You can create art with everything from sticks and mud to glass and air. Of course you can make art with AI.
Now if the question is, can a machine make art, well ultimately someone needed to turn the machine on and design the machine to make art, so arguably that person/people are the ones making the art.
Historically, every question of "is x art" ends up having the answer "yes". I don't know why people fall for the same thing over and over.
are there examples of unions that have started around a focus on the ethics of the services they provide? unions traditionally start locally, around issues for which the locality is a hotspot, which is why they usually focus on pay and working conditions. it's also easier to get a large group to agree on a set of improvements to working conditions vs a set of ethical boundaries.
actually, it looks like this is happening inside Google right now. DeepMind workers are unionizing, and most of their demands revolve around ethical boundaries and the right to refuse to contribute based on ethical grounds.
In that scenario, AI would have to be a public utility, which it is not. Private corporations have no intention to provide services for public good. If they displace a billion jobs, they'll just throw up their hands and go "we're just an Ai company guyz"
Man, this comment made me think of a Kafkaesque future where two AI lawyers and an AI Judge are stuck in an infinite loop arguing over a case, meanwhile the defendant is running around trying to get anyone in the legal system to recognize that the AI is stuck.
More than that, the entire structure of the study is pointless. They set up as a question/response and then had humans rate the response. That's literally what LLM's are trained to do, which ultimately is convincing a human to click the "I like this one better" button on it's response.
LLMs are trained to convince a typical human to click the "I like this one better" on their response.
Convincing a human law professor to click the "I would prefer to deliver this response to a student" button, and to not click the "this response is pedagogically harmful" button is a different task!
I could imagine an LLM convincing a typical human to click the "I like this one better" button with flattery, or with nice-sounding platitudes, or with hand-wavey explanations that sound plausible. And in fact that's exactly what LLMs do when they go wrong - they bluff and output superficially plausible nonsense!
But these weren't typical humans, these were law professors specifically tasked with deciding which response was a better option to give to students as a canonical answer to a contract law question. So I think this is a genuinely impressive result.
This is kind of like saying you can't compare Computer Vision models to Human performance because those models were literally trained to identify objects in images...
I'm not saying you can't compare them, I'm saying it's pointless. LLM's are extremely large scale multivariate regression machines, evaluating it's output within it's own training domain is as pointless as seeing if a ball rolls downhill.
IRDC if the LLMs "understand" anything. They are being used here to produce outputs that are desirable. (Neglecting the real possibility that this "survey" is complete BS, as noted elsewhere.)
My claim is that LLMs waste a lot of time training on all available data.
Math is a sequence of formal rules applied to construct a proof tree. Therefore an AI trained on these rules could be far more efficient, and search far deeper into proof space
It has been tried. Lenat's Automated Mathematician, for example. The problem is that the system succumbs to combinatorial explosion, not knowing which directions are interesting/promising/productive. LLMs seem to pick up some kind of intuition from the data they are fed. The generated data might not have the needed "human touch" or whatever it is.
StockFish amplifies weak "intuition" (heuristic and simple neural estimators) through extensive Alpha-Beta search made possible by the low(ish) branching factor of chess. It doesn't work for Go already (KataGo uses larger neural networks to guide search more efficiently). I doubt either will work for math where branching factor is even bigger and the success criteria (is the result interesting and so on) are not strictly defined.
The thing is, LLM's produce better quality one-shots than any of the products that get returned from overseas ultra-budget contractors in India or SEA. I don't know what that means for Western devs, but I can tell you that the fortune 500 I work for is dialing back on contracting and outsourcing because domestic teams can do higher-quality work faster.
>The thing is, LLM's produce better quality one-shots than any of the products that get returned from overseas ultra-budget contractors in India or SEA.
Interestingly that sort of research is actually what I've used Claude/Chatgpt deep research and openclaw for. If I have an idea, I get an agent to go and do some product research for me and see if there is a market, if anyone has tried it, and if there is anyone doing it.
It has unironically saved me a lot of time I would have otherwise spent going down rabbit holes.
Of the models I've found that claude doesn't gas you up as much as GPT, so for stuff like this where the answer can be "no, that's not a good idea" I usually use claude.
Yup. I do have a 4-step process for this (just for prompts and some bash scripts that call CC). 1. Breadth first 2. Compress 3. Per player deep research 4. Per player compression. Then I just merge all the markdown files, fit it into 250k tokens, load any model that supports that much and you can pretty much "tall to market".
The biggest limitation here is data access though. A lot of market data is gated behind registration or anti-bot captchas, so the project that my CC is working on now is a playwright clone that is not easily detectable + can be used with CLI same as playwright itself.
I find ChatGPT so infuriating the way it always agrees with everything you say. The product is optimised for engagement so it wants its users to be delighted
Why are you wondering? Any law that limits the ability of capital owners to extract wealth will be overturned, and not just from AI, that's global in every industry everywhere there are humans.
Now if the question is, can a machine make art, well ultimately someone needed to turn the machine on and design the machine to make art, so arguably that person/people are the ones making the art.
Historically, every question of "is x art" ends up having the answer "yes". I don't know why people fall for the same thing over and over.
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