343 | Tom Griffiths on The Laws of Thought
episode
Sean Carroll's Mindscape: Science, Society, Philosophy, Culture, Arts, and Ideas
1h 20m
3 speakers
7 chapters
transcribed
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What is the main topic discussed in this episode?
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Hello, everyone, and welcome to the Mindscape Podcast. I'm your host, Sean Carroll. I've always thought that one of the interesting aspects of modern approaches to AI, large language models, other connectionist things, is that very often, or at least in their natural state, an LLM is not good at arithmetic. Thank you very much. about simple problems adding medium-sized numbers together. Other kinds of number-based problems they're not very good at. Counting the number of Rs in the word strawberry. Random numbers. If you ask an LLM to generate a million random integers between 0 and 100 and then made a plot of the frequencies, it would not look uniform. Of course, these are all things that human beings are also intrinsically not very good at.
But part of you thinks, come on, it's a computer. It should be able to do simple arithmetic problems. And of course, the answer is there's no real mystery here. The computer on which the LLM is running has no problem doing arithmetic. But you're not talking to the computer. You're talking to a program. and the program might not be set up to do those kinds of things. And again, it's exactly like humans. It's almost like you tried really hard to make a program that sounded human, and in the course of doing that, it lost the ability to do arithmetic, which is kind of interesting when you think about it. But it's also a reminder that when you say thought or thinking, you're not really referring to a single thing.
The ability to add numbers together and the ability to carry on a conversation, those are two very different abilities. And you might optimize for one over the other in building a program or evolving an organism through natural selection. Nevertheless, we do sort of aim at a sort of standard set of standards for thinking correctly, right? We want to get the right answer when we add numbers together. We want to logic our way through puzzles that we are given. We want to reach rational, reasonable conclusions. So how do you sort of fit together, on the one hand, the pristine rules of logic and reasoning to which we aspire as thinking reasonable creatures, and on the other hand, the reality of our minds and our brains and our embodied intelligence?
which has, number one, a whole bunch of different things that it was selected for over the course of biological time, and number two, all sorts of constraints in terms of energy and fuel and time and things like that. If you had a brain, a human kind of brain, that was able to do arbitrarily good arithmetic, that might make it worse at other things that were more important for survival.
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Chapters
7 chapters
1
What is the main topic discussed in this episode?
0:00–9:26
2
How do logic and probability relate to human thinking?
9:26–22:29
3
What are the laws of thought according to Tom Griffiths?
22:29–37:52
4
How does cognitive science define rationality?
37:52–50:27
5
What is the role of inductive bias in human cognition?
50:27–1:04:10
6
How do neural networks mimic human thought processes?
1:04:10–1:19:42
7
What challenges do AI systems face compared to human cognition?
1:19:42–1:20:38
Speakers
3 identifiedMore from Sean Carroll's Mindscape: Science, Society, Philosophy, Culture, Arts, and Ideas
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364 | Stuart Firestein on How Science Relies on Ignorance and Failure
363 | Chandra Sripada on How LLMs and Humans are Cognitive Cousins