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I think using the word “intelligence” when speaking of computers, beyond a kind of figure of speech, is anthropomorphizing, and it is a common pseudoscientific

I think using the word “intelligence” when speaking of computers, beyond a kind of figure of speech, is anthropomorphizing, and it is a common pseudoscientific habit that must go. What is most characteristic about human intelligence is the ability to abstract from particular, concrete instances of things we experience. This allows us to form general concepts which are the foundation of reason. Analysis requires concepts (as concepts are what are analyzed), inference requires concepts (as we determine logical relations between them). We could say that computers might simulate intelligent behavior in some way or other, but this is observer relative not an objective property of the machine, and it is a category mistake to call computers intelligent in any way that is coherent and not the result of projecting qualities onto things that do not possess them. What makes all of this even more mystifying is that, first, the very founding papers of computer science speak of effective methods, which is by definition about methods that are completely mechanical and formal, and this stripped of the substantive conceptual content it can be applied to. Historically, this practically meant instructions given to human computers who merely completed them without any comprehension of what they were participating in. Second, computers are formal models, not physical machines. Physical machines simulate the computer formalism, but are not identical with the formalism. And as Kripke and Searle showed, there is no way in which you can say that a computer is objectively calculating anything! When we use a computer to add two numbers, you cannot say that the computer is objectively adding two numbers. It isn’t. The addition is merely an interpretation of a totally mechanistic and formal process that has been designed to be interpretable in such ways. It is analogous to reading a book. A book does not objectively contains words. It contains shaped blots of pigment on sheets of cellulose that have been assigned a conventional meaning in a culture and language. In other words, you being the words, the concepts, to the book. You bring the grammar. The book itself doesn’t have them. So we must stop confusing figurative language with literal language. AI, LLMs, whatever can be very useful, but it isn’t even wrong to call them intelligent in any literal sense.

Nevermark 2y ago
> I think using the word “intelligence” when speaking of computers, beyond a kind of figure of Intelligence is what we call problem solving when the class of "problem" that a being or artifact is solving is extremely complex, involves many or near uncountable combinations of constraints, and is impossible to really characterize well. Other than examples, of data points, and some way for the person or artifact to extract something general and useful from them. Like human languages and sensibly weaving together knowledge on virtually every topic known to humans, whether any humans have put those topics together before or not. Human beings have widely ranging abilities in different kinds of thinking, despite our common design. Machines, deep learning architectures, underpinnings are software. There are endless things to try, and they are going to have a very wide set of intelligence profiles. I am staggered how quickly people downplay the abilities of these models. We literally don't know the principles they have learned (post training) for doing the kinds of processing they do. The magic of gradient algorithms. They are far from "perfect", but at what they do there is no human that can hold a candle to them. They might not be creative, but I am, and their versatility in discussing combinations of topics I am fluent in, and am not, is incredibly helpful. And unattainable from human intelligence. Unless I had a few thousand researchers, craftsman, etc. all on a Zoom call 24/7. Which might not work out so well anyway. I get that they have their glaring weaknesses. So do I! So does everyone I have ever had the pleasure to meet. If anyone can write a symbolic or numerical program to do what LLM's are doing now - without training, just code - even on some very small scale, I have yet to hear of it. I.e. someone who can demonstrate they understand the style of versatile pattern logic they learn to do. (I am very familiar with deep learning models and training algorithms and strategies. But they learn patterns suited to the data they are trained on, implicit in the data that we don't see. Knowing the very general algorithms that train them doesn't shed light on the particular pattern logic they learn for any particular problem.)
HappMacDonald 2y ago
All of your descriptions are quite reductivist. Claiming that a computer doesn't do math has a lot in common with the claim that a hammer doesn't drive a nail. While it is true that a standard hammer requires the aid of a human to apply swing force, aim the head, etc it is equally true that a bare-handed human also does not drive a nail. Plus, it's relatively straightforward and inexpensive using contemporary tech to build a roomba-like machine that wanders about on any flat surface cuing up and driving nails of its own accord with no human intervention. If computers do not add numbers, then neither do people. It's not like you can do an addition-style turing test with a human in one room and a computer in another with a judge partitioned off of both of them, feed them each an addition problem and leave the judge in any position where they can determine which result is "really a sum" and which one is only pretending to be. Yet if you reduce far enough to claim that humans aren't "really" adding numbers either, then you are left to justify what it would even mean for numbers to "really" get added together.
vidarh 2y ago
Unless you can demonstrate that humans can solve a function that exceeds the Turing computable, it is reasonable to assume we're non more than Turing complete, and all Turing complete systems can compute the same set of functions. As it stands, we don't even know of any functions that exceeds the Turing complete, but are computable.
- Jensson 2y ago
  > As it stands, we don't even know of any functions that exceeds the Turing complete, but are computable. That would require the universe to be discrete, we don't know that. Otherwise most continuous processes compute something that a Turing machine can't, the Turing machine can only approximate it.
  - vidarh 2y ago
    You can compute with values that are not discrete just fine by expressing them symbolically. I can't write out 1/3 as a discrete value in base 10, but I can still compute with it just fine.

Nevermark 2y ago

> I think using the word “intelligence” when speaking of computers, beyond a kind of figure of Intelligence is what we call problem solving when the class of "problem" that a being or artifact is solving is extremely complex, involves many or near uncountable combinations of constraints, and is impossible to really characterize well. Other than examples, of data points, and some way for the person or artifact to extract something general and useful from them. Like human languages and sensibly weaving together knowledge on virtually every topic known to humans, whether any humans have put those topics together before or not. Human beings have widely ranging abilities in different kinds of thinking, despite our common design. Machines, deep learning architectures, underpinnings are software. There are endless things to try, and they are going to have a very wide set of intelligence profiles. I am staggered how quickly people downplay the abilities of these models. We literally don't know the principles they have learned (post training) for doing the kinds of processing they do. The magic of gradient algorithms. They are far from "perfect", but at what they do there is no human that can hold a candle to them. They might not be creative, but I am, and their versatility in discussing combinations of topics I am fluent in, and am not, is incredibly helpful. And unattainable from human intelligence. Unless I had a few thousand researchers, craftsman, etc. all on a Zoom call 24/7. Which might not work out so well anyway. I get that they have their glaring weaknesses. So do I! So does everyone I have ever had the pleasure to meet. If anyone can write a symbolic or numerical program to do what LLM's are doing now - without training, just code - even on some very small scale, I have yet to hear of it. I.e. someone who can demonstrate they understand the style of versatile pattern logic they learn to do. (I am very familiar with deep learning models and training algorithms and strategies. But they learn patterns suited to the data they are trained on, implicit in the data that we don't see. Knowing the very general algorithms that train them doesn't shed light on the particular pattern logic they learn for any particular problem.)

HappMacDonald 2y ago

All of your descriptions are quite reductivist. Claiming that a computer doesn't do math has a lot in common with the claim that a hammer doesn't drive a nail. While it is true that a standard hammer requires the aid of a human to apply swing force, aim the head, etc it is equally true that a bare-handed human also does not drive a nail. Plus, it's relatively straightforward and inexpensive using contemporary tech to build a roomba-like machine that wanders about on any flat surface cuing up and driving nails of its own accord with no human intervention. If computers do not add numbers, then neither do people. It's not like you can do an addition-style turing test with a human in one room and a computer in another with a judge partitioned off of both of them, feed them each an addition problem and leave the judge in any position where they can determine which result is "really a sum" and which one is only pretending to be. Yet if you reduce far enough to claim that humans aren't "really" adding numbers either, then you are left to justify what it would even mean for numbers to "really" get added together.

vidarh 2y ago

Unless you can demonstrate that humans can solve a function that exceeds the Turing computable, it is reasonable to assume we're non more than Turing complete, and all Turing complete systems can compute the same set of functions. As it stands, we don't even know of any functions that exceeds the Turing complete, but are computable.

Jensson 2y ago
> As it stands, we don't even know of any functions that exceeds the Turing complete, but are computable. That would require the universe to be discrete, we don't know that. Otherwise most continuous processes compute something that a Turing machine can't, the Turing machine can only approximate it.
- vidarh 2y ago
  You can compute with values that are not discrete just fine by expressing them symbolically. I can't write out 1/3 as a discrete value in base 10, but I can still compute with it just fine.