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Seriously. GPT doing math is like using a 737 to drive around on the ground, or if you had the phone number of a prominent astrophysicist and you call him to do
by xp84 3y ago
Seriously. GPT doing math is like using a 737 to drive around on the ground, or if you had the phone number of a prominent astrophysicist and you call him to do long division for you. Wtf is the point. We have computer things to do every math problem. It’s a waste of energy to use LLMs for it in my opinion.
- sheepscreek 3y agoA better example would be - calling your guitar lessons teacher for help on a statistics problem.
- ben_w 3y agoPossibly more like instantiating the statistics problem by getting, say, every 3 members of an orchestra to represent a triplet of doors in the Monty Hall problem and then making a ball-park guess what the results are from a seat in the middle of the back row.
- lumost 3y agoI used GPT-4 to generate a non-cryptographic random 64 character string. It was faster to ask GPT-4 for the string than ask GPT-4 for the instructions to generate the string from my terminal. GPT-4 was faster than google.
- ThrowawayTestr 3y agoSurely navigating to random.org is faster than typing a prompt.
- oefnak 3y agoThat definitely won't be random.
- dgb23 3y agoThat’s likely a very good example of the limitations of LLMs.
- deafpolygon 3y agoI don't think this will be a truly random string.
- reportgunner 3y agoWere you in a room that had it's walls slowly caving in like in indiana jones or why does it matter that it was faster ?
- lumost 3y agoClose! Was in a meeting and needed to see the behavior of a system when passed a string greater than 64 char. My mental capacity was used elsewhere - using chatGPT let me answer an important customer question authoritatively.
- josefx 3y agowouldn't it be less time to just type one yourself? Like open notepad, hit the keys like a deranged monkey, select the first 64 characters, done?
- Traubenfuchs 3y agoThat is very much not random, but generally enough for 99% of all use cases.
- rcxdude 3y agoI would not bet on whether chatgpt's results would be more or less random than this process.
- Traubenfuchs 3y agoAren't chatgpts results deterministic if you use the same seed, temperature and other parameters?
- WithinReason 3y agoIt would be more random than an LLM's output
- 0x000xca0xfe 3y agoObligatory bash solution: $ random_bytes() { xxd -plain -c 0 -l "$1" /dev/urandom; } $ random_bytes 32 e6a4a7bbea69a0164cbb66c89f8f528af93c6d2459fd28d2640e2952c031b618
- ineedasername 3y ago>/dev/urandom Hmm, I’ve been using /usr/games/fortune Is this not a best practice?
- gardenhedge 3y agoI'm glad ChatGPT exists so I don't have to remember any of that
- theelous3 3y agoThe point of doing $non-llm-optimal-thing on an llm is the hope that it let's you skip on formal syntaxes, which are mentally taxing. It's far easier even for an expert to communicate what they want in natural language than it is in a formal syntax for all but the most trivial things. It should be a goal of these tools to do this correctly.
- xp84 3y ago> the hope that it let's you skip on formal syntaxes, which are mentally taxing agree completely, but the LLM should be focused, then, strictly on formulating an "execution plan" of sorts and handing that off, not on performing math itself. In other words, when asked "if i have 349 blueberries and one blueberry turns into a cherry per hour, how many of each fruit will I have in 93478 minutes?" it shouldn't be doing the actual arithmetic, but it should be figuring out what arithmetic would need to be done.
- orange_fritter 3y agoThis is my second ChatGPT comment so I hate to sound like an evangelist, but it basically does this, reproducibly: You describe a complex relationship between several nodes(people, cities, etc), and then ask ChatGPT to draw the relationship as a graph data structure. It will create a formula that Mathematica can render, and then send the formula to Mathematica before presenting an ascii drawing. Usually. Sometimes it just complains that it can't draw and explains what the graph looks like to you in a written formal/human-readable syntax. In other words, yes it just summarizes the problem, converts it to a formal syntax, and sends it off to some other tool.
- reportgunner 3y agoWhat is the point of doing anything if you can't use flashy technologies ? Leave that to the old people. /s
- SanderNL 3y agoIt’s not about the results, it’s about its ability to “reason”. Math is about as close to pure reasoning we get so I don’t get the pessimism. If it is bad at math and can’t be taught, then you have a fundamental problem. It’s a matter of time before this limit gets hit in other domains.
- FeepingCreature 3y agoIn my experience, it can reason usably well but acts like it has dyscalculia. It'll set up a proper algorithm, step through it and trip over digits.
- hutzlibu 3y ago"It'll set up a proper algorithm, step through it and trip over digits." Or it is just pretending to do so. And since it pretends, of course it trips over all small things as it does not understand them.
- FeepingCreature 3y agoI don't think you can pretend to properly describe and evaluate an algorithm, anymore than you can pretend to solve a riddle - the answer is either right or it isn't. And in this case, the shape of the answer is often right; it just makes ... ordinary errors. Ironically, the AI is a lot better at high-level thinking than correct calculation.
- hutzlibu 3y ago"Ironically, the AI is a lot better at high-level thinking than correct calculation." That would be actually a human like feature .. except I do not consider what LLMs are doing as thinking.
- throwaway4aday 3y agoYes and the reason it trips over digits is because those digits wind up being tokenized in ways that seem unexpected to us and would produce the same difficulty if we were presented with them: 100,000 + 987 - 1444 * 25,945.842 / 0.0042 becomes "100" one hundred "," comma "000" triple zero " +" space plus " 9" space nine "87" eighty seven " -" space minus " 14" space 14 "44" forty four " *" space times " 25" space twenty five "," comma "9" nine "45" forty five "." period "8" eight "42" forty two " /" space divide " 0" zero "." period "00" double zero "42" forty two Now imagine someone reading that to you over the phone once and asking you to do the math in your head and you aren't allowed to use paper and pencil and you have to get it right the first time.
- Closi 3y ago> Seriously. GPT doing math is like using a 737 to drive around on the ground, or if you had the phone number of a prominent astrophysicist and you call him to do long division for you. Wtf is the point. We have computer things to do every math problem. It’s a waste of energy to use LLMs for it in my opinion. Using GPT to do maths is probably like using a 737 to drive around on the ground. Teaching GPT to do maths might be like teaching a child the times tables - a skill that can help overall reasoning.
- marci 3y agoApparently, models finetuned for coding are better at logical inquiries than those that are not.
- viraptor 3y agoWell that's... a claim. What's your reasoning for operations on digits within the model itself improving any current metrics?
- Closi 3y agoWell I didn't really make a bold claim, I said "might", but presumably there is a reason why we teach kids basic mental maths before we teach them how to use a calculator. Humans are different to an AI, but putting that aside, my intuition would be that if we never taught kids any mental maths, their concept/understanding of numbers would be fundamentally different to how it is if they learn that 9 x 9 = 81 (also look at how your fingers move - there is a relationship there!). But who knows, AI is strange and there's lots of stuff that needs to be experimented with. I would think training an intuitive sense of numbers would have other fall-outs though. This is half the beauty of LLM's right? You show an LLM some history books and it also learns about biology, politics, grammar, etymology and love. You teach a LLM maths and it also learns ... ?
- latexr 3y ago> GPT doing math is like using a 737 to drive around on the ground, or if you had the phone number of a prominent astrophysicist and you call him to do long division for you. One significant difference is that in both of those examples it is (or quickly becomes) plain why it’s a ridiculous idea. Even if you don’t understand it yourself, you’ll get external feedback fast. Not so with LLMs, where even people with technical needs may fail to see what is or isn’t a good use of the tool. Case in point: https://news.ycombinator.com/item?id=36782446 https://news.ycombinator.com/item?id=36782446 “You’re holding it wrong” isn’t a valid argument in perpetuity. At a certain point it becomes the fault of the designer, not the user.
- jstummbillig 3y ago"We" are never going to stop trying to use LLMs for math. They are obviously mimicking a smart person. What do you do with smart persons? You query them with bad and lazy questions (often without being too honest about that to yourself), and hope/expect helpful answers. Because that is how it works. The limiting factor so far has been the smart persons time and patience. Now, no longer. People moderating their LLMs usage is never happening, from here on out until the end of civilisation. Any LLM service that is designing for that is done. You need to make lazy questions efficient. People do not care about how complicated your sql query is and they will never care. People will not give up on energy, meat, cars, as long as they feel they are giving something up. People will never think twice to not make your LLM think twice. If it seems useful and convenient, people will use it. If it's not giving good answers to lazy questions out of the box, they will go to the thing that does.
- hammock 3y ago>"We" are never going to stop trying to use LLMs for math. They are obviously mimicking a smart person. A lot of words to make a big deal out of nothing. All that is needed is some new abstracted layer that identifies a math question and then proxies it over to the wolfram plugin. That’s it We don’t have crazy debates over whether a polygon should be rendered by the cpu or a gpu. We solved this problem
- zeusk 3y agoSo a Mixture of Experts model; but then isn't ChatGPT using that already? Why're the models so bad at math despite being trained on academic papers and books clearly - or why do they hallucinate and make up non-existing citations?
- edgyquant 3y agoBecause they don’t actually reason about things they just map words based on probability. If they’ve seen a math problem enough they may get it right due to probability, but using that knowledge to solve a new problem isn’t likely.
- remus 3y ago> GPT doing math is like using a 737 to drive around on the ground, or if you had the phone number of a prominent astrophysicist and you call him to do long division for you. I don't think this is a great analogy. if your 737 couldn't drive on the ground and your astrophysicist couldn't answer basic maths questions I wouldn't want to fly in that plane or put much faith in the astrophysicists answers to more complex questions. Maybe maths is not a particular strength of LLMs, but asking questions where it is easy to judge the factual accuracy of the responses seems a pretty reasonable test to be running.
- brushfoot 3y ago> asking questions where it is easy to judge the factual accuracy of the responses seems a pretty reasonable test to be running. It isn't reasonable if that isn't what the system was designed to do. It would be a poor test of my general practitioner's competence to ask him calculus questions and conclude he doesn't know what he's talking about because he can't answer them.
- fragmede 3y agoGiven that a drug's concentration in your bloodstream is absolutely critical for prescribing them, and considering that this is calculated using Calculus, you'd better hope your GP does actually know their Calculus!
- xp84 3y agoMy point was that a 737 can be a land vehicle, but very badly, because it's optimized (to an extreme amount) for flying. I fly on 737s all the time, knowing that have terrible stopping distances and cornering. The 0-60 could be decent, but you can't really accelerate all-out to try it, or you'll overshoot and end up going 175 and crashing. The astrophysicist can do long division in his head, but he'll be about as fast and accurate as the next person, because he doesn't practice arithmetic every day. I agree with the commenter somewhere in my thread who said all an LLM should be optimized for is to classify the type of problem and feed it into a purpose-built, deterministic solver that is trained to interpret math as math and not as language, be it ML-based or algorithmic.
- vicentwu 3y agoAsking models to do math is kind of an effecitve way to measure their capabilities, especially in reasoning and abstraction, which are quite important for problem solving.
- viraptor 3y agoYou don't need to reason and abstraction to do basic calculation. ChatGPT will however happily give you some decent answers about not-too -hard math that requires reasoning. It just won't operate on digits. Those are completely different ideas.
- herdcall 3y agoMy guess is that for most folks good at math => you're smart, so the most obvious way to test an LLM is to ask it a math question, which is trivial to construct. Another motivation IMO is that asking a relatively simple math question and seeing an LLM fall flat kind of makes many to feel good about themselves and gives them a chance to show it off on social media.