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You can make it invent a new language: https://maximumeffort.substack.com/p/i-taught-chatgpt-to-invent-a-language https://maximumeffort.substack.com/p/i-taught-

You can make it invent a new language: https://maximumeffort.substack.com/p/i-taught-chatgpt-to-invent-a-language https://maximumeffort.substack.com/p/i-taught-chatgpt-to-inv... I am sure you will continue to argue that this is still in line with everything-thats-ever-written prediction but my opinion is that at that point, it's a meaningless distinction. The human brain is also just a machine.

intended 3y ago
The brain is a machine, the issue is the difference between 2 claims LLMs are enough to be a brain LLMs are not enough to be a brain.
- deleted 3y ago
  [deleted]
mjburgess 3y ago
So I was with a financial researcher recently, and he wanted to use ChatGPT to summarise some reference financial data -- and it did so, actually correctly. Being sceptical, as every person ought in these matters, I changed the finical data and performed the same analysis (both in a new tab, and within the same convo). The results were the same! How strange? Well, in being reference financial data ChatGPT was reporting prior reference summaries of it. When that data was changed it was reporting the very same reference summaries (which were now wrong). Since it's incapable of actually summarising financial data. It's only capable of selecting combinations of pieces of its training set. Now, is this distinction "meaningless" ? No, it's the difference between this guy being fired for causing a massive loss on a major project; and this guy keeping his job and doing it well.
- kristiandupont 3y ago
  >Since it's incapable of actually summarising financial data It's not, though. It is in fact able to summarize financial data, just as it's able to write code and diagnose a medical condition. It makes mistakes, yes, even grave ones, much more so than experts in those fields would.
  - mjburgess 3y ago
    It isnt making mistakes ... its never actually doing it. Do you see a difference between the process of adding numbers and dividing by their count (taking a mean) and emitting numeric tokens which are most probable for a given input? The former is called "taking a mean" the latter isnt. This system never engages in any method to summarise financial data. It's method is always the same: to emit tokens most probable given a set of historical tokens. It's the difference between saying "the average of 1,2,3" is 2 because that sentence occurs 1,000,000 times and saying it's 2 because you've literally computed it. This system does not run financial summary algorithms. It's a trick
    - sweetgiorni 3y ago
      To add to your point: try asking ChatGPT to do basic arithmetic on numbers it hasn't seen before. You'll see just how good it is at computation.
      - famouswaffles 3y ago
        It's better (GPT-4) than you could manage without an external tool or pad. and that's after being severely hampered by tokenization. https://arxiv.org/abs/2310.02989 https://arxiv.org/abs/2310.02989
  - deleted 3y ago
    [deleted]
- famouswaffles 3y ago
  >Since it's incapable of actually summarising financial data. It's only capable of selecting combinations of pieces of its training set. Third completely off misconception from you today. This is not at all what it is doing. "Supercharged Interpolation" is false and makes no sense. It's not a lookup table either. It doesn't memorize enough of what it needs to to make your assertion possible. https://arxiv.org/abs/2110.09485 https://arxiv.org/abs/2110.09485
  - mjburgess 3y ago
    at 500gb, you can store nearly everything ever written -- let alone compressed. all statistical learning is a variation on k-nn (see the relevant paper on this) but likewise this is obvious a priori k-nn is the ideal learner, and a good starting point for analysis the question for any given system is: what is the learning space, what is the distance function, and how many points are being considered NNs set up a compressed X,y space, in that space choose points via an empirical expectation, and obtain a weighted average as their prediction That's just what they do -- there isn't any other mechanism here. The whole formal structure of the NN can be written down on a page of paper your paper above doesn't deal with this -- it's a reply to the 'forced interpolation' view, which i haven't espoused. but often NNs are forced interpolated 'extrapolation' is of course a part of the possible predictive output of a statical learning system -- in that it's latent space is taken to be embedded in R^n and so one can 'veer off' into R. Whenever you attribute a higher fidelity space to a small latent space you are, in effect, extrapolating
    - famouswaffles 3y ago
      >at 500gb, you can store nearly everything ever written -- let alone compressed. No you cannot. >That's just what they do -- there isn't any other mechanism here. That's not what they do. They are many papers now showing ICL demonstrating some kind of optimization method during inference which would not be happening if all they did was retrieval. I'm come to realize you don't know what you're talking about. Your level of denial is scary to see.
      - 3y ago
Txmm 3y ago
But “everything ever digitised” includes a tonne of linguistics information - it’s still in sample.

intended 3y ago

The brain is a machine, the issue is the difference between 2 claims LLMs are enough to be a brain LLMs are not enough to be a brain.

deleted 3y ago
[deleted]

mjburgess 3y ago

So I was with a financial researcher recently, and he wanted to use ChatGPT to summarise some reference financial data -- and it did so, actually correctly. Being sceptical, as every person ought in these matters, I changed the finical data and performed the same analysis (both in a new tab, and within the same convo). The results were the same! How strange? Well, in being reference financial data ChatGPT was reporting prior reference summaries of it. When that data was changed it was reporting the very same reference summaries (which were now wrong). Since it's incapable of actually summarising financial data. It's only capable of selecting combinations of pieces of its training set. Now, is this distinction "meaningless" ? No, it's the difference between this guy being fired for causing a massive loss on a major project; and this guy keeping his job and doing it well.

kristiandupont 3y ago
>Since it's incapable of actually summarising financial data It's not, though. It is in fact able to summarize financial data, just as it's able to write code and diagnose a medical condition. It makes mistakes, yes, even grave ones, much more so than experts in those fields would.
- mjburgess 3y ago
  It isnt making mistakes ... its never actually doing it. Do you see a difference between the process of adding numbers and dividing by their count (taking a mean) and emitting numeric tokens which are most probable for a given input? The former is called "taking a mean" the latter isnt. This system never engages in any method to summarise financial data. It's method is always the same: to emit tokens most probable given a set of historical tokens. It's the difference between saying "the average of 1,2,3" is 2 because that sentence occurs 1,000,000 times and saying it's 2 because you've literally computed it. This system does not run financial summary algorithms. It's a trick
  - sweetgiorni 3y ago
    To add to your point: try asking ChatGPT to do basic arithmetic on numbers it hasn't seen before. You'll see just how good it is at computation.
    - famouswaffles 3y ago
      It's better (GPT-4) than you could manage without an external tool or pad. and that's after being severely hampered by tokenization. https://arxiv.org/abs/2310.02989 https://arxiv.org/abs/2310.02989
- deleted 3y ago
  [deleted]
famouswaffles 3y ago
>Since it's incapable of actually summarising financial data. It's only capable of selecting combinations of pieces of its training set. Third completely off misconception from you today. This is not at all what it is doing. "Supercharged Interpolation" is false and makes no sense. It's not a lookup table either. It doesn't memorize enough of what it needs to to make your assertion possible. https://arxiv.org/abs/2110.09485 https://arxiv.org/abs/2110.09485
- mjburgess 3y ago
  at 500gb, you can store nearly everything ever written -- let alone compressed. all statistical learning is a variation on k-nn (see the relevant paper on this) but likewise this is obvious a priori k-nn is the ideal learner, and a good starting point for analysis the question for any given system is: what is the learning space, what is the distance function, and how many points are being considered NNs set up a compressed X,y space, in that space choose points via an empirical expectation, and obtain a weighted average as their prediction That's just what they do -- there isn't any other mechanism here. The whole formal structure of the NN can be written down on a page of paper your paper above doesn't deal with this -- it's a reply to the 'forced interpolation' view, which i haven't espoused. but often NNs are forced interpolated 'extrapolation' is of course a part of the possible predictive output of a statical learning system -- in that it's latent space is taken to be embedded in R^n and so one can 'veer off' into R. Whenever you attribute a higher fidelity space to a small latent space you are, in effect, extrapolating
  - famouswaffles 3y ago
    >at 500gb, you can store nearly everything ever written -- let alone compressed. No you cannot. >That's just what they do -- there isn't any other mechanism here. That's not what they do. They are many papers now showing ICL demonstrating some kind of optimization method during inference which would not be happening if all they did was retrieval. I'm come to realize you don't know what you're talking about. Your level of denial is scary to see.
    - 3y ago

Txmm 3y ago

But “everything ever digitised” includes a tonne of linguistics information - it’s still in sample.