yet a "move 37"-style discovery. At the current rate of progress, I'm sure we will get to that level soon. Congratulations to the team. Congratulations in particular to Robin, which generated the hypotheses, proposed the experiments, analyzed the data and generated the figures. And major congratulations also to the human team, which built Robin: Michaela Hinks, Ali Ghareeb, Benjamin Chang, Ludovico Mitchener, Mo Razzak, Kiki Szostkiewicz, and Angela Yiu.

Sam Rodriques

1,106,791 просмотров • 1 год назад

We are building autonomous scientific discovery. And it runs 370x faster than a human scientist. Our AI agents index millions of scientific publications and learn what an entire field has done in the time it takes a researcher to read one paper. Then we bring it to the lab to verify and further fuel learnings. 10 years to discover a material? Not here.
0:32

We are building autonomous scientific discovery. And it runs 370x faster than a human scientist. Our AI agents index millions of scientific publications and learn what an entire field has done in the time it takes a researcher to read one paper. Then we bring it to the lab to verify and further fuel learnings. 10 years to discover a material? Not here.

Radical AI

85,307 просмотров • 3 месяцев назад

We live in a golden age of biology. So why are people still dying from disease? Because discovery and development move slower than they should. Today, we’re partnering with Incyte to change that. Kosmos is now the first agent that can compress months of drug development into weeks, from the earliest stages of scientific discovery through to FDA approval. Incyte will be the first company to deploy it across their pipeline. Work that used to take a team of scientists months now happens in weeks. Patients can't wait, and neither can we.
1:21

We live in a golden age of biology. So why are people still dying from disease? Because discovery and development move slower than they should. Today, we’re partnering with Incyte to change that. Kosmos is now the first agent that can compress months of drug development into weeks, from the earliest stages of scientific discovery through to FDA approval. Incyte will be the first company to deploy it across their pipeline. Work that used to take a team of scientists months now happens in weeks. Patients can't wait, and neither can we.

Sam Rodriques

7,022,703 просмотров • 1 месяц назад

Alex on why AI + fully autonomous labs will compress drug discovery from five years to two: "If scientists are armed with agentic infrastructure that has an intelligence that's a multiple of Einstein, they will do science faster, with higher probability, for lower cost, and have greater impact." "AI coming into the discovery of medicines is one of the biggest unlocks we will see in history because it means we're no longer constrained by our hands or minds. We can identify all n number of hypotheses that could exist, figure out the best experiment to run, and run those 24/7." "The sheer ability to be more comprehensive in our hypothesis set, and to run experimentation at unprecedented scale, means we're going to speed breakthroughs from timelines that take three to five years to timelines that take less than a couple of years."
3:18

Alex on why AI + fully autonomous labs will compress drug discovery from five years to two: "If scientists are armed with agentic infrastructure that has an intelligence that's a multiple of Einstein, they will do science faster, with higher probability, for lower cost, and have greater impact." "AI coming into the discovery of medicines is one of the biggest unlocks we will see in history because it means we're no longer constrained by our hands or minds. We can identify all n number of hypotheses that could exist, figure out the best experiment to run, and run those 24/7." "The sheer ability to be more comprehensive in our hypothesis set, and to run experimentation at unprecedented scale, means we're going to speed breakthroughs from timelines that take three to five years to timelines that take less than a couple of years."

Patrick OShaughnessy

39,866 просмотров • 2 месяцев назад

For decades, the internet has been driven by search. But with AI, it's time for search to evolve. Google is pioneering these changes, led by Robby Stein. We've moved from 10 blue links to a complex multimodal system that expands search, rather than replaces it. Really cool episode.
47:21

For decades, the internet has been driven by search. But with AI, it's time for search to evolve. Google is pioneering these changes, led by Robby Stein. We've moved from 10 blue links to a complex multimodal system that expands search, rather than replaces it. Really cool episode.

Limitless

11,374 просмотров • 7 месяцев назад

OptimAI is leveling up. We’re bringing AI Contextual Search and Tweet Radar to OptimAI agent - a new way to search, filter, and surface the most relevant content in seconds. Try OptimAI: No more endless scrolling. No more noisy feeds. Just precise, contextual results powered by real-time intelligence. Perfect for research, trend discovery, and creating sharper content faster. And it’s only the beginning - the foundations for an agentic system that can reason, decide, and fully automate your workflow. Stay tuned.
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OptimAI is leveling up. We’re bringing AI Contextual Search and Tweet Radar to OptimAI agent - a new way to search, filter, and surface the most relevant content in seconds. Try OptimAI: No more endless scrolling. No more noisy feeds. Just precise, contextual results powered by real-time intelligence. Perfect for research, trend discovery, and creating sharper content faster. And it’s only the beginning - the foundations for an agentic system that can reason, decide, and fully automate your workflow. Stay tuned.

OptimAI

40,330 просмотров • 7 месяцев назад

The Genesis Mission calls for new ways to accelerate scientific discovery. This is our contribution Multimodal search across 25M papers is a step toward science discovery that moves at the speed of curiosity. Releasing, - Visually indexed scientific paper dataset with open access 25M papers, 450M+ visually indexed pages. Total 175TB+. All on Deep Lake - Open-source scientific data agent that achieves 48% SOTA on Humanity's Last Exam with tools including the indexed scientific research dataset. Excited to see what discoveries you all uncover with this. Try it and share your most interesting findings.
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The Genesis Mission calls for new ways to accelerate scientific discovery. This is our contribution Multimodal search across 25M papers is a step toward science discovery that moves at the speed of curiosity. Releasing, - Visually indexed scientific paper dataset with open access 25M papers, 450M+ visually indexed pages. Total 175TB+. All on Deep Lake - Open-source scientific data agent that achieves 48% SOTA on Humanity's Last Exam with tools including the indexed scientific research dataset. Excited to see what discoveries you all uncover with this. Try it and share your most interesting findings.

Davit

238,086 просмотров • 7 месяцев назад

We shipped big changes for Cloudflare Developers AI Search! Every agent needs search and it shouldn't take a week to set up. AI Search is the search primitive for your agents. Create an instance, upload your data, and search it. Comes with hybrid retrieval with vector and BM25.
0:24

We shipped big changes for Cloudflare Developers AI Search! Every agent needs search and it shouldn't take a week to set up. AI Search is the search primitive for your agents. Create an instance, upload your data, and search it. Comes with hybrid retrieval with vector and BM25.

Anni Wang

12,975 просмотров • 2 месяцев назад

Kenya Space Agency's Astrophysicist Discovers a Second Asteroid- Sparking Global Recognition. The Kenya Space Agency (KSA) is pleased to announce the recent confirmation of Asteroid 2024 JJ63 by the Minor Planet Center (MPC), in collaboration with the International Astronomical Search Collaboration (IASC) a NASA partner and the Pan-African Citizen Science e-Laboratory (PAS E-LAB). The asteroid was detected in 2024 by Mr. Harold Safary, an astrophysicist of the Kenya Space Agency. This achievement marks Mr. Safary’s second confirmed asteroid discovery, further strengthening Kenya’s presence in global astronomical research and space science. Previously, the International Astronomical Search Collaboration (IASC) confirmed the discovery of Asteroid 2023 TQ159, also identified by Mr. Safary. This discovery resulted from his participation in an asteroid search project in which teams were provided with astronomical observational data for analysis. Using Astrometrica software, Mr. Safary meticulously examined the data, identified a moving celestial object, and prepared a detailed report that was submitted to the Minor Planet Center through IASC for verification. Both discoveries have since been officially catalogued in the Minor Planet Center database at Harvard University, where the asteroids can be tracked using their designated identification numbers. Together, these achievements highlight the growing impact of Kenya’s space science and astronomy initiatives and underscore the value of space science, education, and international collaboration in advancing scientific discovery. #AsteroidDiscovery #KenyaToTheUniverse #SpaceScience
0:33

Kenya Space Agency's Astrophysicist Discovers a Second Asteroid- Sparking Global Recognition. The Kenya Space Agency (KSA) is pleased to announce the recent confirmation of Asteroid 2024 JJ63 by the Minor Planet Center (MPC), in collaboration with the International Astronomical Search Collaboration (IASC) a NASA partner and the Pan-African Citizen Science e-Laboratory (PAS E-LAB). The asteroid was detected in 2024 by Mr. Harold Safary, an astrophysicist of the Kenya Space Agency. This achievement marks Mr. Safary’s second confirmed asteroid discovery, further strengthening Kenya’s presence in global astronomical research and space science. Previously, the International Astronomical Search Collaboration (IASC) confirmed the discovery of Asteroid 2023 TQ159, also identified by Mr. Safary. This discovery resulted from his participation in an asteroid search project in which teams were provided with astronomical observational data for analysis. Using Astrometrica software, Mr. Safary meticulously examined the data, identified a moving celestial object, and prepared a detailed report that was submitted to the Minor Planet Center through IASC for verification. Both discoveries have since been officially catalogued in the Minor Planet Center database at Harvard University, where the asteroids can be tracked using their designated identification numbers. Together, these achievements highlight the growing impact of Kenya’s space science and astronomy initiatives and underscore the value of space science, education, and international collaboration in advancing scientific discovery. #AsteroidDiscovery #KenyaToTheUniverse #SpaceScience

Kenya Space Agency

31,533 просмотров • 6 месяцев назад

Today I’m excited to introduce Copilot Search in Bing. Copilot Search blends the best of traditional and generative search to help you find what you need. Whether it’s a navigational search result, a quick straightforward answer, or a complex query that leads you on a journey of discovery, Bing is your AI-powered search and answer engine. Copilot Search is rolling out today to everyone. To get started, go to and start exploring. This is a meaningful next step in our evolution of search, building on our learnings from Bing Chat, Copilot, and Bing Generative Search to provide our users the best search experience while supporting and building a healthy web ecosystem. Learn more in today’s announcement:
0:37

Today I’m excited to introduce Copilot Search in Bing. Copilot Search blends the best of traditional and generative search to help you find what you need. Whether it’s a navigational search result, a quick straightforward answer, or a complex query that leads you on a journey of discovery, Bing is your AI-powered search and answer engine. Copilot Search is rolling out today to everyone. To get started, go to and start exploring. This is a meaningful next step in our evolution of search, building on our learnings from Bing Chat, Copilot, and Bing Generative Search to provide our users the best search experience while supporting and building a healthy web ecosystem. Learn more in today’s announcement:

Jordi Ribas

16,724 просмотров • 1 год назад

Proteins are the machinery of life. Scientists have cataloged billions of protein sequences—but their biology is still mostly unknown. Today we're releasing a world model of protein biology: a scientific engine for prediction, design, and discovery that consists of ESMFold2, ESMC, and ESM Atlas. Together, they're helping to open up a new way for researchers to design proteins and speed up scientific discovery. Our mission is to cure or prevent disease. To do that, we need to accelerate science. That's why we're releasing all three openly.
1:08

Proteins are the machinery of life. Scientists have cataloged billions of protein sequences—but their biology is still mostly unknown. Today we're releasing a world model of protein biology: a scientific engine for prediction, design, and discovery that consists of ESMFold2, ESMC, and ESM Atlas. Together, they're helping to open up a new way for researchers to design proteins and speed up scientific discovery. Our mission is to cure or prevent disease. To do that, we need to accelerate science. That's why we're releasing all three openly.

biohub

78,224 просмотров • 1 месяц назад

NVIDIA CEO Jensen Huang just gave us a glimpse into the future of AI, and it's far bigger than text and images. He asked: "If you can understand worlds, is it possible that you understand proteins and chemicals that have structure?” This is the quiet revolution happening in AI right now. We're moving from an AI that understands human language to an AI that understands the language of nature itself. The "letters" are atoms. The "words" are molecules. The "sentences" are the complex structures of life, networks, and reality. The same foundational models that generate video are now being used to: *Decode proteins to fight disease *Design new materials at the atomic level *Simulate complex systems in quantum computing These were once the domain of supercomputers and Nobel laureates. Now, they're becoming solvable problems for a new generation of startups. AI is transitioning from a tool for communication to a tool for creation and discovery in the physical world. This is where the next wave of generational companies will be built. The "hard-to-solve" problems are now within reach.
2:14

NVIDIA CEO Jensen Huang just gave us a glimpse into the future of AI, and it's far bigger than text and images. He asked: "If you can understand worlds, is it possible that you understand proteins and chemicals that have structure?” This is the quiet revolution happening in AI right now. We're moving from an AI that understands human language to an AI that understands the language of nature itself. The "letters" are atoms. The "words" are molecules. The "sentences" are the complex structures of life, networks, and reality. The same foundational models that generate video are now being used to: *Decode proteins to fight disease *Design new materials at the atomic level *Simulate complex systems in quantum computing These were once the domain of supercomputers and Nobel laureates. Now, they're becoming solvable problems for a new generation of startups. AI is transitioning from a tool for communication to a tool for creation and discovery in the physical world. This is where the next wave of generational companies will be built. The "hard-to-solve" problems are now within reach.

Konstantine Buhler

13,014 просмотров • 8 месяцев 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

ReZero: A small model that learns to search - it never gives up 🔥 ReZero trains with synthetic search engines that force the model to retry, refine, and persist until it finds a better answer (never give up 💪). It's built on Meta's Llama 3.2B. Instead of optimizing for recall or speed, we train the model to retry when it's wrong - using reinforcement learning to build persistence into the search process. - Model: - Code: Thanks to AI at Meta for the Llama 3.2B base, Unsloth AI for AutoDidact (the framework we built on), and Colin Kealty for quantizing the model!
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ReZero: A small model that learns to search - it never gives up 🔥 ReZero trains with synthetic search engines that force the model to retry, refine, and persist until it finds a better answer (never give up 💪). It's built on Meta's Llama 3.2B. Instead of optimizing for recall or speed, we train the model to retry when it's wrong - using reinforcement learning to build persistence into the search process. - Model: - Code: Thanks to AI at Meta for the Llama 3.2B base, Unsloth AI for AutoDidact (the framework we built on), and Colin Kealty for quantizing the model!

Menlo Research

13,948 просмотров • 1 год назад

Jensen Huang says agentic AI is not a chatbot. It is a "digital robot" -- and the next bottleneck is not the model. It is first-person data. "Agentic AI is just a digital robot. It understands, it reasons, it plans, and it acts and uses tools." Then he makes the bigger jump: "The world's video data is from a third person, not first person. And so agentic systems, robotic systems, physical AI -- the data is the hardest problem." That is the hidden constraint in robotics. LLMs got the internet. Physical AI needs demonstrations, synthetic worlds, reinforcement learning, and first-person experience. This is why Jensen keeps calling it a stack: robots are not waiting for one magical model. They need the data engine, the simulator, the reward system, the runtime, and the computer. The next wave of AI is not just software that talks. It is software that sees, plans, acts, and eventually moves through the world.
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Jensen Huang says agentic AI is not a chatbot. It is a "digital robot" -- and the next bottleneck is not the model. It is first-person data. "Agentic AI is just a digital robot. It understands, it reasons, it plans, and it acts and uses tools." Then he makes the bigger jump: "The world's video data is from a third person, not first person. And so agentic systems, robotic systems, physical AI -- the data is the hardest problem." That is the hidden constraint in robotics. LLMs got the internet. Physical AI needs demonstrations, synthetic worlds, reinforcement learning, and first-person experience. This is why Jensen keeps calling it a stack: robots are not waiting for one magical model. They need the data engine, the simulator, the reward system, the runtime, and the computer. The next wave of AI is not just software that talks. It is software that sees, plans, acts, and eventually moves through the world.

Karl Mehta

13,608 просмотров • 5 дней назад