AI for Science

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Applications of artificial intelligence

Artificial intelligence is the capability of the computational systems to perform tasks typically associated with human intelligence, such as learning, reasoning, problem-solving, perception, and decision-making. Artificial intelligence has been used in applications throughout industry and academia. Within the field of Artificial Intelligence, there are multiple subfields. The subfield of Machine learning has been used for various scientific and commercial purposes including language translation, image recognition, decision-making, credit scoring, and e-commerce. In recent years, there have been massive advancements in the field of generative artificial intelligence, which uses generative models to produce text, images, videos or other forms of data. This article describes applications of AI in different sectors.

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• AI for Science 2025 - Nature

  Download PDF AI for Science (AI4S) represents the convergence of artificial intelligence (AI) innovation in scientific research and AI-driven scientific discovery, demonstrating their deep integration1, and the establishment of a transformative research paradigm. Traditional research paradigms can be categorized as empirical induction (experimental science), theoretical modeling (theoretical science), computational simulation (computational science), and data-intensive science2. The experimental scientific paradigm generates empirical laws from observations of natural phenomena and reproducible experiments, but does not provide the theoretical foundations that would explain these laws at a fundamental level. [...] Traditional scientific discovery involves generating and validating candidate hypotheses from a large solution space, often characterized by low efficiency and challenges in identifying high-quality solutions4. AI harnesses its powerful data processing and analytical capabilities to navigate solution spaces more efficiently, enabling the generation of high-quality candidate hypotheses. For instance, machine learning can assist mathematicians in uncovering new conjectures and theorems5. [...] With technological advances and the exponential growth of data, a new research paradigm of data-intensive science has emerged, using data mining techniques to automatically identify statistical patterns from large-scale datasets, reducing reliance on priori scientific hypotheses. However, it faces limitations in establishing causal relationships, processing noisy or incomplete data, and discovering principles in complex systems.

• Introducing Anthropic's AI for Science Program

  ## Why AI for Science? At Anthropic, we believe that AI has the potential to significantly accelerate scientific progress. Advanced AI reasoning and language capabilities can help researchers analyze complex scientific data, generate hypotheses, design experiments, and communicate findings more effectively. By reducing the time and resources needed for scientific discovery, we can help address some of humanity's most pressing challenges. [...] ## How to Apply Researchers attached to a research institution interested in the AI for Science program can apply through our application form. Applications will be reviewed by our team, including subject matter experts in relevant fields. We look forward to seeing how researchers use our API to push the boundaries of scientific discovery and create positive impact in the world. ## Related content ### Advancing Claude in healthcare and the life sciences Claude for Healthcare introduces HIPAA-ready infrastructure for providers and payers, while expanded Life Sciences capabilities add connectors to Medidata and ClinicalTrials.gov for clinical trial operations and regulatory work. Read more ### Sharing our compliance framework for California's Transparency in Frontier AI Act [...] This initiative aligns with our vision of building AI systems that bring value to humanity, as discussed in our CEO Dario Amodei's Machines of Loving Grace. We're particularly interested in supporting applications where AI can assist in accelerating processes related to understanding complex biological systems, analyzing genetic data, accelerating drug discovery especially for some of the largest global disease burdens, increasing agricultural productivity, and more. ## Program Details The AI for Science program will offer significant API credits to qualified researchers who will be selected based on their contributions to science, the potential impact of their proposed research, and AI’s ability to meaningfully accelerate their work. ## How to Apply

• GitHub - ai-boost/awesome-ai-for-science

  PaddleScience - SDK & library for AI-driven scientific computing applications Flux.jl - Machine learning in Julia [...] Agentic AI for Scientific Discovery: A Survey of Progress, Challenges, and Future Directions (2025.03) - Comprehensive review of AI agents in science Towards Scientific Intelligence: A Survey of LLM-based Scientific Agents (2025.03) - Scientific AI agent systems [...] ToolUniverse - Democratizing AI scientists by transforming any LLM into research systems with 600+ scientific tools (Harvard MIMS) Aviary - Language agent gymnasium for challenging scientific tasks including DNA manipulation, literature search, and protein engineering Curie - Automated and rigorous experiments using AI agents for scientific discovery POPPER - Automated hypothesis testing with agentic sequential falsifications

• Chapter: 2 Fundamentals of AI in Scientific Research

  ### Using Foundation Models for Science Shirley Ho (Simons Foundation/Flatiron Institute and New York University) spoke about how foundation models can be used to improve the performance of AI in scientific discovery. Foundation models, she explained, are large models that are pretrained with task-agnostic objectives on massive, diverse datasets. These models extract features that can be used as bases for task-specific fine-tuning, leading to performances that are better than can be achieved with supervised training on many types of problems. [...] In the panel’s last presentation, Amy McGovern (University of Oklahoma) talked about how AI is being used for scientific discovery in the areas of weather and climate. In particular, she described work applying AI to increase understanding of the evolution of tropical cyclones (e.g., hurricanes and typhoons) over their lifetimes, including their structure and intensity. The ultimate goal, she said, is to improve the forecasting of hurricanes and other tropical cyclones. [...] In moving toward such a goal, he said, the first stage will be to automate labs as much as possible, creating connected research labs that serve as AI assistants for scientific discovery. From there, the AI science assistants will need to be given the capability to operate autonomously, making discoveries themselves. He mentioned certain steps along the path toward this goal, such as Adam–Eve, the first closed-loop laboratory robotic system (Sparkes et al., 2010). The strategy is to automate the lab and give autonomy (at appropriate levels) to AI scientists. The ultimate goal, he added, is to automate scientific discovery at scale, and achieving that will require success in several areas, most notably vision and leadership, technology platforms, and project strategy/management.

• OpenAI for Science

  ### Built with scientists, for scientists In partnership with scientists and mathematicians, we’re building AI systems that fit naturally into real research, designed to empower researchers to explore more ideas, test hypotheses faster, and uncover patterns that would have taken months or years to find alone. By deeply understanding how science actually happens, we’re working on tools that integrate seamlessly into existing workflows, from literature review and proof generation to modeling, simulation, and automation. ### How researchers and AI are already collaborating [...] ### How researchers and AI are already collaborating We’re at the beginning of something new, where AI systems are starting to meaningfully contribute to science itself. Using GPT‑5, mathematicians have generated correct proofs in minutes, physicists have seen the model rediscover hidden symmetry structures, biologists have validated mechanisms and follow-up experiments it proposed, and researchers across fields have used it to surface deep conceptual links in the literature that humans had missed. These are still early green shoots and require expert judgment, verification, and experimentation, but they mark a meaningful shift—GPT‑5 can now act as a fast, knowledgeable research partner. Learn more ### Prompting strategy for scientists [...] ### Prompting strategy for scientists Working with GPT‑5 for science is a skill. The model is capable of deep reasoning, conceptual search, and formal computation, but scientists get the best results when they use it deliberately. The guidance below captures early lessons from researchers across math, physics, biology, and computer science on how to structure prompts, provide context, and collaborate with the model to accelerate real scientific work. Starting with a simpler version of your question can dramatically improve performance on harder versions. This mirrors how humans work: a warm-up helps orient the model to the right structure.