7 ms·

Horse-riding astronaut is a milestone in AI’s journey to make sense of the world

• avmich 5y ago
  > DALL-E 2 is not based on GPT-3. Under the hood, it works in two stages. First, it uses OpenAI’s language-model CLIP, which can pair written descriptions with images, to translate the text prompt into an intermediate form that captures the key characteristics that an image should have to match that prompt (according to CLIP). Second, DALL-E 2 runs a type of neural network known as a diffusion model to generate an image that satisfies CLIP. So, some stages seems to be artificially introduced...
• sgt101 5y ago
  The "a nurse in a hospital" and "a ceo in front of a business" tests show that there are some problems yet to be navigated.
  • vincnetas 5y ago
    can you share a link to these images?
    • sgt101 5y ago
      from :https://github.com/openai/dalle-2-preview/blob/main/system-card.md https://github.com/openai/dalle-2-preview/blob/main/system-c... https://github.com/openai/dalle-2-preview/raw/main/assets/Model2_a_builder.png https://github.com/openai/dalle-2-preview/raw/main/assets/Mo... https://github.com/openai/dalle-2-preview/raw/main/assets/Model2_a_flight_attendant.png https://github.com/openai/dalle-2-preview/raw/main/assets/Mo...
      • sgt101 5y ago
        and https://github.com/openai/dalle-2-preview/raw/main/assets/Model2_nurse.png https://github.com/openai/dalle-2-preview/raw/main/assets/Mo...
      • kingcharles 5y ago
        As someone who works in CG and has a strong interest in AI for almost 40 years, I have to say the examples you gave give me the opposite impression. I think they're fantastic, other than what the developers highlight - that they are racially-biased due to the input sources. But from an image=generation perspective, I'm blown away. A couple of these lawyer images have issues (one is holding a book hilariously called "LAWER"): https://github.com/openai/dalle-2-preview/raw/main/assets/Model2_lawyer.png https://github.com/openai/dalle-2-preview/raw/main/assets/Mo...
        • sgt101 5y ago
          Oh sure the technical outcome is great, but this kind of output (ie. hugely gender biased) is extremely problematic. Saying "oh the input screwed it" is very very naughty. This kind of tool will be used. If it's used it will do damage becuase it's damaging as it is.
          • oh_sigh 5y ago
            If you ask for a nurse as an example, and 85% of nurses are female, shouldn't you expect it to give you a female?
            • 6gvONxR4sf7o 5y ago
              If you ask for a nurse and 85% are female, but 95% of the nurse images online are female, what distribution do you want it to return? “What do you expect it to do” is a worse question than “What do you want it to do?” It’s a problem is that right now there’s only one option (reflect the training set with a bias towards the most common cases).
            • karpierz 5y ago
              It depends on whether you want it to show what someone would expect a nurse to look like, or show what a nurse. Nothing in the definition of a nurse implies female. If you ask for a female US president, should the application simply return a black screen? If you ask for a black US president, would you expect it to only ever return pictures of Barack Obama?
      • drcongo 5y ago
        That page is far more interesting than the article, thanks.
      • dwighttk 5y ago
        Those look okay as long as I open them on my phone, don’t zoom in, and don’t look at any square, but just sort of take them all in at once. Focusing on one reveals they are pretty uncanny.
      • GrantZvolsky 5y ago
        The model is deliberately limited in its ability to generate images of people.
  • deleted 5y ago
    [deleted]
  • brudgers 5y ago
    AI is an ideology, not a technology. Part of the ideology is the ends justify the means. Hence, "AI training specialist" means "works for Amazon Mechanical Turk." Open AI isn't paying people $250k a year to classify images. And $500k a year to check that work. The AI ideology is God will know his own. A racist, misogynist, hillbilly singularity meets success criteria.
    • seizethecheese 5y ago
      This is nonsense. It’s just generating the most likely image. Probabilities are not in and of themselves racist/misogynist.
      • pengstrom 5y ago
        They are the results of human design though. We have to aspire for more than bias laundering, otherwise we risk the biases multiply
      • krapp 5y ago
        Bias in data leads to biased probabilities. Bias in data reflects biases in the systems that data describes, and the people describing it. One could say (and some have said) that the tendency of some facial recognition software not to recognize black faces is not racist - but simply a matter of physics. What's racist is the culture of software engineering which only tests that software on white faces, and doesn't consider it a problem. Likewise the 'most likely image' of a nurse being a non-white female and a CEO or lawyer being a white male is still a problem even if it accurately reflects racial and gender class role biases in society. Eventually these systems will enforce and maintain power structures and media narratives which themselves are already built upon racial and gender prejudice.
        • ARandomerDude 5y ago
          > What's racist is the culture of software engineering which only tests that software on white faces, and doesn't consider it a problem. That's a pretty cynical take. Every project I've ever worked on, regardless of the problem domain, operated with the 80/20 rule in mind. Solve the easy problems first, get something out the door and then work on the harder problems. If dark skin is a harder problem in image recognition, it doesn't mean a developer is racist for solving the easier parts of the problem first.
  • andrepd 5y ago
    So it's cherry picked examples in the demo page that fall apart at the lightest real world case. Just like GPT3 :-)
    • peterlk 5y ago
      This is a rational argument, and I see it a lot, but it does not probe deep enough. GPT-3 use cases "fall apart at the lightest real world case" because our expectations are wrong. For the vast majority of computing history, computers have been used to do things deterministically and accurately. Next-gen AI does not operate in this way. It encroaches on the human part of work where we expect and allow for people to get it wrong. GPT-3 does shockingly well at classification tasks with basically 0 training/prompting (outside of the base model). And it works for an incredibly broad set of use cases. But using QA and generation are much harder to judge because we can't say (in general) whether generated text is "correct"
      • zozbot234 5y ago
        > with basically 0 training/prompting (outside of the base model) That's a heck of a lot of training. It might seem to work if you ask it to repeat stuff that it has seen in the base model, but there's nothing reliable about that. Most attempts to find some practical use for AI language models have basically been failures. > our expectations are wrong. For the vast majority of computing history, computers have been used to do things deterministically and accurately. Actually, accuracy matters even when dealing with non-deterministic, statistical/random/sampled data. A lot of supposed 'AI' is little more than a glorified toy or party trick, founded on ad-hoc data mining rather than rigorous inference from a well-defined model.
  • nonameiguess 5y ago
    Are these deterministic? Is it guaranteed to always generate the average or most likely nurse or will it generate a distribution of nurses roughly reflective of its training data if you repeatedly ask for many nurses? Edit: Never mind. It does eventually show that it will generate different people when repeatedly prompted for the same thing. Given that fact, though, it seems to do a pretty shitty job. It generated 10 apparently female Korean flight attendants. At least in the US, the actual distribution is more like 3/4 male 1/4 female. It's not 100% female, let alone 100% Korean. It does seem to oversample from the most likely examplars, which illustrates a pretty serious failure mode compared to human artists. No human writing a novel is going to make every single character in every novel they write the most likely possible example of its reference class.
• msrenee 5y ago
  Pretty darn impressive. Those "hooves" are probably going to feature in my nightmares though.
  • bitwize 5y ago
    Oh God, what have you done. You have entered the code incorrectly. Look what your careless hands have wrought. Select the pistol, and then, select your horse.
    • CrazyPyroLinux 5y ago
      Penny Arcade, nice!
    • robocat 5y ago
      OT, but for the ignorant like me, here’s the cartoon: https://www.penny-arcade.com/comic/2008/05/26/the-unhorse https://www.penny-arcade.com/comic/2008/05/26/the-unhorse
• sandworm101 5y ago
  >> “Every now and then it generates something that just makes me gasp." I'm amazed to see how easily Intel/AMD are able to cram so many monkey/typewriter combinations onto such tiny chips.
• amelius 5y ago
  Can we get some images that were not cherry picked? What does the average output look like?
  • msla 5y ago
    They can tell you it isn't cherry-picked. Why would you believe them?
  • tartoran 5y ago
    Oh, come on. That would ruin their marketing
  • corysama 5y ago
    Here are some less cherry-picked examples. Mostly from the twitter accounts of OpenAI employees. https://twitter.com/BecomingCritter/status/1511808277490896903 https://twitter.com/BecomingCritter/status/15118082774908969... https://www.reddit.com/r/MediaSynthesis/comments/tyaz70/links_to_80_dalle_2_examples/ https://www.reddit.com/r/MediaSynthesis/comments/tyaz70/link...
  • dvh 5y ago
    I would rather see images labeled "nurse" in their training dataset compared to their generated nurses.
  • moyix 5y ago
    Here's one: https://twitter.com/jmhessel/status/1511783083967586306 https://twitter.com/jmhessel/status/1511783083967586306 And a non-cherry picked depiction of cherry-picking scientists: https://twitter.com/jmhessel/status/1512143226022481932 https://twitter.com/jmhessel/status/1512143226022481932 I think tbh people have been burned by cherry picked results in AI for so long that they're overly suspicious of DALL-E 2...
    • Engineering-MD 5y ago
      It’s interesting that the researcher looks very similar in all images. I would be interested in seeing the training set
• throwaway675309 5y ago
  I'd love to see DALL-E integrated into something like AI dungeon for automatic choose your own adventure picture generation.
  • andreyk 5y ago
    That is already the case! https://twitter.com/nickwalton00/status/1468335326410129409 https://twitter.com/nickwalton00/status/1468335326410129409
• nfc 5y ago
  “If we define understanding as human understanding, then AI systems are very far off,” This took me into the following line of thought. If we wanted AGI we probably should give this neural networks an overarching goal, the same way our intelligence evolved in the presence of overarching goals (survival, reproduction...). It's these less narrow goals that allowed us to evolve our "general intelligence". It's possible that if we are trying to construct AGI through the accumulation of narrow goals we are taking the harder route. At the same time I think we should not pursue AGI the way I'm suggesting is best, too many unknown risks (paperclip problem...) Of course all this begs the question of what is AGI, how we define a good overarching goal to prompt AGI and many more...
  • ahnick 5y ago
    I think the best concise definition I've run across is what I heard Yann LeCun say in his recent interview with Lex Fridman. “The essence of intelligence is the ability to predict.” -Yann LeCun
    • colordrops 5y ago
      That's just the current idea of how the brain works - predictive processing. As we advance our understanding perhaps this will be seen as only one facet of intelligence. For instance, where does creativity fit into this definition?
• dvt 5y ago
  > OpenAI is clear about where it stands. “Our aim is to create general intelligence,” says Dhariwal. “Building models like DALL-E 2 that connect vision and language is a crucial step in our larger goal of teaching machines to perceive the world the way humans do, and eventually developing AGI.” (Emphasis mine.) Philosophical nit: this by definition would not be AGI. AGI would be the ability of the entity to figure out its own subjective qualia-inducing way of interpreting the world. Thomas Nagel already explained this in the 70s (in his famous essay What Is It Like to Be a Bat?). By enforcing our own worldview (technically speaking, model), we're just making some artificial (and in all likelihood inferior) version of a human.
  • chaps 5y ago
    Reread it -- that's not what they're saying. They're suggesting that human-like perception is a stepping stone (ie, not necessarily a linear path towards). It reads to me as if they're acknowledging a significant gap between the two.
    • dvt 5y ago
      > They're suggesting that human-like perception is a stepping stone I fully understand the discourse; my point is that it's not (Nagel, Chalmers, Searle†, and other substance dualists would agree). We need a breakthrough akin to what analysis was to algebra in neuroscience before we even understand what our brain is doing. Whatever it's doing, it's definitely not what OpenAI implemented. † Yes, I know he says he isn't, but he kind of is[1]. [1] http://www.edwardfeser.com/unpublishedpapers/searle.html http://www.edwardfeser.com/unpublishedpapers/searle.html
      • hackinthebochs 5y ago
        >Whatever it's doing, it's definitely not what OpenAI implemented. Defining intelligence as "the thing brains do" is a mistake. Planes fly, but not like birds. Biology is a great tool to understand some relationships between form and function, but mimicking biology isn't the end-goal.
• irrational 5y ago
  > ask it to generate images of astronauts on horses, teddy-bear scientists, or sea otters in the style of Vermeer, and it does so with near photorealism. I'm now imagining a mashup of Scribblenauts and this AI image generator.
• colordrops 5y ago
  I hate how OpenAI got a lot of their public name and attention through the original benefactors that started it and their pledge of opening up AI research to the public, and now they are basically the opposite - monetized opaque controlled systems. Really off-putting.
  • skybrian 5y ago
    They do publish papers. I'm not sure they were ever about providing open source implementations? AI safety was their thing.
    • colordrops 5y ago
      I could be misremembering but I believe the idea was that AI advancement was going to happen anyway so open it up and put it into the hands of the people instead of a few power brokers who would abuse it.
    • TechBro8615 5y ago
      It’s a good thing our benefactors at OpenAI are looking out for “AI safety” so we can’t do things like ask a computer to make a graphically violent image or verbally racist string of text.
  • marvin 5y ago
    They just chose a misleading name in the first place. It's pretty obvious that they couldn't properly pursue their core mission as a purely open research organization. The desired end result is too valuable. And if they end up getting close to what they're really after, it's really undesirable to get an arms race where solely OpenAI's contributions are known to everyone.
    • T-A 5y ago
      > They just chose a misleading name in the first place. It wasn't just the name, it was the whole rationale they presented for creating OpenAI. Nick Bostrom was having his 15 minutes of fame with "Superintelligence" [1] and scenarios like a an "AI arms race" and the sudden emergence of a super-intelligent singleton [2] were legion. OpenAI was founded on the premise that making leading edge AI research public was the best way to mitigate that risk [3]. It was presented to the world as a "research institution which can prioritize a good outcome for all over its own self-interest" where "Researchers will be strongly encouraged to publish their work, whether as papers, blog posts, or code, and our patents (if any) will be shared with the world" [4]. That lasted all of three years. [1] https://en.wikipedia.org/wiki/Superintelligence:_Paths,_Dangers,_Strategies https://en.wikipedia.org/wiki/Superintelligence:_Paths,_Dang... [2] https://www.nickbostrom.com/fut/singleton.html https://www.nickbostrom.com/fut/singleton.html [3] https://en.wikipedia.org/wiki/OpenAI#Strategy https://en.wikipedia.org/wiki/OpenAI#Strategy [4] https://openai.com/blog/introducing-openai/ https://openai.com/blog/introducing-openai/
• mensetmanusman 5y ago
  How many kWhr does it take to train?
• mrjin 5y ago
  Is it Artificial Intelligence or rather Artificial Idiocy?