They’re made out of weights

When Dreyfus' book appeared in 1972, it received really harsh criticism from the then AI community. Dreyfus actually comments on that criticism in the revised 1992 edition.

I just don't see how Dreyfus critique of AI has been dealt with in modern AI: the critique is aimed at fundamental issues, not at the technical issues.

It is true that the critique written by Dreyfus is based on GOFAI algorithms from the 60s, but it is also true that if you read the book today, you'll find lots of similar situations and a similar way of thinking about the possibilities of AI, as well as the same underlying assumptions.

And as a side note, outdated means that it does not apply anymore, or that is not relevant anymore. Which is different from 'establishing a dialogue' with the text/author, in a way that 'seems' not to be relevant anymore. If you say that Dreyfus' book is outdated just because the 4th edition of Norvig's text only mentions it in a footnote, you are assuming that Norvig and Russell's opinion are definitive. They might be not.

I have authors like Norvig, Russel, LeCun, Minsky and other in the field in high regard. But they are normally not trained in either modern linguistics nor philosophy. Let alone the rest, large amount of researchers in the field. AI research is a complex field, and maybe (in this we could follow Foucault) not even a science. Doing research in an area of study does not turn it into science.

It is precisely philosophy, and even more contemporary philosophy, the discipline that focuses on how we build knowledge, and how we experience the world. Two really important, almost fundamental, topics that directly contribute to how AI is developed as a field of knowledge.

You are free not to believe me and dismiss the whole point. I do have evidence and I know it, no need to prove that (to begin with, the references are there. Read them if you want to expand your knowledge).

> By now, there's plenty of works, up to and including direct neural interfaces. Utah arrays, Michigan arrays. Stab the brain, dump the spike trains, decode. You crack the manifold open by correlating to known stimuli using ML, and generalize from there to unknown stimuli. There is no need to "know the exact configuration", and few bother - you put your hardware into the part of the brain you want (top level map is consistent enough brain to brain), gather a set of reference points, and use them to anchor the rest of the decoding process.

I am familiar with those works. Seeing the stimuli/activation correlation does not imply causal representation of the stimuli. It implies the causal activation of neural structures, at most.

> What follows is: if you can represent "thinking" as a computational process, you can implement it with a Turing machine, and thus, an LLM can be made to think. That proves LLMs can think. But not that the existing ones do or don't! Because that's the entire thing about upper bounds!

Alas! assumption spotted. IF you can represent "thinking" as a computational process, then you could implement a thinking machine. You need to prove first that thinking _is_ a computational process, _then_ you could go and try to implement such machine, and because you proved that thinking is a computational process, you are certain that theoretically such a machine can be built. But until you prove your assumption right, you are just trying blindfolded. The harm in the actual field/society regarding AI is that _we don't even know if thinking can be modeled as a computational process_. And no, this does not have anything to do with science. (By the way, I would not regard AI research as science since it is strictly studying an engineered artifact, but that's another story).

And yet the US is a dominant player and Germany is not.

> And we do not have a problem of resources today, we have so much food

I never said there needs to be scarcity for an ecosystem to emerge. These properties emerge wherever living things compete for finite resources. Imagine a pond where you introduce a few small catfish. At first, food is plentiful. Yet the fish will grow and reproduce until it is scare. It's the same with resources. Individuals/companies/societies will grow to consume the maximum they can sustain, because this is the optimum behaviour in an ecosystem.

The point is that organisms which avoid these behaviours tend to lose, in every sense of the word, to organisms that do. The behaviours are emergent from the constraints of the system.

> that its sometimes just spoils and no one cares

Transportation costs are a major driver of food shortage. It's not at all free to get large quantities of food from where they are produced to where they are consumed. People are generally not willing to transport food (or do much of anything else) at a loss.

There are exactly two possibilities: thinking can be expressed as computation, or thinking requires hypercomputation.

I did acknowledge both, explicitly.

Which one?

I'm betting hard against the second one, by the way. Because it requires hypercomputational magic fairy dust to:

1) exist - physical Church-Turing thesis has to be proven wrong empirically

2) be so involved in the functioning of human brain that it cannot be substituted for anything else

Wishful thinking, in my eyes.

But that's the name of the game, isn't it? Anything but admitting that your mind is a glorified math construct implemented in wet meat.

Comment is now to old to edit: for convenience, here's the link without the parameter -- https://youtu.be/LRq_SAuQDec