The Right Abstraction Changes the Problem
More compute and larger models are not always the most interesting way forward.
LamaLab
Laboratory for AI for Materials. Led by Kevin Jablonka.
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Latest posts
Scale Isn't Everything in Chemical AI
Over the past few years, the dominant narrative in Deep Learning has been simple: larger models, more data, and more compute lead to better performance.
What We Think We Know
Noisy synthetic data can improve pretraining.
The Appearance Behind the Epistemic Numbers
AI scientists often look like they are doing science. Their traces tell a more insightful story.
Are AI Scientists Scientists?
Over the past weeks, we found ourselves repeatedly returning to the same broader question: what kinds of reasoning, structures, and practices are actually needed for AI systems to become reliable partners in scientific work, and if AI can do science on its own?
How does a L(l)ama see AI in science?
Last week, we organized a one-day group retreat to reflect on our projects, how we work together, and where we see the field heading.
Introducing “What is LamaLab reading?”
Keeping up with the current pace of published papers seems close to impossible — especially if you work in a cross-domain field.