Lee Cronin
speaker
545 appearances
2 recordings
1 series
first heard Dec 2023
last heard Jun 2024
Lee Cronin’s voice in public audio — every appearance, attributed to the second.
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Appearances
Because when I asked chat GPT, it made me feel really happy. I got a hit from it. But actually, it just exposed how little intelligence I use in every moment. Yeah. Because I'm easily fooled. So what I would like to do is to say, well, hey, hang on. What is it about the brain?
So the brain has this incredible connectivity and it has the ability to, you know, as I said earlier about my nephew, you know, I just I went from Bill to Billy and he went, all right, Leroy. Like, how did he make that leap? That he was able to basically, without any training, I extended his name. He went gay and he doesn't like, he wants to be called Bill.
He went back and said, you like to be called Lee? I'm going to call you Leroy. So human beings have a brilliant ability or intelligent beings appear to have a brilliant ability to integrate across all domains all at once and to synthesize something which allows us to generate knowledge and becoming true and complete is
on our own, although AIs are built in true and complete things, their thinking is not true and complete in that they are not able to build universal explanations. And that lack of universal explanation means that they're just inductivists. Inductivism doesn't get you anywhere. It's just basically a party trick.
I think it's in The Fabric of Reality from David Deutsch, where basically the farmer is feeding the chicken every day, and the chicken's getting fat and happy, and the chicken's like, I'm really happy. Every time the farmer comes in and feeds me, and then one day the farmer comes in and instead of feeding the chicken, just wrings its neck.
Oh, I'm impressed. Right. I'm impressed. Increasingly so. But we're mining the past. Yes. And what the human brain appears to be able to do is mine the future. Yes.
I can show on the back of a piece of paper why that's impossible. And it's like the problem is that, and again, there's domain experts kind of bullshitting each other. The term generative, right? Average person, oh, it's generative. No, no, no.
Look, if I take the numbers between 0 and 1,000 and I train a model to pick out the prime numbers by giving them all the prime numbers between 0 and 1,000, it doesn't know what a prime number is. Mm-hmm. Occasionally, if I can cheat a bit, we'll start to guess. It never will produce anything out with the data set because you mine the past.
The thing that I'm getting to is I think that actually current machine learning technologies might actually help reveal why time is fundamental. It's kind of insane because they tell you about what's happened in the past, but they can never help you understand what's happening in the future without training examples. Sure, if that thing happens again...
It's like, so I think, so let's think about what large language models are doing. We have all the internet as we know it, you know, language, but also they're doing something else. We're having human beings correcting it all the time. Those models are being corrected. Steered. Corrected.
I...
don't think i think again we can show that on a piece of paper that's sure i think there has you have to have so this is the failure in epistemology like i'm i'm glad i even can say that word let me know what it means you said it multiple times i know it's like three times now without failure quit while you're ahead just don't say it again all right you did really well thanks so i i but i i think the so what is reasoning so coming back to the chemical brain if i could basically if i could show that in a
Because, I mean, I'm never going to make an intelligence in CanMachina because we don't have brain cells. They don't have glial cells. They don't have neurons. But if I can take a gel and engineer the gel to be a hybrid hardware for reprogramming, which I think I know how to do, I will be able to process a lot more information and train models billions of times cheaper for...
and use cross-domain knowledge. And there's certain techniques I think we can do, but it's still missing the abilities of human beings that have had to become true and complete. And so I guess the question to give back at you is like, how do you tell the difference between trial and error and the generation of new knowledge.
I think the way you can do it is this, is that you come up with a theory, an explanation, inspiration comes from out, yeah, and then you then test that, and then you see that's going towards a truth. And human beings are very good at doing that, and the transition between philosophy, mathematics, physics, and natural sciences. And I think that we can see that.
Where I get confused is why people misappropriate the term artificial intelligence to say, hey, there's something else going on here. Because I think you and I both agree, machine learning is really good. It's only going to get better. We're going to get happier with the outcome. But why would you ever think the model was thinking or reasoning? Reasoning requires intention.
And the intention, if the model isn't reasoning, the intentions come from the prompter. And the intention has come from the person who programmed it to do it. So I... But don't you think...
But those initial conditions came from someone starting it.
And that causal chain in there. So that intention comes from the outside. I think that there is something in that causal chain of intention that's super important. I don't disagree we're going to get to AGI. It's a matter of when and what hardware. I think we're not going to do it in this hardware. And I think we're unnecessarily fetishizing really cool outputs and dopamine hits.
Because obviously that's what people want to sell us.
Showing 461–480 of 545 · page 24 of 28
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