Comment by mjburgess
4 years ago
How much of this is just "AI is bad at everything", but in the math case, it's easier for the lay person to tell.
It's all just passable garbled nonesense that the reader (goes to lengths) to interept based on their prior knowledge, which is not expressed in the syntax of what these systems output.
In the case of mathematics, we're far less willing to "BS away" the interpretive failures. But if we were equally demanding, likewise, all prose generated by these systems isnt AI "getting" anything either.
Pass a film reel thru' a shredder and an art student would still call it a film. Pass math thru' and a mathematician wont. This says more about our ability and inclination to make sense out of nonesense when in apparent communicative situations (since, when speaking to a person, this actually improves our mutual understanding).
So, how much of AI is just hacking people's cognitive failures: (1) people's willingness to attribute intention; (2) people's willingness to impart sense "at all costs" to apparent communication; and (3) "hopeium".
Have you ever used Github CoPilot? It does a lot of useful work, automating away rote typing in programming. Have you tried Dall-E or Stable Diffusion? They make good looking images. This comment seems completely unmoored from where the state of the art is right now.
Math follows a completely different approach with respect to how machine-learning AIs do their thing.
Reason derives its strength in having a few primitives and creating new assertions through the transformation of symbols by following precise rules (which is how algorithms work).
In ML-based AIs, everything is imprecise and probabilistic, and this kind of generation gets its strength from building recognizable from utterly imprecise inputs and training - quite the opposite of how logic and reason evolve. Now, "classic" AI was a powerful way to derive new knowledge, and automatic theorem proving is a strong discipline; but the recent breakthroughs in AI are not directly applicable to classic techniques.
Do you know what machine-learning AIs could be good for? Generating "insight" in problem solvers for guiding the theorem demonstrations through the proof search space, trying to find the best sub-spaces to explore. If there's a way to create human-like general AI, it will likely combine both kinds of generation - the rational methods of symbolic logic and the "irrational" statistical methods of ML.
Automated theorem proving is the same problem as “complete and label the diagram”, which image generation is okay at.
Work in progress for sure, though.
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Dall-E produces good looking images within certain parameters.
When you are in its bounds it seems magical, once you go outside it seems like a weak joke.
And many of the reasons it is bad outside its sweet spot are fundamental to how it works not a flaw that can be iterated away.
> fundamental to how it works not a flaw that can be iterated away.
Can you elaborate?
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I haven't used copilot because I'm not sure I'm allowed, but I'll try it on a personal project eventually.
I'm hoping it's not as bad as Dall-E and Stable Diffusion - I've tried to use those to generate some generic product looking stock photos for a demo and it's spectacularly bad. The only context I see it get praised is fantasy style art - and that is visually appealing nonsense by definition.
If the code generated by copilot has the same "looks convincing but is fundamentally flawed" quality then it sounds like an insidious bug generator.
sure, but co-pilot is mostly just copying code (see, for example, the issue with it producing quake source code).
If you think of AI as a dial from sample(data) to mean(data), then as the dial is turned towards the mean() you get more "generic" results, but also more garbled ones.
Copilot is more like a search engine, having turned the dial more towards sample().
The real invention of the NN is simply to provide that dial in a trainable way.
The only change to the "state of the art" is the size of the weights, and how long they take to train. This "advancement" is no more impressive than google indexing more webpages.
There has been no step-change advancement in AI in, perhaps, 50 years. All we see today is a product of hardware, in GPU/CPUs able to compress TBs of data into c. 300GB of weights. And likewise, the internet to provide it and SSDs to hold it.
The "magic" of AI is no more the magic of wikipida, here: copilot is good only because million+ programmers made github good.
It's still little more than a fancy search.
> It's all just passable garbled nonesense that the reader (goes to lengths) to interept based on their prior knowledge, which is not expressed in the syntax of what these systems output.
> It's still little more than a fancy search.
I feel like the goalposts have been moved between your two comments. CoPilot is obviously not producing garbled nonsense, and it's also not just printing the top result from StackOverflow. It is producing code that references my variables, does the right thing 50% of the time, and usually compiles.
One of the nice little things is error messages- when I type `if (!foo) { throw ... ` CoPilot is able to complete a nicely formatted and descriptive error message from its understanding of my code. It's not garbled nonsense, and it's not just a search engine.
Does AI deserve the hype it sometimes gets? Not yet. But I think you're going to have to start digging a little deeper for your commentary.
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Even if AI got to the point of perfectly passing every expert-level Turing test your degree of rigor as to what "thinking" is would never truly permit any belief of AI having struck the golden nugget of intelligence.
Imagine if we were all self-replicating computers, and certain members of this silicon race began experimenting with making creatures with carbon macro-molecules to create organic intelligence, you could make the same claim in the other direction:
"There has been no step-change advancement in Organic Intelligence in, perhaps, 50 years. All we see today is a product of cell count, in neurotransmitter chemistry able to compress TBs of experiences into c. 300B neurons."
I think you are missing the conditional, contextual nature of language models. They mix things in coherent ways, they adapt to the request. Google doesn't create new things when they don't exist, and the pre-written code examples on the internet will never adapt to your needs.
But I agree with you that everything they do seems intelligent because 'intelligence' was in the training data. Not much different from us, if you raise a human removed from society (take his intelligent training data away) he will not accomplish almost anything on his own.
I agree. It's possible to point out the clear limitations of current AI without being oblivious to the huge, indisputable advances that have occurred.
People thought it might take centuries for a computer to defeat a top human in Go. Then deep learning showed up and a few years later it's the opposite.
A lot of the things deep learning methods are doing now are things no one had any idea how long research would take to achieve, or if they were even possible.
Personally, I think we are currently hitting some walls that might take a while to climb before we get to AGI, but I am very impressed at the recent progress.
> How much of this is just "AI is bad at everything"
"AI Language Models" are not touted as some general AI that is smart at everything, like a clever person with multiple intellectual skills integrated into one.
AI language models are for modeling language, not for math problem solving, or anything else. People good at language aren't always good at math.
DeepL produces very good, correct translations for "Alice has five more balls than Bob, who has two balls after he gives four to Charlie. How many balls does Alice have?" into numerous languages, even though it doesn't offer a solution.
I have little doubt that an AI system could be trained to translate word problems like this into systems of equations, which could be dumped into a some decades-old CAS to obtain a solution, which the AI could map back into the verbal domain through the identities between the math variables like x and Alice's apples.
"Hey look, that human who is supposedly good at math can't produce a painting of the Grand Canyon in the style of Monet, even if given eight months to do it, and is easily defeated in chess."
> How much of this is just "AI is bad at everything", but in the math case, it's easier for the lay person to tell
Honestly, even as someone generally pretty dismissive of the AI hype, I'm not sure you can go that far. The whole reason we have specific mathematical notation is that human languages often are not super great at dealing with it, and English in particular is pretty abysmal for being both unambiguous and precise (and I'd be surprised if language models didn't end up suffering from biases analogous to how many image recognition AI models have been found to not deal well with a diverse set of human appearances). We don't teach math the same way we teach English, and we certainly don't expect people to be experts at teaching both, so why would we expect an AI model designed for language to be able to do math?
Because there is an algorithm for it. Convert the strings into floating point numbers, add them, convert them back to strings. It’s a leetcode medium question. It should be learnable.
The article talks about abstract math questions, but even arithmetic is hard for language models.
That even arithmetic is hard for language models may suggest language models (at least in their current form) are not the most optimum for the task.
Language models aren't built for math. Their improvement/training cycles aren't sensitive to the exactness and rule-based nature of mathematical language, plus there are probably a lot of bad/misleading examples of math in the source data.
You'd have to be unrealistically pessimistic to call what GPT-3 and other huge language models produce "nonsense".
It's not that they were not built for math, but more like verification is hard. But it's hard for humans as well. A large generative model + a fast verifier could do wonders.
AlphaGo was built on that - the model can propose moves, but you can verify who won in the end. There are some code generation models that write their own tests as well, or use externally provided tests to verify their solutions. The DeepMind matrix multiplication algorithm was also "learning from verification" of generated solutions, because it's trivial to do that. In general verification remains an open problem.
I disagree. It is that they were not built for math. While brain analogies are shittier than most people assume, this is like trying to do math in your head without being allowed to think through calculations.
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AI is bad at music also. Even the state of the art transformer models can't produce more than a few seconds of coherent melodic phrases.
I think if we replaced "AI" with "taking averages over subsets of historical examples", then there'd be no mystery for when "AI" will be good or bad at anything.
Would we expect a discrete melodic structure to be expressible as averages of prior music? No.
Have you heard the piano continuations of AudioLM?
https://google-research.github.io/seanet/audiolm/examples/
Pretty sure the first continuation is a famous piece with a few notes messed up. Can't remember the name. Honestly it only sounds marginally better than the old markov chain continuations.
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Indeed, there is lots of denial or ignorance in this thread (ignorance in the technical sense). AudioLM already produced impressive results and it's a tiny fraction of what is already possible because performance simply improves with scale. One can probably solve music generation today with a ~$1B budget for most purposes like film or game music, or personalized soundtracks. This is not science fiction.
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Which is extra funny, because GOFAI models (e.g. David Cope's work) were doing a pretty OK job back in the 1990s!
It doesn't surprise me that an AI model for language can't grok maths or music. I can't see how a language model can map to maths. Hell, I don't even know how to describe music in words. It's possible to articulate some maths in words, but that often involves using words with unexpected definitions.
AI can be quite good at music,
but yes there is not yet at on-demand button rendering from a text prompt of bitstreams encoding composed performed and mastered music.
AI is bad at Audio. AI can do MIDI fine.
MIDI is extraordinarily expressive and is likely used to sequence a large majority of music produced within the last three decades. A lot of the instruments you hear are synthesizers or samplers running directly from MIDI. There is a lot more to what MIDI can do, and is used for, than the conception most people have from "canyon.mid" or old website background music. If an AI can do MIDI just fine then it's an extremely small leap to doing audio just fine.
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Which is a real shame. AI-powered restoration of poor-quality audio would be highly useful.
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That's wrong, and shows how ignorant you are of SOTA techniques for music generation. They are far ahead of that.
That’s what a musician does. They make short loops and loop them.
This reads like someone who knows sheet music and theory but does not listen to music. It’s repetition of short phrases over and over.
I’m not really sure what people expect of general AI trained on human generated outputs. It can’t make up anything anything “net new” only compose based upon what we feed it.
I like to think AI is just showing us how simple minded we really are and how our habit of sharing vain fairy tales about history makes us believe we’re masters of the universe.
Those models are not trained on short loops. They are trained on whole songs just like image generation models are trained on whole images. And yet they struggle to repeat sections, modulate to a different key, create bridges, intros and outros. After a few seconds of hallucinating a melodic line they simply abandon the idea and migrate to another one. There is no global structure whatsoever.
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See the "joke explaining" examples on Page 36 of the paper: https://arxiv.org/abs/2204.02311 and tell me if it's just "passable garbled nonsense".
> 500 bn parameter model
That solution has a compressed representation of half the internet.
NNs are "garabled nonesense" insofar as they try to generalise; insofar as they are search engines, they provide apparent sense by just repeating something in their database (= weights).
Google, likewise, returns joke explanations.
"The Internet" is much, much bigger than 500bn floats. The latest snapshot of archive.org has 505 billion web pages itself.
That's astonishing.
Oo this reminds me. One of my favourite sci-fi novels is The Moon is a Harsh Mistress.
In it, it depicts the growth of a nascent AI from its attempts at understanding humor. The AI befriends a technician and gets the human to rate its own crafted jokes.
Eventually the AI gets really good at telling jokes, and becomes sentient as a result.
It was a very fun take on AI gaining sentience, highly recommended!
This might make sense as a response solely to the title of the article, but I have to admit I find it puzzling as a reaction to its content. Notwithstanding the title, the article mentions a model called Minerva that scored fully 50% on the MATH dataset of high-school/undergrad mathematical problems. For comparison, a human computer science PhD student scored 40%. [1]
For context, Minerva came out this July. When it was tested on a national math exam, it scored higher than that year's class of graduating high school seniors. [2] A mere eight months (!) earlier, OpenAI had announced [3] they'd trained a language model that solved math word problems almost as well as an average middle-schooler. So even if you believe — rightly or wrongly — that current capabilities aren't very impressive, it's worth remembering that your understanding of current capabilities might not be entirely accurate, even if it's only a few months out of date.
Incidentally, it may be worth looking at some examples of these models' outputs before deciding what they can or can't do. Here's Minerva solving some math problems, for example:
https://dl.airtable.com/.attachmentThumbnails/32f10fc59039c7...
https://dl.airtable.com/.attachmentThumbnails/f6f86e0edd14d1...
https://dl.airtable.com/.attachmentThumbnails/d9bc743dca1015...
https://dl.airtable.com/.attachmentThumbnails/51562ef70c2b14...
https://dl.airtable.com/.attachmentThumbnails/c081906cc4d23b...
I'll admit I find it challenging to interpret these results as "passable garbled nonesense [sic]", though perhaps I'm not being demanding enough. At any rate, when these models go from beating 10-year olds at math to beating 18-year olds at math in the span of 8 months, one does start to wonder how much of the hype is really due to over-interpretation — and what the next 8 months have in store.
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[1] https://arxiv.org/pdf/1709.04326.pdf (For comparison, a 3-time Mathematics Olympiad gold-medalist scored 90%.)
[2] This was Poland's National Math Exam. Minvera scored 65%; the 2021 national average was 57%. See https://www.aitracker.org/ and Section 3 of the Minerva paper at https://arxiv.org/pdf/2206.14858.pdf
[3] https://openai.com/blog/grade-school-math/