Thread by @IntuitMachine

Stumbled upon this new videocast with @GaryMarcus @luislamb @ykilcher . I must say that introduction was quite good.

I'm a bit perplexed about the achievement of Cyc. Have symbolic systems ever achieved a level of robustness to be of real utility? Not sure why this panel is all praise about it.

Yanic asked a good question that you can't just throw around buzzwords like 'abstractions' and 'semantics' without proposing an approach on how to achieve it. It's not clear how you get from symbolic manipulation into common sense.

The host (keith duggar?) makes a good point that language models are continuous on the inside and discrete on the outside.

I guess the root of the problem of the current hostility between symbolists and connectionists is the competition for grant money. It's a problem in general of many fields that must incentivize maximalist approaches.

Research funding isn't very kind to interdisciplinary or pluralistic approaches.

I also don't know what to make of the argument that all of computer science is symbolic and therefore it must be valuable for general intelligence. Conventional computer science is extremely valuable, but general intelligence is something different.

A massive amount of effort has been spent to craft semantic knowledge bases. Yet they remain extremely brittle. Search engines have fared much better with respect to utility. Strictness is a nice thing to have, but it's inflexible in real-world applications.

The world is really messy and navigating this world requires the same kind of general intelligence that you would find in autonomous systems. None of today's AI is nowhere near as competent as a honey bee.

Does symbolic reasoning or semantic knowledge help in achieving autonomous systems? I doubt it does.

I honestly don't know where these guests have been in the past year! Have they not played around with GPT-3? Yes, language is discrete, but it's interpretation and thus understanding is continuous.

Proof systems already provide mathematical proofs that are beyond the capabilities of humans. But these systems don't move the needle in developing general intelligence.

Well, at least they are looking at transformer and graphical networks. Which incidentally are both deep learning approaches!