11 ms·

Basically the linked article argues like this: > That’s because cognition, or the ability to observe, learn and gain new insight, is incredibly hard to replica

Basically the linked article argues like this: > That’s because cognition, or the ability to observe, learn and gain new insight, is incredibly hard to replicate through AI on the scale that it occurs in the human brain. (no other more substantial arguments were given) I'm also very skeptical on seeing AGI soon, but LLMs do solve problems that people thought were extremely difficult to solve ten years ago.

babyshake 2y ago
It's possible we see some ways in which AI becomes increasingly AGI like in some ways but not in others. For example, AI that can create novel scientific discoveries but can't make a song as good as your favorite musician who creates a strong emotional effect with their music.
- KoolKat23 2y ago
  This I'm very sure will be the case, but everyone will still move the goalposts and look past the fact that different humans have different strengths and weaknesses too. A tone deaf human for instance.
  - jltsiren 2y ago
    There is another term for moving the goalposts: ruling out a hypothesis. Science is, especially in the Popperian sense, all about moving the goalposts. One plausible hypothesis is that fixed neural networks cannot be general intelligences, because their capabilities are permanently limited by what they currently are. A general intelligence needs the ability to learn from experience. Training and inference should not be separate activities, but our current hardware is not suited for that.
    - KoolKat23 2y ago
      If that's the case, would you say we're not generally intelligent as future humans tend to be more intelligent? That's just a timescale issue, if its learned experience of gpt4 is being fed into the model on training gpt5, then gptx (i.e. including all of them) can be said to be a general intelligence. Alien life one may say.
      - threeseed 2y ago
        > That's just a timescale issue Every problem is a timescale issue. Evolution has shown that. And no you can't just feed GPT4 into GPT5 and expect it to become more intelligent. It may be more accurate since humans are telling it when conversations are wrong or not. But you will still need advancements in the algorithms themselves to take things forward. All of which takes us back to lots and lots of research. And if there's one thing we know is that research breakthroughs aren't a guarantee.
        KoolKat23 2y ago
        I think you missed my point slightly, sorry my explaining probably. I mean timescale as in between two points in time. Between the two points it meets the intelligence criteria you mentioned. Feeding human vetted GPT4 data into GPT5 is no different to a human receiving inputs from its interaction with the world and learning. More accurate means smarter, gradually it's intrinsic world model improves as does reasoning etc. I agree those are the things that will advance it but taking a step back it potentially meets that criteria even if less useful day to day (given its an abstract viewpoint over time and not at the human level).
- godelski 2y ago
  More importantly, there's many ways that AI can seemingly look to becoming more intelligent without making any progress in that direction. That's of real concern. As a silly example, we could be trying to "make a duck" by making an animatronic. You could get this thing to be very life like looking and trick ducks and humans alike (we have this already btw). But that's very different from being a duck. Even if it were indistinguishable until you opened it up, progress on this animatronic would not necessarily be progress towards making a duck (though it need not be either). This is a concern because several top researchers -- at OpenAI -- have explicitly started that they think you can get AGI by teaching the machine to act as human as possible. But that's a great way to fool ourselves. Just as a duck may fall in love with an animatronic and never realize the deciept. It's possible they're right, but it's important that we realize how this metric can be hacked.
- hmcq6 2y ago
  Ironic cuz it's actually exactly the opposite. In the same way 1,000,000 monkeys on typewriters will eventually write Shakespeare AI is plenty capable of creating "art". It is however currently completely unable to "think". It can't make a novel scientific discovery because it can't even add 2 + 2. It can give you the most common answer to "what is 2 + 2" but it's not actually pulling up the calculator app and doing the computation, it's just giving the most probabilistic answer. And even if it could pull up a predefined list of apps to double check it's work, that still isn't AGI.
godelski 2y ago
> but LLMs do solve problems that people thought were extremely difficult to solve ten years ago. Well for something to be G or I you need them to solve novel problems. These things have interested most of the Internet and I've yet to see a "reasoning" disentangle memorization from reasoning. Memorization doesn't mean they aren't useful (not sure why this was ever conflated since... Computers are useful...), but it's very different from G or I. And remember that these tools are trained for human preferential output. If humans prefer things to look like reasoning then that's what they optimize. [0] Sure, maybe your cousin Throckmorton is dumb but that's besides the point. That said, I see no reason human level cognition is impossible. We're not magic. We're machines that follow the laws of physics. ML systems may be far from capturing what goes on in these computers, but that doesn't mean magic exists. [0] If it walks like a duck, quacks like a duck, and swims like a duck, and looks like a duck it's probably a duck. But probably doesn't mean it isn't a well made animatronic. We have those too and they'll convince many humans they are ducks. But that doesn't change what's inside. The subtly matters.
- stroupwaffle 2y ago
  I think it will be an organoid brain bio-machine. We can already grow organs—just need to grow a brain and connect it to a machine.
  - Moosdijk 2y ago
    The keyword being “just”.
    - ggm 2y ago
      Just grow, just connect, just sustain, just avoid the many pitfalls. Indeed just is key
    - godelski 2y ago
      just adverb to turn a complex thing into magic with a simple wave of the hands E.g. To turn lead into gold you _just_ need to remove 3 protons
tptacek 2y ago
Are you talking about the press release that the story on HN currently links to, or the paper that press release is about? The paper (I'm not vouching for it; I just skimmed it) appears to reduce AGI to a theoretical computational model, and then supplies a proof that it's not solvable in polynomial time.
- Gehinnn 2y ago
  I was referring to the press release article. I also looked at the paper now, and to me their presented proof looked more like a technicality than a new insight. If it's not solvable in polynomial time, how did nature solve it in a couple of million years?
  - deleted 2y ago
    [deleted]
  - tptacek 2y ago
    Probably by not modeling it as a discrete computational problem? Either way: the logic of the paper is not the logic of the summary of the press release you provided.
- Veedrac 2y ago
  That paper is unserious. It is filled with unjustified assertions, adjectives and emotional appeals, M$-isms like ‘BigTech’, and basic misunderstandings of mathematical theory clearly being sold to a lay audience.
  - tptacek 2y ago
    It didn't look especially rigorous to me (but I'm not in this field). I'm really just here because we're doing that thing where we (as a community) have a big 'ol discussion about a press release, when the paper the press release is about is linked right there.
  - dekhn 2y ago
    Yes, I place it roughly in the "Stochastic Parrots" cluster of articles.
more_corn 2y ago
Pretty sure anyone who tries can build an ai with capabilities indistinguishable from or better than humans.
ryandvm 2y ago
> but LLMs do solve problems that people thought were extremely difficult to solve ten years ago Agreed. I would have laughed you out of the room 5 years ago if you told me AI's would be writing code or carrying on coherent discussions on pretty complex topics in 2024. As far as I'm concerned, all bets are off after the collective jaw drop that the entire software engineering industry did when we saw GPT4 released. We went from Google AI responses of "I'm sorry, I can't help with that." to ChatGPT writing pages of code that mostly works. It turns out that the larger these models get, the more unexpected emergent capabilities they have, so I'm mostly in the camp of thinking AGI is just a matter of time and resources.
- gizmo686 2y ago
  > It turns out that the larger these models get, the more unexpected emergent capabilities they have, so I'm mostly in the camp of thinking AGI is just a matter of time and resources. AI research has a long history of people saying this. Whenever there is a new fundamental improvement, it looks like you can just keep getting better results by throwing more resources at it. However, eventually we end up reaching a point where throwing more resources at it stops meaningfully improving performance. LLMs have an additional problem related to training data. We are already throwing all the data we can get our hands on at them. However, unlike most other AI systems we have developed, LLMs are actively polluting their data pool, so this intitial generation of LLMs are probably going to have the best data set of any that we ever develop. Of course, today's data will continue to be available, but will loose value as it ages.
  - scotty79 2y ago
    Currently we are throwing everything at LLMs and hope good things stick. At one point we might use AI to select best training data from what's available to best train the next AI.
    - oco101 2y ago
      "That is just one very narrow task that basically anyone can do regardless of intelligence or talent. It takes less than a year to train a distracted 16 year old to do it, but three decades to train an AI and even then you probably need to hand tune it for specific locations because it won’t know what do in unusual road layouts." It takes three decades to train an AI, but of course, like everything humanity does is not linear, it is exponential. Before the Wright brothers, it was believed that powered, controlled, heavier-than-air flight was impossible. Then, in 1903, they achieved the first successful airplane flight, which lasted 12 seconds and covered 120 feet. By 1914, the first commercial flight covered approximately 21 miles and took about 23 minutes. This is just one example and I don't see why AI should be any different

babyshake 2y ago

It's possible we see some ways in which AI becomes increasingly AGI like in some ways but not in others. For example, AI that can create novel scientific discoveries but can't make a song as good as your favorite musician who creates a strong emotional effect with their music.

KoolKat23 2y ago
This I'm very sure will be the case, but everyone will still move the goalposts and look past the fact that different humans have different strengths and weaknesses too. A tone deaf human for instance.
- jltsiren 2y ago
  There is another term for moving the goalposts: ruling out a hypothesis. Science is, especially in the Popperian sense, all about moving the goalposts. One plausible hypothesis is that fixed neural networks cannot be general intelligences, because their capabilities are permanently limited by what they currently are. A general intelligence needs the ability to learn from experience. Training and inference should not be separate activities, but our current hardware is not suited for that.
  - KoolKat23 2y ago
    If that's the case, would you say we're not generally intelligent as future humans tend to be more intelligent? That's just a timescale issue, if its learned experience of gpt4 is being fed into the model on training gpt5, then gptx (i.e. including all of them) can be said to be a general intelligence. Alien life one may say.
    - threeseed 2y ago
      > That's just a timescale issue Every problem is a timescale issue. Evolution has shown that. And no you can't just feed GPT4 into GPT5 and expect it to become more intelligent. It may be more accurate since humans are telling it when conversations are wrong or not. But you will still need advancements in the algorithms themselves to take things forward. All of which takes us back to lots and lots of research. And if there's one thing we know is that research breakthroughs aren't a guarantee.
      - KoolKat23 2y ago
        I think you missed my point slightly, sorry my explaining probably. I mean timescale as in between two points in time. Between the two points it meets the intelligence criteria you mentioned. Feeding human vetted GPT4 data into GPT5 is no different to a human receiving inputs from its interaction with the world and learning. More accurate means smarter, gradually it's intrinsic world model improves as does reasoning etc. I agree those are the things that will advance it but taking a step back it potentially meets that criteria even if less useful day to day (given its an abstract viewpoint over time and not at the human level).
godelski 2y ago
More importantly, there's many ways that AI can seemingly look to becoming more intelligent without making any progress in that direction. That's of real concern. As a silly example, we could be trying to "make a duck" by making an animatronic. You could get this thing to be very life like looking and trick ducks and humans alike (we have this already btw). But that's very different from being a duck. Even if it were indistinguishable until you opened it up, progress on this animatronic would not necessarily be progress towards making a duck (though it need not be either). This is a concern because several top researchers -- at OpenAI -- have explicitly started that they think you can get AGI by teaching the machine to act as human as possible. But that's a great way to fool ourselves. Just as a duck may fall in love with an animatronic and never realize the deciept. It's possible they're right, but it's important that we realize how this metric can be hacked.
hmcq6 2y ago
Ironic cuz it's actually exactly the opposite. In the same way 1,000,000 monkeys on typewriters will eventually write Shakespeare AI is plenty capable of creating "art". It is however currently completely unable to "think". It can't make a novel scientific discovery because it can't even add 2 + 2. It can give you the most common answer to "what is 2 + 2" but it's not actually pulling up the calculator app and doing the computation, it's just giving the most probabilistic answer. And even if it could pull up a predefined list of apps to double check it's work, that still isn't AGI.

godelski 2y ago

> but LLMs do solve problems that people thought were extremely difficult to solve ten years ago. Well for something to be G or I you need them to solve novel problems. These things have interested most of the Internet and I've yet to see a "reasoning" disentangle memorization from reasoning. Memorization doesn't mean they aren't useful (not sure why this was ever conflated since... Computers are useful...), but it's very different from G or I. And remember that these tools are trained for human preferential output. If humans prefer things to look like reasoning then that's what they optimize. [0] Sure, maybe your cousin Throckmorton is dumb but that's besides the point. That said, I see no reason human level cognition is impossible. We're not magic. We're machines that follow the laws of physics. ML systems may be far from capturing what goes on in these computers, but that doesn't mean magic exists. [0] If it walks like a duck, quacks like a duck, and swims like a duck, and looks like a duck it's probably a duck. But probably doesn't mean it isn't a well made animatronic. We have those too and they'll convince many humans they are ducks. But that doesn't change what's inside. The subtly matters.

stroupwaffle 2y ago
I think it will be an organoid brain bio-machine. We can already grow organs—just need to grow a brain and connect it to a machine.
- Moosdijk 2y ago
  The keyword being “just”.
  - ggm 2y ago
    Just grow, just connect, just sustain, just avoid the many pitfalls. Indeed just is key
  - godelski 2y ago
    just adverb to turn a complex thing into magic with a simple wave of the hands E.g. To turn lead into gold you _just_ need to remove 3 protons

tptacek 2y ago

Are you talking about the press release that the story on HN currently links to, or the paper that press release is about? The paper (I'm not vouching for it; I just skimmed it) appears to reduce AGI to a theoretical computational model, and then supplies a proof that it's not solvable in polynomial time.

Gehinnn 2y ago
I was referring to the press release article. I also looked at the paper now, and to me their presented proof looked more like a technicality than a new insight. If it's not solvable in polynomial time, how did nature solve it in a couple of million years?
- deleted 2y ago
  [deleted]
- tptacek 2y ago
  Probably by not modeling it as a discrete computational problem? Either way: the logic of the paper is not the logic of the summary of the press release you provided.
Veedrac 2y ago
That paper is unserious. It is filled with unjustified assertions, adjectives and emotional appeals, M$-isms like ‘BigTech’, and basic misunderstandings of mathematical theory clearly being sold to a lay audience.
- tptacek 2y ago
  It didn't look especially rigorous to me (but I'm not in this field). I'm really just here because we're doing that thing where we (as a community) have a big 'ol discussion about a press release, when the paper the press release is about is linked right there.
- dekhn 2y ago
  Yes, I place it roughly in the "Stochastic Parrots" cluster of articles.

more_corn 2y ago

Pretty sure anyone who tries can build an ai with capabilities indistinguishable from or better than humans.

ryandvm 2y ago

> but LLMs do solve problems that people thought were extremely difficult to solve ten years ago Agreed. I would have laughed you out of the room 5 years ago if you told me AI's would be writing code or carrying on coherent discussions on pretty complex topics in 2024. As far as I'm concerned, all bets are off after the collective jaw drop that the entire software engineering industry did when we saw GPT4 released. We went from Google AI responses of "I'm sorry, I can't help with that." to ChatGPT writing pages of code that mostly works. It turns out that the larger these models get, the more unexpected emergent capabilities they have, so I'm mostly in the camp of thinking AGI is just a matter of time and resources.

gizmo686 2y ago
> It turns out that the larger these models get, the more unexpected emergent capabilities they have, so I'm mostly in the camp of thinking AGI is just a matter of time and resources. AI research has a long history of people saying this. Whenever there is a new fundamental improvement, it looks like you can just keep getting better results by throwing more resources at it. However, eventually we end up reaching a point where throwing more resources at it stops meaningfully improving performance. LLMs have an additional problem related to training data. We are already throwing all the data we can get our hands on at them. However, unlike most other AI systems we have developed, LLMs are actively polluting their data pool, so this intitial generation of LLMs are probably going to have the best data set of any that we ever develop. Of course, today's data will continue to be available, but will loose value as it ages.
- scotty79 2y ago
  Currently we are throwing everything at LLMs and hope good things stick. At one point we might use AI to select best training data from what's available to best train the next AI.
  - oco101 2y ago
    "That is just one very narrow task that basically anyone can do regardless of intelligence or talent. It takes less than a year to train a distracted 16 year old to do it, but three decades to train an AI and even then you probably need to hand tune it for specific locations because it won’t know what do in unusual road layouts." It takes three decades to train an AI, but of course, like everything humanity does is not linear, it is exponential. Before the Wright brothers, it was believed that powered, controlled, heavier-than-air flight was impossible. Then, in 1903, they achieved the first successful airplane flight, which lasted 12 seconds and covered 120 feet. By 1914, the first commercial flight covered approximately 21 miles and took about 23 minutes. This is just one example and I don't see why AI should be any different