Comment by geor9e
13 hours ago
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
13 hours ago
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
What people are discovering with the latest models is that often their errors are due to entirely reasonable choices and assumptions... which happen to be wrong in your specific case. They call a library you don't have installed, or something like that. Short of inventing either telepathy or spice which can allow LLMs to see the future, it will increasingly be the case that you cannot use the best models efficiently without giving them extensive context. Writing 'reports' where you dump in everything even tangentially relevant is the obvious way to do so, and so I would expect future LLMs to be even more so than o1-preview/pro.
I get much better output from o1* models when I dump a lot of context + leave a detailed but tightly scoped prompt with minimal ambiguity. Sometimes I even add - don’t assume, ask me if you are unsure. What I get back is usually very very high quality. To the point that I feel my 95th percentile coding skills have diminishing returns. I find that I am more productive researching and thinking about the what and leaving the how (implementation details) to the model - nudging it along.
One last thing, anecdotally - I find that it’s often better to start a new chat after implementing a chunky bit/functionality.
Alternatively, we can standardize the environment. It takes humans weeks to adapt to a new interface or starting point. Why would ai be different?
Maximum likelihood training tinges, nay, corrupts, everything it touches. That’s before you pull apart the variously-typed maximum likelihood training processes that the artifice underwent..
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.
There was a debate over whether to integrate Stable Diffusion into the curriculum in a local art school here.
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...
The skill that's worth learning is how to investigate, experiment and think about these kinds of tools.
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.
Very true regarding the subjects of a hypothetical AI art class.
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.
All knowledge degrades with time. Medical books from the 1800's wouldn't be a lot of use today.
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.
> whatever you learn will be obsolete next month
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 answer in formal education is probably somewhere in the middle. The stuff you learn shouldn't be obsolete by the time you graduate but at the same time they should be integrating new advancements sooner.
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.
Integrating it into the curriculum is strange. They should do one time introductory lectures instead.
To be fair, the article basically says "ask the LLM for what you want in detail"
great advice, but difficult to apply given very small context window of o1 models
The churn is real. I wonder if so much churn due to innovation in a space can prevent enough adoption such that it actually reduces innovation
It’s churn because every new model may or may not break strategies that worked before.
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.
IMO the lack of real version control and lack of reliable programmability have been significant impediments to impact and adoption. The control surfaces are more brittle than say, regex, which isn’t a good place to be.
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.
A constantly changing "API" coupled with a inherently unreliable output is not conducive to stable business.
It's interesting that despite all these real issues you're pointing out a lot of people nevertheless are drawn to interact with this technology.
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
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Unless your business is customer service reps, with no ability to do anything but read scripts, who have no real knowledge of how things actually work.
Then current AI is basically the same, for cheap.
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Great summary of how AI compresses the development (and hype) product cycle
Modern AI both shortens the useful lifespan of software and increases the importance of development speed. Waiting around doesn’t seem optimal right now.