4 ms·

I'd like to see posts on LLMs written from a different perspective. For me, the surprise comes not from the sudden emergent capability of language models, but t

I'd like to see posts on LLMs written from a different perspective. For me, the surprise comes not from the sudden emergent capability of language models, but that the understanding (and synthesis!) of ideas encoded in language has succumbed to literally nothing more than statistical analysis. Or at least come that much closer to doing so. That it bears so close a resemblance to actual thinking says more about the importance of language to cognition than the other way around.

throwbadubadu 4y ago
Interesting take.. especially as I often wondered, watching and loving animals, if not our ""intelligence separation"" from them is not to a large degree because of the language.. (and the wisdom storage and transfer that comes with it).
guns 4y ago
This is what Stephen Wolfram concludes in a recent article about ChatGPT: > The specific engineering of ChatGPT has made it quite compelling. But ultimately (at least until it can use outside tools) ChatGPT is “merely” pulling out some “coherent thread of text” from the “statistics of conventional wisdom” that it’s accumulated. But it’s amazing how human-like the results are. And as I’ve discussed, this suggests something that’s at least scientifically very important: that human language (and the patterns of thinking behind it) are somehow simpler and more “law like” in their structure than we thought. https://writings.stephenwolfram.com/2023/02/what-is-chatgpt-doing-and-why-does-it-work/ https://writings.stephenwolfram.com/2023/02/what-is-chatgpt-...
- criddell 4y ago
  I wonder if different languages lead to different capabilities? If I ask the same question in English, Japanese, and German, will I reliably get “better” answers from one language over another.
  - whimsicalism 4y ago
    The models transfer knowledge between languages, so probably some difference in capabilities but not a ton in core capabilities. It can solve a physics problem in Telugu close to as well as in English.
- Animats 4y ago
  > (at least until it can use outside tools) This is key. ChatGPT/GPT-4 alone are limited to reformulating what they know from their training data. Linked to search engines, databases, and computational tools such as Wolfram Alpha, they acquire much more capability. We're already seeing that with Microsoft Bing. (Update: what happens as large language models learn Excel? Especially since Microsoft is already connecting them to Excel.) What's striking is how fast this field is advancing. Huge advances over months, not years or decades. We now have a much better idea of how intelligence evolved. It's mostly just more neurons. One of the great philosophical questions has, inadvertently, been answered. Is the Singularity happening right now?
  - petilon 4y ago
    > ChatGPT/GPT-4 alone are limited to reformulating what they know from their training data. That's not true. They are extrapolating. If they weren't, they wouldn't have the problem known as "hallucination".
  - YeGoblynQueenne 4y ago
    >> Is the Singularity happening right now? No, but it seems everyone loves to LARP it anyway.
  - electrondood 4y ago
    > Linked to search engines, databases, and computational tools such as Wolfram Alpha, they acquire much more capability. I see this as analogous to the human brain; there are different structures which are particularly good at specific tasks/functions. They all work together. The only difference between a human brain and an ANN is a difference of degree. A neuron and an artificial neuron are functionally identical. I think as we start interconnecting these models we see surprising emergent properties.
- deleted 4y ago
  [deleted]
LeanderK 4y ago
I think the task of predicting the next word can be misunderstood. The better you want to be the more you have to "understand" how the previous words interacted. From the style of writing to the current topic discussed, the task gets increasingly complex if you want to be really, really good. How could the next sentence start? Will the author end the sentence here or keep going? These questions are very complex. This does not mean that we humans might predict all the time, in fact I would argue that LLMs only predict during training. They generate otherwise. We might also learn by trying to predict. I can imagine babies doing it.
- svachalek 4y ago
  I had the same thought watching my son as a baby. So much of his day seemed to be focused on predicting what happens next, and he got so much joy when he succeeded. So many life skills are predicated on knowing that if I do this, that will happen, which gets me closer to my goal. I started to wonder if intelligence and prediction are really the same thing.
whimsicalism 4y ago
I find the phrase "statistical analysis" a frustrating one nowadays as it seems to have become a signal for "I hold a particular philosophy of the mind". I don't understand this use of "statistical" as a diminutive to describe these models. Why can't incredibly complicated behavior be emergent from matrix multiplication subject to optimization in the same way that our biological matter has developed complicated emergent properties also being subject to optimization? The loss function is very different, the optimization techniques as well, but the fundamental idea of complex behavior emerging out of a substrate subject to optimization seems common. I haven't seen a single good answer to that
- mpalmer 4y ago
  I can't say that my philosophy affected my choice of words in the way that you mean. I'm just expressing wonder at how well the abstraction performs.
- Jensson 4y ago
  > I find the phrase "statistical analysis" a frustrating one nowadays as it seems to have become a signal for "I hold a particular philosophy of the mind". LLMs are trained to reproduce human text, that is different from for example AlphaGo that is trained to win Go games. Trained to reproduce data is what we mean with a statistical model, trained to win is how we got superhuman performance before, while trained to reproduce data performs worse than the original creators of the data.
  - pixl97 4y ago
    This is reductive in the sense is that things like AlphaGo were bad at Go for a very long time, and then with more compute power and algorithm changes suddenly they were far better. And the problem space for go is absolutely huge it is still an insignificant portion of the problem space for knowledge.
- DanHulton 4y ago
  Well, to a degree, because we just don't do science like that. You're the one supposing a thing, so the burden of proof is on you. You need to demonstrate that the "incredibly complicated behaviour" that you're referring to (I assume this is longhand for "thinking", but please correct me if I'm wrong) is indeed emerging from matrix multiplication. Especially given that what you're suggesting is unexpected, given the known way these models work and the explanations that have been put forth already that extrapolate from the known way these models work. If science were so credulous as to accept the first proffered theory about a new development, well, we wouldn't have these interesting AI models in the first place!
felipeerias 4y ago
Some anthropologists suggest that our main evolutionary advantage was not so much our individual ability for reasoning, but our collective capacity to accumulate, evolve and transmit cultural knowledge over the centuries and millennia. Skills like fire or language, for example, had a major influence in the development of our species and are mainly culturally transmitted: trying to reason your way into creating one or the other from scratch is a surprisingly difficult task. If that point of view is true, then it shouldn’t be surprising that a large part of what we consider human-like behaviours should be tractable simply by analysing large amounts of data. AI systems are not modelling cognition, but culture.
- BeFlatXIII 4y ago
  To use my favorite Stephen Hawking quotation (that's also been sampled by Pink Floyd), "for millions of years, mankind lived just like the animals, then something happened that unleashed the power of our imagination: we learned to talk."

throwbadubadu 4y ago

Interesting take.. especially as I often wondered, watching and loving animals, if not our ""intelligence separation"" from them is not to a large degree because of the language.. (and the wisdom storage and transfer that comes with it).

guns 4y ago

This is what Stephen Wolfram concludes in a recent article about ChatGPT: > The specific engineering of ChatGPT has made it quite compelling. But ultimately (at least until it can use outside tools) ChatGPT is “merely” pulling out some “coherent thread of text” from the “statistics of conventional wisdom” that it’s accumulated. But it’s amazing how human-like the results are. And as I’ve discussed, this suggests something that’s at least scientifically very important: that human language (and the patterns of thinking behind it) are somehow simpler and more “law like” in their structure than we thought. https://writings.stephenwolfram.com/2023/02/what-is-chatgpt-doing-and-why-does-it-work/ https://writings.stephenwolfram.com/2023/02/what-is-chatgpt-...

criddell 4y ago
I wonder if different languages lead to different capabilities? If I ask the same question in English, Japanese, and German, will I reliably get “better” answers from one language over another.
- whimsicalism 4y ago
  The models transfer knowledge between languages, so probably some difference in capabilities but not a ton in core capabilities. It can solve a physics problem in Telugu close to as well as in English.
Animats 4y ago
> (at least until it can use outside tools) This is key. ChatGPT/GPT-4 alone are limited to reformulating what they know from their training data. Linked to search engines, databases, and computational tools such as Wolfram Alpha, they acquire much more capability. We're already seeing that with Microsoft Bing. (Update: what happens as large language models learn Excel? Especially since Microsoft is already connecting them to Excel.) What's striking is how fast this field is advancing. Huge advances over months, not years or decades. We now have a much better idea of how intelligence evolved. It's mostly just more neurons. One of the great philosophical questions has, inadvertently, been answered. Is the Singularity happening right now?
- petilon 4y ago
  > ChatGPT/GPT-4 alone are limited to reformulating what they know from their training data. That's not true. They are extrapolating. If they weren't, they wouldn't have the problem known as "hallucination".
- YeGoblynQueenne 4y ago
  >> Is the Singularity happening right now? No, but it seems everyone loves to LARP it anyway.
- electrondood 4y ago
  > Linked to search engines, databases, and computational tools such as Wolfram Alpha, they acquire much more capability. I see this as analogous to the human brain; there are different structures which are particularly good at specific tasks/functions. They all work together. The only difference between a human brain and an ANN is a difference of degree. A neuron and an artificial neuron are functionally identical. I think as we start interconnecting these models we see surprising emergent properties.
deleted 4y ago
[deleted]

LeanderK 4y ago

I think the task of predicting the next word can be misunderstood. The better you want to be the more you have to "understand" how the previous words interacted. From the style of writing to the current topic discussed, the task gets increasingly complex if you want to be really, really good. How could the next sentence start? Will the author end the sentence here or keep going? These questions are very complex. This does not mean that we humans might predict all the time, in fact I would argue that LLMs only predict during training. They generate otherwise. We might also learn by trying to predict. I can imagine babies doing it.

svachalek 4y ago
I had the same thought watching my son as a baby. So much of his day seemed to be focused on predicting what happens next, and he got so much joy when he succeeded. So many life skills are predicated on knowing that if I do this, that will happen, which gets me closer to my goal. I started to wonder if intelligence and prediction are really the same thing.

whimsicalism 4y ago

I find the phrase "statistical analysis" a frustrating one nowadays as it seems to have become a signal for "I hold a particular philosophy of the mind". I don't understand this use of "statistical" as a diminutive to describe these models. Why can't incredibly complicated behavior be emergent from matrix multiplication subject to optimization in the same way that our biological matter has developed complicated emergent properties also being subject to optimization? The loss function is very different, the optimization techniques as well, but the fundamental idea of complex behavior emerging out of a substrate subject to optimization seems common. I haven't seen a single good answer to that

mpalmer 4y ago
I can't say that my philosophy affected my choice of words in the way that you mean. I'm just expressing wonder at how well the abstraction performs.
Jensson 4y ago
> I find the phrase "statistical analysis" a frustrating one nowadays as it seems to have become a signal for "I hold a particular philosophy of the mind". LLMs are trained to reproduce human text, that is different from for example AlphaGo that is trained to win Go games. Trained to reproduce data is what we mean with a statistical model, trained to win is how we got superhuman performance before, while trained to reproduce data performs worse than the original creators of the data.
- pixl97 4y ago
  This is reductive in the sense is that things like AlphaGo were bad at Go for a very long time, and then with more compute power and algorithm changes suddenly they were far better. And the problem space for go is absolutely huge it is still an insignificant portion of the problem space for knowledge.
DanHulton 4y ago
Well, to a degree, because we just don't do science like that. You're the one supposing a thing, so the burden of proof is on you. You need to demonstrate that the "incredibly complicated behaviour" that you're referring to (I assume this is longhand for "thinking", but please correct me if I'm wrong) is indeed emerging from matrix multiplication. Especially given that what you're suggesting is unexpected, given the known way these models work and the explanations that have been put forth already that extrapolate from the known way these models work. If science were so credulous as to accept the first proffered theory about a new development, well, we wouldn't have these interesting AI models in the first place!

felipeerias 4y ago

Some anthropologists suggest that our main evolutionary advantage was not so much our individual ability for reasoning, but our collective capacity to accumulate, evolve and transmit cultural knowledge over the centuries and millennia. Skills like fire or language, for example, had a major influence in the development of our species and are mainly culturally transmitted: trying to reason your way into creating one or the other from scratch is a surprisingly difficult task. If that point of view is true, then it shouldn’t be surprising that a large part of what we consider human-like behaviours should be tractable simply by analysing large amounts of data. AI systems are not modelling cognition, but culture.

BeFlatXIII 4y ago
To use my favorite Stephen Hawking quotation (that's also been sampled by Pink Floyd), "for millions of years, mankind lived just like the animals, then something happened that unleashed the power of our imagination: we learned to talk."