Comments by "Gilad Barlev" (@GSBarlev) on "Mastery Learning" channel.

I'm an expert in the field and I still think it's "autocorrect on steroids." It's just that I think that autocorrect was a revolutionary tool, even when it was just Markov chains.
5
It's literally true, though. It's all about probabilities and choosing the most appropriate response. What differentiates Transformer models from previous Markov chains and Naïve Bayes algorithms is that Transformers encode the input into a more useful vector space before applying the predictions. You may find the "on steroids" shorthand as somewhat short-selling the importance of that shift, but the alternative is that we talk about artifical neural network models as if they have intelligence or agency (using terms like "attention," "understanding," "learn" and "halluciante") which, while useful shorthand, is preposterous.
2
 @alakani +1 for the shout-out to Dayan & Abbot. I taught myself computational neuroscience (as a physicist) from that book.
1
@alakani Going to ignore your wider point and just give you an FYI—you can pretty safely disregard quantum effects when it comes to ideas of "consciousness." Yes, the probability that a K+ ion will quantum tunnel through a cell membrane is nonzero, but it's infinitesimal, especially compared to the real quantum tunneling that poses a major limitation to how tightly we can pack transistors on a CPU die.
1
@diadetediotedio6918 I have a PhD in Physics and a decade of industry experience working with large network models. So yes, I'd say I'm pretty well qualified to speak to both quantum mechanics and the mechanisms behind consciousness.
1
@diadetediotedio6918 I mean, I have a PhD in Physics and ten years of industry experience in data science and machine learning, so... yes
1
If you don't understand what your code is doing, you shouldn't be running it, much less committing it. That's true whether you get it from Stackoverflow or [Stackoverflow through] ChatGPT
1