Difference between revisions of "Science Agents"
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==AI/ML Methods tailored to Science== | ==AI/ML Methods tailored to Science== | ||
===Regression (Data Fitting)=== | ===Regression (Data Fitting)=== | ||
| + | * 2024-06: [https://arxiv.org/abs/2406.14546 Connecting the Dots: LLMs can Infer and Verbalize Latent Structure from Disparate Training Data]: training on (x,y) pairs enables inferring underlying function (define it in code, invert it, compose it) | ||
* 2024-12: [https://arxiv.org/abs/2402.14547 OmniPred: Language Models as Universal Regressors] | * 2024-12: [https://arxiv.org/abs/2402.14547 OmniPred: Language Models as Universal Regressors] | ||
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* [https://www.markov.bio/ Markov Bio]: [https://www.markov.bio/research/mech-interp-path-to-e2e-biology Through a Glass Darkly: Mechanistic Interpretability as the Bridge to End-to-End Biology] ([https://x.com/adamlewisgreen/status/1853206279499751531 quick description], [https://markovbio.github.io/biomedical-progress/ background info on recent bio progress]) | * [https://www.markov.bio/ Markov Bio]: [https://www.markov.bio/research/mech-interp-path-to-e2e-biology Through a Glass Darkly: Mechanistic Interpretability as the Bridge to End-to-End Biology] ([https://x.com/adamlewisgreen/status/1853206279499751531 quick description], [https://markovbio.github.io/biomedical-progress/ background info on recent bio progress]) | ||
* [https://www.arxiv.org/abs/2412.16247 Towards scientific discovery with dictionary learning: Extracting biological concepts from microscopy foundation models] | * [https://www.arxiv.org/abs/2412.16247 Towards scientific discovery with dictionary learning: Extracting biological concepts from microscopy foundation models] | ||
| + | * [https://arxiv.org/abs/2412.12101 InterPLM: Discovering Interpretable Features in Protein Language Models via Sparse Autoencoders] | ||
===Uncertainty=== | ===Uncertainty=== | ||
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* 2024-12-06: [https://www.biorxiv.org/content/10.1101/2024.11.11.623004v1 The Virtual Lab: AI Agents Design New SARS-CoV-2 Nanobodies with Experimental Validation] (writeup: [https://www.nature.com/articles/d41586-024-01684-3 Virtual lab powered by ‘AI scientists’ super-charges biomedical research: Could human–AI collaborations be the future of interdisciplinary studies?]) | * 2024-12-06: [https://www.biorxiv.org/content/10.1101/2024.11.11.623004v1 The Virtual Lab: AI Agents Design New SARS-CoV-2 Nanobodies with Experimental Validation] (writeup: [https://www.nature.com/articles/d41586-024-01684-3 Virtual lab powered by ‘AI scientists’ super-charges biomedical research: Could human–AI collaborations be the future of interdisciplinary studies?]) | ||
* 2024-12-11: Google [https://blog.google/products/gemini/google-gemini-deep-research/ Deep Research] | * 2024-12-11: Google [https://blog.google/products/gemini/google-gemini-deep-research/ Deep Research] | ||
| + | * 2024-12-30: [https://arxiv.org/abs/2412.21154 Aviary: training language agents on challenging scientific tasks] | ||
=AI Science Systems= | =AI Science Systems= | ||
Latest revision as of 15:49, 31 December 2024
Contents
AI Use-cases for Science
Literature
AI finding links in literature
- 2019-07: Unsupervised word embeddings capture latent knowledge from materials science literature
- 2024-11: Large language models surpass human experts in predicting neuroscience results
Autonomous Ideation
- 2024-09: Mining Causality: AI-Assisted Search for Instrumental Variables
- 2024-12: Thinking Fast and Laterally: Multi-Agentic Approach for Reasoning about Uncertain Emerging Events
- 2024-12: LLMs can realize combinatorial creativity: generating creative ideas via LLMs for scientific research
Adapting LLMs to Science
- 2023-06: Domain-specific chatbots for science using embeddings
- 2024-10: Personalization of Large Language Models: A Survey
- 2024-11: Adapting While Learning: Grounding LLMs for Scientific Problems with Intelligent Tool Usage Adaptation
AI/ML Methods tailored to Science
Regression (Data Fitting)
- 2024-06: Connecting the Dots: LLMs can Infer and Verbalize Latent Structure from Disparate Training Data: training on (x,y) pairs enables inferring underlying function (define it in code, invert it, compose it)
- 2024-12: OmniPred: Language Models as Universal Regressors
Symbolic Regression
Literature Discovery
Commercial
- Cusp AI: Materials/AI
AI/ML Methods co-opted for Science
Mechanistic Interpretability
Train large model on science data. Then apply mechanistic interpretability (e.g. sparse autoencoders, SAE) to the feature/activation space.
- Mechanistic interpretability for protein language models (visualizer, code, SAE)
- Markov Bio: Through a Glass Darkly: Mechanistic Interpretability as the Bridge to End-to-End Biology (quick description, background info on recent bio progress)
- Towards scientific discovery with dictionary learning: Extracting biological concepts from microscopy foundation models
- InterPLM: Discovering Interpretable Features in Protein Language Models via Sparse Autoencoders
Uncertainty
- 2024-10: entropix: Entropy Based Sampling and Parallel CoT Decoding
- 2024-10: Taming Overconfidence in LLMs: Reward Calibration in RLHF
Science Agents
- 2024-01-13: ORGANA: A Robotic Assistant for Automated Chemistry Experimentation and Characterization (video)
- 2024-06-19: LLMatDesign: Autonomous Materials Discovery with Large Language Models
- 2024-08-12: Sakana AI: AI Scientist; The AI Scientist: Towards Fully Automated Open-Ended Scientific Discovery (code)
- 2024-09-09: SciAgents: Automating scientific discovery through multi-agent intelligent graph reasoning (code)
- 2024-09-11: PaperQA2: Language Models Achieve Superhuman Synthesis of Scientific Knowledge (𝕏 post, code)
- 2024-10-17: Rapid and Automated Alloy Design with Graph Neural Network-Powered LLM-Driven Multi-Agent Systems
- 2024-10-28: Large Language Model-Guided Prediction Toward Quantum Materials Synthesis
- 2024-12-06: The Virtual Lab: AI Agents Design New SARS-CoV-2 Nanobodies with Experimental Validation (writeup: Virtual lab powered by ‘AI scientists’ super-charges biomedical research: Could human–AI collaborations be the future of interdisciplinary studies?)
- 2024-12-11: Google Deep Research
- 2024-12-30: Aviary: training language agents on challenging scientific tasks
AI Science Systems
Inorganic Materials Discovery
- 2023-11: Scaling deep learning for materials discovery
- 2023-11: An autonomous laboratory for the accelerated synthesis of novel materials
- 2024-10: Open Materials 2024 (OMat24) Inorganic Materials Dataset and Models (code, datasets, checkpoints, blogpost)
Chemistry
- 2023-12: Autonomous chemical research with large language models (Coscientist)
Impact of AI in Science
Related Tools
Data Visualization
- 2024-10: Data Formulator: Create Rich Visualization with AI iteratively (video, code)
- Julius AI: Analyze your data with computational AI
See Also
- AI agents
- Nanobot.chat: Intelligent AI for the labnetwork @ mtl.mit.edu forum
