Lee Cronin

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.

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.

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...

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...

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.

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.

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.

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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

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

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...

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...

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.