6 ms·

> 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

> 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.)
theptip 4y ago
I think he's proposing that the LLM should know how to call out to a knowledge engine at inference time. He thinks the knowledge engine continues to be its own (human-curated) system of knowledge that is valuable. I am suggesting the LLM will (effectively) call out to a knowledge engine at training time, learn everything the knowledge engine knows, and render it obsolete. So it's similar in some sense (collaboration between the two systems), but crucially, a diametrically opposed prediction in terms of the long-term viability of Wolfram Alpha. Crucially, he says "[an LLM] just isn’t a good fit in situations where there are structured computational things to do", but I think it's dubious to claim this; LLMs can learn structured domains too, if they are well-represented in the training set. edit to add: I see that you're specifically noting the LaMDA point, yes, you're right that this is more like what he's proposing. My main claim is that things will not move in that direction, rather the direction of the Mind's Eye paper I linked.
- tablatom 4y ago
  > learn everything the knowledge engine knows Isn't this an effectively infinite set? Wolfram Alpha could be said to know "all the numbers", and "all the formulas". > LLMs can learn structured domains too if they are well-represented in the training set But can they learn how to apply structured knowledge in precise ways? In mathematical or computational ways? I don't follow the field in great detail but the commentary I read seems to be saying this is not at all the case. And my own experiments with ChatGPT show it has no systematic grasp of logic.
  - theptip 4y ago
    > Isn't this an effectively infinite set? No, the thing you'd want the LLM to be learning would be the rules. > But can they learn how to apply structured knowledge in precise ways? I personally believe: clearly yes, already. You can already get a LLM to generate code for simple logical problems. You can ask ChatGPT to modify a solution in a particular way, showing it has some understanding of the underlying logic, rather than just regurgitating solutions it saw. In other domains, you can already give IQ tests to GPT-N: https://lifearchitect.ai/ravens/ https://lifearchitect.ai/ravens/. Others have written in more detail than I could do justice to: https://www.gwern.net/Scaling-hypothesis#scaling-hypothesis https://www.gwern.net/Scaling-hypothesis#scaling-hypothesis. I'd just note that a lot of commentators make quite simple errors of either goalpost-moving or a failure to extrapolate capabilities a year or two ahead. Of course, no linear or exponential growth curve continues indefinitely. But betting against this curve, now, seems to me a good way of losing money.

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.)

theptip 4y ago

I think he's proposing that the LLM should know how to call out to a knowledge engine at inference time. He thinks the knowledge engine continues to be its own (human-curated) system of knowledge that is valuable. I am suggesting the LLM will (effectively) call out to a knowledge engine at training time, learn everything the knowledge engine knows, and render it obsolete. So it's similar in some sense (collaboration between the two systems), but crucially, a diametrically opposed prediction in terms of the long-term viability of Wolfram Alpha. Crucially, he says "[an LLM] just isn’t a good fit in situations where there are structured computational things to do", but I think it's dubious to claim this; LLMs can learn structured domains too, if they are well-represented in the training set. edit to add: I see that you're specifically noting the LaMDA point, yes, you're right that this is more like what he's proposing. My main claim is that things will not move in that direction, rather the direction of the Mind's Eye paper I linked.

tablatom 4y ago
> learn everything the knowledge engine knows Isn't this an effectively infinite set? Wolfram Alpha could be said to know "all the numbers", and "all the formulas". > LLMs can learn structured domains too if they are well-represented in the training set But can they learn how to apply structured knowledge in precise ways? In mathematical or computational ways? I don't follow the field in great detail but the commentary I read seems to be saying this is not at all the case. And my own experiments with ChatGPT show it has no systematic grasp of logic.
- theptip 4y ago
  > Isn't this an effectively infinite set? No, the thing you'd want the LLM to be learning would be the rules. > But can they learn how to apply structured knowledge in precise ways? I personally believe: clearly yes, already. You can already get a LLM to generate code for simple logical problems. You can ask ChatGPT to modify a solution in a particular way, showing it has some understanding of the underlying logic, rather than just regurgitating solutions it saw. In other domains, you can already give IQ tests to GPT-N: https://lifearchitect.ai/ravens/ https://lifearchitect.ai/ravens/. Others have written in more detail than I could do justice to: https://www.gwern.net/Scaling-hypothesis#scaling-hypothesis https://www.gwern.net/Scaling-hypothesis#scaling-hypothesis. I'd just note that a lot of commentators make quite simple errors of either goalpost-moving or a failure to extrapolate capabilities a year or two ahead. Of course, no linear or exponential growth curve continues indefinitely. But betting against this curve, now, seems to me a good way of losing money.