Comment by the_af
4 years ago
Thanks for the conversation.
We obviously disagree. I don't think we are near general AI (and yes, I know the objection that everyone who says this is simply moving the goalposts-- regardless, I'm unconvinced). I think GPT et al are very interesting tricks, but still not general AI; and that the path to it doesn't lie in this direction.
I subscribe to the view we think of the human mind as a pattern matching computer simply because this is the current major tech, much like people in the past thought of "humors" or "steam machines". I think some of the analogies are useful, to a point, but I don't think there's hard evidence the mind is like a neural net (irony notwithstanding) or a pattern marching GPT-like algorithm.
Re: Thinking Fast and Slow, I see there are serious doubts about the validity of the book's foundations and conclusions, and that it's been challenged.
> I don't think we are near general AI
Nor do I. But I don't agree with your definition of general AI at all.
I think by your definition we are well on the way towards it. I'd note that you didn't address the idea that compression of input concepts into a lower dimensional space is exactly the "reliably turning symbols into meaning" idea you suggest.
The fact the latent representations of concepts can be manipulated in ways that make logical sense is a good indication that the symbols have meaning. The classic Word2Vec experiments showing how the relationships Paris->France ~= London->England and King - Man + Woman = Queen show this well. Modern large language models are much more complicated of course, but the principles remain.
> I subscribe to the view we think of the human mind as a pattern matching computer simply because this is the current major tech, much like people in the past thought of "humors" or "steam machines". I think some of the analogies are useful, to a point, but I don't think there's hard evidence the mind is like a neural net (irony notwithstanding) or a pattern marching GPT-like algorithm.
I don't think the computational approach is an interesting question. No one thinks brains operate like software neural networks (not sure what "pattern marching GPT-like algorithm" means - it is just a neural network). That doesn't matter because all computational methods are ultimately equivalent.
I think outcomes on metrics like benchmarks is important, and while some benchmarks have issues that some approaches exploit I think things like Chollet's "On the Measure of Intelligence" (https://arxiv.org/abs/1911.01547) are reasonable frameworks for discussion of progress.
> Re: Thinking Fast and Slow, I see there are serious doubts about the validity of the book's foundations and conclusions, and that it's been challenged.
This in inaccurate. There are some reasonable criticisms of the studies on the priming affect (one chapter of the book). These and Kahneman's response (he accepts the criticisms) are outlined in https://replicationindex.com/category/thinking-fast-and-slow... and https://retractionwatch.com/2017/02/20/placed-much-faith-und...
These issues don't detract from the overall theme of the book about the two systems of decision making (rational and reflex) and how often we use the reflex decision system but convince ourselves we are using the rational system.
If you can point at any additional doubts about the books conclusions I'd appreciate a reference.
> I don't think the computational approach is an interesting question.
Great, so we agree then!
> No one thinks brains operate like software neural networks
A bold claim.
> (not sure what "pattern marching GPT-like algorithm" means - it is just a neural network).
Yet you understood me perfectly.
Anyway, you are more well read than me on this subject, yet we both agree we are not near general AI.
>> I don't think the computational approach is an interesting question.
> Great, so we agree then!
Maybe? I think all forms of computation are equivalent, so if the brain is implemented in the same way as a neural network is uninteresting. They can do the same thing (which is interesting).
> yet we both agree we are not near general AI.
Sure. My disagreement is with the idea with your original statement:
"Translating ambiguous symbols to meaning and combining them in meaningful ways is a big deal which, apparently, these AI models cannot do. They can just parrot things."
Modern AI systems can do this, and do not just parrot things.
They are capable of novel outputs.
This is because the models have sufficient "understanding" to manipulate "things" (latent vector representations, which you call symbols) to output novel but meaningful outputs.