Wolfram Alpha and ChatGPT
281–290 of 309 posts
Re: Wolfram Alpha and ChatGPT
#282Earlier quoted context omitted.
> It's just going to be whatever arbitrary text that already exists in the training dataset is closest to the semantic phrasing of the question. My understanding of machine learning in general is that this is not how it works, and rather it uses a neural network for a lot of what it does (which isn't merely picking the closest arbitrary text in its training set), though I don't know details about NLP specifically. I…
> but the fact is that it didn't offer these options, and instead it offered the correct one ...because you gave it a "correct" prompt; or in other words, you gave it a prompt that ChatGPT can respond "successfully" to. But if you keep trying, you will surely find yourself with "failed" responses. And ChatGPT has no way of knowing the difference. That's my point. > Is the other "completely valid and possible output"…
I was disagreeing with this, and showing ways it can advance. Your reply focuses on the fact that it will still get things wrong. However, I never claimed that it wouldn't make mistakes. I agree it will, and frequently. I just wanted to show that it can do more, and valuably and recognisably more.
The next step up for ChatGPT isn't perfection of mathematics and logic. And with the examples we've been playing with, experimentations with prompts, and further training, when combined with other systems, I don't see why better couldn't be done, and I gave one such example. Sometimes the output it gives is what we're after, sometimes it isn't. Sometimes, we'd be able to feed its own input back to itself and ask it to evaluate if the previous output it gave is what we're after. E.g., I recall hearing about someone feeding the output to the Rust compiler, then feed error messages back, in an iterative loop until they get something that at least compiles. I don't know the best techniques yet, but I have no doubt that a variety of techniques combined with other systems could improve the output of ChatGPT, significantly.
Regarding fair prompts, here's another example of a prompt and its reply, trying at a more generic prompt:
Provide a list of mathematical questions contained in the following text, phrased as equations: "Bob was asked to add 234241.24211 and 58352342.52544, and he wanted to know the result. What is the result? An answer was given by Suzie, who had recently had to count how many new potatoes she had in her pile, after putting the seven new ones in the pile of 15 she already had."
1. 234241.24211 + 58352342.52544 = ___________
2. 15 + 7 = ___________
Or maybe we can ask it to do something like a school assignment: Assuming the following text is given to students, create a list of mathematical equations that could serve as questions based on this text: "Bob was asked to add 234241.24211 and 58352342.52544, and he wanted to know the result. What is the result? An answer was given by Suzie, who had recently had to count how many new potatoes she had in her pile, after putting the seven new ones in the pile of 15 she already had."
1. 234241.24211 + 58352342.52544 = __?
2. 7 + 15 = __?
Doesn't matter if it fails sometimes, but it can do a lot better.You might be right about your claims about conceptualising, and seemingly focusing on perfection as the next step. Perfection isn't the next step in this journey, because there's a lot more value to be added before trying to hit anything remotely close to that (if we even can!). (here, I'm referring to your comment: "I don't see any way to seed such a system to always produce logically correct results")
Side note: I think that humans (and probably many other animals) are conscious, and have a non-physical mind, while computers never will, or at least won't in virtue of us improving the physical characteristics of these systems. But that being said, I think there's a lot about the way human brains work that can be mimicked by machines, and I think that human brains do an awful lot of guessing in our day to day moving throughout the world. That doesn't strike me as different to ChatGPT. It guesses a lot of things, and so do we.
Re: Wolfram Alpha and ChatGPT
#283Earlier quoted context omitted.
> 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, bas…
It's possible to enhance mathematical abilities of LLMs by enabling them to externally run symbolic mini programs e.g. https://arxiv.org/abs/2211.12588 . It's also possible to create fact-grounded retrieval-enhanced language models e.g. https://proceedings.mlr.press/v162/borgeaud22a.html .
Personally I think hybridization is the way to go.
Re: Wolfram Alpha and ChatGPT
#284In the WolframAlpha query, it gives 6313 miles
But in the `GeoDistance[Chicago, Tokyo]` query, it gives 6296 miles
Is there something different about the two queries? Is one Haversine and the other Eucledean? Or does one compare city-centers and the other compares minimum edge-to-edge distance?
Re: Wolfram Alpha and ChatGPT
#285Earlier quoted context omitted.
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 semanti…
Much like ChatGPT, you seem to have comprehended the words I said, but not their meaning.
If you were to consider carefully enough the language example that I gave, you would see that it already refutes your speculation.
Another way would be to apply your argument to humans, who have managed to be useful despite having been trained only on (a broader class of) semantics (i.e., the sum of all qualia) and the logical imperfections that this entails (cf. your comments).
Happy new year!
Re: Wolfram Alpha and ChatGPT
#286It is a shame that Mr. Wolfram cannot write about things without making it 75% about himself. I once bought a book he wrote about great scientists, each chapter about a different scientist. I thought "This guy's stuff is usually so self promotional it is kind of gross, but this will be fun to see his take on these other people". The book was still about him. Amazing.
Re: Wolfram Alpha and ChatGPT
#287Earlier quoted context omitted.
> It's obviously ridiculous but is also a fairly simply dimensional analysis problem. Wouldnt that much icecream would have so much mass it would form some kind of icecream blackhole? A cubic lightyear of blackhole-density icrecream seems like it would be (infinitely?) more than what Wolfram Alpha calculated. I wouldnt even know where to start calculating that, but im pretty sure its not a simple dimensional analysis…
> Wouldnt that much icecream would have so much mass it would form some kind of icecream blackhole? Absolutely; a cubic lightyear of ice cream would have about the same mass as the observable universe, with a Schwarzchild radius of billions of light years.
Re: Wolfram Alpha and ChatGPT
#288Earlier 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…
Glad to see others recognize Wolfram's assertion that he is god's gift to the field of computer science
> Be kind. Don't be snarky. Converse curiously; don't cross-examine. Edit out swipes.
> Please don't fulminate. Please don't sneer, including at the rest of the community.
Re: Wolfram Alpha and ChatGPT
#289Earlier quoted context omitted.
This comment reminds me of this xkcd: https://xkcd.com/1958/ You don't need GPT to do this. Humans have been making propaganda for centuries.
People already are running influence operations like I describe using human authors [1]. The difference is GPT would allow a much larger volume of content to be created at lower cost. A few engineers could run a system that argues with millions of people. [1] https://www.lawfareblog.com/brief-history-online-influence-o...
Re: Wolfram Alpha and ChatGPT
#290Very good at well spoken elaborate stories. Will have a confident answer to all of your questions. Will prefer to tell you bullshit instead of just saying "I don't know".
And there lies also the problem, you will never know if ChatGPT really knows the answer, or is just bullshitting you. Just like a sleazy sales guy.
So as an engineer, I'm not scared yet that my job is in jeopardy ;D.