AI for science

“It's not just emerging, it's exploding, I would say. That's the better term, because I know you go from investments into like in the hundreds of millions, now in the billions. So there's now actually a startup by Jeff Bezos that, you know, is that 6.2 billion …”

“The same bitter lessons or lessons that you can draw in LLM space are eventually going to be true in this space as well, I think.”

“Now we can treat this as a search engine. Like we search the internet, we now search the space of all possible molecules, not just the ones that people have made, or that they're in the universe, but all of them.”

“I think there's two big examples, you know, protein folding is a big one, and the other one is machine learning force fields, or something called machine learning inter-atomic potentials. Both of them have been actually very successful.”

“We do talk to a lot of companies in our field who make materials at scale in, in the alloy space who are thinking about this, and they look at it from a different lens. You know, they're not all hype on AI for science, and actually I'd say a lot of them are ki…”

“One of the themes that has run through the podcast is how the lab and experimentation and the real world have probably the biggest impact and have the most relevance to whether something is AI for science or something like B to B SAS.”

“A lot of regulatory frameworks equate AI with language models and Yes, language models can, you know, manipulate people, can have all these kinds of harmful impacts that we should think about controlling, but AI for science is different. So I think this one si…”