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As an AI researcher myself I can definitely see why people used to have these sentiments, but I also have to point out how the big papers of the last ~12 months

As an AI researcher myself I can definitely see why people used to have these sentiments, but I also have to point out how the big papers of the last ~12 months or so have changed the landscape. Especially multi-modal models like Gato. To my surprise, a lot of people (even inside the community) still tend to put human intelligence on some sort of pedestal, but I believe once a system (or host of systems) achieves a sufficient level of multi-modality, these same people will no longer be able to tell human and machine intelligence apart - and in the end that is the only thing that counts, since we lack a rigorous definition of "understanding" or consciousness as a whole. Transformers are certainly going to become even more general purpose in the future and I'm sure we haven't even seen close to what's possible with RL. Regarding the AGI timescale, I'm a bit more bullish. Not Elon-type "possible end-all singularity in 5 years" but more Carmack-like "learning disabled toddler running on 10k+ GPUs by the end of the decade." Whether RL+transformers will be able to go all the way there is impossible to tell right now, but if I was starting an AGI company today that's definitely the path I would pursue.

godelski 4y ago
I'm not sure Gato actually demonstrates intelligence. It definitely does demonstrate the ability to perform a wide variety of tasks, but I don't think that is actually intelligence. Gato has yet to demonstrate that it understands things. I do think it is easy to fall prone to the gullibility gap (and also take too strong of an opposing view, which I may be victim to).
- darawk 4y ago
  How are you defining "understands things"? Or more precisely, what are the observable consequences of understanding things that you are waiting to see?
  - godelski 4y ago
    "Understands things" means a lot. Mostly I'm referring the causality and generalization of inference though.
    - darawk 4y ago
      So, what would be the observable, empirical consequences of the kind of causal understanding and generalization of inference you're describing?
      - godelski 4y ago
        The common current example? A text prompt of "A horse riding an astronaut" without prompt engineering. Though I don't think the successful production of this image will demonstrate intelligence/causal understanding either (but it is a good counter example). Causal understanding is going to be a bit difficult to prove tbh.
        aquaduck 4y ago
        Counter-argument: DALL-E is smart enough to understand that an astronaut riding a horse makes more sense than a horse riding an astronaut, and therefore assumes that you meant "a horse-riding astronaut" unless you go out of your way to specify that you definitely do, in fact, want to see a horse riding an astronaut.
        godelski 4y ago
        Because intelligence is more than frequentism. Being able to predict that a dice lands on a given side with probability 1/6 is not a demonstration of intelligence. It feels a bit naive to even suggest this and I suspect that you aren't a ML researcher (and if I'm right, maybe don't have as much arrogance because you don't have as much domain knowledge).
        Roland3 4y ago
        ML layman, asshole expert here. You’re a jerk dude. Stop condescending to your peers.
        random314 4y ago
        You will have to do better than make rhetorical arguments.
andreyk 4y ago
Gato is definitely cool, but it's worth remembering it was based on supervised learning with data from a bunch of tasks, so data availability is a strong bottleneck with that approach. Still, I'd agree the past year or two have made me more optimistic about HLAI (if not scary AGI).
- sigmoid10 4y ago
  Yeah, I'm definitely not saying this is already the ultimate solution. But if you look at their scaling experiments and extrapolate the results, it roughly shows how a model with number of parameters on the same order of magnitude as the human brain could more or less achieve overall human level performance. Of course that doesn't mean the architecture has to extrapolate, but it's definitely a path worth walking unless someone proves it's a dead end. In that case I'd replace "end of decade" with 10 to 20 years in my above statement.
  - godelski 4y ago
    Except it is pretty naive to extrapolate like that. A high order polynomial looks flat if you zoom in enough. You can't extrapolate far beyond what you have empirical data for without potentially running into huge problems.
    - sigmoid10 4y ago
      How do you know what the function looks like? Unless someone shows that this scaling behaviour breaks down, it is definitely worth pursuing, since the possible benefits are obvious. Everyone who just says it will break down eventually is almost certainly right, but this is also a completely trivial and worthless prediction by itself. The important distinction is that noone knows where the breakdown happens. And from what we can tell today, it's not even close.
      - godelski 4y ago
        I think your viewpoint is the more naive one and I think you have the burden of proof to argue why local trends are representative of global trends. This is because the non-local argument accepts a wider range of possibilities and the local argument is the the more specific one. Think of it this way, I'm saying that every function looks linear given the right perspective and you're arguing that a particular function _is_ linear. I'm arguing that we don't know that. The burden of proof would be on you to prove that this function were linear beyond what we have data for. (Path towards HLI doesn't have to be linear, exponential, nor logarithmic but you do have to make a pretty strong argument to convince others that the local trend is generalizable).
nradov 4y ago
If you review the history of AGI research going back to the 1960s, there have been multiple breakthroughs which convinced some researchers that success was close. Yet those all turned out to be mirages. You're probably being overly optimistic, and the Gato approach will eventually run into a wall.
- sigmoid10 4y ago
  Gato is just one tiny glimpse out of many, and the combined research effort both from academia and industry and the level of success has never even come close throughout history to the things we are seeing right now.

godelski 4y ago

I'm not sure Gato actually demonstrates intelligence. It definitely does demonstrate the ability to perform a wide variety of tasks, but I don't think that is actually intelligence. Gato has yet to demonstrate that it understands things. I do think it is easy to fall prone to the gullibility gap (and also take too strong of an opposing view, which I may be victim to).

darawk 4y ago
How are you defining "understands things"? Or more precisely, what are the observable consequences of understanding things that you are waiting to see?
- godelski 4y ago
  "Understands things" means a lot. Mostly I'm referring the causality and generalization of inference though.
  - darawk 4y ago
    So, what would be the observable, empirical consequences of the kind of causal understanding and generalization of inference you're describing?
    - godelski 4y ago
      The common current example? A text prompt of "A horse riding an astronaut" without prompt engineering. Though I don't think the successful production of this image will demonstrate intelligence/causal understanding either (but it is a good counter example). Causal understanding is going to be a bit difficult to prove tbh.
      - aquaduck 4y ago
        Counter-argument: DALL-E is smart enough to understand that an astronaut riding a horse makes more sense than a horse riding an astronaut, and therefore assumes that you meant "a horse-riding astronaut" unless you go out of your way to specify that you definitely do, in fact, want to see a horse riding an astronaut.
        godelski 4y ago
        Because intelligence is more than frequentism. Being able to predict that a dice lands on a given side with probability 1/6 is not a demonstration of intelligence. It feels a bit naive to even suggest this and I suspect that you aren't a ML researcher (and if I'm right, maybe don't have as much arrogance because you don't have as much domain knowledge).
        Roland3 4y ago
        ML layman, asshole expert here. You’re a jerk dude. Stop condescending to your peers.
        random314 4y ago
        You will have to do better than make rhetorical arguments.

andreyk 4y ago

Gato is definitely cool, but it's worth remembering it was based on supervised learning with data from a bunch of tasks, so data availability is a strong bottleneck with that approach. Still, I'd agree the past year or two have made me more optimistic about HLAI (if not scary AGI).

sigmoid10 4y ago
Yeah, I'm definitely not saying this is already the ultimate solution. But if you look at their scaling experiments and extrapolate the results, it roughly shows how a model with number of parameters on the same order of magnitude as the human brain could more or less achieve overall human level performance. Of course that doesn't mean the architecture has to extrapolate, but it's definitely a path worth walking unless someone proves it's a dead end. In that case I'd replace "end of decade" with 10 to 20 years in my above statement.
- godelski 4y ago
  Except it is pretty naive to extrapolate like that. A high order polynomial looks flat if you zoom in enough. You can't extrapolate far beyond what you have empirical data for without potentially running into huge problems.
  - sigmoid10 4y ago
    How do you know what the function looks like? Unless someone shows that this scaling behaviour breaks down, it is definitely worth pursuing, since the possible benefits are obvious. Everyone who just says it will break down eventually is almost certainly right, but this is also a completely trivial and worthless prediction by itself. The important distinction is that noone knows where the breakdown happens. And from what we can tell today, it's not even close.
    - godelski 4y ago
      I think your viewpoint is the more naive one and I think you have the burden of proof to argue why local trends are representative of global trends. This is because the non-local argument accepts a wider range of possibilities and the local argument is the the more specific one. Think of it this way, I'm saying that every function looks linear given the right perspective and you're arguing that a particular function _is_ linear. I'm arguing that we don't know that. The burden of proof would be on you to prove that this function were linear beyond what we have data for. (Path towards HLI doesn't have to be linear, exponential, nor logarithmic but you do have to make a pretty strong argument to convince others that the local trend is generalizable).

nradov 4y ago

If you review the history of AGI research going back to the 1960s, there have been multiple breakthroughs which convinced some researchers that success was close. Yet those all turned out to be mirages. You're probably being overly optimistic, and the Gato approach will eventually run into a wall.

sigmoid10 4y ago
Gato is just one tiny glimpse out of many, and the combined research effort both from academia and industry and the level of success has never even come close throughout history to the things we are seeing right now.