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13 results for “ai limitations”
You can go and and kinda train it against that, but you can never be certain with any strong degree of accuracy that it won't happen again. This does start to feel like a little bit like the Aliven problem where, like, in theory, you know, it's like
1:16:26 / 1:17:30
from transcript1:16:26 – 1:17:30
there is no single right way of doing it. You can decide how to constrain that autonomy. Right? A very I mean, a different example of how you could constrain autonomy is preauthorization use cases. Insurance preauthorization is a very ripe use case f
16:28 / 17:30
from transcript16:28 – 17:30
using new technology, in this case, general purpose technology and repurposing it for harmful ends. And that's a huge, risk. And I think that has a lot of complications because, generally, you know, I'm in huge favor of open science and open source.
2:07:29 / 2:08:32
from transcript2:07:29 – 2:08:32
especially with this nondeterministic API again. Right? So you're kind of stuck because, there are tons of instructions that you could inject within your prompt and then, yeah, it's it's going really bad. Okay. I let's actually spend a little time he
23:15 / 24:18
from transcript23:15 – 24:18
We still could have humans checking because they can still verify. If if a if an AI mathematician is better than a human, humans can still run the proof through a proof checker and then confirm that it was correct. So in that way, humans can still, u
34:34 / 35:41
from transcript34:34 – 35:41
...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 that makes me less optimist
6:46 / 7:48
from transcript6:46 – 7:48
But the things you will see a lot are models being tricked into generating, like, porn, or hate speech or phishing messages or viruses, computer viruses. And these are truly harmful impacts and truly an AI safety slash security problem, but the bigge
56:10 / 57:19
from transcript56:10 – 57:19
energy problems, the not the the the scarcity problem and then travel to the stars and consciousness of the stars and maximum human flourishing, on the other hand, is this sort of PDoom scenarios. So given the uncertainty around it and the importance
2:05:47 / 2:07:06
from transcript2:05:47 – 2:07:06
Does it change our ability to evaluate them? So I think the intelligence frontier is just so jagged. What things they can do and can't do is often surprising. They still can't fold clothes. They can answer a lot of tough physics problems, though. Why
33:50 / 34:51
from transcript33:50 – 34:51
you can put a human into a pool game, and within two hours, they can chew pool and be able to do it. Right now, there's no way for us to have machines learn skills like that on the fly. Everything requires enormous amount of data and computing time a
8:50 / 10:00
from transcript8:50 – 10:00
accurate, which is you you gotta think about these AI systems as as almost biological systems that are grown, not coded up. Right? Because, sure, they express as software. But in many ways, when you're training a model, it you are actually growing in
34:31 / 35:32
from transcript34:31 – 35:32
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
that you're always better off when you're trying to solve an AI problem, taking a general learning approach that can scale with computation because it ultimately proves to be the most effective. And the alternative would be something that's much more
21:44 / 22:53
from transcript21:44 – 22:53
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