Searching...
16 results for “ai limitations”
And so I think those are I almost want to do I basically somewhat disagree with with your assertion that Ellen's got harder to use. Like, yes, we we expose more capabilities, but they're they're in minor forms, like using Canvas, like web search in i
47:15 / 48:26
from transcript47:15 – 48:26
And so the grad students are trained. They're taking classes to do this. I I do recommend to, like, you know, I've worked with grad students. I work with fewer now because we don't publish as much. But when I was at FAIR publishing papers, like, I wo
1:05:50 / 1:07:08
from transcript1:05:50 – 1:07:08
and r and d, the threat models that we're primarily primarily interested in. What one misreading of the time horizon graph is this is referring to the full distribution of any tasks that you might keep AIs. And I think that's clearly not right. In pa
5:23 / 7:30
from transcript5:23 – 7:30
...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
5:58 / 7:05
from transcript5:58 – 7:05
It probably wouldn't change what's possible, but it would really, really change, like, ease of adoption for people who maybe aren't as in the weeds on AI tools. And and if you combine, though, the latency with the cost reduction as well, I do think t
47:18 / 48:20
from transcript47:18 – 48:20
I think So okay. So that's what what you mean is that, like, some in some way, system wants distinction encoded in purpose of the model somehow. Yes. Yeah. I mean, it's just whether at the end of the day, you you just believe in, like, this, like, co
17:12 / 18:27
from transcript17:12 – 18:27
...the limitations of LMs. Can they do transfer learning or just learning in general? You seem to ask, can people do these things? Yes. Can people do these things? Well, it's like lateral thinking. Right? So, yeah. So it's like reasoning in or out of di
3:34 / 4:50
from transcript3:34 – 4:50
I'm no. I'm You just have to go. I'm not speaking for everyone at meter. Yeah. I'm a relatively longer timelines quote unquote person at meter. We have Nicola, my colleague, who helped out with AI 2027 who's on the who's on the shortest timelines end
26:57 / 28:44
from transcript26:57 – 28:44
...limitations? That's a terrifying place to be as one of these frontier labs and and companies because millions of people are using them. There was a lot of backlash last year with the GPT four o getting removed. And I personally never used the model,
1:38:13 / 1:39:17
from transcript1:38:13 – 1:39:17
So there there's still classes of there are classes of problems that are, like, very easy for the model and very hard for humans. There are some things that are very easy for humans, very hard for modern world experience and stuff. So it's still very
42:42 / 43:47
from transcript42:42 – 43:47
I think once it hits kind of 95% or something, you get very diminishing returns from really focusing on that benchmark. Cause it's sort of, it's either the case that you've now achieved that capability, or there's also the issue of leakage in public
12:01 / 13:05
from transcript12:01 – 13:05
I just think there's a lot of complexity involved in adding features to complex systems. Like, if you look at the browser, Chrome. If I wanted to add a feature if I wanted to have tabs as as opposed to up top, I want them on the left side. Mhmm. Inte
3:24:38 / 3:25:46
from transcript3:24:38 – 3:25:46
in America and in a number of Western countries is a problem. And in contrast, I see China building platforms left and right, including nuclear, so that will be an interesting dynamic. And then semiconductors is another bottleneck. But AI is so compl
4:21 / 5:31
from transcript4:21 – 5:31
Does any of this make you think differently about what tools to give an LLM? You know? I know they deleted the the tweet, but it's basically like, well, before, if you're putting all these MCPs, like, yeah, you have email access and all of this. And
17:57 / 19:00
from transcript17:57 – 19:00
And so it's it's I wouldn't say it's it's it's it's like the the question really comes down to what does it take to understand the full extent of what's possible? And honestly, that that's just getting more and more involved over time. Yep. I I have
48:44 / 49:54
from transcript48:44 – 49:54
And as the code becomes bigger or more complex or more input, it's gonna just start kind of sloshing back and forth between bugs. And so if you don't have those hard skills and you're not ultimately the driver at the end of the day, like, you're gonn
4:38:58 / 4:40:00
from transcript4:38:58 – 4:40:00
Have a podcast?
Run your podcast through Clypt
Get ranked clips, hooks, and ready-to-post copy from your own episodes. Free to try.