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I don’t think it’s a foregone conclusion that human reasoning doesn’t operate on the basis of statistical prediction of the next most probable “token” at its mo
by K0balt 3y ago
I don’t think it’s a foregone conclusion that human reasoning doesn’t operate on the basis of statistical prediction of the next most probable “token” at its most granular level. Humans are certainly capable of hallucination in the LLM sense, and without training we often struggle to produce (or even outright fabricate) the rationale behind our “conclusions”.
We stopped intentionally modelling human reasoning because we have no clue how it works.
We roughly copied the physical devices then figured out how to slap them together with an algorithm that makes them behave similarly to human thought, when trained with a massive quantity of cultural-linguistic-memetic data.
It should not be surprising that if you take a bucketful of engine parts and keep messing with them until they kinda work as an engine that you will probably wind up with something along similar lines as the original intention of the parts. Frankly, it would be quite surprising if you didn’t.
Biological neural networks are fundamentally linear algebra processing device that integrate data into functions through training, so the fact that we understand the process as linear algebra is actually an argument that the process is similar.
As to whether “superintelligence” can exist, I think we are in exactly the same page there. It is a matter of definition. Personally I doubt the existence of a fundamentally superior system of logic type of superintelligence, but certainly a device can made to iterate faster and flawless memory and instant, accurate calculation is going to beat the hell out of my notebook and HP28s lol.
I suspect that superintelligence can be (has been?) achieved in some respects, but in the same sense that a room full of ten educated people with regular computing tools is “superintelligence”.
- seanhunter 3y ago> Biological neural networks are fundamentally linear algebra processing device that integrate data into functions through training, so the fact that we understand the process as linear algebra is actually an argument that the process is similar. Do you have a reference for this? My understanding of biological neural networks is very different from this. Specifically I find it really hard to believe that a biological brain is a linear algebra processing device.
- K0balt 3y agoMatrix multiplication with a different function and weights for each input/output. That’s the primary operation. There are many other operational modes overlaid that we typically do not model with mainstream models, but they primarily act as broad modifiers (suppression, gain, etc) we understand very well how it works, for example, when we get a few brain cells together to play pong or doom. We can transcribe the functionality of these simple biological networks, once trained, onto models and they function as expected. The simplified computational model we use for neural networks closely models the primary mechanism, but lacks self reinforcement systems, biochemical moderators, self directed connectivity modifications, etc. Basically we do the training with a separate process for most of the systems in common use, in contrast with biological systems which self organise and train in-situ.
- seanhunter 3y agoI notice that you’re consistently just claiming that the biological system does linear algebra without any source for this.
- K0balt 3y agoI’m not claiming that biological systems “do” linear algebra, I’m claiming that their behaviour can be partially modelled using linear algebra. If I gave the impression that I thought they were actually computing linear algebra I apologise for my unclear writing. But AFAIK modeling neural network nodes with linear algebra is a foundational principle and has been for quite some time, since I first started messing with neural networks 30 years ago. But hey, maybe you’re right and computational neural networks aren’t roughly modelled after their biological counterparts and I’ve just misunderstood the field all this time. I guess the good thing about neural networks is you don’t really have to understand them completely to use them lol. That said, if you’ve got information that refutes the notion that they are roughly modelled after their biological counterparts, or that somehow linear algebra is not the right tool for modelling them, I’d sure be interested to see it. Just because I’ve ben doing something for decades doesn’t mean I haven’t been doing it -wrong- for decades lol. Wouldn’t be the first thing.