[famouswaffles]

>so far. I'm not sure if you are suggesting there is?

No i wasn't. I guess i wasn't very clear in my first reply.

I was mainly getting at this,

>and, consequently, its' a very bad idea to try and make machines that "think like humans", because that way we'll only make machines with none of the advantages of machines and all the disadvantages of computers.

No one is scaling up and pouring millions of compute into LLMs for general intelligence because they thought it was an excellent idea before the fact(virtually no one did, even some of the most verbal proponents).

They're doing it because it's seems to be working in a way logic failed to. and logic had the headstart, both in research and public consciousness. Nearly all of fictional ai is an envisioning of the hard symbolic logic general intelligence system that dominated early ai research. Logic was not the underdog here.

The point i was really driving at is that you say "because that way we'll only make machines with none of the advantages of machines and all the disadvantages of computers." almost like it's a choice, like Logic and GPT are both on the field and people are going for the worse player. Logic is not even in consideration because ot couldn't make the cut.

[YeGoblynQueenne]

Like I say in my earlier comment, that's not right. Logic-based AI is still dominant in many fields. There is a lot of excitement about statistical machine learning (I know, it's an understatement) but that's only because statistical machine learning is finally working and doing things that couldn't be done with logic- not because logic can't do the things that statistical machine learning can't do (it can), and not because statistical machine learning can do the things that logic can do (it can't).

There are two worlds, if you want. For me it's a mistake to try and keep them separated. All the great pioneers of AI were not only this or only that people. e.g. Shannon's MSc thesis gave us boolean logic-based circuits (logic gates) and he also introduced information theory. The people who have made real contributions to AI and to computer science were never one-trick ponies.

An analogy I like to make is that we have both airplanes and helicopters. A flying machine is something so useful to have that we 're going to use any kind we can make. Obviously a helicopter will not compete with a jet for speed, but a jet isn't anywhere as manoeuverable or flexible as a helicopter. So we use both.

>> Logic was not the underdog here.

It wasn't, but there was a bit of a Triassic extinction event, with the last AI winter of the '90s that took the expert systems and basically severed the continuity of logic-based AI research. The story is more complex than that, but logic-based AI was dealt a powerful blow, and progress slowed down. Although again like I say in my other comment, it didn't get completely extinguished. Perhaps, like we recognise birds today as the remaining dinosaurs, we'll recognise the old-new wave of logic-based AI that is hidden by the AI effect.