improving and integrating the technology is raising harder and more complex questions than first envisioned

Many people not only envisioned but predicted these problems as soon as the hype cycle began.

Interesting article. I’d have loved to see some stats on how LLM investment and LLM startups are doing.

They are both different parts of the same problem. Prolog can solve logical problems using symbolism. ChatGPT cannot solve logical problems, but it can approximate human language to an astonishing degree. If we ever create an AI, or what we now call an AGI, it will include elements of both these approaches.

In “Computing Machinery and Intelligence”, Turing made some really interesting observations about AI (“thinking machines” and “learning machines” as they were called then). It demonstrates stunning foresight:

An important feature of a learning machine is that its teacher will often be very largely ignorant of quite what is going on inside… This is in clear contrast with normal procedure when using a machine to do computations: one’s object is then to have a clear mental picture of the state of the machine at each moment in the computation. This object can only be achieved with a struggle.

Intelligent behaviour presumably consists in a departure from the completely disciplined behaviour involved in computation, but a rather slight one, which does not give rise to random behaviour, or to pointless repetitive loops.

You can view ChatGPT and Prolog as two ends of the spectrum Turing is describing here. Prolog is “thinking rationally”: It is predictable, logical. ChatGPT is “acting humanly”: It is an unpredictable, “undisciplined” model but does exhibit very human-like behaviours. We are “quite ignoerant of what is going on inside”. Neither approach is enough to achieve AGI, but they are such fundamentally different approaches that it is difficult to conceive of them working together except by some intermediary like Subsumption Architecture.

I don’t think your characterisation of the Dartmouth Project and machine learning are quite correct. It was extremely broad and covered numerous avenues of research, it was not solely related to machine learning though that was certainly prominent.

The thing that bothers me is how reductive these recent narratives around AI can be. AI is a huge field including actionism, symbolism, and connectionism. So many people today think that neural nets are AI (“the proper term for the study of machine learning”), but neural nets are connectionism, ie just one of the three major fields of AI.

Anyway, the debate as to whether “AI” exists today or not is endless. But I don’t agree with you. The term AGI has only come along recently, and is used to move the goalposts. What we originally meant by AI has always been an aspirational goal and one that we have not reached yet (and might never reach). Dartmouth categorised AI into various problems and hoped to make progress toward solving those problems, but as far as I’m aware did not expect to actually produce “an AI” as such.

The human brain can run on a hamburger once every day or two. Investing immense amounts of energy into statistical models is only taking us further away from true, meaningful AI, not closer.

You are talking about a guy who takes economic advice from a “psychic medium” who he believes is in turn talking psychically to his dogs, who he believes are clones of resurrected jesus-dog.