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Wolfram Alpha and ChatGPT

writings.stephenwolfram.com

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Re: Wolfram Alpha and ChatGPT

#221

Post by thomasahle »

The "Moons larger than Mercury" example is interesting. ChatGPT first gives the wrong answer, but then goes on to actually explain the opposite (the correct answer).

It seems it got tricked by doing chain of thought in the wrong order. If it had done the thinking first, it would probably have answered correctly.

Another option would be to use a multi-pass algorithm. It would be easy for a language model reading the first output to generate a new answer correct answer.

I wonder if this kind of boosted model (similar to diffusion) could be the future of text generators.

Re: Wolfram Alpha and ChatGPT

#222

Post by pdonis »

dragonwriter wrote: post #192
pdonis wrote: post #190

Earlier quoted context omitted.

> Do the damn math Wolfram's point, which is valid, is that ChatGPT can't do the damn math. That's simply not what it does. To do things like do accurate math, you need a different kind of model, one that is based on having actual facts about the world, generated by a process that is semantically linked to the world. For example, Wolfram uses the example of asking ChatGPT the distance from Chicago to Tokyo; it gives…

> To do things like do accurate math, you need a different kind of model, one that is based on having actual facts about the world, generated by a process that is semantically linked to the world. Or you just need a model that can recognize math, and then pass it to a system that can do math. Math is actually something traditional, non-AI systems are very good at doing (it is the raison d’être of traditional computin…

> Or you just need a model that can recognize math, and then pass it to a system that can do math.

Wolfram Alpha already does that. But that's because Wolfram Alpha is built as a model whose purpose is "recognize what kind of problem this natural language query requires, then pass it on to the problem engine for that kind of problem", where each problem engine is an actual solution model for that kind of problem, based on actual facts about the world.

ChatGPT, though, is built as a completely different type of model, whose purpose is "find a pattern that this natural language query matches, then generate a greatest probability sequence of natural language words for that pattern based on the training data set". That's a completely different structure.

Re: Wolfram Alpha and ChatGPT

#223

Post by amelius »

Winsaucerer wrote: post #210
thomastjeffery wrote: post #202

The one thing I want everyone to understand about ChatGPT: ChatGPT interfaces with semantics , and not logic . -- That means that any emergent behavior that appears logically sound is only an artifact of the logical soundness of its training data. It can only echo reason. The trouble is, it can't choose which reason to echo! The entire purpose of ChatGPT is to disambiguate, but it will always do so by choosing the mo…

> As I see it, there is clearly no way to advance ChatGPT into anything more than it is today. Impressive as it is, the curtain is wide open for all to see, and the art can be viewed plainly as what it truly is: magic, and nothing more. (I only RTFA after writing this comment, and I now see that the below is what they're doing) I'm an outsider to this field. My unexpert thought was that perhaps this model could be us…

You are asking ChatGPT to interface with a calculator. This is sort of what Wolfram proposes too: interface ChatGPT with a math language. It could work for simple problems. But extracting a mathematical description from a piece of text can require logic too, I suppose, which is where the difficulties start.

Re: Wolfram Alpha and ChatGPT

#224

Post by janalsncm »

theptip wrote: post #129
janalsncm wrote: post #115

Earlier quoted context omitted.

> 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. And when facts change, your only option will be to retrain. Since facts are always changing, you’ll always be training.

I don't think most of the interesting knowledge encoded in Wolfram Alpha changes. Mathematics and pure Logic is true, and immutable. Most of Physics, ditto.

Thats true for the physics and math but some of the things are updated all the time. For example you can get a current weather report. And they have structured data about movies, TV shows, music, and notable people [1]. Every time any country has an election you’re going to retrain your model? That gets really expensive really fast.

On top of that, the training process isn’t that trustworthy. There’s no guarantee your model won’t accidentally say that e.g. Obama is the president.

All of this is to say that the best path forward is to translate questions into queries to an auditable knowledge base and then integrate the responses back into the conversation. It’s a couple years old but the best I’ve seen in this area is Retrieval Augmented Generation [2]. And even that’s imperfect, in my experience.

[1] https://www.wolframalpha.com/

[2] https://arxiv.org/abs/2005.11401

Re: Wolfram Alpha and ChatGPT

#225

Post by CrushItNerds519 »

thomastjeffery wrote: post #202

The one thing I want everyone to understand about ChatGPT: ChatGPT interfaces with semantics , and not logic . -- That means that any emergent behavior that appears logically sound is only an artifact of the logical soundness of its training data. It can only echo reason. The trouble is, it can't choose which reason to echo! The entire purpose of ChatGPT is to disambiguate, but it will always do so by choosing the mo…

This comment and many others speculate on the limits of ChatGPT based on assumptions about what ChatGPT does that are not quite accurate. In particular, ChatGPT does not simply output the “most semantically popular result”. That description applies only to the base model, before instruction tuning and RLHF.

As for the speculation itself, e.g., “as soon as you merge two subjects, you are right back to gambling semantics as logic”, it may be suggestive to consider that such models have learned to translate between English and French after having only trained on the two languages separately.

Re: Wolfram Alpha and ChatGPT

#226

Post by [deleted] »

thomastjeffery wrote: post #202

The one thing I want everyone to understand about ChatGPT: ChatGPT interfaces with semantics , and not logic . -- That means that any emergent behavior that appears logically sound is only an artifact of the logical soundness of its training data. It can only echo reason. The trouble is, it can't choose which reason to echo! The entire purpose of ChatGPT is to disambiguate, but it will always do so by choosing the mo…

[deleted]

Re: Wolfram Alpha and ChatGPT

#227

Post by caf »

pdonis wrote: post #190
agentwiggles wrote: post #18

I'm almost offended by the "cubic light year of ice cream" answer from ChatGPT. It's obviously ridiculous but is also a fairly simply dimensional analysis problem. Do the damn math, don't wag your finger at me and crush my dreams! I'm pretty bullish on ChatGPT and its ilk, but I _really_ dislike when ChatGPT lectures me because my request is against its "moral values." I recently pasted in the lyrics from Sleep's tit…

> Do the damn math Wolfram's point, which is valid, is that ChatGPT can't do the damn math. That's simply not what it does. To do things like do accurate math, you need a different kind of model, one that is based on having actual facts about the world, generated by a process that is semantically linked to the world. For example, Wolfram uses the example of asking ChatGPT the distance from Chicago to Tokyo; it gives…

It's perhaps a minor point, but I don't believe Wolfram Alpha has the distances between each pair of cities in its store of facts: it has the locations of each city, and it knows how to calculate the distance between two locations on Earth, and it can decompose the query into such a calculation on two facts from its database.

Re: Wolfram Alpha and ChatGPT

#228

Post by cj »

thomastjeffery wrote: post #202

The one thing I want everyone to understand about ChatGPT: ChatGPT interfaces with semantics , and not logic . -- That means that any emergent behavior that appears logically sound is only an artifact of the logical soundness of its training data. It can only echo reason. The trouble is, it can't choose which reason to echo! The entire purpose of ChatGPT is to disambiguate, but it will always do so by choosing the mo…

This is the best description of the state of ChatGPT that I’ve read thus far.

…Unless this whole comment was generated by ChatGPT. (I strongly dislike that I’m starting to second guess whether comments are written by humans or not)

Re: Wolfram Alpha and ChatGPT

#229

Post by superposeur »

eternalban wrote: post #59
sushisource wrote: post #28

Earlier quoted context omitted.

> 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 c…

> 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.

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.)

Re: Wolfram Alpha and ChatGPT

#230

Post by WoodenChair »

theptip wrote: post #27

> 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…

> 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.

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