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> And, yes, one can imagine finding a way to “fix this particular bug”. But the point is that the fundamental idea of a generative-language-based AI system like

> And, yes, one can imagine finding a way to “fix this particular bug”. But the point is that the fundamental idea of a generative-language-based AI system like ChatGPT just isn’t a good fit in situations where there are structured computational things to do. Put another way, it’d take “fixing” an almost infinite number of “bugs” to patch up what even an almost-infinitesimal corner of Wolfram|Alpha can achieve in its structured way. I can see why Wolfram is bearish on what might be termed "the naive scaling hypothesis", i.e. that given more data, LLMs will naturally cease making false utterances by learning more systems of knowledge. If the naive scaling hypothesis is true, it recapitulates and invalidates a good chunk of the hand-coded work that he's built over the last decade or two. But I am not so sure; my money is on the robots for now. For example, it should be really easy to generate training data for a LLM using more-formal systems like Wolfram Alpha; not least by having your LLM generate an arbitrary large list of "questions for Wolfram Alpha", then take that query and put it into WA, then attach the results to your LLM training set. In other words, systems like Wolfram Alpha will be used to boost LLMs; the other way round is less obvious. Given the recent success, I'd put my money on "LLM can learn any structured system that can be wrapped in a text interface". An example that's even more impressive than "wrap Wolfram Alpha" has already been demonstrated: LLMs plugged into Physics models (MuJoCo) at Google (https://arxiv.org/abs/2210.05359 https://arxiv.org/abs/2210.05359). There is (currently) no reason that these models can't be plugged in to learn any given simulator or oracle. And on a more prosaic note, Google's LaMDA is clearly ahead of ChatGPT (it's just not public), and explicitly tackles the bullshit/falsehood problem by having a second layer that fact-checks the LLM by querying a fact database / knowledge-graph. Of course, perhaps at some point before AGI the "naive scaling" approach will break down. It just seems to be a bad bet to be making right now; we are seeing no evidence of a slowdown in capabilities gains (quite the opposite, if anything).

sushisource 4y ago
> And on a more prosaic note, Google's LaMDA is clearly ahead of ChatGPT (it's just not public), and explicitly tackles the bullshit/falsehood problem by having a second layer that fact-checks the LLM by querying a fact database / knowledge-graph. Isn't that more-or-less what he's proposing, though? It does feel intuitive to me that something based on probabilistic outcomes (neural nets) would have a very hard time consistently returning accurate deterministic answers. Of course (some) humans get there too, but that assumes what we're doing now with ML can ever reach human-brain level which is of course very much not an answered question.
- eternalban 4y ago
  > what he’s proposing Sure, but as usual (just like the cellular automata business) Wolfram gives/has the impression that he is discussing something novel. And it ain’t novel, to say nothing of the fact that it is also a fairly obvious thing to do. Symbolic AI folks are not taking this LM business well. They are all coping.
  - hgsgm 4y ago
    How is the guy who says "combine symbolic with probabilistic" the one who is "coping" with his system not being powerful enough, but the team who deployed a bot that is almost always wrong, is not "coping"?
    - eternalban 4y ago
      You're right. How could I know this? I have formed an opinion about a person I have never personally met. Mea culpa. My impression then is that Stephan Wolfram, in spite of his considerable and quite & justifiably impressive brain, refuses to apply the necessary corrective measures to adjust for the fact of the existence of external agencies in the world when formulating his personal theory of the world. > a bot that is almost always wrong "It’s always amazing when things suddenly “just work”. It happened to us with Wolfram|Alpha back in 2009. It happened with our Physics Project in 2020. And it’s happening now with OpenAI’s ChatGPT. " It is possible I missed the widespread excitement about Wolfram|Alpha and Project Physics. The former did make waves in geek circles, I remember that. The latter did not make it to the New York Times, did it? https://www.google.com/search?q=Wolfram%7Calpha+stephan+wolfram https://www.google.com/search?q=Wolfram%7Calpha+stephan+wolf... https://www.google.com/search?q=Project%20Physics%20stephan%20wolfram https://www.google.com/search?q=Project%20Physics%20stephan%... https://www.google.com/search?q=ChatGPT https://www.google.com/search?q=ChatGPT Coping with that, in his specific case.
  - superposeur 4y ago
    These decades, whenever the word "Wolfram" comes up, reliably discussion will center on his tone and style. My advice: don't confuse the message with the messenger. Mathematica (yes even I can't bring myself to call it "Wolfram Language" or whatever) is an exquisite and indispensable software system, a true aid to thought. Likewise for Wolfram Alpha. (And yes, agree his cellular automata stuff is unconvincing.)
cs702 4y ago
Great comment! I didn't see Wolfram's OP as being bearish on the "naive" scaling hypothesis, but more as him being a bit upset and jealous about ChatGPT recently getting so much attention. Maybe he's a bit scared too about what future improvements to ChatGPT-like models could do to his business. In any case, I think his proposal of combining the two approaches makes a lot of sense in the near term, as I wrote here: https://news.ycombinator.com/item?id=34329654 https://news.ycombinator.com/item?id=34329654
- gfodor 4y ago
  Weird - I thought his post was very kind to the work of OpenAI and was extremely balanced in how it weighed the two approaches against each other. It seems like with Wolfram there is always a lot of people who presume the worst intentions.
- ModernMech 4y ago
  > him being a bit upset and jealous about ChatGPT recently getting so much attention. Was it given away by the fact that he couldn't wait a single paragraph before claiming to have not one but two successes on the scale of ChatGPT? Stephen Wolfram is consistent if anything.
ghghgfdfgh 4y ago
> I can see why Wolfram is bearish on what might be termed "the naive scaling hypothesis" Forgive me for asking, but what does "bearish" mean in this context?
- TheCoreh 4y ago
  It's stock market slang: - bullish - "bull market" - a positive outlook - bearish - "bear market" - a negative outlook So being "bearish" means he's skeptical about that being the case
- ncann 4y ago
  Bearish = pessimistic (in finance, thinking stock will go down), the opposite of bullish = optimistic (thinking stock will go up)
- moyix 4y ago
  "Bearish" and "bullish" are terms from finance; bearish means you're pessimistic, bullish is optimistic. https://en.wikipedia.org/wiki/Bull_(stock_market_speculator) https://en.wikipedia.org/wiki/Bull_(stock_market_speculator) https://en.wikipedia.org/wiki/Market_trend#Bear_market https://en.wikipedia.org/wiki/Market_trend#Bear_market
WoodenChair 4y ago
> For example, it should be really easy to generate training data for a LLM using more-formal systems like Wolfram Alpha; not least by having your LLM generate an arbitrary large list of "questions for Wolfram Alpha", then take that query and put it into WA, then attach the results to your LLM training set. I don't think this would be "really easy." I think you would quickly run into memory limits because the size of "arbitrary large" would need to be really really large to be enough training data to be able to have enough grounding for arbitrary problems (of course there are an infinite number of possible questions for Wolfram Alpha; how would you even determine what the right subset are to train on?). At least that's my educated guess.

sushisource 4y ago

> And on a more prosaic note, Google's LaMDA is clearly ahead of ChatGPT (it's just not public), and explicitly tackles the bullshit/falsehood problem by having a second layer that fact-checks the LLM by querying a fact database / knowledge-graph. Isn't that more-or-less what he's proposing, though? It does feel intuitive to me that something based on probabilistic outcomes (neural nets) would have a very hard time consistently returning accurate deterministic answers. Of course (some) humans get there too, but that assumes what we're doing now with ML can ever reach human-brain level which is of course very much not an answered question.

eternalban 4y ago
> what he’s proposing Sure, but as usual (just like the cellular automata business) Wolfram gives/has the impression that he is discussing something novel. And it ain’t novel, to say nothing of the fact that it is also a fairly obvious thing to do. Symbolic AI folks are not taking this LM business well. They are all coping.
- hgsgm 4y ago
  How is the guy who says "combine symbolic with probabilistic" the one who is "coping" with his system not being powerful enough, but the team who deployed a bot that is almost always wrong, is not "coping"?
  - eternalban 4y ago
    You're right. How could I know this? I have formed an opinion about a person I have never personally met. Mea culpa. My impression then is that Stephan Wolfram, in spite of his considerable and quite & justifiably impressive brain, refuses to apply the necessary corrective measures to adjust for the fact of the existence of external agencies in the world when formulating his personal theory of the world. > a bot that is almost always wrong "It’s always amazing when things suddenly “just work”. It happened to us with Wolfram|Alpha back in 2009. It happened with our Physics Project in 2020. And it’s happening now with OpenAI’s ChatGPT. " It is possible I missed the widespread excitement about Wolfram|Alpha and Project Physics. The former did make waves in geek circles, I remember that. The latter did not make it to the New York Times, did it? https://www.google.com/search?q=Wolfram%7Calpha+stephan+wolfram https://www.google.com/search?q=Wolfram%7Calpha+stephan+wolf... https://www.google.com/search?q=Project%20Physics%20stephan%20wolfram https://www.google.com/search?q=Project%20Physics%20stephan%... https://www.google.com/search?q=ChatGPT https://www.google.com/search?q=ChatGPT Coping with that, in his specific case.
- superposeur 4y ago
  These decades, whenever the word "Wolfram" comes up, reliably discussion will center on his tone and style. My advice: don't confuse the message with the messenger. Mathematica (yes even I can't bring myself to call it "Wolfram Language" or whatever) is an exquisite and indispensable software system, a true aid to thought. Likewise for Wolfram Alpha. (And yes, agree his cellular automata stuff is unconvincing.)

cs702 4y ago

Great comment! I didn't see Wolfram's OP as being bearish on the "naive" scaling hypothesis, but more as him being a bit upset and jealous about ChatGPT recently getting so much attention. Maybe he's a bit scared too about what future improvements to ChatGPT-like models could do to his business. In any case, I think his proposal of combining the two approaches makes a lot of sense in the near term, as I wrote here: https://news.ycombinator.com/item?id=34329654 https://news.ycombinator.com/item?id=34329654

gfodor 4y ago
Weird - I thought his post was very kind to the work of OpenAI and was extremely balanced in how it weighed the two approaches against each other. It seems like with Wolfram there is always a lot of people who presume the worst intentions.
ModernMech 4y ago
> him being a bit upset and jealous about ChatGPT recently getting so much attention. Was it given away by the fact that he couldn't wait a single paragraph before claiming to have not one but two successes on the scale of ChatGPT? Stephen Wolfram is consistent if anything.

ghghgfdfgh 4y ago

> I can see why Wolfram is bearish on what might be termed "the naive scaling hypothesis" Forgive me for asking, but what does "bearish" mean in this context?

TheCoreh 4y ago
It's stock market slang: - bullish - "bull market" - a positive outlook - bearish - "bear market" - a negative outlook So being "bearish" means he's skeptical about that being the case
ncann 4y ago
Bearish = pessimistic (in finance, thinking stock will go down), the opposite of bullish = optimistic (thinking stock will go up)
moyix 4y ago
"Bearish" and "bullish" are terms from finance; bearish means you're pessimistic, bullish is optimistic. https://en.wikipedia.org/wiki/Bull_(stock_market_speculator) https://en.wikipedia.org/wiki/Bull_(stock_market_speculator) https://en.wikipedia.org/wiki/Market_trend#Bear_market https://en.wikipedia.org/wiki/Market_trend#Bear_market

WoodenChair 4y ago

> For example, it should be really easy to generate training data for a LLM using more-formal systems like Wolfram Alpha; not least by having your LLM generate an arbitrary large list of "questions for Wolfram Alpha", then take that query and put it into WA, then attach the results to your LLM training set. I don't think this would be "really easy." I think you would quickly run into memory limits because the size of "arbitrary large" would need to be really really large to be enough training data to be able to have enough grounding for arbitrary problems (of course there are an infinite number of possible questions for Wolfram Alpha; how would you even determine what the right subset are to train on?). At least that's my educated guess.