Will AI outsmart human intelligence? - with 'Godfather of AI' Geoffrey Hinton

It was a Royal Institution public lecture, "Will AI outsmart human intelligence? - with 'Godfather of AI' Geoffrey Hinton", [VIDEO] Ultimately I somehwat disagreed with some of Hintons points in this talk, and after some thought I came up with specific reasons/doubts, and yet at the same time, his intuitive explanations helped shift my views somewhat as well.

I muck about on HN and get ~80% of my video content on youtube. I think it's great overall. The rest ~5% twitter, 0% TV although I have one sitting switched off with a £174 bbc fee. 5% pirated maybe. If you go with the official HN guide of "anything that gratifies one's intellectual curiosity" and avoid "stories about politics, or crime, or sports, or celebrities" then youtube is much better - watching Hinton explain AI and neural networks at the mo. [LINK] and [VIDEO] which is kind of interesting.

Geoffrey Hinton's recent lecture at the Royal Institute[1] is a fascinating watch. His assertion that human use of language being exactly analogous to neural networks with back-propagation really made me think about what LLMs might be able to do, and indeed, what happens in me when I "think". A common objection to LLM "intelligence" is that "they don't know anything". But in turn... what do biological intelligences "know"? For example, I "know" how to do things like write constructs that make complex collections of programmable switches behave in certain ways, but what do I really "understand"? I've been "taught" things about quantum mechanics, electrons, semiconductors, transistors, integrated circuits, instruction sets, symbolic logic, state machines, assembly, compilers, high-level-languages, code modules, editors and formatting. I've "learned" more along the way by trial and error. But have I in effect ended up with anything other than an internalised store of concepts and interconnections? (c.f. features and weights). Richard Sutton takes a different view in an interview with Dwarkesh Patel[2] and asserts that "learning" must include goals and reward functions but his argument seemed less concrete and possibly just a semantic re-labelling. [1] [VIDEO] [2] [LINK]