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In many of its outputs, it's doing far more than stretching, and essentially rotating new entities into scene-appropriate relationships. True, it has some prob
by gojomo 4y ago
In many of its outputs, it's doing far more than stretching, and essentially rotating new entities into scene-appropriate relationships.
True, it has some problems fully abstracting, and then logically-enforcing, some object-to-object relationships that most people are trivially able to apply as 'acceptance tests' on candidate images. That is evidence its scene-understanding is not yet at human-level, in that aspect – even as it's exceeded human-level capabilities in other aspects.
Whether this is inherent or transitory remains to be seen. The current publicly-available renderers tend to have a hard time delivering requested meaningful text in the image. But Google's PARTI claims that simply growing the model fixes this: see, for example: https://twitter.com/gojomo/status/1540095089615089665 https://twitter.com/gojomo/status/1540095089615089665
We also should be careful using DALL-E as an accurate measure of what's possible, because OpenAI has intentionally crippled their offering in a number of ways to avoid scaring or offending people, under the rubric of "AI safety". Some apparent flaws might be intentional, or unintentional, results of the preferences of the designers/trainers.
Ultimately, I understand the practicality of setting tangible tests of the form, "To say an agent 'understands', it MUST be able to X".
However, to be honest in perceiving the rate-of-progress, we need to give credit when agents defeat all the point-in-time MUSTs, and often faster than even optimists expected. At that point, searching for new MUSTs that agent fails at is a valuable research exercise, but retroactively adding such MUSTs to the definition of 'understanding' risks self-deception. "It's still not 'understanding' [under a retconned definition we specifically updated with novel tough cases, to comfort us about it crushing all of our prior definition's MUSTs]." It obscures giant (& accelerating!) progress under a goalpost-moving binary dismissal driven by motivated-reasoning.
This is especially the case as the new MUSTs increasingly include things many, or most, humans don't reliably do! Be careful who your rules-of-thumb say "can't possibly be coceptually intelligent", lest you start unpersoning lots of humanity.
- joe_the_user 4y agoI read a modest amount of popular "AI safety" text (lesswrong basically) and this is the first time I've heard of Dall-E being crippled for safety reasons. Your argument overall seems to take "you skeptics keep moving the bar, give me a benchmark I can pass and I'll show you", which seems reasonable on it's face but I don't think actually works. The problem is that while algorithm may be defined by theory and tested by benchmark, the only "definition" we have for general intelligence except "what we can see people doing". If I or anyone had a clear, accepted benchmark for general intelligence, we'd be quite a bit further towards creating it but we're not there. That said, I think one thing that current AIs lack is an understanding of it's own processing and an understanding of the limits of that processing. And there are many levels of this. But I won't promise that if this problem is corrected, I won't look at other things. IDK, achieving AGI isn't like just passing some test, no reason it should be like that.
- astrange 4y ago> I read a modest amount of popular "AI safety" text (lesswrong basically) and this is the first time I've heard of Dall-E being crippled for safety reasons. You can read OpenAI's paper or try using it. They've intentionally not taught it many things; it doesn't know most celebrities' names, or copyrighted characters, and there's several filters before and after the big model to prevent NSFW generations of any type. (Oddly, it does know "Homer Simpson" and "Hatsune Miku".)
- joe_the_user 4y agoAh, OK That sound data sanitizing rather than the AI safety/danger that Bostrom and company worry about. For them, it's "OMG, must limit it's capabilities" rather than "Must keep it from doxing or offending people". The parent I was replying to was kind of ambiguous what sort of crippling they meant (We also should be careful using DALL-E as an accurate measure of what's possible, because OpenAI has intentionally crippled their offering in a number of ways to avoid scaring or offending people, under the rubric of "AI safety".)
- gojomo 4y agoSome of what they've done is described here – https://openai.com/blog/reducing-bias-and-improving-safety-in-dall-e-2/ https://openai.com/blog/reducing-bias-and-improving-safety-i... – & in other statements. I'm not sure they've claimed, or we can be sure, that every one of their training or output limits has been disclosed. It seems one of their techniques is to silently add extra words to some prompts to increase the variety of returned images in certain dimensions, eg: https://twitter.com/rzhang88/status/1549472829304741888 https://twitter.com/rzhang88/status/1549472829304741888 While I can't find the link right now, someone also tweeted examples of asking DALL-E2 for certain historical roles that would have been, by actual history and likely representation in training data, fairly uniform in race/sex – but the new OpenAI 'bias mitigations' generate ahistorical race/gender-varied examples. That's interesting – I'm a big fan of non-traditional castings like 'Hamilton!' etc – but serves to make the AI look ignorant of history, when in fact it has just been design-limited to simulate ignorance.