3 ms·

A decade to AGI is still insanely optimistic, borderline delusional. A century or never might is way more realistic timeline.

by Lionga 1y ago

tobias3 1y ago
We cannot even guess what the timeline would be. This is why it is insane to invest in it and expect a positive return in usual investing horizons.
- bogzz 1y ago
  ORCL seems to be the one who'll be left without a chair when the music stops.
  - deleted 1y ago
    [deleted]
Cornbilly 1y ago
Delusion seems to be the main product of SV these days.
riskable 1y ago
I don't think it's possible to say how far away or "never" it is. All we know is that LLMs cannot become AGI. My prediction: AGI will come from a strange place. An interesting algorithm everyone already knew about that gets applied in a new way. Probably discovered by accident because someone—who has no idea what they're doing—tried to force an LLM to do something stupid in their code and yet somehow, it worked. What wouldn't surprise me: A novel application of the Archimedes principle or the Brazil nut effect. You might be thinking, "What TF to those have to do with AI‽ LOL!" and you're probably right... Or are you?
- ACCount37 1y ago
  Why not? What's the fundamental, absolutely insurmountable capability gap between the two? What is it that can't be bridged with architectural tweaks, better scaffolding or better training? I see a lot of people make this "LLMs ABSOLUTELY CANNOT hit AGI" assumption, and it never seems to be backed by anything at all.
  - fnord77 1y ago
    The view that LLMs alone are insufficient for AGI is based on their fundamental mathematical architecture
    - ACCount37 1y ago
      What is the exact limitation imposed by "their fundamental mathematical architecture"? I'm not aware of any such thing.
      - Jensson 1y ago
        They don't update themselves as they work on a problem. Humans solve hard problems by forming new neural connections, LLM cannot do that. If you make a model that does that then its no longer the LLM architecture we use today and it would be called something else.
        ACCount37 1y ago
        Even if we assume that "update themselves as they work on a problem" is vital for AGI (not proven, it's just an assumption), then what stops us from just giving an LLM an "update itself" tool call? As the most naive implementation: the LLM just collects the relevant training data, and then runs PEFT on itself. Then it tests the tuned version of itself to see whether the tune was any good and is worth retaining. Sure, that naive approach would require an LLM to be good at assembling training sets, and validating performance gains, and it would be very computationally expensive. But none of those things would somehow make it not-an-LLM-anymore.
- OJFord 1y ago
  > All we know is that LLMs cannot become AGI. A part of it, perhaps: I think of it like 'computer vision'; LLMs offer 'computer speech/language' as it were. But not a 'general intelligence' of motives and reasoning, 'just' an output. At the moment we have that output hooked up to a model that has data from the internet and books etc. in excess of what's required for convincing language, so it itself is what drives the content. I think the future will be some other system for data and reasoning and 'general intelligence', that then uses a smaller language model for output in a human-understood form.
- red75prime 1y ago
  I guess AGI will come from identifying and grinding away limitations of transformers (and/or diffusion networks). As it has been with almost all technologies. Then someone (or someAI) will probably find something unexpected (but less unexpected at this stage) and more suitable for general intelligence.
- tim333 1y ago
  Not the current ones but that doesn't mean you can't build something that uses the ideas from LLMs but adds to them.
XorNot 1y ago
I agree, though I would argue the issue is closer to no one's trying to measure how close we should be. If you take the materialist view - and in this business you obviously have to - then the question is "do we have human level computational capacity with some level of real-time learning ability?" So the yardstick we need is (1): what actually is the computational capacity of the human brain? (i.e. the brain obviously doesn't simulate it's own neurons, so what is a neuron doing and how does that map to compute operations?) and then (2) - is any computer system running an AI model plausibly working within those margins or exceeding them? With the second part being: and can that system implement the sort of dynamic operations which humans obviously must, unaided? i.e. when I learn something new I might go through a lot of repetitions, but I don't just stop being able to talk about a subject for months while ingesting sum content of the internet to rebuild my model.
- naasking 1y ago
  > do we have human level computational capacity with some level of real-time learning ability We don't build airplanes by mimicking birds. AGIs computational capacity won't be directly comparable to human computation capacity that was formed by messy, heuristic evolution. You're right that there is some core nugget in there that needs to be replicated though, and it will probably be by accident, as with most inventions. Hardware is still progressing exponentially, and performance improvements from model and algorithmic improvements have been outpacing hardware progress for at least a decade. The idea that AGI will take a century or more is laughable. Now that's borderline deluded.
  - XorNot 1y ago
    > We don't build airplanes by mimicking birds. We don't build airplanes by having them flap wings, but we do build planes which are considerably more aerodynamically efficient then a bird. i.e. we considerably exceed the underlying performance metric governed by the physics. Which is my point: an AI need not work anything like a human, but it seems obvious that model training taking months of time to incorporate new information fundamentally cannot be close to AGI since even far simpler creatures can learn and retain information on much shorter timescales. I'd give the notion more credence if we plausibly had say, a model with a context window which could hold a day of information, and then do could train overnight on that such that it would incorporate that context window into the next day of activity.

A decade to AGI is still insanely optimistic, borderline delusional. A century or never might is way more realistic timeline.

by Lionga 1y ago

A decade to AGI is still insanely optimistic, borderline delusional. A century or never might is way more realistic timeline.

tobias3 1y ago

We cannot even guess what the timeline would be. This is why it is insane to invest in it and expect a positive return in usual investing horizons.

bogzz 1y ago
ORCL seems to be the one who'll be left without a chair when the music stops.
- deleted 1y ago
  [deleted]

Cornbilly 1y ago

Delusion seems to be the main product of SV these days.

riskable 1y ago

I don't think it's possible to say how far away or "never" it is. All we know is that LLMs cannot become AGI. My prediction: AGI will come from a strange place. An interesting algorithm everyone already knew about that gets applied in a new way. Probably discovered by accident because someone—who has no idea what they're doing—tried to force an LLM to do something stupid in their code and yet somehow, it worked. What wouldn't surprise me: A novel application of the Archimedes principle or the Brazil nut effect. You might be thinking, "What TF to those have to do with AI‽ LOL!" and you're probably right... Or are you?

ACCount37 1y ago
Why not? What's the fundamental, absolutely insurmountable capability gap between the two? What is it that can't be bridged with architectural tweaks, better scaffolding or better training? I see a lot of people make this "LLMs ABSOLUTELY CANNOT hit AGI" assumption, and it never seems to be backed by anything at all.
- fnord77 1y ago
  The view that LLMs alone are insufficient for AGI is based on their fundamental mathematical architecture
  - ACCount37 1y ago
    What is the exact limitation imposed by "their fundamental mathematical architecture"? I'm not aware of any such thing.
    - Jensson 1y ago
      They don't update themselves as they work on a problem. Humans solve hard problems by forming new neural connections, LLM cannot do that. If you make a model that does that then its no longer the LLM architecture we use today and it would be called something else.
      - ACCount37 1y ago
        Even if we assume that "update themselves as they work on a problem" is vital for AGI (not proven, it's just an assumption), then what stops us from just giving an LLM an "update itself" tool call? As the most naive implementation: the LLM just collects the relevant training data, and then runs PEFT on itself. Then it tests the tuned version of itself to see whether the tune was any good and is worth retaining. Sure, that naive approach would require an LLM to be good at assembling training sets, and validating performance gains, and it would be very computationally expensive. But none of those things would somehow make it not-an-LLM-anymore.
OJFord 1y ago
> All we know is that LLMs cannot become AGI. A part of it, perhaps: I think of it like 'computer vision'; LLMs offer 'computer speech/language' as it were. But not a 'general intelligence' of motives and reasoning, 'just' an output. At the moment we have that output hooked up to a model that has data from the internet and books etc. in excess of what's required for convincing language, so it itself is what drives the content. I think the future will be some other system for data and reasoning and 'general intelligence', that then uses a smaller language model for output in a human-understood form.
red75prime 1y ago
I guess AGI will come from identifying and grinding away limitations of transformers (and/or diffusion networks). As it has been with almost all technologies. Then someone (or someAI) will probably find something unexpected (but less unexpected at this stage) and more suitable for general intelligence.
tim333 1y ago
Not the current ones but that doesn't mean you can't build something that uses the ideas from LLMs but adds to them.

XorNot 1y ago

I agree, though I would argue the issue is closer to no one's trying to measure how close we should be. If you take the materialist view - and in this business you obviously have to - then the question is "do we have human level computational capacity with some level of real-time learning ability?" So the yardstick we need is (1): what actually is the computational capacity of the human brain? (i.e. the brain obviously doesn't simulate it's own neurons, so what is a neuron doing and how does that map to compute operations?) and then (2) - is any computer system running an AI model plausibly working within those margins or exceeding them? With the second part being: and can that system implement the sort of dynamic operations which humans obviously must, unaided? i.e. when I learn something new I might go through a lot of repetitions, but I don't just stop being able to talk about a subject for months while ingesting sum content of the internet to rebuild my model.

naasking 1y ago
> do we have human level computational capacity with some level of real-time learning ability We don't build airplanes by mimicking birds. AGIs computational capacity won't be directly comparable to human computation capacity that was formed by messy, heuristic evolution. You're right that there is some core nugget in there that needs to be replicated though, and it will probably be by accident, as with most inventions. Hardware is still progressing exponentially, and performance improvements from model and algorithmic improvements have been outpacing hardware progress for at least a decade. The idea that AGI will take a century or more is laughable. Now that's borderline deluded.
- XorNot 1y ago
  > We don't build airplanes by mimicking birds. We don't build airplanes by having them flap wings, but we do build planes which are considerably more aerodynamically efficient then a bird. i.e. we considerably exceed the underlying performance metric governed by the physics. Which is my point: an AI need not work anything like a human, but it seems obvious that model training taking months of time to incorporate new information fundamentally cannot be close to AGI since even far simpler creatures can learn and retain information on much shorter timescales. I'd give the notion more credence if we plausibly had say, a model with a context window which could hold a day of information, and then do could train overnight on that such that it would incorporate that context window into the next day of activity.