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Wouldn't "general intelligence" require so much more than scoring well (or even amazingly) on benchmarks? Like what about having some "AGI model" embodied in s

by uludag 23d ago

Wouldn't "general intelligence" require so much more than scoring well (or even amazingly) on benchmarks? Like what about having some "AGI model" embodied in something (maybe humanoid), and test it by having it step in an assortment of cars and park them. Does bodily-kinesthetic intelligence account for nothing? Humans are intelligent creatures and can dynamically adapt to the physical shape of a variety of vehicles and their movement characteristics. And there's so many things like this that are extremely basic, which some people dismiss since practically every human has the capability to do it, but actually requires a high degree of intelligence.

cryptoz 23d ago
What you've described is just a new benchmark, though. It'll be called CarParkBench, various embodied LLMs will then be run against that benchmark, and some will score better than others. I do see where you're going, but that's already what's happening: we have so many different benchmarks because there's no real single way to test for general intelligence. Also, it takes a human probably at least a decade of world experience, growth, learning, etc, to pass your benchmark. I'm quite confident that it will be very soon that an embodied LLM will pass your new benchmark, much sooner than a human would take if born today.
- strken 22d ago
  I think the issue is less about creating a new benchmark and more that the existing benchmarks shouldn't be called anything related to AGI unless they measure AGI. If a model couldn't go to work as e.g. a first year apprentice plumber on their first day and perform anywhere remotely close to the median, but can pass a benchmark that claims to measure AGI, the benchmark is wrong and the model is not exhibiting general intelligence yet. ApprenticePlumberBench sounds like it's genuinely better than ARC-AGI at measuring AGI and that's a bit silly. (Edit: I wrote ARC-GIS the first time around, for some silly reason)
  - visarga 22d ago
    It's not measuring AGI at all, it starts from human "core knowledge" so it is parochial. It is made of tests that still fail so by definition next version will also start low. Moving goalpost.
dist-epoch 22d ago
AGI has a pretty precise definition, covering only cognitive tasks. Running a marathon is not needed to claim AGI.
- visarga 22d ago
  > can match or exceed human cognitive abilities across a wide range of tasks If you go by definition AGI is not general, just "smart ape" shaped.
- Barrin92 22d ago
  >AGI has a pretty precise definition, covering only cognitive tasks. OpenAI's own charter defines AGI as "Highly autonomous systems that outperform humans at most economically valuable work". This is actually fairly sensible and involves obviously a ton of non-cognitive, emotional, social and physical activity. In other words, if you can replace most or all human beings with a machine, you have something that's generally intelligent. That's obviously not even remotely where we're at, AI chatbots do well on narrow usually text based or programmatic problems, but can't even replace a barista or a plumber.
  - ilaksh 22d ago
    There is rapid progress in generality in humanoid robotics though. I think within the next year or less we will get the ChatGPT moment for humanoid robots. If you look at progression of capabilities such as the recent Skild AI demos.
    - unknownfuture 22d ago
      Hard disagree. The problem is sensors. There are simply no technologies today that can replicate the density, precision, and versatility of human touch sensors. Until then, there is simply no way to create generally capable robots that can operate at the level of a human. And unlike LLMs, advancement is held back by physical limitations like materials science, so progress has been and will continue to be much slower.
fidotron 22d ago
This is a much underappreciated point. That said, a look at the state of self driving and the recent robot olympics shows that advancement on that has accelerated enormously, though whether it's reflected in any of the LLMs is something else entirely.
dgunay 22d ago
This is a big reason why I feel like even though LLMs are _effectively_ AGI in some regard, they also are a hack around what most people figured AGI would look like before the advent of LLMs. Humans can do metacognition, output multimodally at the same time (verbal _and_ physical intelligence go together to produce an expressive face while one talks), have a good sense for what they do and don't know, continuously take in and respond to the world around them in a (mostly) uninterrupted fashion without "turns", learn knew knowledge and retain it for their whole lives, etc. When you reduce a human to a text generator, yes obviously SOTA LLMs perform way better, but rather than invent something that can operate as an always-running "being", we've grafted a harness around an intelligence that is bound purely to speak only when spoken to. Maybe organic intelligence is already that, playing out at a super high refresh rate, but I don't know.
- dinfinity 22d ago
  Current AI is arguably much more capable of multimodal output than humans. It can produce an incredibly vast variety of audio, images, and video. Humans are limited to producing the sounds we can make with meatflaps in our throats, and contorting various parts of our bodies to produce crude symbols and shapes. (Very capable!) Embodiment, persistent operation and continuous learning are indeed things that still set us apart from AI. None of those are fundamentally difficult to solve, though. More importantly, none of those are particularly relevant for being "intelligent": If a criminal threatened to kill your family unless you solve some difficult problem that requires only intelligence and you could choose any single person, animal, or AI to help you with it, which would you choose? Be honest.
  - uludag 22d ago
    Before "AI," humans have created a vast array of "multimodal output" (computer art, instruments, dance, architecture, etc.). Why are you giving the AI a harness and a plethora of tools and not the human in this comparison? Without these, the LLM too would be utterly useless. Also, that situation is extremely contrived. If a criminal threatened to kill me if I misspelled a word, I would choose a dictionary. If a criminal challenged me to predict a next token, I'd choose the LLM. For all real precarious dangerous situations, I would obviously choose a human. Like immagine the hilarity (or tragedy) that would pursuit if ChatGPT tried to handle a hostage situation or a plane hijacking. and those meatflaps are normally called vocal folds/cords btw

by uludag 23d ago

cryptoz 23d ago

What you've described is just a new benchmark, though. It'll be called CarParkBench, various embodied LLMs will then be run against that benchmark, and some will score better than others. I do see where you're going, but that's already what's happening: we have so many different benchmarks because there's no real single way to test for general intelligence. Also, it takes a human probably at least a decade of world experience, growth, learning, etc, to pass your benchmark. I'm quite confident that it will be very soon that an embodied LLM will pass your new benchmark, much sooner than a human would take if born today.

strken 22d ago
I think the issue is less about creating a new benchmark and more that the existing benchmarks shouldn't be called anything related to AGI unless they measure AGI. If a model couldn't go to work as e.g. a first year apprentice plumber on their first day and perform anywhere remotely close to the median, but can pass a benchmark that claims to measure AGI, the benchmark is wrong and the model is not exhibiting general intelligence yet. ApprenticePlumberBench sounds like it's genuinely better than ARC-AGI at measuring AGI and that's a bit silly. (Edit: I wrote ARC-GIS the first time around, for some silly reason)
- visarga 22d ago
  It's not measuring AGI at all, it starts from human "core knowledge" so it is parochial. It is made of tests that still fail so by definition next version will also start low. Moving goalpost.

dist-epoch 22d ago

AGI has a pretty precise definition, covering only cognitive tasks. Running a marathon is not needed to claim AGI.

visarga 22d ago
> can match or exceed human cognitive abilities across a wide range of tasks If you go by definition AGI is not general, just "smart ape" shaped.
Barrin92 22d ago
>AGI has a pretty precise definition, covering only cognitive tasks. OpenAI's own charter defines AGI as "Highly autonomous systems that outperform humans at most economically valuable work". This is actually fairly sensible and involves obviously a ton of non-cognitive, emotional, social and physical activity. In other words, if you can replace most or all human beings with a machine, you have something that's generally intelligent. That's obviously not even remotely where we're at, AI chatbots do well on narrow usually text based or programmatic problems, but can't even replace a barista or a plumber.
- ilaksh 22d ago
  There is rapid progress in generality in humanoid robotics though. I think within the next year or less we will get the ChatGPT moment for humanoid robots. If you look at progression of capabilities such as the recent Skild AI demos.
  - unknownfuture 22d ago
    Hard disagree. The problem is sensors. There are simply no technologies today that can replicate the density, precision, and versatility of human touch sensors. Until then, there is simply no way to create generally capable robots that can operate at the level of a human. And unlike LLMs, advancement is held back by physical limitations like materials science, so progress has been and will continue to be much slower.

fidotron 22d ago

This is a much underappreciated point. That said, a look at the state of self driving and the recent robot olympics shows that advancement on that has accelerated enormously, though whether it's reflected in any of the LLMs is something else entirely.

dgunay 22d ago

This is a big reason why I feel like even though LLMs are _effectively_ AGI in some regard, they also are a hack around what most people figured AGI would look like before the advent of LLMs. Humans can do metacognition, output multimodally at the same time (verbal _and_ physical intelligence go together to produce an expressive face while one talks), have a good sense for what they do and don't know, continuously take in and respond to the world around them in a (mostly) uninterrupted fashion without "turns", learn knew knowledge and retain it for their whole lives, etc. When you reduce a human to a text generator, yes obviously SOTA LLMs perform way better, but rather than invent something that can operate as an always-running "being", we've grafted a harness around an intelligence that is bound purely to speak only when spoken to. Maybe organic intelligence is already that, playing out at a super high refresh rate, but I don't know.

dinfinity 22d ago
Current AI is arguably much more capable of multimodal output than humans. It can produce an incredibly vast variety of audio, images, and video. Humans are limited to producing the sounds we can make with meatflaps in our throats, and contorting various parts of our bodies to produce crude symbols and shapes. (Very capable!) Embodiment, persistent operation and continuous learning are indeed things that still set us apart from AI. None of those are fundamentally difficult to solve, though. More importantly, none of those are particularly relevant for being "intelligent": If a criminal threatened to kill your family unless you solve some difficult problem that requires only intelligence and you could choose any single person, animal, or AI to help you with it, which would you choose? Be honest.
- uludag 22d ago
  Before "AI," humans have created a vast array of "multimodal output" (computer art, instruments, dance, architecture, etc.). Why are you giving the AI a harness and a plethora of tools and not the human in this comparison? Without these, the LLM too would be utterly useless. Also, that situation is extremely contrived. If a criminal threatened to kill me if I misspelled a word, I would choose a dictionary. If a criminal challenged me to predict a next token, I'd choose the LLM. For all real precarious dangerous situations, I would obviously choose a human. Like immagine the hilarity (or tragedy) that would pursuit if ChatGPT tried to handle a hostage situation or a plane hijacking. and those meatflaps are normally called vocal folds/cords btw