Guillaume Lample: recommendation

Transcript context

…Okay. Let’s focus on science and the forward deployed. We actually are strong believers in science. We started the our new science pod that focuses specifically on the air for science. What areas do you think are the most promis. What we’re pretty excited about right now, and something we have already started doing or that we’d probably be able to share more about this in a couple of months, is that we are exploring AI for science. And there are a lot of areas where we think that you could get some extremely promising buzz. If you were to apply AI in these domains. There are a lot of long inputs. You just have to find these domains where actually AI has not been yet applied, and it’s usually hard to do because the people working in those domains don’t necessarily know the capability of these models. They don’t know. How I would just have to pair them with Yeah, exactly. Your researcher slashing, which is actually hard to do. But this matching, we’re doing it naturally with our customers. So we have some company we are very closely with. So for instance, ISM Andreesen are one of our partners, so we’re doing some research with them on their other, like tons of extremely interesting problems. Columns in physics, in science matter science that they’re essentially the only ones to work on. ‘cause they’re doing something No, no one else is doing on the, yeah. So there are many domains where AI can actually revolutionize things. Just you have to think about it on you familiar with what can do or to apply it. So yeah, it’s something where more modeling with our partners, with our customers sort AI for s, but. Yeah. Okay. And then for deployed what it makes a good four deployed engineer, what do they need? Where do people fail?…

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