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15 results for “ai models”
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
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from transcript12:00 – 13:02
incremental improvements, like matrix multiplication. Like, it has to sit there for days thinking how to incrementally improve a thing, and that it does so recursively. And as you do more and more improvement, it'll slow down Right. Because there'll
1:08:16 / 1:09:22
from transcript1:08:16 – 1:09:22
...language models, but reasoning is going to impact every single industry from biology to, self driving cars to robotics. And so reasoning, I think, is is the big huge breakthrough that that, is going to transform a lot of different applications and in
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from transcript30:52 – 31:51
that tells you, okay. That move in the end was good. That strategy was good. And then you can go back and analyze that and and and and and explain even to yourself a little bit more why. Explore around it. And that's how chess analysis and things lik
1:04:52 / 1:06:15
from transcript1:04:52 – 1:06:15
“MIT Professor compares proactive AI agents to raising children until age 18”
But AI agents can convert words to actions. But proactive AI is the agents really being autonomous and doing actions on their own, you know, without we having to stimulate them or prompt them and so o
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framework1:11 – 1:29
...There's no reason in some number of years that you can't train a language model to maximize time spent on a chat app.
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from transcript2:38:05 – 2:39:17
But, you know, basically, I remember from that time, that was sort of in one of the multi you know, there's been what, five five, six, eight AI winters, as they say, sort of boom bust cycles where people have made claims that there's, like, you know,
5:48 / 8:27
from transcript5:48 – 8:27
And between the fifties, sixties, seventies, and eighties, it was the early days of AI exploration. And we had logic systems. We had, expert systems. We also had early exploration of neural network. And then it came to around the late eighties, the n
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from transcript12:45 – 13:46
and it gets the wrong answer, it doesn't know how to solve the problem. You look at it and go, okay, I'm using AI to try to do a hard math problem. This language model versus the other one got it right, got it wrong. I my prompt was changed. It's all
19:20 / 20:35
from transcript19:20 – 20:35
coding, but the question is so I think what will happen is, yeah, you will just say build that website. It will make a very good website, and then you maybe refine it. But will it do things independently where so will you be still having humans askin
3:22:21 / 3:23:34
from transcript3:22:21 – 3:23:34
Outside of that, the the default conventional AI weapon would be drones, which is, I don't know. It that makes sense that come or that that countries would compete on that. And, I think that it would be a mistake if The US weren't, trying to do more
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from transcript11:15 – 12:28
sort of categorized yet. And it's not just tacit knowledge. It's actually knowledge that you could, you know, ask them about, and they could describe it. How you know, because one question people have with the alarms is, like, how much we've already
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from transcript40:28 – 41:31
billions of interactions, you really are almost allowed to fail never. When you have embodied systems that are put out there in the real world, you you just have to solve so many problems you never thought you'd have to solve when you're just thinkin
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from transcript3:12:45 – 3:13:49
eventually you end up with evolutionary systems is really how you build AI because and maybe I'm over extrapolating up a biology where, you know, effectively, your brain has a series of modules that have different functions or tasks. Right? You have
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from transcript24:04 – 25:07
and text generation or summarization. That's what they're seeing at a high level. And within each I mean, which are the areas that we've seen? Legal has been caught. You have accounting that's been really hot. You have code generation or migrating co
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from transcript15:08 – 16:09
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