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That's not at all on par with what I'm saying. There exists a generally accepted baseline definition for what crosses the threshold of intelligent behavior. We

That's not at all on par with what I'm saying. There exists a generally accepted baseline definition for what crosses the threshold of intelligent behavior. We shouldn't seek to muddy this. EDIT: Generally its accepted that a core trait of intelligence is an agent’s ability to achieve goals in a wide range of environments. This means you must be able to generalize, which in turn allows intelligent beings to react to new environments and contexts without previous experience or input. Nothing I'm aware of on the market can do this. LLMs are great at statistically inferring things, but they can't generalize which means they lack reasoning. They also lack the ability to seek new information without prompting. The fact that all LLMs boil down to (relatively) simple mathematics should be enough to prove the point as well. It lacks spontaneous reasoning, which is why the ability to generalize is key

highfrequency 2y ago
What is that baseline threshold for intelligence? Could you provide concrete and objective results, that if demonstrated by a computer system would satisfy your criteria for intelligence?
- no_wizard 2y ago
  see the edit. boils down to the ability to generalize, LLMs can't generalize. I'm not the only one who holds this view either. Francois Chollet, a former intelligence researcher at Google also shares this view.
  - highfrequency 2y ago
    Are you able to formulate "generalization" in a concrete and objective way that could be achieved unambiguously, and is currently achieved by a typical human? A lot of people would say that LLMs generalize pretty well - they certainly can understand natural language sequences that are not present in their training data.
    - whilenot-dev 2y ago
      > A lot of people would say that LLMs generalize pretty well What do you mean here? The trained model, the inference engine, is the one that makes an LLM for "a lot of people". > they certainly can understand natural language sequences that are not present in their training data Keeping the trained model as LLM in mind, I think learning a language includes generalization and is typically achieved by a human, so I'll try to formulate: Can a trained LLM model learn languages that hasn't been in its training set just by chatting/prompting? Given that any Korean texts were excluded from the training set, could Korean be learned? Does that even work with languages descending from the same language family (Spanish in the training set but Italian should be learned)?
  - stevenAthompson 2y ago
    > Francois Chollet, a former intelligence researcher at Google also shares this view. Great, now there are two of you.
  - voidspark 2y ago
    Chollet's argument was that it's not "true" generalization, which would be at the level of human cognition. He sets the bar so high that it becomes a No True Scotsman fallacy. The deep neural networks are practically generalizing well enough to solve many tasks better than humans.
    - daveguy 2y ago
      No. His argument is definitely closer to LLMs can't generalize. I think you would benefit from re-reading the paper. The point is that a puzzle consisting of simple reasoning about simple priors should be a fairly low bar for "intelligence" (necessary but not sufficient). LLMs performs abysmally because they have a very specific purpose trained goal that is different from solving the ARC puzzles. Humans solve these easily. And committees of humans do so perfectly. If LLMs were intelligent they would be able to construct algorithms consisting of simple applications of the priors. Training to a specific task and getting better is completely orthogonal to generalized search and application of priors. Humans do a mix of both search of the operations and pattern matching of recognizing the difference between start and stop state. That is because their "algorithm" is so general purpose. And we have very little idea how the two are combined efficiently. At least this is how I interpreted the paper.
      - voidspark 2y ago
        He is setting a bar, saying that that is the "true" generalization. Deep neural networks are definitely performing generalization at a certain level that beats humans at translation or Go, just not at his ARC bar. He may not think it's good enough, but it's still generalization whether he likes it or not.
        fc417fc802 2y ago
        I'm not convinced either of your examples is generalization. Consider Go. I don't consider a procedural chess engine to be "generalized" in any sense yet a decent one can easily beat any human. Why then should Go be different?
david-gpu 2y ago
> There exists a generally accepted baseline definition for what crosses the threshold of intelligent behavior. Go on. We are listening.
byearthithatius 2y ago
"There exists a generally accepted baseline definition for what crosses the threshold of intelligent behavior" not really. The whole point they are trying to make is that the capability of these models IS ALREADY muddying the definition of intelligence. We can't really test it because the distribution its learned is so vast. Hence why he have things like ARC now. Even if its just gradient descent based distribution learning and there is no "internal system" (whatever you think that should look like) to support learning the distribution, the question is if that is more than what we are doing or if we are starting to replicate our own mechanisms of learning.
- dingnuts 2y ago
  How does an LLM muddy the definition of intelligence any more than a database or search engine does? They are lossy databases with a natural language interface, nothing more.
  - tibbar 2y ago
    Ah, but what is in the database? At this point it's clearly not just facts, but problem-solving strategies and an execution engine. A database of problem-solving strategies which you can query with a natural language description of your problem and it returns an answer to your problem... well... sounds like intelligence to me.
    - uoaei 2y ago
      > problem-solving strategies and an execution engine Extremely unfounded claims. See: the root comment of this tree.
      - travisjungroth 2y ago
        …things that look like problem solving strategies in performance, then.
  - madethisnow 2y ago
    datasets and search engines are deterministic. humans, and llms are not.
nmarinov 2y ago
I think the confusion is because you're referring to a common understanding of what AI is but I think the definition of AI is different for different people. Can you give your definition of AI? Also what is the "generally accepted baseline definition for what crosses the threshold of intelligent behavior"?
voidspark 2y ago
You are doubling down on a muddled vague non-technical intuition about these terms. Please tell us what that "baseline definition" is.
appleorchard46 2y ago
> Generally its accepted that a core trait of intelligence is an agent’s ability to achieve goals in a wide range of environments. Be that as it may, a core trait is very different from a generally accepted threshold. What exactly is the threshold? Which environments are you referring to? How is it being measured? What goals are they? You may have quantitative and unambiguous answers to these questions, but I don't think they would be commonly agreed upon.
aj7 2y ago
LLM’s are statistically great at inferring things? Pray tell me how often Google’s AI search paragraph, at the top, is correct or useful. Is that statistically great?
nurettin 2y ago
> intelligence is an agent’s ability to achieve goals in a wide range of environments. This means you must be able to generalize, which in turn allows intelligent beings to react to new environments and contexts without previous experience or input. I applaud the bravery of trying to one shot a definition of intelligence, but no intelligent being acts without previous experience or input. If you're talking about in-sample vs out of sample, LLMs do that all the time. At some point in the conversation, they encounter something completely new and react to it in a way that emulates an intelligent agent. What really makes them tick is language being a huge part of the intelligence puzzle, and language is something LLMs can generate at will. When we discover and learn to emulate the rest, we will get closer and closer to super intelligence.
nl 2y ago
> Generally its accepted that a core trait of intelligence is an agent’s ability to achieve goals in a wide range of environments. This is the embodiment argument - that intelligence requires the ability to interact with its environment. Far from being generally accepted, it's a controversial take. Could Stephen Hawking achieve goals in a wide range of environments without help? And yet it's still generally accepted that Stephen Hawking was intelligent.

highfrequency 2y ago

What is that baseline threshold for intelligence? Could you provide concrete and objective results, that if demonstrated by a computer system would satisfy your criteria for intelligence?

no_wizard 2y ago
see the edit. boils down to the ability to generalize, LLMs can't generalize. I'm not the only one who holds this view either. Francois Chollet, a former intelligence researcher at Google also shares this view.
- highfrequency 2y ago
  Are you able to formulate "generalization" in a concrete and objective way that could be achieved unambiguously, and is currently achieved by a typical human? A lot of people would say that LLMs generalize pretty well - they certainly can understand natural language sequences that are not present in their training data.
  - whilenot-dev 2y ago
    > A lot of people would say that LLMs generalize pretty well What do you mean here? The trained model, the inference engine, is the one that makes an LLM for "a lot of people". > they certainly can understand natural language sequences that are not present in their training data Keeping the trained model as LLM in mind, I think learning a language includes generalization and is typically achieved by a human, so I'll try to formulate: Can a trained LLM model learn languages that hasn't been in its training set just by chatting/prompting? Given that any Korean texts were excluded from the training set, could Korean be learned? Does that even work with languages descending from the same language family (Spanish in the training set but Italian should be learned)?
- stevenAthompson 2y ago
  > Francois Chollet, a former intelligence researcher at Google also shares this view. Great, now there are two of you.
- voidspark 2y ago
  Chollet's argument was that it's not "true" generalization, which would be at the level of human cognition. He sets the bar so high that it becomes a No True Scotsman fallacy. The deep neural networks are practically generalizing well enough to solve many tasks better than humans.
  - daveguy 2y ago
    No. His argument is definitely closer to LLMs can't generalize. I think you would benefit from re-reading the paper. The point is that a puzzle consisting of simple reasoning about simple priors should be a fairly low bar for "intelligence" (necessary but not sufficient). LLMs performs abysmally because they have a very specific purpose trained goal that is different from solving the ARC puzzles. Humans solve these easily. And committees of humans do so perfectly. If LLMs were intelligent they would be able to construct algorithms consisting of simple applications of the priors. Training to a specific task and getting better is completely orthogonal to generalized search and application of priors. Humans do a mix of both search of the operations and pattern matching of recognizing the difference between start and stop state. That is because their "algorithm" is so general purpose. And we have very little idea how the two are combined efficiently. At least this is how I interpreted the paper.
    - voidspark 2y ago
      He is setting a bar, saying that that is the "true" generalization. Deep neural networks are definitely performing generalization at a certain level that beats humans at translation or Go, just not at his ARC bar. He may not think it's good enough, but it's still generalization whether he likes it or not.
      - fc417fc802 2y ago
        I'm not convinced either of your examples is generalization. Consider Go. I don't consider a procedural chess engine to be "generalized" in any sense yet a decent one can easily beat any human. Why then should Go be different?

david-gpu 2y ago

> There exists a generally accepted baseline definition for what crosses the threshold of intelligent behavior. Go on. We are listening.

byearthithatius 2y ago

"There exists a generally accepted baseline definition for what crosses the threshold of intelligent behavior" not really. The whole point they are trying to make is that the capability of these models IS ALREADY muddying the definition of intelligence. We can't really test it because the distribution its learned is so vast. Hence why he have things like ARC now. Even if its just gradient descent based distribution learning and there is no "internal system" (whatever you think that should look like) to support learning the distribution, the question is if that is more than what we are doing or if we are starting to replicate our own mechanisms of learning.

dingnuts 2y ago
How does an LLM muddy the definition of intelligence any more than a database or search engine does? They are lossy databases with a natural language interface, nothing more.
- tibbar 2y ago
  Ah, but what is in the database? At this point it's clearly not just facts, but problem-solving strategies and an execution engine. A database of problem-solving strategies which you can query with a natural language description of your problem and it returns an answer to your problem... well... sounds like intelligence to me.
  - uoaei 2y ago
    > problem-solving strategies and an execution engine Extremely unfounded claims. See: the root comment of this tree.
    - travisjungroth 2y ago
      …things that look like problem solving strategies in performance, then.
- madethisnow 2y ago
  datasets and search engines are deterministic. humans, and llms are not.

nmarinov 2y ago

I think the confusion is because you're referring to a common understanding of what AI is but I think the definition of AI is different for different people. Can you give your definition of AI? Also what is the "generally accepted baseline definition for what crosses the threshold of intelligent behavior"?

voidspark 2y ago

You are doubling down on a muddled vague non-technical intuition about these terms. Please tell us what that "baseline definition" is.

appleorchard46 2y ago

> Generally its accepted that a core trait of intelligence is an agent’s ability to achieve goals in a wide range of environments. Be that as it may, a core trait is very different from a generally accepted threshold. What exactly is the threshold? Which environments are you referring to? How is it being measured? What goals are they? You may have quantitative and unambiguous answers to these questions, but I don't think they would be commonly agreed upon.

aj7 2y ago

LLM’s are statistically great at inferring things? Pray tell me how often Google’s AI search paragraph, at the top, is correct or useful. Is that statistically great?

nurettin 2y ago

> intelligence is an agent’s ability to achieve goals in a wide range of environments. This means you must be able to generalize, which in turn allows intelligent beings to react to new environments and contexts without previous experience or input. I applaud the bravery of trying to one shot a definition of intelligence, but no intelligent being acts without previous experience or input. If you're talking about in-sample vs out of sample, LLMs do that all the time. At some point in the conversation, they encounter something completely new and react to it in a way that emulates an intelligent agent. What really makes them tick is language being a huge part of the intelligence puzzle, and language is something LLMs can generate at will. When we discover and learn to emulate the rest, we will get closer and closer to super intelligence.

nl 2y ago

> Generally its accepted that a core trait of intelligence is an agent’s ability to achieve goals in a wide range of environments. This is the embodiment argument - that intelligence requires the ability to interact with its environment. Far from being generally accepted, it's a controversial take. Could Stephen Hawking achieve goals in a wide range of environments without help? And yet it's still generally accepted that Stephen Hawking was intelligent.