Instead of learning the latest workarounds for the kinks and quirks of a beta AI product, I'm going to wait 3 weeks for the advice to become completely obsolete
One last thing, anecdotally - I find that it’s often better to start a new chat after implementing a chunky bit/functionality.
Your model attempts to give you a reasonably maximum likelihood output (in terms of kl-ball constrained preference distributions not too far from language), and expects you to be the maximum likelihood user (since its equilibriation is intended for the world in which you the user are just like the people who ended up in the training corpus) for which the prompt that you gave would be a maximum likelihood query (implying that there are times it’s better to ignore you-specific contingencies in your prompt to instead rather re-envision your question instead as being a noisily worded version of a more normal question).
I think there are probably some ways to still use maximum likelihood but you switch out over the ‘what’ that is being assumed as likely - eg models that attenuate dominant response strategies as needed by the user, and easy ux affordances for the user to better and more fluidly align the model with their own dispositional needs.
In that context, how is convincing intelligent people to pay OpenAI to help train their own replacements while agreeing not to compete with them anything but the biggest, dumbest, most successful nerd snipe in history?
Dumping more context just implies getting brain raped even harder. Y’all are the horses paying to work at the glue factory. “Pro” users paying extra for that privilege, no thank you!
Personally while I consider AI a useful tool, I think it's quite pointless to teach it in school, because whatever you learn will be obsolete next month.
Of course some people might argue that the whole art school (it's already quite a "job-seeking" type, mostly digital painting/Adobe After Effect) will be obsolete anyway...
There is just a different decay curve for different topics.
Part of 'knowing' a field is to learn it and then keep up with the field.
this is exactly the kind of attitude that turns university courses into dinosaurs with far less connection to the “real world” industry than ideal. frankly its an excuse for laziness and luddism at this point. much of what i learned about food groups and economics and politics and writing in school is obsolete at this point, should my teachers not have bothered at all? out of what? fear?
the way stable diffusion works hasn’t really changed, and in fact people have just built comfyui layers and workflows on top of it in the ensuing 3 years, and the more you stick your head in the sand because you already predetermined the outcome you are mostly piling up the debt that your students will have to learn on their own because you were too insecure to make a call without trusting that your students can adjust as needed
The problem has also always been that those who know enough about cutting edge stuff are generally not interested in teaching for a fraction of what they can get doing the stuff.
A "Stable Diffusion" class might be a waste of time, but a "Generative art" class where students are challenged to explore what's available, share their own experiments and discuss under what circumstances these tools could be useful, harmful, productive, misleading etc feels like it would be very relevant to me, no matter where the technology goes next.
What's also important is the teaching of how commercial art or art in general is conceptualized, in other words:
What is important and why? Design thinking. I know that phrase might sound dated but that's the work what humans should fear being replaced on / foster their skills.
That's also the line that at first seems to be blurred when using generative text-to-image AI, or LLMs in general.
The seemingly magical connection between prompt and result appears to human users like the work of a creative entity distilling and developing an idea.
That's the most important aspect of all creative work.
If you read my reply, thanks Simon, your blog's an amazing companion in the boom of generative AI. Was a regular reader in 2022/2023, should revisit! I think you guided me through my first local LLama setup.
There is a creative purist idea that the best thing that can happen to an art student is to be thrown out of school early on before it ruins your creativity.
If you put that aside, a stable diffusion art school class just sounds really cool to me as an elective class. Especially the group that would be in this class. The problem I find with these tools is they are overwhelmed by the average person, non-artist, making pictures of cats and darth vader so it is so hard to find what real artists are doing in the space.
Then current AI is basically the same, for cheap.
Once you get past the tier 1 incoming calls, support is pretty specialized.
I said "Unless your business is customer service reps, with...". It's a conditional. It doesn't mean all service reps are clueless, or scripted.
But I bet you've encountered the ones that are!
It looks as if it touches some deep psychological lever: have an assistant that can help to carry out tasks that you don't have to bother learning the boring details of a craft.
Unfortunately lead cannot yet be turned into gold
To look at this statement cynically, a lot of people are drawn to anything with billions of dollars behind it… like literally anything.
Not to mention the amount companies spend on marketing AI products.
> It looks as if it touches some deep psychological lever: have an assistant that can help to carry out tasks
That deep lever is “make value more cheaply and with less effort”
From what I’ve seen, most of the professional interest in AI is based on cost cutting.
There are a few (what I would call degenerate) groups who believe there is some consciousness behind these AI, but theyre very small group.
Current AI has brought back a lot of that wonder and interest for me, and I'm sure the same is true for a lot of other computer nerds.
I was mystified by LLMs a couple years ago. But after really understanding how they work and running into their limitations, a lot of that sheen was lost.
There’s not a ton of interesting technology happening with LLM, more a ton of interesting math. (Math, especially linalg, is not the part of computer science I, personally, fell in love with.)
The outputs of LLMs, unlike programming languages, is pretty random and trial and error based. There’s never any real skill or expertise being built by playing with these tools. My control over the output isn’t as direct or understandable as with programming.
There’s no joy of discovery, only joy of getting the slot machine to give me what I want once in a while.
I’ve regained a lot of that wonder, recently, by doing graphics programming and learning lisp. Going against industry trends in my recreational programming has helped the field feel fresh to me.
Regardless, I don’t think the extreme minority of people who are truly nerdily passionate about tech are the “a lot of people” OC or I was talking about.
Nobody is designing how to prompt models. It’s an emergent property of these models, so they could just change entirely from each generation of any model.
I would quibble that there is a modicum of design in prompting; RLHF, DPO and ORPO are explicitly designing the models to be more promptable. But the methods don’t yet adequately scale to the variety of user inputs, especially in a customer-facing context.
My preference would be for the field to put more emphasis on control over LLMs, but it seems like the momentum is again on training LLM-based AGIs. Perhaps the Bitter Lesson has struck again.
People are trying to design how to prompt, but it’s very different in both implementation and result than designing a programming language or a visual language, ofc.