6 ms·

AI is going to continue to have incremental progress, particularly now in hardware gains. No one can even define what AGI is or what it will look like, let alon

AI is going to continue to have incremental progress, particularly now in hardware gains. No one can even define what AGI is or what it will look like, let alone be something that OpenAI would own? Features progress is too incremental to suddenly pop out with "AGI". Fighting about it seems a distraction.

root_axis 3y ago
There's also no reason to believe that incremental progress in transformer models will eventually lead to "AGI".
- snapcaster 3y ago
  Yes, but I think everyone would agree that the chance isn't 0%
  - root_axis 3y ago
    I don't agree, I think many people would argue the chance is 0%.
    - snapcaster 3y ago
      Are you one of those people? how can you be so confident? I think everyone should have updated their priors after how surprising the emergent behavior in GPT3+ are
      - nicklecompte 3y ago
        Perhaps you should update your priors about "emergent behavior" in GPT3+: https://arxiv.org/abs/2304.15004 https://arxiv.org/abs/2304.15004
        famouswaffles 3y ago
        This is like saying that nothing special happens to water at 100 degrees because if you look at the total thermal energy, it's a smooth increase.
        nicklecompte 3y ago
        Please read the paper. The authors are using more precise and specific metrics that qualitatively measure the same thing. Instead of having exact string match being 1 if 100% correct, 0 if there is any failure, they use per-token error. The crux of their argument is that per-token error is a better choice of metric anyway, and the fact that "emergent abilities" do not occur when using this metric is a strong argument that those abilities don't really exist. However thermal energy does not more precisely or specifically measure a phase transition. They are only indirectly linked - nobody would say that thermal energy is a better measure of state-of-matter than solid/liquid/gas. Your argument makes absolutely zero sense. Frankly it seems intentionally ignorant.
        famouswaffles 3y ago
        I read the paper. Per token error is a fairly useless metric. It's not predictive and it tells you absolutely nothing. They say it's a superior metric but clearly the wider research community disagrees since no one has cared to adopt per token error as a metric in subsequent papers. >and the fact that "emergent abilities" do not occur when using this metric is a strong argument that those abilities don't really exist. If your conclusion is that those abilities don't exist then you clearly didn't read the paper very well. They never argue those abilities don't exist, they simply argue whether we should call them "emergent" or not. >However thermal energy does not more precisely or specifically measure a phase transition. They are only indirectly linked - nobody would say that thermal energy is a better measure of state-of-matter than solid/liquid/gas. Your argument makes absolutely zero sense. Frankly it seems intentionally ignorant. Phase Changes are literally driven by changes in thermal energy. Water boils when it absorbs enough thermal energy to break intermolecular forces keeping its liquid state together. solid/liquid/gas is descriptive. It's not a measure of anything. Anyway, the point is simple. Despite thermal energy driving state change after a certain threshold, that "point" doesn't look like anything special. Smooth quantitative change sometimes results in sudden qualitative changes.
      - root_axis 3y ago
        I don't think GPT3's "emergent behavior" was very surprising, it was a natural progression from GPT2, and the entire purpose of GPT3 was to test the assumptions about how much more performance you could gain by growing the size of the model. That isn't to say GPT3 isn't impressive, but its behavior was within the cone of anticipated possibilities. Based on a similar understanding, the idea that transformer models will lead to AGI seems obviously incorrect, as impressive as they are, they are just statistical pattern matchers of tokens, not systems that understand the world from first principles. And just in case you're among those that believe "humans are just pattern matchers", that might be true, but humans are modeling the world based on real time integrated sensory input, not on statistical patterns of a selection of text posted online. There's simply no reason to believe that AGI can come out of that.
        andoando 3y ago
        I agree. I am baffled as to why there isn't more thought on developing AI starting from simple sensory input.
    - JohnFen 3y ago
      I don't think the chance is 0%, but I do think that the chance is very, very close to 0%, at least if we're talking about it happening with current technology within the next hundred years or so.
  - jayveeone 3y ago
    Non-zero chances don't deserve the hype AGI is receiving, is the issue. And a lot of AI experts outside of the AGI grift have stated that it's zero.
  - pton_xd 3y ago
    It's a static single-pass feed-forward network. How could that possibly result in AGI?! (Queue famous last words ...)
    - Akronymus 3y ago
      IMO it could become an AGI IFF it has an infinitely long context window. Otherwise I see absolutely no chance of it becoming a true agi
    - Vecr 3y ago
      Just feed the output through a quality system (with retry if the quality is too bad), scaffold it a bit, then run it back into the LLM. Should work(tm)
  - nicklecompte 3y ago
    Transformer neural networks are not capable of true recursion, which is an excellent reason to think that the chance truly is 0%.
    - CamperBob2 3y ago
      That seems easy enough to fix
xiphias2 3y ago
Progress is definitely not inremental, it's exponential. The same performance (training an LLM with a given perplexity) can be achieved 5x cheaper next year while the amount of money deep learning infrastructure gets increases exponentially right now. If this method is able to get to AGI (which I believe but many people are debating), human intelligence will just be mostly ,,skipped'', and won't be a clear point.
- blibble 3y ago
  how long do you think the "exponential" (that looks very linear to me) growth in funding can continue? until it's more than US GDP? world GDP? universe GDP? either way you're close to the point it will have to go logistic
- deleted 3y ago
  [deleted]
- CatWChainsaw 3y ago
  In nature, exponential curves reveal themselves to be sigmoidal on a long enough time scale. Since you're on HN you probably have a mathematical bent, and you should know that.

root_axis 3y ago

There's also no reason to believe that incremental progress in transformer models will eventually lead to "AGI".

snapcaster 3y ago
Yes, but I think everyone would agree that the chance isn't 0%
- root_axis 3y ago
  I don't agree, I think many people would argue the chance is 0%.
  - snapcaster 3y ago
    Are you one of those people? how can you be so confident? I think everyone should have updated their priors after how surprising the emergent behavior in GPT3+ are
    - nicklecompte 3y ago
      Perhaps you should update your priors about "emergent behavior" in GPT3+: https://arxiv.org/abs/2304.15004 https://arxiv.org/abs/2304.15004
      - famouswaffles 3y ago
        This is like saying that nothing special happens to water at 100 degrees because if you look at the total thermal energy, it's a smooth increase.
        nicklecompte 3y ago
        Please read the paper. The authors are using more precise and specific metrics that qualitatively measure the same thing. Instead of having exact string match being 1 if 100% correct, 0 if there is any failure, they use per-token error. The crux of their argument is that per-token error is a better choice of metric anyway, and the fact that "emergent abilities" do not occur when using this metric is a strong argument that those abilities don't really exist. However thermal energy does not more precisely or specifically measure a phase transition. They are only indirectly linked - nobody would say that thermal energy is a better measure of state-of-matter than solid/liquid/gas. Your argument makes absolutely zero sense. Frankly it seems intentionally ignorant.
        famouswaffles 3y ago
        I read the paper. Per token error is a fairly useless metric. It's not predictive and it tells you absolutely nothing. They say it's a superior metric but clearly the wider research community disagrees since no one has cared to adopt per token error as a metric in subsequent papers. >and the fact that "emergent abilities" do not occur when using this metric is a strong argument that those abilities don't really exist. If your conclusion is that those abilities don't exist then you clearly didn't read the paper very well. They never argue those abilities don't exist, they simply argue whether we should call them "emergent" or not. >However thermal energy does not more precisely or specifically measure a phase transition. They are only indirectly linked - nobody would say that thermal energy is a better measure of state-of-matter than solid/liquid/gas. Your argument makes absolutely zero sense. Frankly it seems intentionally ignorant. Phase Changes are literally driven by changes in thermal energy. Water boils when it absorbs enough thermal energy to break intermolecular forces keeping its liquid state together. solid/liquid/gas is descriptive. It's not a measure of anything. Anyway, the point is simple. Despite thermal energy driving state change after a certain threshold, that "point" doesn't look like anything special. Smooth quantitative change sometimes results in sudden qualitative changes.
    - root_axis 3y ago
      I don't think GPT3's "emergent behavior" was very surprising, it was a natural progression from GPT2, and the entire purpose of GPT3 was to test the assumptions about how much more performance you could gain by growing the size of the model. That isn't to say GPT3 isn't impressive, but its behavior was within the cone of anticipated possibilities. Based on a similar understanding, the idea that transformer models will lead to AGI seems obviously incorrect, as impressive as they are, they are just statistical pattern matchers of tokens, not systems that understand the world from first principles. And just in case you're among those that believe "humans are just pattern matchers", that might be true, but humans are modeling the world based on real time integrated sensory input, not on statistical patterns of a selection of text posted online. There's simply no reason to believe that AGI can come out of that.
      - andoando 3y ago
        I agree. I am baffled as to why there isn't more thought on developing AI starting from simple sensory input.
  - JohnFen 3y ago
    I don't think the chance is 0%, but I do think that the chance is very, very close to 0%, at least if we're talking about it happening with current technology within the next hundred years or so.
- jayveeone 3y ago
  Non-zero chances don't deserve the hype AGI is receiving, is the issue. And a lot of AI experts outside of the AGI grift have stated that it's zero.
- pton_xd 3y ago
  It's a static single-pass feed-forward network. How could that possibly result in AGI?! (Queue famous last words ...)
  - Akronymus 3y ago
    IMO it could become an AGI IFF it has an infinitely long context window. Otherwise I see absolutely no chance of it becoming a true agi
  - Vecr 3y ago
    Just feed the output through a quality system (with retry if the quality is too bad), scaffold it a bit, then run it back into the LLM. Should work(tm)
- nicklecompte 3y ago
  Transformer neural networks are not capable of true recursion, which is an excellent reason to think that the chance truly is 0%.
  - CamperBob2 3y ago
    That seems easy enough to fix

xiphias2 3y ago

Progress is definitely not inremental, it's exponential. The same performance (training an LLM with a given perplexity) can be achieved 5x cheaper next year while the amount of money deep learning infrastructure gets increases exponentially right now. If this method is able to get to AGI (which I believe but many people are debating), human intelligence will just be mostly ,,skipped'', and won't be a clear point.

blibble 3y ago
how long do you think the "exponential" (that looks very linear to me) growth in funding can continue? until it's more than US GDP? world GDP? universe GDP? either way you're close to the point it will have to go logistic
deleted 3y ago
[deleted]
CatWChainsaw 3y ago
In nature, exponential curves reveal themselves to be sigmoidal on a long enough time scale. Since you're on HN you probably have a mathematical bent, and you should know that.