this post was submitted on 24 Jan 2024
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Again, this isn't quite correct. They can do this, but it isn't the only way they can achieve completion of tokens.
(It also developed representations of what constituted legal vs non-legal moves.)
You are getting closer to the point. Think about a model asked to complete Pythagorean theorem sequences based on a, b inputs to arrive at c inputs.
What's the most efficient way to represent that data for successfully completing sequences?
So somewhere in there I'd expect nodes connected to represent the Othello grid. They wouldn't necessarily be in a grid, just topologically the same graph.
Then I'd expect millions of other weighted connections to represent the moves within the grid including some weightings to prevent illegal moves. All based on mathematics and clever statistical analysis of the training data. If you want to refer to things as tokens then be my guest but it's all graphs.
If you think I'm getting closer to your point can you just explain it properly? I don't understand what you think a neural network model is or what you are trying to teach me with Pythag.
The most efficient way for a neural network to predict Pythagorean results given inputs would be to reverse engineer a Pythagorean function within itself rather than simply trying to model statistical relationships between inputs and results. To effectively build a world model of Pythagorean calculation.
Training to autocomplete doesn't mean that the way it achieves this is limited to any one approach or solution, and it would be useful to keep in mind that a neural network of unbounded size can model any possible function.
It wouldn't reverse engineer anything. It would start by weighting neurons based on it's training set of Pythagorean triples. Over time this would get tuned to represent Pythag in the form of mathematical graphs.
This is not "understanding" as most people would know it. More like a set of encoded rules.
Seems to me you are attempting to understand machine learning mathematics through articles.
That quote is not a retort to anything I said.
Look up Category Theory. It demonstrates how the laws of mathematics can be derived by forming logical categories. From that you should be able to imagine how a neural network could perform a similar task within its structure.
It is not understanding, just encoding to arrive at correct results.
What I quoted isn't an article, it was a mathematics dissertation.
And you disputed that a NN could arrive at the theorem before being corrected about it.
There you go arguing in bad faith again by putting words in my mouth and reducing the nuance of what was said.
You do know dissertations are articles and don't constitute any form or rigorous proof in and of themselves? Seems like you have a very rudimentary understanding of English, which might explain why you keep struggling with semantics. If that is so, I apologise because definitions are difficult when it comes to language, let alone ESL.
I didn't dispute that NNs can arrive at a theorem. I debate whether they truly understand the theorem they have encoded in their graphs as you claim.
This is a philosophical/semantical debate as to what "understanding" actually is because there's not really any evidence that they are any more than clever pattern recognition algorithms driven by mathematics.
Where did I claim that? Cite the exact phrase.
I said reverse engineer. Not deduce or prove.
Title of your post is literally "New Theory Suggests Chatbots Can Understand Text".
You also hinted at it with your Pythag analogy.
I didn't write the headline, and I happen to interpret it the same way I interpreted it in "Bees understand the concept of zero." Language can have more than one narrowly scoped meaning, and the article body makes it clear it isn't saying anything about human consciousness or introspective understanding.
No, I correctly stated that a model happening upon the Pythagorean function would outperform ones approximating it by statistical correlations. That, as Hinton has said in the past, "predicting the next thing takes knowledge." It makes sense that the development of world models and abstractions from the training data and not simply surface statistics would correlate with both increased next token prediction and network complexity increases.
You interpreted what I was saying as implying the network has some woo woo interpretation of 'understanding' because you seem to be more committed to debating a straw man using inaccurate and overly narrow semantics than actually discussing the topic at hand in good faith.
You posted the article rather than the research paper and had every chance of altering the headline before you posted it but didn't.
You questioned why you were downvoted so I offered an explanation.
Your attempts to form your own arguments often boil down to "no you".
So as I've said all along we just differ on our definitions of the term "understanding" and have devolved into a semantic exchange. You are now using a bee analogy but for a start that is a living thing not a mathematical model, another indication that you don't understand nuance. Secondly, again, it's about definitions. Bees don't understand the number zero in the middle of the number line but I'd agree they understand the concept of nothing as in "There is no food."
As you can clearly see from the other comments, most people interpret the word "understanding" differently from yourself and AI proponents. So I infer you are either not a native English speaker or are trying very hard to shoehorn your oversimplified definition in to support your worldview. I'm not sure which but your reductionist way of arguing is ridiculous as others have pointed out and full of logical fallacies which you don't seem to comprehend either.
Regarding what you said about Pythag, I agree and would expect it to outperform statistical analysis. That is due to the fact that it has arrived at and encoded the theorem within its graphs but I and many others do not define this as knowledge or understanding because they have other connotations to the majority of humans. It wouldn't for instance be able to tell you what a triangle is using that model alone.
I spot another apeal to authority... "Hinton said so and so..." It matters not. If Hinton said the sky is green you'd believe it as you barely think for yourself when others you consider more knowledgeable have stated something which may or may not be true. Might explain why you have such an affinity for AI...
Lol
Lol indeed, just seen you moderate a Simulation Theory sub.
Congratulations, you have completed the tech evangelist starter pack.
Next thing you'll be telling me we don't have to worry about climate change because we'll just use carbon capture tech and failing that all board Daddy Elon's spaceship to teraform Mars.