Searching...
15 results for “ai research”
Person1 mention across episodes
AI Research
1 episode · View guest page
we now have a thinking machine. But, that was just a more anecdotal inspiration. The field really began in the fifties, when computer scientists came together and look at how we can use computer programs and algorithms to, to build these programs tha
12:00 / 13:02
from transcript12:00 – 13:02
...AI researcher. And in that, there's a term of research taste that's really interesting. So in everything you've seen, do you think it's possible for AI systems to have research taste, to help you in the way that AI coscientist does, to help steer hum
43:28 / 44:30
from transcript43:28 – 44:30
Omar doesn't think we need artificial general intelligence. He thinks we need artificial programmable intelligence, and the difference matters more than you think. Here's the paradox. Khattab has built one of the most widely used frameworks for worki
1:31 / 2:36
from transcript1:31 – 2:36
so I think there's a good chance that that could happen. Part of it is what what is your definition of AGI? Of course, people are arguing about that now, and and, mine's quite a high bar and always has been of, like, can we match the cognitive functi
59:15 / 1:00:19
from transcript59:15 – 1:00:19
a couple of office rooms and ask the the model to count the number of chairs. And this is something a toddler could do or maybe maybe a a elementary school kid could do, and AI could not do that. Right? So, there's just so much AI today could not do.
27:37 / 28:46
from transcript27:37 – 28:46
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
colleagues. Still, the workhorse model for, comp evaluation. That's some good trivia, Joe. Yeah. Very good. And no comment. Well, I assume just on the hard number evaluation, people are also ranking these on data usage, energy, that sort of thing as
11:54 / 13:00
from transcript11:54 – 13:00
...is fully automated. And from there, humans would be doing AI research together with that system, and they will quickly be able to develop a system that's actually can do the research for you. That's the idea. And then initially, their prediction was
3:15:44 / 3:16:46
from transcript3:15:44 – 3:16:46
People are spending a lot on these models. They're presumably doing this because they're getting value from them. You can maybe argue, like, oh, well, I don't think that value is real. I think people are just playing around. Whatever. But, like, what
0:00 / 1:54
from transcript0:00 – 1:54
Are we building towards the biggest economic boom in human history or the fastest collapse? Right now, AI labs are burning billions on compute. Anthropic just built a data center that uses as much power as Indiana state capital and Microsoft's planni
2:15 / 3:25
from transcript2:15 – 3:25
human designed and very carefully constructed pipelines for post training where we really encode a lot of the things we wanna do. You see massive emphasis on retrieval and web search and tool use and agent training. There is clearly a a sense in whic
0:45 / 1:45
from transcript0:45 – 1:45
most economically valuable tasks. By this definition, you know, of automating most economically valuable tasks, if we did have AGI, that would truly be a profound thing in our society. So now for the CEO predictions. I think one thing that's helpful
22:18 / 23:31
from transcript22:18 – 23:31
Is it fair, like, as a starting point to say that nobody really agrees on the definitions of AGI and ASI? I kinda think there's a lot of disagreement, but among I've been getting pushback where a lot of people kind of say the same thing, which is lik
3:14:13 / 3:15:17
from transcript3:14:13 – 3:15:17
...research in a controlled environment. Well, should should they do it at all? In other words, if I were to make the gain of function argument, I would say, as a former biologist, I spent, you know, almost a decade at the bench, and I care deeply about
31:36 / 32:39
from transcript31:36 – 32:39
Have a podcast?
Run your podcast through Clypt
Get ranked clips, hooks, and ready-to-post copy from your own episodes. Free to try.