Google just dropped 145 pages documenting how researchers use Gemini to tackle scientific problems.
𝘚𝘢𝘷𝘦 & 𝘙𝘦𝘵𝘸𝘦𝘦𝘵 (𝘵𝘰 𝘩𝘦𝘭𝘱 𝘺𝘰𝘶𝘳 𝘯𝘦𝘵𝘸𝘰𝘳𝘬)
A few things that stood out to me (in simple terms):
- In one case, the AI was used as an adversarial reviewer and caught a serious flaw in a cryptography proof that had passed human review. That’s a very different use than “summarise this PDF.”
- The model links tools from very different fields (for example, using theorems from geometry/measure theory to make progress on algorithms questions). This is where its wide reading really matters.
- They don’t let the model run wild. Humans still choose the problems, check every proof, and decide what’s actually new. The model is there to suggest ideas, spot gaps, and do the heavy algebra.
- Agentic loops, not just chat
In some projects, they plug Gemini into a loop where it:
-- proposes a mathematical expression,
-- writes code to test it,
-- reads the error messages, and
-- fixes itself. (humans only step in when something promising appears)
We are moving past the era of simple chat prompts and into a more sophisticated era of research.
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