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In an era where the quality of journalism and media has moved from objective analysis toward editorialized hysteria on all topics, the approach you take is especially refreshing.
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Thanks for the comment and the kind words about me being nice. I'll try to live up to that. On the technical depth point, I agree. A lot of folks tell me they love the "meaning of life" questions. I love both the technical and the philosophical. My hope is to more and more try to go deep technically with the ML, CS, math, physics folks on the topic of their expertise, and find productive points of passionate disagree…
If you want to get into philosophical questions you should get some actual philosophers on the show because right now it seems like most of your guests are just making up answers to your questions on the spot and it's not very insightful. At the very least keep the questions more open ended, ask them what are the biggest road blocks to AGI and how do we get over them, or if they have some contrarian opinions about AG…
People watch JRE, because it's fun and he has a lot of very influential people as guests nowadays. It doesn't mean that more interviewers should be like him, God forbid...
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I believe this is overstated and makes it sound like all of his work is bs, when the majority of the book is just fine. (I could be wrong and would be happy to be proven so. Just not a fan of the all-or-nothing attitude people apply toward this book when that doesn't seem warranted.)
If some data is wrong and you can't tell which is which, how can you trust just some parts of this book? Are we to fact-check bit by bit until everything gets sorted out and we know for sure what parts of the book to trust? Besides, why go through all that trouble if there are heaps of good psychology books out there that aren't plagued with errors as this one? Since our time is so limited, I think it is a good heuri…
All of psychology has suffered in the replication crisis, but my understanding is that Kahneman & Tversky's stuff is better than most. Their work was mostly solid and in a different era. The real bullshit began in the era of celebrities doing TED talks.
Edit it would be better for me to distinguish Kahneman & Tversky's own work from the work of others described in the book. Eg there is stuff in the book on priming which is definitely TED-era and doesn't replicate.
I'll echo the comments here and just say thank you Lex for your calm, incisive, and enlightening interview style. In an era where the quality of journalism and media has moved from objective analysis toward editorialized hysteria on all topics, the approach you take is especially refreshing.
I am not sure if this is covered in the podcast, but from the title, I wonder if in deep neural net research there are papers and implementations of a system 1 and 2 like model. One part of the neural net could be system 1 for fast decision making, and other could be system 2, with more deliberate analysis. They could communicate over some shared medium.
Note that both system 1 and 2 are trainable and able to perform complex tasks (he takes the example of a chess player for whom only strong moves come to mind, which is system 1 playing chess in that case; and system 2 is more of a "validation" for system 1's output in such a case).
He doesn't go into it but I think you could make the reverse argument, that system 2 is "checked" by system 1, when we "feel" that something, even though "correct", is just "not right" for instance. That kind of judgement over a thought or idea is nearly instant, it's very system-1 like, and keeps popping up in our thinking as we "judge" said thoughts and do some triage as we go along.
As for AI and system 2, the problem is that system 2 is conscious, deliberate, and aware of causality and meaning — and the last two are really hard problems for now. He mentions earlier ML models pre-DL (when they tried to do it the hard, symbolic way iirc?), and indeed the question of whether current architecture can or cannot generalize up to system 2 is opened. Yann Lecun apparently thinks it can (just that we don't know if it's right around the corner or very, very far away), Lex (and most AI experts I heard) think not, that there's a fundamentally 'other' kind of architecture(s) required.
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The things Kahneman worked on himself all did pretty well out of the replication crisis as far as I can tell. The fact that his findings got so much pushback when introduced probably helped a lot with making them more rigorous. And Kahneman didn't dig in his heels when the crisis hit. But there is an awful lots of stuff that needs to be expunged from his book because it was based on what turned out to be bad science.
> But there is an awful lots of stuff that needs to be expunged from his book because it was based on what turned out to be bad science. Can you give an example of this? I haven't seen anything he said in here that fundamentally needs to rest on a theory that could be the outcome of some study, but maybe I'm interpreting somehow differently.
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Those are very strong words. Why?
For me it's the questions. He gets these incredible guests with deep understanding of their respective fields and yet they keep being asked nonsensical question they can't possible give a meaningful answer to. For example almost every guest some variation of "Do you think one day we'll have superhuman AI?" Then the guest is struggling to come up with some platitude, because there is nothing else to say. It's a waste…
The few times I've tried to read Kahneman's book I must admit that I stopped pretty early. I found his writing style too authoritative, without presenting much evidence for the theories he lays out.
I can see where you might feel this way. I recommend giving it another try and come at from two considerations. The first is observing their experimental designs. They are very clever and well described. The second is the extent to which these experiments have been replicated with consistent results using populations with a range of socioeconomic and cultural differences. Given the consistency of these experimental o…
https://replicationindex.com/2017/02/02/reconstruction-of-a-...
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For me it's the questions. He gets these incredible guests with deep understanding of their respective fields and yet they keep being asked nonsensical question they can't possible give a meaningful answer to. For example almost every guest some variation of "Do you think one day we'll have superhuman AI?" Then the guest is struggling to come up with some platitude, because there is nothing else to say. It's a waste…
To each their own. I enjoy observing brilliant people a bit out of their element. There's plenty material available from/about Lex's guests' work; why not ask them to speculate about superhuman AI or whatever?