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The fall of the theorem economy

davidbessis.substack.com

111–120 of 127 posts

Re: The fall of the theorem economy

#111

People think mathematics is about proving theorems. I think that's just an accident of history. When we write software, we very seldom write proofs that our algorithms are correct. We just write tests, and we also run the algorithms and when they fail we know we have a bug and then we proceed to debug, fix, and add new tests (if we are disciplined, but most of us are). In time, by usage and testing, we gain confidenc…

> When we write software, we very seldom write proofs that our algorithms are correct. We just write tests, and we also run the algorithms and when they fail we know we have a bug and then we proceed to debug, fix, and add new tests

But this is due to a "failure" of programming language progress. We've had formal languages for a long time (see Ada and SPARK) and we've simply failed to use them for most scenarios, instead regressing to imperative manually memory managed languages like C and then deriving from that branch.

Re: The fall of the theorem economy

#113
post #77

To be devil's advocate, two things may offer a glimmer of hope: First, math, generally, is useless. I mean, yes there are of course practical uses of basic thru undergrad-level math, and some beyond that. But for many mathematicians, the sum result of their entire career may lead to exactly zero results that have any real-world value. The entire field they work in may have meaning only to the handful of other individ…

Plenty of math showed zero real-world value for centuries until it suddenly did.

Re: The fall of the theorem economy

#114
post #101

Earlier quoted context omitted.

LLMs sure, but AlphaZero had no visual cortex yet can smash Magnus Carlsen easily. I think that we're not that far away from AI that can be superhuman at all facets of theorem proving. I think that we're far away from an AI that can create good abstractions and construct a theory to prove theorems.

A convolutional neural network is really somewhat like a visual cortex. Obviously AlphaZero doesn't literally have a visual cortex -- actual literal visual cortices are features of actual literal brains made out of meat -- but it definitely has something that does something akin to visual processing , in a way that LLMs don't. Or at least they don't on the face of it ; maybe well trained large enough LLMs have effect…

That's fair. Two things that are heavily underrated are architecture and encoders

Re: The fall of the theorem economy

#115
post #31

Greg Egan's description of how mathematics evolves into "truth mining" in his novel Diaspora is seeming more and more prescient. It essentially describes what mathematics would look like after formalization records all theorems discovered so far in a huge, collective database and proof assistants can instantly work out the details of a given proof. What remains of mathematics? According to Egan, visualization, intuit…

I'd be very surprised if there aren't huge areas of undiscovered math that can't be explained with either geometric or algebraic views. Math is entirely subjective. "Proof" essentially means "Other educated practitioners have the same experience when trying to understand this." The logical steps that proofs are built on all have that common foundation. Our concept of logic based on our subjective experience of "truth…

> Our concept of logic based on our subjective experience of "truth."

The idea that we experience objectivity subjectively, thus there is no objectivity, seems like an ultimately nonsensical and self-defeating sleight of hand to me.

Re: The fall of the theorem economy

#116
post #97

Earlier quoted context omitted.

This is a serious misconception of human cognitive abilities. We have the ability to abstract generally - there is no abstraction for which we lack the capacity to comprehend. We regularly visualize, contextualize, and satisfactorily explain systems with dozens of dimensions. The fact that we cannot hold 4,5+ spatial dimensions in our imaginations sufficiently to develop an intuition for navigation in that space and…

> There's also a huge issue with your use of the word subjective - math is objective. Proofs remain stable whether it's humans or any other system that does the processing. We test that objectivity by comparing the subjective readings from individual humans, and if the tests all return the same results, we can confidently say that the resulting proof is an objective fact about reality. Subjective fundamentally means…

[deleted]

Re: The fall of the theorem economy

#117
post #97

Earlier quoted context omitted.

This is a serious misconception of human cognitive abilities. We have the ability to abstract generally - there is no abstraction for which we lack the capacity to comprehend. We regularly visualize, contextualize, and satisfactorily explain systems with dozens of dimensions. The fact that we cannot hold 4,5+ spatial dimensions in our imaginations sufficiently to develop an intuition for navigation in that space and…

> There's also a huge issue with your use of the word subjective - math is objective. Proofs remain stable whether it's humans or any other system that does the processing. We test that objectivity by comparing the subjective readings from individual humans, and if the tests all return the same results, we can confidently say that the resulting proof is an objective fact about reality. Subjective fundamentally means…

> [...] claiming that math is producing objective facts ignores at least a few hundred years of philosophy of mathematics (if not more). Even practicing mathematicians like Chaitin have described math as being more about inventing than discovering.

Chaitin's work (including in his own opinion) moved the philosophical needle on maths towards "discovery" and away from "invention".

Re: The fall of the theorem economy

#118
post #101

Earlier quoted context omitted.

LLMs sure, but AlphaZero had no visual cortex yet can smash Magnus Carlsen easily. I think that we're not that far away from AI that can be superhuman at all facets of theorem proving. I think that we're far away from an AI that can create good abstractions and construct a theory to prove theorems.

A convolutional neural network is really somewhat like a visual cortex. Obviously AlphaZero doesn't literally have a visual cortex -- actual literal visual cortices are features of actual literal brains made out of meat -- but it definitely has something that does something akin to visual processing , in a way that LLMs don't. Or at least they don't on the face of it ; maybe well trained large enough LLMs have effect…

I believe the architecture for convolution neural networks were directly inspired by how vision works and some of the core design choices map onto real features of the visual cortex.

Re: The fall of the theorem economy

#119
post #31

Greg Egan's description of how mathematics evolves into "truth mining" in his novel Diaspora is seeming more and more prescient. It essentially describes what mathematics would look like after formalization records all theorems discovered so far in a huge, collective database and proof assistants can instantly work out the details of a given proof. What remains of mathematics? According to Egan, visualization, intuit…

I'd be very surprised if there aren't huge areas of undiscovered math that can't be explained with either geometric or algebraic views. Math is entirely subjective. "Proof" essentially means "Other educated practitioners have the same experience when trying to understand this." The logical steps that proofs are built on all have that common foundation. Our concept of logic based on our subjective experience of "truth…

>> Animal brains can't abstract like (some of) our brains can.

You mean they can't come up with mathematical abstractions? That's right of course, but they seem perfectly able to "abstract" in the sense of drawing general rules from specific observations. For example, I noticed recently how my cat friend was happy to exit and enter the house from either of two doors and a window as the opportunity presented itself (i.e. depending on which one was open at the time he wanted to get in or out). E.g. I just happened to open the window and he hopped onto the ledge and out into the mystery of the night, entirely unaided by human hands or voices.

That's an abstraction: one door is like another door and they're both like a window. Both things lead in and out of the house and they can be open or closed at different times. If one is closed another may be open. Something like that, obviously I have no idea what concepts, exactly, he has in his head. And btw, "in", "out", "house", etc are also abstractions, so a house in the UK is like a house in France, or like one in Italy, or one in Greece: that's where the human things are; I guess.

I think it's important to understand this about animal intelligence, that it is very much capable of broad generalisation and abstract thought. Absolutely not to the same degree as humans because we got language and that probably comes with a whole other level of ability for abstraction and reasoning, but I don't believe it's possible for an animal to survive in the physical world if it can't go from the concrete to the abstract, the specific to the general, to some extent.

You mention ASI so I'm comfortable making this comment which is about AI, without feeling I'm hijacking your conversation. I think one only begins to understand how powerful the animal brain is, and how overlooked (in AI discourse) its ability to form such broad, useful abstractions that allow animals to navigate the physical world autonomously while setting and pursuing their own goals, when one tries to reproduce the same behaviour artificially, in computers.

Like you say, yes, maybe when we stop understanding what our computers produce, it will be because they moved a level up from where we are, as ASI. Maybe there is such a level; maybe there isn't. For the time being, there is no artificial system that can spontaneously move in the physical world as easily and effortlessly as my cat friend can (and he's a graceful animal; really, much closer to an African wildcat than its domestic descendant). If you want an AI system to understand the difference between "in" and "out" and that there are doors and windows that connect the two you have to somehow explicitly feed that information into it, either by hand-coding (e.g. the PDDL models used in Planning and Scheduling) or by training on carefully chosen examples of those concepts (as in machine learning), or at least some kind of objective that will lead the AI to develop them, or develop similar concepts (as in Reinforcement Learning). We're still very far from the capabilities of the animal brain.

Also, may I quibble about the fact that our brain, too, is an animal brain?

Re: The fall of the theorem economy

#120

Earlier quoted context omitted.

I'd be very surprised if there aren't huge areas of undiscovered math that can't be explained with either geometric or algebraic views. Math is entirely subjective. "Proof" essentially means "Other educated practitioners have the same experience when trying to understand this." The logical steps that proofs are built on all have that common foundation. Our concept of logic based on our subjective experience of "truth…

This is a serious misconception of human cognitive abilities. We have the ability to abstract generally - there is no abstraction for which we lack the capacity to comprehend. We regularly visualize, contextualize, and satisfactorily explain systems with dozens of dimensions. The fact that we cannot hold 4,5+ spatial dimensions in our imaginations sufficiently to develop an intuition for navigation in that space and…

>> Crude attempts, such as OpenCyc and other formal ontological reasoner systems, would need trillions of low level rules to have a rough approximation of the world model as complex as that of a human child. AI with trillions of parameters could probably start getting to the point where there's parity with human scale, but even if you turned the entire planet earth into computronium and turned it toward the task of understanding all the theory and science of the universe, there will always be far more left to explore and understand than the sum total of all knowledge.

I thought CyC was a heroic attempt doomed to failure but not because it was rule-based. IIUC it used (custom) first-order logic and that's as expressive as expressive can be. There's no reason why a sufficiently large first-order rule-base could not capture every human concept. There's also no reason why "trillions of parameters" would be any better at that than "trillions of rules". It really comes down to what those rules or parameters are encoding.

What doomed CyC to failure, I think, is that its rule-base was mainly manually encoded. CyC was basically the world's biggest ever expert system, and it came with the biggest ever knowledge acquisition bottleneck. I don't think there's any magick to my claim, either. Human minds can't handle the complexity of a few dozen, let alone a few million, interconnected rules without making mistakes. It's hopeless trying to create such a system by hand. From a certain point onward you have no idea what your system can and can't do, because you have no idea what information it has and hasn't access to.

But it's hopeless trying to create such a system by chance, too, i.e. by feeding the system all the data we can find in the hope that it will somehow spontaneously acquire all the knowledge we want it to; much of which is not even in the deductive closure of the information we feed it (and LLMs are not deductive inference engines, unlike expert systems).

Some kind of automatic knowledge acquisition is clearly a much better idea than manually coding rules by hand, but I don't see how peta-scale machine learning has moved the needle much either. We're still stuck with systems that can do some spectacular things but can't do simple things. Or things that look simple to us.

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