Searching...
12 results for “enterprise ai”
Topic3 mentions across episodes
enterprise AI
Topic3 mentions across episodes
enterprise
And it's like, that is the thing that a Chinese company with mediumly strong export controls, there will always be loopholes, might not be able to do at all. And if that the main result for o three is also a spectacular coding performance and if that
1:17:13 / 1:18:16
from transcript1:17:13 – 1:18:16
And we're papering over the limitations, and we're kind of working around them in all sorts of ways, whether it's the and the LLM itself and the training data or and the infrastructure around and everything that we're doing to make them work. But tha
6:46 / 7:48
from transcript6:46 – 7:48
...the enterprise and your needs are much more narrower, there's several reasons to go narrow. First of all, is, is just, cost. It's it's cost not only in terms of dollars but also latency. And so,
8:14 / 9:22
from transcript8:14 – 9:22
enterprise market, but it is a hard like, I don't know how you get people to try doing that. I guess if ChatGPT can do it, like, people are trying ChatGPT. I think it boils down to the the scientific question of how hard is tool use to solve. Because
3:32:14 / 3:33:28
from transcript3:32:14 – 3:33:28
Like, that's then that's gonna be, like, pretty quick and easy to do, I think. So it's gonna be be a whole range of, like, business outcomes, and it's gonna be tons of tons of sort of optimism around. People can just figure out ways to make money. To
4:38:59 / 4:40:02
from transcript4:38:59 – 4:40:02
Yet at the same time, if you, you know, do actually go and analyze, I think there are exactly, you know, the considerations that you're talking about, which is, yeah, the models at the time, which I think at the time it was just, 3.5, which was out.
4:33 / 5:36
from transcript4:33 – 5:36
So, like, the recursive language model. That that's a cool idea to kind of take this even a bit further. So just to explain, so, Nathan, you also mentioned earlier, it's harder to do cool research in academia because of the compute budget. If I recal
2:50:13 / 2:51:20
from transcript2:50:13 – 2:51:20
And any sequence, long enough is fundamentally spatial and temporal. Right? Which I think is so by kneeling spatial and temporal reasoning, you go after the rooticular problem of intelligence itself. What the world looks like is we want to have eight
1:02:14 / 1:03:24
from transcript1:02:14 – 1:03:24
Sometimes we do intellectually really easy tasks. Like if someone asked me, like, yesterday my daughter, she misspelled the word butterfly, so I need to tell her how to spell butterfly. It's a low, you know, it's an easy intellectual task. And someti
33:39 / 34:48
from transcript33:39 – 34:48
But typically, when we speak about agents, what do we have in mind? We have in mind a system that's not the transactional calls in an LLM. There's something that's more ongoing. There's, the AI system, you know, the agent can be proactive, not just r
7:30 / 8:36
from transcript7:30 – 8:36
Have a podcast?
Run your podcast through Clypt
Get ranked clips, hooks, and ready-to-post copy from your own episodes. Free to try.