Ben, By examples do you mean like array reversal in your article?
I agree. This problem may not be addressed by their learning paradigm at all. But I disagree this has been the biggest problem for symbol grounding. I think the biggest problem for symbol grounding has been ambiguity. Manifest in language. So I agree GPT-3 may not be capturing necessary patterns for the kind of reason used in array reversal etc. But I disagree that this kind of reasoning has been the biggest problem for symbol grounding. Where GPT-3 may point the way is by demonstrating a solution to the ambiguity problem. That solution may be hidden. They may have stumbled on to the solution simply by virtue of the fact that they have no theory at all! No preconceptions. I would contrast this with traditional grammar learning. Which does have preconceptions. Traditional grammar learning starts with the preconception that grammar will not contradict. The GPT-x algorithm may not have this expectation. So they may be capturing contradictions and indexing them on context, by accident. So that's my thesis. The fundamental problem which has been holding us back for symbol grounding is that meaning can contradict. A solution to this, even by accident (just because they had no theory at all?) may still point the way. And the way it points in my opinion is towards infinite parameters. "Parameters" constantly being generated (and contradiction is necessary for that, because you need to be able to interpret data multiple ways in order to have your parameters constantly grow in number 2^2^2^2....) Grok that problem - contradictions inherent in human meaning - and it will be a piece of cake to build the particular patterns you need for abstract reasoning on top of that. Eliza did it decades ago. The problem was it couldn't handle ambiguity. -Rob On Sat, Aug 1, 2020 at 9:40 AM Ben Goertzel <[email protected]> wrote: > Rob, have you looked at the examples cited in my article, that I > linked here? Seeing this particular sort of stupidity from them, > it's hard to see how these networks would be learning the same sorts > of "causal invariants" as humans are... > > Transformers clearly ARE a full grammar learning architecture, but in > a non-AGI-ish sense. They are learning the grammar of the language > underlying their training corpus, but mixed up in a weird and > non-human-like way with so many particulars of the corpus. > > Humans also learn the grammar of their natural languages mixed up with > the particulars of the linguistic constructs they've encountered -- > but the "subtle" point (which obviously you are extremely capable to > grok) is that the mixing-up of abstract grammatical patterns with > concrete usage patterns in human minds is of a different nature than > the mixing-up of abstract grammatical patterns with concrete usage > patterns in GPT3 and other transformer networks. The human form of > mixing-up is more amenable to appropriate generalization. > > ben ------------------------------------------ Artificial General Intelligence List: AGI Permalink: https://agi.topicbox.com/groups/agi/T21c073d3fe3faef0-M89e52548cccd0a556249a9e8 Delivery options: https://agi.topicbox.com/groups/agi/subscription
