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Here's an example that I think garners more agreement that properties of a limit ("really understanding") don't necessarily mean that any path towards that limi

by kmod 4y ago

Here's an example that I think garners more agreement that properties of a limit ("really understanding") don't necessarily mean that any path towards that limit has the properties of the limit. I think there's a lot of room for disagreement about whether this is a factually-accurate analogy and I'm not trying to argue either way on that, just trying to answer your question about how one might make these sorts of arguments if one has a certain belief about the facts. Let's say we're trying to build a calculator that only needs to do integer addition. And we decide to build it by building a giant if-else chain that hardcodes the answer to each and every possible addition. And due to finite resources, we're going to hardcode all the additions of integers up to absolute value N, but we will increase N over time. Everything you said applies equally to this situation: it quacks like a duck, and when we talk about things it can't do we have to continually move the goalposts each time a new version comes out. It also has the property that there is a "scaling law" that says that each time you double N you get predictably better performance from the system, and you can do this without bound and continually approach a limit where it can answer any question indistinguishably from something we might call "true understanding". But I think it's a bit easier to agree that in this case that it's not "really doing" addition and is a bit short of our wish to have an artificial addition system. And if someone touts this system as the way to automate addition we might feel a bit irritated. Again, many people will say that this is a bad analogy because LLMs operate quite differently, and I'm not trying to argue for or against that. Just trying to give my explanation for how a certain understanding of the facts can imply the kind of conclusion that you are trying to understand.

brotchie 4y ago
re: Calculator examples, I kinda see them both as information systems that achieve a result but there's a phase difference in where the information is stored. Similar to how space is 4D such that with relativity going faster in a spatial dimension kind of "borrows" from the time dimension (in a hand wavy way). By analogy, you can have something that's purely a lookup table, or on the other hand, completely based on an algorithm, and the full lookup table is kind of "borrowing" from the algorithmic dimension of the information system space and vice-verse the fully algorithmic version is borrowing from the hardcoded dimension of the information system space. Under the condition that you're adding integers below N, then if you consider BOTH the (hardcoded, algorithmic) as a singular space (as with 4D space time) then they are equivalent. Need to work on this theory further to make it more understandable, but I think this way about intelligence. Intelligence sits as a pattern in the information system space that can range anywhere from hardcoded to algorithmic (if we choose to orthogonalize the space this way). But what actually matters is the system's future impact on it's local laws of physics, and for that purpose both implementations are equivalent. Edit: Conversation with GPT-4 about this https://sharegpt.com/c/Sbs4XgI https://sharegpt.com/c/Sbs4XgI
lordnacho 4y ago
I think what this points towards is that we care about the internal mechanism. If we prod it externally and it gives the wrong answer, then the internal mechanism is definitely wrong. But if we get the right answers and then open it up and find the internals are still wrong, it's still wrong. This illuminates a contradiction: the walks like a duck thing is incompatible with the internals being a duck. If you see a creature with feathers that waddles and can fly, it might still be a robot when you open it. So your test cannot just rely on external tests. But you also want to create a definition of artificial intelligence that doesn't depend on being made of meat and electricity.
- pegasus 4y ago
  I think @dvt's comment above is a good attempt at answering this question. I agree with him that intrinsic motivation and a capacity for suffering, hope and all the other emotions (which we share with pretty much all animals, if not plants too) are at the top of the list. Cleverness is there also, but not at the top of the list.
- simonh 4y ago
  > I think what this points towards is that we care about the internal mechanism. The mechanism is what makes a system interesting. In software this is why we develop libraries of algorithms and code we can reuse and compose into new solutions. The programmer is providing the intellectual flexibility, while the code is the set of capabilities. It’s why this is a superior approach, compared to building a single monolithic mass of procedural code from scratch in a single variable scope for every program we write. Solutions matter because it’s not just about what a system can do now, it’s about what it can learn or be adapted to do next.
continuational 4y ago
The only thing that separates your mechanism for doing addition from what computers actually do is efficiency. Computers can only add numbers up to some fixed size, e.g. 64 bits, and you have to use repetition to add anything larger. Does that mean computers are not "really doing" addition?
- simonh 4y ago
  There’s a lot more different than efficiency. We can program computers with algorithms capable of computing any possible addition, the limitation being only the memory of the computer and time, not the algorithm itself. Those algorithms are genuinely doing addition in a way that a pre-computed lookup table is not. It’s the difference between computing an addition in your head and just remembering that 2 + 2 is 4.

by kmod 4y ago

brotchie 4y ago

re: Calculator examples, I kinda see them both as information systems that achieve a result but there's a phase difference in where the information is stored. Similar to how space is 4D such that with relativity going faster in a spatial dimension kind of "borrows" from the time dimension (in a hand wavy way). By analogy, you can have something that's purely a lookup table, or on the other hand, completely based on an algorithm, and the full lookup table is kind of "borrowing" from the algorithmic dimension of the information system space and vice-verse the fully algorithmic version is borrowing from the hardcoded dimension of the information system space. Under the condition that you're adding integers below N, then if you consider BOTH the (hardcoded, algorithmic) as a singular space (as with 4D space time) then they are equivalent. Need to work on this theory further to make it more understandable, but I think this way about intelligence. Intelligence sits as a pattern in the information system space that can range anywhere from hardcoded to algorithmic (if we choose to orthogonalize the space this way). But what actually matters is the system's future impact on it's local laws of physics, and for that purpose both implementations are equivalent. Edit: Conversation with GPT-4 about this https://sharegpt.com/c/Sbs4XgI https://sharegpt.com/c/Sbs4XgI

lordnacho 4y ago

I think what this points towards is that we care about the internal mechanism. If we prod it externally and it gives the wrong answer, then the internal mechanism is definitely wrong. But if we get the right answers and then open it up and find the internals are still wrong, it's still wrong. This illuminates a contradiction: the walks like a duck thing is incompatible with the internals being a duck. If you see a creature with feathers that waddles and can fly, it might still be a robot when you open it. So your test cannot just rely on external tests. But you also want to create a definition of artificial intelligence that doesn't depend on being made of meat and electricity.

pegasus 4y ago
I think @dvt's comment above is a good attempt at answering this question. I agree with him that intrinsic motivation and a capacity for suffering, hope and all the other emotions (which we share with pretty much all animals, if not plants too) are at the top of the list. Cleverness is there also, but not at the top of the list.
simonh 4y ago
> I think what this points towards is that we care about the internal mechanism. The mechanism is what makes a system interesting. In software this is why we develop libraries of algorithms and code we can reuse and compose into new solutions. The programmer is providing the intellectual flexibility, while the code is the set of capabilities. It’s why this is a superior approach, compared to building a single monolithic mass of procedural code from scratch in a single variable scope for every program we write. Solutions matter because it’s not just about what a system can do now, it’s about what it can learn or be adapted to do next.

continuational 4y ago

The only thing that separates your mechanism for doing addition from what computers actually do is efficiency. Computers can only add numbers up to some fixed size, e.g. 64 bits, and you have to use repetition to add anything larger. Does that mean computers are not "really doing" addition?

simonh 4y ago
There’s a lot more different than efficiency. We can program computers with algorithms capable of computing any possible addition, the limitation being only the memory of the computer and time, not the algorithm itself. Those algorithms are genuinely doing addition in a way that a pre-computed lookup table is not. It’s the difference between computing an addition in your head and just remembering that 2 + 2 is 4.