AI for scientific discovery
See:
Resources
- The AI revolution in science: applications and new research directions
- The AI revolution in scientific research (The Royal Society, The Alan Turing Institute)
- The AI revolution in science
- The researchers using AI to analyse peer review
- Consensus - Ask a question, get conclusions from research papers
AI for medicine
- AI for Medicine | The Batch | AI News & Insights
- MedLLMsPracticalGuide - A curated list of practical guide resources of Medical LLMs (Medical LLMs Tree, Tables, and Papers)
Talks
Code
- #CODE Gt4sd-core (IBM) - GT4SD, an open-source library to accelerate hypothesis generation in the scientific discovery process
- #CODE Deep Search
- https://ds4sd.github.io/
- https://research.ibm.com/interactive/deep-search/
- Deep Search extracts and structures data from documents in four steps: Parse, Interpret, Index, and Integrate
- Handling Scientific Articles with Deep Search
Events
- NeurIPS - Machine Learning and the Physical Sciences
- Machine Learning and the Physical Sciences, NeurIPS 2024
References
- #PAPER Artificial intelligence in research (Musib 2017)
- #PAPER #REVIEW A Survey of Deep Learning for Scientific Discovery (Raghu & Schmidt, 2020)
- #PAPER DeepXDE: A deep learning library for solving differential equations (Lu 2020)
- #PAPER Machine Learning for Scientific Discovery (Surana 2021)
- #PAPER Machine Intelligence for Scientific Discovery and Engineering Invention (Daniels 2021). White paper
- #PAPER Data-driven discovery of Green’s functions with human-understandable deep learning (Boulle 2022)
- #PAPER Leakage and the Reproducibility Crisis in ML-based Science (Kapoor 2022)
- #PAPER Journal Impact Factor and Peer Review Thoroughness and Helpfulness: A Supervised Machine Learning Study (Severin 2022)
- #PAPER Galactica: A Large Language Model for Science (2022)
- #PAPER How Generative AI models such as ChatGPT can be (Mis)Used in SPC Practice, Education, and Research? An Exploratory Study (2023)
- #PAPER Artificial intelligence and scientific discovery: a model of prioritized search - ScienceDirect (2024)
- #PAPER Artificial Intelligence, Scientific Discovery, and Product Innovation (2024)
- #PAPER SciAgents: Automating scientific discovery through multi-agent intelligent graph reasoning (2024)
- #CODE GitHub - lamm-mit/SciAgentsDiscovery
- See Agents. Built with Autogen
AI for physical sciences
- #PAPER Machine learning and the physical sciences (Carleo 2019)
- #PAPER SciANN: A Keras/Tensorflow wrapper for scientific computations and physics-informed deep learning using artificial neural networks (Haghighat 2020)
- #PAPER Learning an Accurate Physics Simulator via Adversarial Reinforcement Learning (Jiang 2021)
- #PAPER Optimizing the synergy between physics and machine learning (2021). Editorial
AI for Earth sciences
- #PAPER Deep learning and process understanding for data-driven Earth system science (Reichstein, 2019)
- http://www.ccpo.odu.edu/~klinck/Reprints/PDF/reichsteinNature2019.pdf
- ML approaches are increasingly used to extract patterns and insights from the ever-increasing stream of geospatial data, but current approaches may not be optimal when system behaviour is dominated by spatial or temporal context
- Earth system data are exemplary of all four of the ‘four Vs’ of ‘big data’: volume, velocity, variety and veracity
- One key challenge is to extract interpretable information and knowledge from this big data, possibly almost in real time and integrating between disciplines
- Two major tasks in the coming years: (1) extracting knowledge from the data deluge, and (2) deriving models that learn much more from data than traditional data assimilation approaches can, while still respecting our evolving understanding of nature’s laws
- #TALK Machine-learning-model-data-integration for a better understanding of the Earth System (Markus Reichstein)
- #PAPER Machine learning for weather and climate are worlds apart (Watson-Parris 2021)
- #PAPER Artificial Intelligence Revolutionises Weather Forecast, Climate Monitoring and Decadal Prediction (Dewitte 2021)
- #PAPER Towards neural Earth system modelling by integrating artificial intelligence in Earth system science (Irrgang 2021)
- #PAPER Evolution of machine learning in environmental science—A perspective (Hsieh 2022)
- #PAPER Machine Learning in Weather Prediction and Climate Analyses—Applications and Perspectives (Bochenek 2022)
AI for medicine
- #PAPER OpenMedIA: Open-Source Medical Image Analysis Toolbox and Benchmark under Heterogeneous AI Computing Platforms (Zhuang 2022)
- #CODE https://git.openi.org.cn/OpenMedIA
- OpenMedIA is an open-source toolbox library containing a rich set of deep learning methods for medical image analysis under heterogeneous AI computing platforms
- #PAPER Discovery of a structural class of antibiotics with explainable deep learning (2023)
- #PAPER Accurate structure prediction of biomolecular interactions with AlphaFold 3 (2024)
- Google DeepMind and Isomorphic Labs introduce AlphaFold 3 AI model
- DeepMind's AlphaFold 3 Enhances 3D Biomolecular Modeling
- AlphaFold 3 is a triumph of machine learning. It extends the utility of the previous version beyond proteins, and it computes with unprecedented accuracy how biological molecules will combine, allowing for a more comprehensive understanding of how drugs interact with the body
- #PAPER Towards Conversational Diagnostic AI (2024)
- #PAPER #REVIEW A Survey of Large Language Models in Medicine: Progress, Application, and Challenge (2024)
- #PAPER A generalist vision–language foundation model for diverse biomedical tasks | Nature Medicine (2024)
- #PAPER Med42-v2: A Suite of Clinical LLMs (2024)
- #PAPER #REVIEW The application of large language models in medicine: A scoping review (2024)