SAIR Arrives With AI for Science: Kick-Off 2026
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San Francisco, Feb 3, 2026 - The Foundation for Science and AI Research (SAIR) has launched. Co-founded by Terence Tao with contributions from Nobel Prize, Turing Award, and Fields Medal laureates, the initiative centers on two tracks: AI for Science and Science for AI.
AI for Science aims to accelerate discovery across disciplines using artificial intelligence. Science for AI applies the scientific method to how we design, evaluate, and govern AI. Together, SAIR's goal is to build the foundations for responsible scaling and, over time, figure out how to make AI think more like humans.
Why this matters for researchers
- Clearer evaluation: rigorous, testable standards for AI models used in scientific work.
- Faster iteration: tooling and methods that shorten the gap between hypothesis and result.
- Safer deployment: governance grounded in evidence, not hype or pure intuition.
- Deeper integration: models that respect prior knowledge, uncertainty, and reproducibility.
What "AI for Science" could mean in practice
- Benchmarks tied to real research objectives, not just leaderboard metrics.
- Datasets and protocols that make replication straightforward across labs.
- Model evaluation that includes calibration, error bars, and failure modes.
- Workflows that combine simulation, theory, and data with transparent assumptions.
What "Science for AI" could bring to your lab
- Hypothesis-driven development of models and agents, with preregistered evaluations.
- Standardized reporting for datasets, training runs, and compute budgets.
- Governance aligned to documented risks and measurable outcomes, not vague guidelines.
What to watch next in 2026
- Kick-off events and research agendas that outline early priorities.
- Calls for proposals or collaborations with universities and institutes.
- Initial benchmarks, datasets, or challenge problems to seed community progress.
How to prepare your team
- Audit your datasets and pipelines for reproducibility and documentation gaps.
- Add uncertainty, stress tests, and ablations to your evaluation checklist.
- Stand up a simple registry for experiments (configs, seeds, artifacts, and results).
- Plan cross-lab replications on at least one high-value result this quarter.
If you're aligning processes for responsible AI in research, the NIST AI Risk Management Framework is a solid reference point. See the overview at NIST AI RMF.
Looking to upskill your team on practical AI workflows and tools? Explore curated options by role at Complete AI Training - Courses by Job.
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