Machine Learning Street Talk
This week Dr. Tim Scarfe and Dr. Keith Duggar discuss Explainability, Reasoning, Priors and GPT-3. We check out Christoph Molnar’s book on intepretability, talk about priors vs experience in NNs, whether NNs are reasoning and also cover articles by Gary Marcus and Walid Saba critiquing deep learning. We finish with a brief discussion of Chollet’s ARC challenge and intelligence paper.
00:00:00 Intro
00:01:17 Explainability and Christoph Molnars book on Intepretability
00:26:45 Explainability – Feature visualisation
00:33:28 Architecture / CPPNs
00:36:10 Invariance and data parsimony, priors and experience, manifolds
00:42:04 What NNs learn / logical view of modern AI (Walid Saba article)
00:47:10 Core knowledge
00:55:33 Priors vs experience
00:59:44 Mathematical reasoning
01:01:56 Gary Marcus on GPT-3
01:09:14 Can NNs reason at all?
01:18:05 Chollet intelligence paper/ARC challenge
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Wow! Amazing video π
didn't know Gru was into machine learning
Thank you for the video, guys.
I wasn't totally on board with the GOFAI article, particularly the example "house is larger than chair –> chair fits inside a house".
This isn't logically deducible. It's easy to create counterexamples to the implication, like if the house is larger in volume, but extremely short, or it's solid and admits nothing inside.
The implicit logic rests on a good world model, which fills in the premises that a house is hollow, it's much larger than a chair in all dimensions, etc. A good world model isn't going to be developed by learning some narrow dataset like MultiNLI, that's quite ridiculous. So I suspect that those results aren't totally fair arguments against the current machine learning paradigm. Try those questions with GPT-X and I'm sure it will perform better. I generally agree that deep learning is getting way too much emphasis, but unfair criticism of it does the alternatives no favour.
Interesting discussion but posting some links brought up in your discussions would really help those viewing:
https://leanpub.com/interpretable-machine-learning
https://distill.pub/2017/feature-visualization/
https://arxiv.org/abs/1912.01412
Loved it. I want to read the article against gpt3 now
Anybody built a model to predict next video's topic ?
Very interesting conversation. I can't help but feel that too many critiques of deep learning take the form "what we need is some basic rules spelled out…" Next thing you know, it's 1989 again, and we're trying to exhaustively code a description of some "simple" real-world phenomenon.
Very Interesting conversation. Thanks for brining our attention to the article questioning the GPT-3 reasoning. There are loads of posts and videos admiring this language model, but a fair AI system would look at both sides of the agreement π
Dr. Keith Duggar is living in the mandelbrot set. π
Hi guys, just listened to the whole thing π and I loved the discussion on "static" results/outputs vs. computed results/outputs. Keith had a very interesting example of a multiplication circuit all laid-out (I would say folded – reminds me of fold and unfold in pure functional programming) — this is a very very interesting discussion and is at the heart of NNs … it is related to memorizing/remembering vs. computing from scratch. As we know, a NN is some sort of a gigantic hash table (where every combination of weights is a key – more or less. no?) And so I agree that we should not be bias as to what we call "computing" (or "inferencing") – if I got the right output, then I got the right output, regardless of how – end of story. So I agree on that. But isn't the deal breaker infinity – or, more correctly, infinite objects. I mean if my domain is infinite, then how much I can store/hash becomes an issue. Take computing simple arithmetic expressions… e.g, (3 * (2 + (8 / 2)) or, closer to my heart, language – formal or natural, btw. I mean a Python compiler is ready to interpret and execute ANY syntactically correct Python program (and here ANY = Infinity) – that's why I cannot see how a NN can ever learn to check the syntax of a python program no matter how many examples you give it – because the set is infinite No? (related also to the discussion on alphaGo and regardless of how large the search space is in the end it is finite…)
Anyway, I liked the discussion and the issues raised, and it is perhaps all valid (what Keith suggests) except that I think this argument breaks in infinite domains… great discussion nevertheless.
BTW, I'm now a regular listener π