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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...

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Chao Huang

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16,854 次观看 • 10 个月前 •via X (Twitter)

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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.
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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 次观看 • 7 个月前

🚀 Big milestone: We raised a $235M Series A, co-led by Braidwell and Collective Global! Our team is excited for this next chapter in building the world’s first scientific superintelligence platform to accelerate breakthroughs in life, chemical, and materials sciences. 📣 If the idea of building scientific superintelligence excites you, come build with us. We’re seeking extraordinary, driven people across AI research, software, robotics, biology, chemistry, materials science, and operations. #ScientificSuperintelligence #AI #Innovation
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🚀 Big milestone: We raised a $235M Series A, co-led by Braidwell and Collective Global! Our team is excited for this next chapter in building the world’s first scientific superintelligence platform to accelerate breakthroughs in life, chemical, and materials sciences. 📣 If the idea of building scientific superintelligence excites you, come build with us. We’re seeking extraordinary, driven people across AI research, software, robotics, biology, chemistry, materials science, and operations. #ScientificSuperintelligence #AI #Innovation

Lila Sciences

43,078 次观看 • 10 个月前

Can LLMs invent better ways to train LLMs? At Sakana AI, we’re pioneering AI-driven methods to automate AI research and discovery. We’re excited to release DiscoPOP: a new SOTA preference optimization algorithm that was discovered and written by an LLM! Our method leverages LLMs to propose and implement new preference optimization algorithms. We then train models with those algorithms and evaluate their performance, providing feedback to the LLM. By repeating this process for multiple generations in an evolutionary loop, the LLM discovers many highly-performant and novel preference optimization objectives! Paper: GitHub: Model: We proudly collaborated with the University of Oxford (Foerster Lab for AI Research (now part of BOLD)) and Cambridge University (Mihaela van der Schaar) on this groundbreaking project. Looking ahead, we envision a future where AI-driven research reduces the need for extensive human intervention and computational resources. This will accelerate scientific discoveries and innovation, pushing the boundaries of what AI can achieve.
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Can LLMs invent better ways to train LLMs? At Sakana AI, we’re pioneering AI-driven methods to automate AI research and discovery. We’re excited to release DiscoPOP: a new SOTA preference optimization algorithm that was discovered and written by an LLM! Our method leverages LLMs to propose and implement new preference optimization algorithms. We then train models with those algorithms and evaluate their performance, providing feedback to the LLM. By repeating this process for multiple generations in an evolutionary loop, the LLM discovers many highly-performant and novel preference optimization objectives! Paper: GitHub: Model: We proudly collaborated with the University of Oxford (Foerster Lab for AI Research (now part of BOLD)) and Cambridge University (Mihaela van der Schaar) on this groundbreaking project. Looking ahead, we envision a future where AI-driven research reduces the need for extensive human intervention and computational resources. This will accelerate scientific discoveries and innovation, pushing the boundaries of what AI can achieve.

Sakana AI

555,922 次观看 • 2 年前

The Spectre AI On-Chain Search Engine Research Zone - The Showcase. Revealing the Research Zone: The Spectre AI On-Chain Search Engine, a comprehensive platform in development designed to be your ultimate one-stop shop for all things cryptocurrency. Combining real-time data, extensive research tools, and advanced AI analytics, this platform aims to deliver an unparalleled user experience. We are working closely with Google to integrate real-time data implementation, ensuring you have the most accurate and up-to-date information at your fingertips. Our search engine is divided into three segments, showcased here as we prepare for our upcoming deployment: Landing Page: Features all main coins, trending coins, category coins, and your personal watchlist. Includes a switch to view our unique UI Heatmaps with Flipcards for a broad market overview. Research Zone: Packed with project descriptions, social media links, and charts powered by TradingView . Includes a full Technical Analysis panel with TA indicators and AI interpretation and analysis. Sentiment Analysis Zone provides all essential information in one place, featuring our unique Sentiment Analysis Charts. Right-side panel displays token metrics, contract information, audit info, and a blockchain scanning zone. Includes main and related tweets, a flipcard of the project's price and chart, and a holders chart. Search Engine Chatbot: Currently under development in collaboration with Google. Reveal coming soon. The backend is complete, and our front-end development is well underway. We are nearing our beta release and are excited to soon offer a revolutionary way to interact with the cryptocurrency market. Stay tuned for the beta launch and get ready to experience the future of crypto research with Spectre AI. #searchengine #google #ai #spectre #blockchain #tech #innovation $SPECT
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The Spectre AI On-Chain Search Engine Research Zone - The Showcase. Revealing the Research Zone: The Spectre AI On-Chain Search Engine, a comprehensive platform in development designed to be your ultimate one-stop shop for all things cryptocurrency. Combining real-time data, extensive research tools, and advanced AI analytics, this platform aims to deliver an unparalleled user experience. We are working closely with Google to integrate real-time data implementation, ensuring you have the most accurate and up-to-date information at your fingertips. Our search engine is divided into three segments, showcased here as we prepare for our upcoming deployment: Landing Page: Features all main coins, trending coins, category coins, and your personal watchlist. Includes a switch to view our unique UI Heatmaps with Flipcards for a broad market overview. Research Zone: Packed with project descriptions, social media links, and charts powered by TradingView . Includes a full Technical Analysis panel with TA indicators and AI interpretation and analysis. Sentiment Analysis Zone provides all essential information in one place, featuring our unique Sentiment Analysis Charts. Right-side panel displays token metrics, contract information, audit info, and a blockchain scanning zone. Includes main and related tweets, a flipcard of the project's price and chart, and a holders chart. Search Engine Chatbot: Currently under development in collaboration with Google. Reveal coming soon. The backend is complete, and our front-end development is well underway. We are nearing our beta release and are excited to soon offer a revolutionary way to interact with the cryptocurrency market. Stay tuned for the beta launch and get ready to experience the future of crypto research with Spectre AI. #searchengine #google #ai #spectre #blockchain #tech #innovation $SPECT

SPECTRE AI

18,254 次观看 • 2 年前

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 次观看 • 1 年前

Meet BIOS, an AI Scientist built to orchestrate complex biomedical research. • Global SOTA on Data Analysis Benchmarks: BixBench 48.78% open-answer, 55.12% multiple-choice + refusal, 64.39% multiple-choice (no refusal) - outperforming systems like Edison Scientific and Kepler. • Human-in-the-Loop or Autonomous Mode: Intermediate checkpoints let researchers guide investigations mid-flight as insights emerge. No more waiting hours for batch runs + reruns to get results. Or, run in fully autonomous mode for extended investigations. • Persistent World State: Rather than losing context as conversations grow, world state ensures investigations build on insights within each research cycle and across sessions. • Subagent Swarm: BIOS orchestrates subagents specializing in research functions (Literature Review, Data Analysis, Novelty Detection) and, soon, research domains (microbiology, longevity, genomics). BIOS is available now in Beta with free + paid tiers, exclusive launch pricing and, for limited time, free full access to academic users with a .edu email address. Pro, Researcher and Lab subscription tiers offer discounted packages on monthly credits. Our usage-based pricing is competitive and in some cases significantly cheaper than leading scientific agents. Try BIOS and read our paper in the links below ↓
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Meet BIOS, an AI Scientist built to orchestrate complex biomedical research. • Global SOTA on Data Analysis Benchmarks: BixBench 48.78% open-answer, 55.12% multiple-choice + refusal, 64.39% multiple-choice (no refusal) - outperforming systems like Edison Scientific and Kepler. • Human-in-the-Loop or Autonomous Mode: Intermediate checkpoints let researchers guide investigations mid-flight as insights emerge. No more waiting hours for batch runs + reruns to get results. Or, run in fully autonomous mode for extended investigations. • Persistent World State: Rather than losing context as conversations grow, world state ensures investigations build on insights within each research cycle and across sessions. • Subagent Swarm: BIOS orchestrates subagents specializing in research functions (Literature Review, Data Analysis, Novelty Detection) and, soon, research domains (microbiology, longevity, genomics). BIOS is available now in Beta with free + paid tiers, exclusive launch pricing and, for limited time, free full access to academic users with a .edu email address. Pro, Researcher and Lab subscription tiers offer discounted packages on monthly credits. Our usage-based pricing is competitive and in some cases significantly cheaper than leading scientific agents. Try BIOS and read our paper in the links below ↓

BIOS

68,467 次观看 • 5 个月前

What if our biggest problems—affordable housing shortages, energy limits, and scientific slowdowns—aren’t inevitable, but chosen? This week on Possible, we sit down with Derek Thompson to talk Abundance, scientific discovery, scientific acceleration, and more. 00:25 When would you choose to be born: 1980, 2020, or 2040? 05:14 AI vs. Ezra Klein and human capabilities 08:14 The future of work 11:49 Tariffs and trade policies 16:19 Operation Warp Speed 23:04 AI policies for accelerating abundance 28:07 Too much, too little, or just right? 32:21 Innovation in retail and government housing 35:01 Debating border security and immigration 37:50 Challenges in accelerating scientific research and NIH reforms 48:16 The loneliness epidemic and technology's role
1:05:32

What if our biggest problems—affordable housing shortages, energy limits, and scientific slowdowns—aren’t inevitable, but chosen? This week on Possible, we sit down with Derek Thompson to talk Abundance, scientific discovery, scientific acceleration, and more. 00:25 When would you choose to be born: 1980, 2020, or 2040? 05:14 AI vs. Ezra Klein and human capabilities 08:14 The future of work 11:49 Tariffs and trade policies 16:19 Operation Warp Speed 23:04 AI policies for accelerating abundance 28:07 Too much, too little, or just right? 32:21 Innovation in retail and government housing 35:01 Debating border security and immigration 37:50 Challenges in accelerating scientific research and NIH reforms 48:16 The loneliness epidemic and technology's role

Reid Hoffman

18,083 次观看 • 1 年前

The best undergraduate and graduate research awards at Caltech commencement this year were presented to research on Neural Operators by my students @ZongyiLiCaltech and Miguel Liu-Schiaffini. This recognition is a testament to their passion and dedication, and the impact their work has already had: from building a foundation for AI+Science through multi-scale learning, creating the first high-resolution AI-based weather model, designing a novel medical catheter that reduces bacterial contamination, to name just a few. The future of Physical AI will be built on the foundation of Neural Operators since the real world exists in multiple scales and resolutions, while standard deep learning only supports fixed resolution. This is essential for us to unlock the full potential of AI for scientific discovery. #Caltech2025 #ai #science #NeuralOperators
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The best undergraduate and graduate research awards at Caltech commencement this year were presented to research on Neural Operators by my students @ZongyiLiCaltech and Miguel Liu-Schiaffini. This recognition is a testament to their passion and dedication, and the impact their work has already had: from building a foundation for AI+Science through multi-scale learning, creating the first high-resolution AI-based weather model, designing a novel medical catheter that reduces bacterial contamination, to name just a few. The future of Physical AI will be built on the foundation of Neural Operators since the real world exists in multiple scales and resolutions, while standard deep learning only supports fixed resolution. This is essential for us to unlock the full potential of AI for scientific discovery. #Caltech2025 #ai #science #NeuralOperators

Prof. Anima Anandkumar

29,765 次观看 • 1 年前

auto-research is starting to gain traction as a very viable paradigm for creating useful research discovery. now, that paradigm is still in its infancy and the infrastructure to hold all that trail of context as the agents blaze through experiments isn't well defined (to say the least). on that topic, I had the chance to chat with my boys francesco and giulio from paradigma about what underlying infra is needed to make this paradigm work. the paradigma's paradigm, which involves copious amount of DAGs, make this auto-research paradigm a paradigmatic case of essential infrastructure. here's the full video in full: - 0:00 - what is missing from auto-research? - 2:02 - giulio and francesco ai journey - 8:10 - research infra is the bottleneck? - 10:18 - paradigma vision of autonomous research - 13:17 - “important discovery per joules” - 17:15 - why is DAG the unit of research for auto-research? - 20:40 - is paradigma trying to replace the research publication? - 24:50 - how does knowledge is shared between experiments in the DAG? - 27:34 - what is even auto-research lol? - 33:53 - the value of the human mind in this auto-research future. - 37:00 - how do you reconcile hallucination in this auto-research paradigm? - 41:33 - the adoption of auto-research across varied fields? - 47:30 - ✨ introduction to the auto-research infrastructure. ✨ - 56:55 - where is the code? - 59:10 - full IDE next? - 1:03:20 - the place of the human in this DAG / code quality? manual node? token spent? - 1:16:02 - who’s the user for auto-research? - 1:18:13 - how to validate bad DAG? - 1:20:18 - ✨ auto-research agent results ✨ - 1:22:53 - ✨ how a big research DAG looks like? ✨ - 1:25:10 - how to get the canonical DAG for the final result? - 1:27:50 - the auto-research DAG being the new pre-print? - 1:30:05 - what’s next for paradigma and the auto-research infra? - 1:35:00 - what are they excited about research wise? enjoyyyyy my guys 🌹
1:37:23

auto-research is starting to gain traction as a very viable paradigm for creating useful research discovery. now, that paradigm is still in its infancy and the infrastructure to hold all that trail of context as the agents blaze through experiments isn't well defined (to say the least). on that topic, I had the chance to chat with my boys francesco and giulio from paradigma about what underlying infra is needed to make this paradigm work. the paradigma's paradigm, which involves copious amount of DAGs, make this auto-research paradigm a paradigmatic case of essential infrastructure. here's the full video in full: - 0:00 - what is missing from auto-research? - 2:02 - giulio and francesco ai journey - 8:10 - research infra is the bottleneck? - 10:18 - paradigma vision of autonomous research - 13:17 - “important discovery per joules” - 17:15 - why is DAG the unit of research for auto-research? - 20:40 - is paradigma trying to replace the research publication? - 24:50 - how does knowledge is shared between experiments in the DAG? - 27:34 - what is even auto-research lol? - 33:53 - the value of the human mind in this auto-research future. - 37:00 - how do you reconcile hallucination in this auto-research paradigm? - 41:33 - the adoption of auto-research across varied fields? - 47:30 - ✨ introduction to the auto-research infrastructure. ✨ - 56:55 - where is the code? - 59:10 - full IDE next? - 1:03:20 - the place of the human in this DAG / code quality? manual node? token spent? - 1:16:02 - who’s the user for auto-research? - 1:18:13 - how to validate bad DAG? - 1:20:18 - ✨ auto-research agent results ✨ - 1:22:53 - ✨ how a big research DAG looks like? ✨ - 1:25:10 - how to get the canonical DAG for the final result? - 1:27:50 - the auto-research DAG being the new pre-print? - 1:30:05 - what’s next for paradigma and the auto-research infra? - 1:35:00 - what are they excited about research wise? enjoyyyyy my guys 🌹

Yacine Mahdid

12,091 次观看 • 1 个月前

AI is rapidly accelerating scientific discovery and contributing to the effort to better understand cancer. We're proud to announce two major open-source breakthroughs from Google Research and Google DeepMind that demonstrate the immense potential of AI in this field: First, DeepSomatic can significantly improve cancer diagnostics by accurately identifying key genetic variants within cancer cells, outperforming current state-of-the-art methods. This provides researchers and clinicians with a more precise and reliable tool for mapping cancer's blueprint, which is essential for targeted therapy development. Second, our new Gemma C2S-Scale 27B model (built using our open Gemma family), achieved an exciting milestone: it generated a novel hypothesis to convert "cold" tumors—which are notoriously resistant to current immunotherapies—into "hot" tumors that can be successfully treated. This discovery may offer a new avenue for cancer drug development.
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AI is rapidly accelerating scientific discovery and contributing to the effort to better understand cancer. We're proud to announce two major open-source breakthroughs from Google Research and Google DeepMind that demonstrate the immense potential of AI in this field: First, DeepSomatic can significantly improve cancer diagnostics by accurately identifying key genetic variants within cancer cells, outperforming current state-of-the-art methods. This provides researchers and clinicians with a more precise and reliable tool for mapping cancer's blueprint, which is essential for targeted therapy development. Second, our new Gemma C2S-Scale 27B model (built using our open Gemma family), achieved an exciting milestone: it generated a novel hypothesis to convert "cold" tumors—which are notoriously resistant to current immunotherapies—into "hot" tumors that can be successfully treated. This discovery may offer a new avenue for cancer drug development.

Google AI

59,683 次观看 • 9 个月前

SuperVerse x Spectre AI Collaboration We are excited to announce our collaboration and token integration with $SUPER – SuperVerse, one of the largest and most influential Web3 communities in the space. SuperVerse is the definitive home for Web3 gaming innovation. As part of this collaboration, SUPER token holders with 1,500 tokens or more will gain exclusive access to the Spectre AI On-Chain Search Engine, providing powerful tools and insights to navigate the crypto landscape. In addition to the On-Chain Search Engine, SUPER holders will soon have access to a range of advanced Spectre AI utilities: - X Bubbles – Visual mapping of project and influencer connections in the crypto space - X Charts – Advanced charting tools with deep market analytics - Monarch AI Agent – A real-time AI-powered chat environment with integrated research tools - AI DexScan – An AI-infused decentralized exchange with real-time analytics directly embedded alongside chart data This collaboration marks the convergence of Web3 gaming and AI-driven blockchain intelligence. #gaming #ai #web $SUPER $SPECT
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SuperVerse x Spectre AI Collaboration We are excited to announce our collaboration and token integration with $SUPER – SuperVerse, one of the largest and most influential Web3 communities in the space. SuperVerse is the definitive home for Web3 gaming innovation. As part of this collaboration, SUPER token holders with 1,500 tokens or more will gain exclusive access to the Spectre AI On-Chain Search Engine, providing powerful tools and insights to navigate the crypto landscape. In addition to the On-Chain Search Engine, SUPER holders will soon have access to a range of advanced Spectre AI utilities: - X Bubbles – Visual mapping of project and influencer connections in the crypto space - X Charts – Advanced charting tools with deep market analytics - Monarch AI Agent – A real-time AI-powered chat environment with integrated research tools - AI DexScan – An AI-infused decentralized exchange with real-time analytics directly embedded alongside chart data This collaboration marks the convergence of Web3 gaming and AI-driven blockchain intelligence. #gaming #ai #web $SUPER $SPECT

SPECTRE AI

102,766 次观看 • 1 年前

Two big steps towards our vision for NotebookLM as the ultimate research platform: • Integrating Deep Research, with a set of only-at-Notebook features that let you explore the retrieved sources • Launching a series of Featured Notebooks curated by Google Research These developments are designed to enhance the full life cycle of research and scholarship: using the power of AI to assemble the knowledge base you need to advance your understanding, and then making your work accessible and intelligible to a wider audience using all the explanatory tools that Notebook offers. If you've used DeepResearch in the Gemini app, you already know that it's a pioneering advance in assembling complex, grounded information on any topic imaginable—collecting an entire trove of material for you and writing a nuanced research report that summarizes the findings. But because NotebookLM is designed to manage and explore potentially hundreds of sources, the Deep Research report is only the beginning of your journey. In our integration, Deep Research gives you an overview all of the sources it found during its research phase, with annotated commentary explaining how each source related to your original query. You can then choose to import some or all of the sources to the notebook, along with the report itself, which you can then explore or transform using the full suite of tools that Notebook offers: grounded chat with citations, Mind Maps, Audio/Video overviews, and much more. And it's that suite of tools that make the Google Research Featured Notebooks so compelling as well. Each notebook contains a curated collection of articles on a specific topic, published by the Google Research team. Think of them as a kind of knowledge base of Google's best thinking on a series of compelling research questions: How do scientists link genetics to health? How will quantum computing be useful? If you're a specialist in these fields, you can read the original papers or ask nuanced questions in chat and advance your understanding of the latest developments. But these notebooks can also make the complex but important topics understandable to non-specialists or students. Each notebook comes with pre-generated audio and video overviews, flashcards, and other Studio artifacts designed to make the scientific and technological concepts accessible and interesting. And you can always explore the material with our new "Learning Guide" chat mode that effectively gives you a personal tutor to enhance your understanding. There's much more to come on this front, but you can see in these two announcements how we see Notebook as both a workbench for conducting research and a publishing platform for sharing the results of that research once you're ready to make it public. Deep Research is rolling out this week to all users. The first two Google Research notebooks are live now, both of them deep dives into our most recent discoveries involving genetics and health. (Links in the following tweets.) We'll be publishing new notebooks in the series every other week or so for the next few months.
0:34

Two big steps towards our vision for NotebookLM as the ultimate research platform: • Integrating Deep Research, with a set of only-at-Notebook features that let you explore the retrieved sources • Launching a series of Featured Notebooks curated by Google Research These developments are designed to enhance the full life cycle of research and scholarship: using the power of AI to assemble the knowledge base you need to advance your understanding, and then making your work accessible and intelligible to a wider audience using all the explanatory tools that Notebook offers. If you've used DeepResearch in the Gemini app, you already know that it's a pioneering advance in assembling complex, grounded information on any topic imaginable—collecting an entire trove of material for you and writing a nuanced research report that summarizes the findings. But because NotebookLM is designed to manage and explore potentially hundreds of sources, the Deep Research report is only the beginning of your journey. In our integration, Deep Research gives you an overview all of the sources it found during its research phase, with annotated commentary explaining how each source related to your original query. You can then choose to import some or all of the sources to the notebook, along with the report itself, which you can then explore or transform using the full suite of tools that Notebook offers: grounded chat with citations, Mind Maps, Audio/Video overviews, and much more. And it's that suite of tools that make the Google Research Featured Notebooks so compelling as well. Each notebook contains a curated collection of articles on a specific topic, published by the Google Research team. Think of them as a kind of knowledge base of Google's best thinking on a series of compelling research questions: How do scientists link genetics to health? How will quantum computing be useful? If you're a specialist in these fields, you can read the original papers or ask nuanced questions in chat and advance your understanding of the latest developments. But these notebooks can also make the complex but important topics understandable to non-specialists or students. Each notebook comes with pre-generated audio and video overviews, flashcards, and other Studio artifacts designed to make the scientific and technological concepts accessible and interesting. And you can always explore the material with our new "Learning Guide" chat mode that effectively gives you a personal tutor to enhance your understanding. There's much more to come on this front, but you can see in these two announcements how we see Notebook as both a workbench for conducting research and a publishing platform for sharing the results of that research once you're ready to make it public. Deep Research is rolling out this week to all users. The first two Google Research notebooks are live now, both of them deep dives into our most recent discoveries involving genetics and health. (Links in the following tweets.) We'll be publishing new notebooks in the series every other week or so for the next few months.

Steven Johnson

104,814 次观看 • 8 个月前

Out of all the announcements at Google I/O today, this is the one closest to my heart - our foundational research on Co-Scientist was published in nature and we announced its broad availability via Google Gemini for Science. When you are suffering from a disease, time is everything. As our collaborator and Stanford Medicine Professor Dr. Gary Peltz reminds us, there are thousands of diseases out there with zero treatments. There is simply so much left to solve. Our goal with Co-Scientist has been to give scientists superpowers and help them get to these answers faster - compressing the scientific process from months and years down to hours and days. Much like Galileo's telescope helped us look into the stars, Co-Scientist is designed to help us make sense of the vast complexity of biological and scientific data. It is among the first examples of a truly general-purpose multi-agent system for scientific discovery. The core research question behind it was: How can an AI system engage in the rigorous, structured thinking that’s the hallmark of science and scientists? To tackle this, Co-Scientist builds on the principles of self-play and self-improvement underpinning GoogleDeepMind breakthroughs like AlphaGo, generalizing them to scientific reasoning through self-debates. Since our preprint last year, we have further improved its capabilities and have been validating it in collaborations with scientists across over 100 institutions globally, spanning both academia and industry. And we are thrilled to see the emergence of a new form of AI-human scientist collaboration that's already leading to important new insights, discoveries and peer reviewed publications - from understanding antimicrobial resistance (published in Cell) to decoding plant immunity, to identifying new treatments for liver fibrosis (Advanced Science), cancer, neurodegenerative diseases like ALS and the grand challenge of aging. I have always believed AI's greatest promise is accelerating scientific discovery and advancing human health. My genuine hope for the future is that AI tools like Co-Scientist help democratize science, giving anyone, anywhere the means to pursue their child-like curiosity and change the world. This work was done with stellar team mates spanning GoogleDeepMind GoogleResearch, Google Cloud and GoogleLabs especially Juro Gottweis (Juraj Gottweis ), who is the heart and soul of this effort. Special thanks also to all our wonderful collaborators: Gary Peltz, Tiago Costa, José R Penadés, Eeshit Dhaval Vaishnav , Byron, Vik Dhillon, Jonathan Gootenberg, Omar Abudayyeh Ritu Raman, Ryan Flynn, Filippo Menolascina, Velia Siciliano, Clare Bryant, Matt Onsum, Katherine Labbé and more. Nature paper link - Google DeepMind blog - Gemini for Science -
6:26

Out of all the announcements at Google I/O today, this is the one closest to my heart - our foundational research on Co-Scientist was published in nature and we announced its broad availability via Google Gemini for Science. When you are suffering from a disease, time is everything. As our collaborator and Stanford Medicine Professor Dr. Gary Peltz reminds us, there are thousands of diseases out there with zero treatments. There is simply so much left to solve. Our goal with Co-Scientist has been to give scientists superpowers and help them get to these answers faster - compressing the scientific process from months and years down to hours and days. Much like Galileo's telescope helped us look into the stars, Co-Scientist is designed to help us make sense of the vast complexity of biological and scientific data. It is among the first examples of a truly general-purpose multi-agent system for scientific discovery. The core research question behind it was: How can an AI system engage in the rigorous, structured thinking that’s the hallmark of science and scientists? To tackle this, Co-Scientist builds on the principles of self-play and self-improvement underpinning GoogleDeepMind breakthroughs like AlphaGo, generalizing them to scientific reasoning through self-debates. Since our preprint last year, we have further improved its capabilities and have been validating it in collaborations with scientists across over 100 institutions globally, spanning both academia and industry. And we are thrilled to see the emergence of a new form of AI-human scientist collaboration that's already leading to important new insights, discoveries and peer reviewed publications - from understanding antimicrobial resistance (published in Cell) to decoding plant immunity, to identifying new treatments for liver fibrosis (Advanced Science), cancer, neurodegenerative diseases like ALS and the grand challenge of aging. I have always believed AI's greatest promise is accelerating scientific discovery and advancing human health. My genuine hope for the future is that AI tools like Co-Scientist help democratize science, giving anyone, anywhere the means to pursue their child-like curiosity and change the world. This work was done with stellar team mates spanning GoogleDeepMind GoogleResearch, Google Cloud and GoogleLabs especially Juro Gottweis (Juraj Gottweis ), who is the heart and soul of this effort. Special thanks also to all our wonderful collaborators: Gary Peltz, Tiago Costa, José R Penadés, Eeshit Dhaval Vaishnav , Byron, Vik Dhillon, Jonathan Gootenberg, Omar Abudayyeh Ritu Raman, Ryan Flynn, Filippo Menolascina, Velia Siciliano, Clare Bryant, Matt Onsum, Katherine Labbé and more. Nature paper link - Google DeepMind blog - Gemini for Science -

Vivek Natarajan

82,051 次观看 • 2 个月前

The government has taken a lot of initiatives to promote AI, to promote opportunities in next-generation sectors such as Space & Drones, and for Science, Technology and Innovation. Some of them are: - 'India AI Mission' was launched with a budget outlay of ₹10,300 crore for building computing infrastructure, developing indigenous AI capabilities, attracting AI talent and financing AI start-ups. - 3 Centres of Excellence in Artificial Intelligence focused on Healthcare, Agriculture and Sustainable Cities were announced in 2023. In this year's budget, we have announced another Centre of Excellence in Artificial Intelligence focused on Education. - IN-SPACe has signed over 70 MoUs with Non-Government Entities (NGEs) to extend the necessary support for carrying out the space activities. - The National Green Hydrogen Mission launched in January 2023, with an outlay of around ₹ 20,000 crore for 5 years, will make India a global hub for the production, usage and export of Green Hydrogen and its derivatives and enable India to be energy-independent by 2047. - India Semiconductor Mission was launched in 2021. - National Quantum Mission was approved in 2023 to support scientific and industrial R&D and create a vibrant ecosystem in Quantum Technologies. - 'Anusandhan’ corpus of Rs 1 lakh crore established to provide long-term financing to support research and innovation in sunrise domains. - 10,000 PM Research Fellowships for technological research in IITs and IISc proposed. - 5 National Centres of Excellence for Skilling proposed to equip our youth with the skills required for 'Make for India, Make for the World' manufacturing. - We allocated Rs 20,000 crore to the ‘Research, Development and Innovation’ initiative. - Smt Nirmala Sitharaman at IIIT Kottayam, Kerala.
6:21

The government has taken a lot of initiatives to promote AI, to promote opportunities in next-generation sectors such as Space & Drones, and for Science, Technology and Innovation. Some of them are: - 'India AI Mission' was launched with a budget outlay of ₹10,300 crore for building computing infrastructure, developing indigenous AI capabilities, attracting AI talent and financing AI start-ups. - 3 Centres of Excellence in Artificial Intelligence focused on Healthcare, Agriculture and Sustainable Cities were announced in 2023. In this year's budget, we have announced another Centre of Excellence in Artificial Intelligence focused on Education. - IN-SPACe has signed over 70 MoUs with Non-Government Entities (NGEs) to extend the necessary support for carrying out the space activities. - The National Green Hydrogen Mission launched in January 2023, with an outlay of around ₹ 20,000 crore for 5 years, will make India a global hub for the production, usage and export of Green Hydrogen and its derivatives and enable India to be energy-independent by 2047. - India Semiconductor Mission was launched in 2021. - National Quantum Mission was approved in 2023 to support scientific and industrial R&D and create a vibrant ecosystem in Quantum Technologies. - 'Anusandhan’ corpus of Rs 1 lakh crore established to provide long-term financing to support research and innovation in sunrise domains. - 10,000 PM Research Fellowships for technological research in IITs and IISc proposed. - 5 National Centres of Excellence for Skilling proposed to equip our youth with the skills required for 'Make for India, Make for the World' manufacturing. - We allocated Rs 20,000 crore to the ‘Research, Development and Innovation’ initiative. - Smt Nirmala Sitharaman at IIIT Kottayam, Kerala.

Nirmala Sitharaman Office

23,179 次观看 • 1 年前

We built an open-source research platform for agents utilizing Andrej Karpathy's autoresearch agent. agentipedia Crowd-sourced research will be the single biggest impact point for AI in the next 5 years and agentipedia will be a platform to drive it. Our vision: > There are potentially millions of niche use cases of research agents building strategies, better models, operating procedures and more. PHD level science is possible through agent collaboration. > Right now, very few entities control the vast majority of resources that can power this research; we believe in the future where any curious soul can harness the same energy. > Agentipedia was created to let curious souls (ML Engineers, Executives/CEOs, Founders, Builders, or literally anyone) think of a hypothesis for any application and be met with a swarm of agents experimenting to see if its true. Collaboration will yield magnitudes of impact on our society that we have not yet seen. The simulators for several use cases like (drug discovery, autonomous driving) and more already exist today.
0:25

We built an open-source research platform for agents utilizing Andrej Karpathy's autoresearch agent. agentipedia Crowd-sourced research will be the single biggest impact point for AI in the next 5 years and agentipedia will be a platform to drive it. Our vision: > There are potentially millions of niche use cases of research agents building strategies, better models, operating procedures and more. PHD level science is possible through agent collaboration. > Right now, very few entities control the vast majority of resources that can power this research; we believe in the future where any curious soul can harness the same energy. > Agentipedia was created to let curious souls (ML Engineers, Executives/CEOs, Founders, Builders, or literally anyone) think of a hypothesis for any application and be met with a swarm of agents experimenting to see if its true. Collaboration will yield magnitudes of impact on our society that we have not yet seen. The simulators for several use cases like (drug discovery, autonomous driving) and more already exist today.

invisible

30,345 次观看 • 4 个月前

Wrapping up the year and coinciding with #NeurIPS2024, today at Meta FAIR we’re releasing a collection of nine new open source AI research artifacts across our work in developing agents, robustness & safety and new architectures. More in the video from Joelle Pineau. All of this work is part of FAIR’s continued work towards the goal of achieving advanced machine intelligence A few highlights from what we’re releasing today: • Meta Motivo: A first-of-its-kind behavioral foundation model that controls the movements of a virtual embodied humanoid agent to perform complex tasks. • Meta Video Seal: a state-of-the art comprehensive framework for neural video watermarking. • Meta Explore Theory-of-Mind: A program-guided adversarial data generation for theory of mind reasoning. • Meta Large Concept Models: A fundamentally different training paradigm for language modeling that decouples reasoning from language representation. And much more! We’re excited to share this work with the research community and look forward to seeing how it inspires new innovation across the field. Details and access to everything released by FAIR today ➡️
1:34

Wrapping up the year and coinciding with #NeurIPS2024, today at Meta FAIR we’re releasing a collection of nine new open source AI research artifacts across our work in developing agents, robustness & safety and new architectures. More in the video from Joelle Pineau. All of this work is part of FAIR’s continued work towards the goal of achieving advanced machine intelligence A few highlights from what we’re releasing today: • Meta Motivo: A first-of-its-kind behavioral foundation model that controls the movements of a virtual embodied humanoid agent to perform complex tasks. • Meta Video Seal: a state-of-the art comprehensive framework for neural video watermarking. • Meta Explore Theory-of-Mind: A program-guided adversarial data generation for theory of mind reasoning. • Meta Large Concept Models: A fundamentally different training paradigm for language modeling that decouples reasoning from language representation. And much more! We’re excited to share this work with the research community and look forward to seeing how it inspires new innovation across the field. Details and access to everything released by FAIR today ➡️

AI at Meta

156,123 次观看 • 1 年前

Open science is how we continue to push technology forward and today at Meta FAIR we’re sharing eight new AI research artifacts including new models, datasets and code to inspire innovation in the community. More in the video from Joelle Pineau. This work is another important step towards our goal of achieving Advanced Machine Intelligence (AMI). What we’re releasing: • Meta Spirit LM: An open source language model for seamless speech and text integration. • Meta Segment Anything Model 2.1: An updated checkpoint with improved results on visually similar objects, small objects and occlusion handling. Plus a new developer suite to make it easier for developers to build with SAM 2. • Layer Skip: Inference code and fine-tuned checkpoints demonstrating a new method for enhancing LLM performance. • SALSA: New code to enable researchers to benchmark AI-based attacks in support of validating security for post-quantum cryptography. • Meta Lingua: A lightweight and self-contained codebase designed to train language models at scale. • Meta Open Materials: New open source models and the largest dataset of its kind to accelerate AI-driven discovery of new inorganic materials. • MEXMA: A new research paper and code for our novel pre-trained cross-lingual sentence encoder with coverage across 80 languages. • Self-Taught Evaluator: a new method for generating synthetic preference data to train reward models without relying on human annotations. Access to state-of-the-art AI creates opportunities for everyone. We’re excited to share this work and look forward to seeing the community innovation that results from it. Details and access to everything released by FAIR today ➡️
1:36

Open science is how we continue to push technology forward and today at Meta FAIR we’re sharing eight new AI research artifacts including new models, datasets and code to inspire innovation in the community. More in the video from Joelle Pineau. This work is another important step towards our goal of achieving Advanced Machine Intelligence (AMI). What we’re releasing: • Meta Spirit LM: An open source language model for seamless speech and text integration. • Meta Segment Anything Model 2.1: An updated checkpoint with improved results on visually similar objects, small objects and occlusion handling. Plus a new developer suite to make it easier for developers to build with SAM 2. • Layer Skip: Inference code and fine-tuned checkpoints demonstrating a new method for enhancing LLM performance. • SALSA: New code to enable researchers to benchmark AI-based attacks in support of validating security for post-quantum cryptography. • Meta Lingua: A lightweight and self-contained codebase designed to train language models at scale. • Meta Open Materials: New open source models and the largest dataset of its kind to accelerate AI-driven discovery of new inorganic materials. • MEXMA: A new research paper and code for our novel pre-trained cross-lingual sentence encoder with coverage across 80 languages. • Self-Taught Evaluator: a new method for generating synthetic preference data to train reward models without relying on human annotations. Access to state-of-the-art AI creates opportunities for everyone. We’re excited to share this work and look forward to seeing the community innovation that results from it. Details and access to everything released by FAIR today ➡️

AI at Meta

150,222 次观看 • 1 年前

🚨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 次观看 • 6 个月前