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ZorbaTHut  1yr ago (text post)   44987 thread views

Culture War Roundup for the week of September 5, 2022

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Anyways, over the course of the next few years, I imagine there will be a few scandals, from niche to mainstream, of artists using AI but representing it as human-made.

Already here, technically:

https://www.washingtonpost.com/technology/2022/09/02/midjourney-artificial-intelligence-state-fair-colorado/

So the average artist will be able to step in, using AI to create ideas and starting points, and then build off of that. AI will be the go to for reference images.

The problem with this reasoning is that AI capabilities scale up FAST. Just a year ago the predecessors of the current models were barely passable at art. One year from now, they could be exponentially better still.

And artists who use it as a tool are actually helping it learn to replace them, eventually! So this isn't like handing someone a tool which will make their life easier, its hiring them an assistant who will learn how to do their job better and more cheaply and ultimately surpass them.

My favorite illustration of this is something called Centaur Chess.

Early chess engines would occasionally make dumb moves that were obvious to human players. Even when their overall level improved enough to beat the top human players they still often did things that skilled players could see were sub-optimal.

This meant that in the late 90s / early 00s the best "players" were human-computer teams. A chess engine would suggest moves, then a human grandmaster would make their move based on that - either playing the way the computer suggested, or substituting their own move if they saw something the computer had missed.

But as AI continued to develop the engine's suggestions kept getting better. Eventually they reached a point where any "corrections" were more likely to be the human misunderstanding what the computer was trying to do rather than a genuine mistake. Human plus computer became weaker than the computer alone, and the best tactic was to just directly play the AI's moves and resist the temptation to make improvements.

Just a year ago the predecessors of the current models were barely passable at art. One year from now, they could be exponentially better still.

https://xkcd.com/605/

Here's another relevant XKCD:

https://xkcd.com/1425/

8 years ago when this comic was published the task of getting a computer to identify a bird in a photo was considered a phenomenal undertaking.

Now, it is trivial. And further, the various art-generating AIs can produce as many images of birds, real or imagined, as you could possibly desire.

So my point is that I'm not extrapolating from a mere two data points.

And my broader point, that AI will continue to improve in capability with time, seems obviously and irrefutably true.

And my broader point, that AI will continue to improve in capability with time, seems obviously and irrefutably true.

I'll give a caveat, here. AI will certainly get better within its existing capabilities and within some set of new capabilities, but there are probably at least some capabilities that will require changes in type rather than degree, or where requirements grow very quickly.

These examples are easier to talk about in the sense of text. GPT-3 is very good at human-like sentences, and GPT-4/5 will definitely be much better at that. It very likely handle math questions better. It more likely than not will still fail to rhyme well. It is also unlikely to hold context for 50k tokens (eg, a novel) in comparison to GPT-3's ~2k (ie, a long post), because the current implementations go badly quadratic. There are some interesting possible alternative approaches/fixes -- that Gwern link is as much about them as the problem -- but they are not trivial changes to design philosophies.

Very interesting.

I do wonder if certain architectures/frameworks for machine learning will start to break as they exceed certain sizes, or at least see massively diminished returns that are only partially solved by throwing more compute at them, indicating there's issues with the core design.

It is interesting to consider that no HUMAN can hold the full text of a Novel in their head, they make notes, they have editors to help, and obviously they can refer back to and refine the manuscript itself.

It more likely than not will still fail to rhyme well.

Well this, I'd assume, is because it can't have any way to know what 'rhyming' is in terms of the auditory noises we associate with words, because text doesn't convey that unless you already know the sounds of said words.

Perhaps there'll be some way to overcome that by figuring out how to get a text-to-speech AI and GPT-type AI to work together?

Well this, I'd assume, is because it can't have any way to know what 'rhyming' is in terms of the auditory noises we associate with words, because text doesn't convey that unless you already know the sounds of said words.

Unfortunately, it's a dumber problem than that. Neural nets can pick up a lot of very surprising things from their source data. StableDiffusion can pick up artists and connotations that aren't obvious from its input data, and GPT is starting to 'learn' some limited math despite not being taught what the underlying mathematical symbols are (albeit with some often-sharp limitations). GPT does actually have a near-encyclopedic knowledge of IPA pronunciation, and you can easily prompt it to rewrite whole sentences in phonetic pronunciation. And we're not talking a situation where these programs try to do something rhyme-like and fail, like match up words with large number of letter overlaps without understanding pronunciation. Indeed, one of the limited ways people have successfully gotten rhymes out of it have involved prompting it to explain the pronunciation first. (Though not that this runs into and very quickly fills up the available Attention.) Instead, GPT and GPT-like approaches struggle to rhyme even when trained on a corpus of poetry or limericks: the information is in the training data, it's just inaccessible at the scope the model is working at : either it does transparent copy or it doesn't get very close.

Gwern makes the credible argument that (at least part of) GPT's problem is that it works in fairly weird byte-pair encodings to avoid hitting some of those massively diminishing returns as early as had it been trained on phonetic or character-level minimum units, but at the cost of completely eliminating the ability to handle or even examine certain sub-encoding concepts. It's possible that we'll eventually get enough input data and parameters to just break these limits from an unintuitive angle, but the split from how we suspect human brains handle things may just mean that this scope of BPEs cause bad results in this field and a better work-around needs to be designed (at least where you need these concepts to be examined).

((Other tools using a similar tokenizer have similar constraints.))

How does this work? My understanding was that the only "learning" that took place is when the model is trained on the dataset (which is done only once, requiring a huge amount of computational resources), and any subsequent usage of the model has no effect on the training.

I'm far from an expert here.

If they want to make the AI 'smarter' at the cost of longer/more expensive training, they can add parameters (i.e. variables that the AI considers when interpreting an input and translating it into an output), and more data to train on to better refine said parameters. Very roughly speaking, this is the difference between training the AI to recognize colors in terms of 'only' the seven colors of the rainbow vs. the full palette of Crayola crayons vs. at the extreme end the exact electromagnetic frequency of every single shade and brightness of visible light.

My vague understanding is that the current models are closer to the crayola crayons than to the full electromagnetic frequency.

Tweaking an existing model can also achieve improvements, think in terms of GANs.

If the AI produces an output and receives feedback from a human or another AI as to how well the output satisfices the input, and is allowed to update its own internals based on this feedback, it will become better able to produce outputs that match the inputs.

This is how a model can get refined without needing to completely retrain it from scratch.

Although with diffusion models like DallE, outputs can also be improved by letting the model take more 'steps' (i.e. run it through the model again and again) to refine the output as far as it can.

As far as I know there's very little benefit to manually tweaking the models once they're trained, other than to e.g. implement a NSFW filter or something.

And as we produce and concentrate more computational power, it becomes more and more feasible to use larger and larger models for more tasks.

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