KalMann

This interview lasted over an hour. If he was answering wrong they should have said something.

I think this is disingenuous comparison. When we read a book we can estimate the amount of data we're taking in based on the character count (each character being represented by some fixed amount of bits).

What you're suggesting on the other hand is something akin to counting the number of pixels on each page we look at. That's absurd overestimate of the amount of data a person reading is actually taking in.

I really don't think your argument is very convincing. Some of your examples are downright ridiculous? "Jobs" Making a movie about one of the creation of some of one of the most famous companies on Earth is obviously interesting. That's not comparable at all.

I think there's been natural but steady progress with since 2024 with the release of the o1 model, which showed impressive reasoning capabilities. But I think it's wrong to look at the magnitude of the accomplishments and assume that will be field independent. We don't know the range of problems reasoning techniques are useful for. What we see here is refinement of capabilities that have been noticeable for years.

Maybe you need to phrase it better. Like with a more specific direction of thinking.

I think he's talking about reasoning models.

I think your analogy is good but I don't believe modern LLMs use Lean or any lean-like structure in their proofs. At least recent open source ones like DeepSeek can do advanced math without it (maybe the most cutting edge ones are doing it I can't say).

Not a physicist either but my understanding is that is that if you believe that we can discover all the laws of physics that explain how the world operates then it needs to have a solution.

Like we have formulas describing how gravity works. We can test these formulas by observing the motion of the planets and galaxies. Is this theory true? There's lots of evidence for it so it feels like it's gotta be pretty close to "the truth"

We also have formulas describing how elementary particles behave. These formulas have been tested to a very high degree of precision so it seems they've got to be close to the truth as well. But if you use both our formulas for gravitation and formulas for elementary particles you can derive a contradiction. So these two theories cannot simultaneously be true. There's got to be something wrong with them.

I suppose there's the possibility that at a certain point nature simply doesn't follow any laws and you can't possibly make sense of it.

Are you sure? I think "normies" would prefer to see and try on the clothes they buy.

I can give you the exact mathematical formula used to statistically optimize the output of a neural network from input examples. Can you do the same for the brain?

> But we are? That's our education system.

That is not what the education system does. That's an obvious distortion of reality. People train over billions of documents to statistically predict the next word to gain and understanding of language. LLMs do this statistical processing in order to mimic humans natural language learning ability. And there has been continued evidence of the limitations of this approach to accurately mimic the totality of human cognition.

> So are humans.

AI advocates are _way_ too confident about the nature human cognition. Questions that have been debated by philosophers and cognitive scientists for decades are now "obvious" according to you people, though you never provide any argument to support your statements.

Did you misstate your comment? The Jordan normal form is more general than spectral decomposition so it should come after.

I don't really see how this implies the above commenter's statement is "simply not true".

I think this was a failure. The gold standard should be that the if every human driver was replaced with an AI how well could the system function. This makes it look like things would be catastrophic. Thus, showing how humans continue to be much more versatile and capable than AI.

I suppose if you lower the standards for what you hope AI can accomplish it wouldn't be considered a failure.