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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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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 🌹

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Vivek Natarajan

83,106 Aufrufe • vor 3 Monaten

Chai Discovery cofounder Jack Dent says AI drug discovery hit a major inflection point in 2025. As a result, it's now being deployed at the biggest pharma companies as part of their core discovery engines. "Over the course of 2025, these models went from being research curiosities... to being able to design molecules without needing to fine-tune them on a lot of data." "You can really just prompt them in the way that you'd prompt an LLM with a target, maybe some disease you're going after. And they will design a number of candidates that would help do that, essentially." "A year, year and a half ago, basically none of this stuff worked." "In 2025 we really crossed the Rubicon. And now these technologies are actively being deployed in companies like Eli Lilly, Novartis, Pfizer. These are some of the most scientific, highest-tech companies in the world. And they're putting them into real drug programs and making them part of their core discovery engines."
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Chai Discovery cofounder Jack Dent says AI drug discovery hit a major inflection point in 2025. As a result, it's now being deployed at the biggest pharma companies as part of their core discovery engines. "Over the course of 2025, these models went from being research curiosities... to being able to design molecules without needing to fine-tune them on a lot of data." "You can really just prompt them in the way that you'd prompt an LLM with a target, maybe some disease you're going after. And they will design a number of candidates that would help do that, essentially." "A year, year and a half ago, basically none of this stuff worked." "In 2025 we really crossed the Rubicon. And now these technologies are actively being deployed in companies like Eli Lilly, Novartis, Pfizer. These are some of the most scientific, highest-tech companies in the world. And they're putting them into real drug programs and making them part of their core discovery engines."

TBPN

29,322 Aufrufe • vor 1 Monat

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.
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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 Aufrufe • vor 5 Monaten

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.
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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

23,187 Aufrufe • vor 1 Jahr

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 Aufrufe • vor 1 Jahr

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

150,435 Aufrufe • vor 1 Jahr