9 ms·

Limitations of DALL-E

213 points by amrrs 5y ago

bricemo 5y ago
Great roundup. AI are great at technical optimization problems (for example, reducing the usage of electricity in a data center). For creative and subjective works, it seems like a machine-human hybrid model will be the way to go for the next decade. The AI spits out some rough drafts, then the human artist picks it up from there. All of these last-mile problems would be trivial for an artist to touch up.
- lumost 5y ago
  I suspect that we'll see a big wave of Large Transformer models getting built into high cost professional tools. Need to write some random background NPC dialogue for a game? Need to generate a draft Cad design for "Airframe supporting a large turbojet engine with X newtons thrust"? These are problems where the high cost of inference won't be significant, and there is an expert available to revise and discard the result if its not working. I'd be curious if there is a good startup model for anything in this space, seems like you would already need to be a vendor of the underlying technical software to make a viable business (CAD,Game Engines,Creative Tools etc.)
  - flywithit 5y ago
    Sounds about right based on my experience with GitHub copilot. It does make me fear a little bit. I mean I’m not experienced with cad but I could see you might train on a particular cad program, that program may have undiscovered bug X that allows a model to pass all the synthetic tests while optimizing for something like cost (which goes undiscovered because no human would ever design in such a way)… the reviewing engineers rubber stamp under pressure and then bam, 9/10 airframes fall apart after 1,000 miles. That’s the copilot analogue at least.
- maroonblazer 5y ago
  Yes, and that could inspire a massive creative explosion. One application I'm pining for: I'd love Apple to introduce AI into Logic Pro X (LPX) such that it could take a 4, 6, 8 or 16 bar set of tracks and generate infinite variations, from which I can pick and choose ones to embellish. Or, similarly, I have innumerable LPX projects that are just a single idea that, in the hands of someone more capable (or with more motivation) than myself, could be turned into full pieces. I'd love to be able to give LPX 2 or more of these projects and it generates transitional pieces between them. All LPX upgrades are free, but I'd happily pay for this feature.
phaedrus 5y ago
Some of the proposed models for human thought can't handle negation. An example of this is map-based models. Theories which propose human thought is a form of a production system (as in logic) get a boost from the fact they can explain negation as well as other things. So to see DALL-E can't handle negation is, to my thinking, an important difference between the way it "thinks" (or what passes for that) and how we do.
- astrange 5y ago
  Its training data might not have negations in it - I'm not sure anyone has tried to explain what CLIP understands of text. Besides that, we know it doesn't because models don't recurse because no matter how big they are, they still have constant execution times. And we gave up on systematically parsing text in favor of just vibing anyway (technical term). That's just as well since most parsers didn't really work in practice; I think I vaguely remember one as kind of working (https://www.link.cs.cmu.edu/link/ https://www.link.cs.cmu.edu/link/) but it can't handle bad grammar or misspellings like ML can.
  - visarga 5y ago
    That's not true, the autoregressive LMs (GPTs) can generate variable length outputs allowing for step by step computation. Recent papers get them to generate the full chain of thought reasoning process to the solution (also called rationales in some papers), improving accuracy. But this version of Dall-E is not autoregressive, they've gone for diffusion models in the generator and for a single vector embedding (no internal spatial grid structure) for the encoder. That explains why it's bad at negations, counting and stacking coloured blocks. I believe these skills were not the top priority of OpenAI this time, they understandably went for pretty images.
    - astrange 5y ago
      I agree GPT as a whole generates variable length outputs, but the model isn't generating them in a single pass, is it? I thought there was some sampling process implemented in traditional code outside the model that made it do that, and it wasn't learned. Have forgotten the details. If Dall-E took variable length sequences as inputs, its execution time would still be O(N) on the length of the sequence and it would still have a fixed amount of memory to work with. Whereas a computer program of length N can do pretty much anything, and a human given a description can go off and think about it.
atty 5y ago
It’s kind of crazy to think that Alex-net was just ~10 years ago, and look at what DALL-E can do now. I can’t imagine what the ML landscape is going to look like in another 10-20 years. Excited to be working in the field to experience it all first hand.
- sydthrowaway 5y ago
  Is it too late to join?
  - kanonieer 5y ago
    Better today than tomorrow.
  - natly 5y ago
    You only have one life
  - delusional 5y ago
    Now is the perfect time to join. There's tons of open chat-rooms filled with people excited about the space. Computing resources are basically thrown at you. I'm not an ML guy, but I find the terminology and explanations very approachable. If you want to get started I can recommend the EleutherAI[1] discord. It seems to have a good mix of experience and excitement. [1]: https://www.eleuther.ai/ https://www.eleuther.ai/
    - sydthrowaway 5y ago
      Hmm, I’d feel like a johnny come lately
    - webmaven 5y ago
      > Now is the perfect time to join. There's tons of open chat-rooms filled with people excited about the space. Computing resources are basically thrown at you. Any pointers on where one has to stand in order to be targeted by said computing resources?
  - 5y ago
cl42 5y ago
So what? I too wouldn't know how to draw anything related to "ophanim" unaided by a dictionary. I'd also argue that its ability to come up with symbols that look oddly like letters but aren't actual letters could be a form of creativity. Don't get me wrong, I'm not a GPT-3/DALL-E fanboy but I don't get why this is interesting. Anyone who has worked with a creative designer/artist/architect/etc. would know that there are so many "wrong answers" when it comes to creating visual imagery. Some of these aren't limitations of DALL-E but rather misinterpretations of our own expectations.
- balaji1 5y ago
  It would have been cool if there were a few tweets with the caveat of "tho here's what DALL-E is good at". Maybe the twitter account has showcased that separately. otherwise so what if DALL-E has limitations on some weird obscure prompts. As I am hearing about DALL-E the first time, I am still impressed with the images in this tweet thread.
- jonas21 5y ago
  When something is new and mysterious, knowing what it is not is just as interesting as knowing what it is.
  - visarga 5y ago
    True, but the "blue cube over the red cube" limitation is caused by a deliberate choice to maximise other abilities of the model at the expense of exact word for word attribute memory. The reason is that CLIP is encoding the text to a simple fixed length vector, the more text you put in the more gets ignored.
- Vetch 5y ago
  Such failures are interesting, in the physicist's sense. That it struggles with composition sometimes but not other times might indicate model has learned a particular distribution factorization which is good much of the time but makes it unable to properly account for conditional dependencies in certain queries. The issue of losing track of lots of details may also be related. Its struggles with negation is also fascinating because that has been a thorn since the early days of GOFAI. Negation is trickier and more involved than what one might naively assume. For those taking relief from such weaknesses, it's worth remembering that this is as bad as it is going to be and probably not for long. Model size is surprisingly small when compared to recent behemoths. That it can already do all that it does is certainly cause for concern.
yosito 5y ago
As someone who appreciates the craftsmanship and care that goes into human-created art, I found the abilities of DALL-E to be a little distressing. These limitations really point out how much DALL-E is still very different from humans, in a way that I find reassuring. Human reasoning still seems to be out of the reach of these kinds of models. What they really seem to be is very very good imitators which, after being trained on very large amounts of data, can imitate what they've seen. You could make an argument that that's essentially what humans do too, just in a more complex way. But I think it underscores why humans and machines will always be different. We'll always be trained on different inputs, and have our own unique way of combining those inputs, even as the abilities of AI rival and even surpass our own in many ways.
- ravi-delia 5y ago
  Honestly, I'd assume that pretty much any of these flaws are more fixable than you'd expect. We probably won't hit upon the right way for a while, since large models are so expensive, but with some trickery on the architectural level and bigger models, they'll spit out better and better art until it's indistinguishable from human level. Most of your brain is doing the same kind of dumb learning these models are, just much better and faster. There are some fundamental architectural differences, especially relating to the higher-level functions we associate with "consciousness" (working and short term memory, attention, maybe the executive?), but for almost every task you stick with the big dumb blob of compute and it gets the job done better than your higher faculties ever could. Don't underestimate the extent to which your brain plows through difficulties by throwing neurons at the problem until it goes away. Our biggest models are still a fraction of the size and a smaller fraction of the complexity of the bits of brain which perform the same function. I won't rule out anything until we see models of that scale.
  - Jimmy 5y ago
    >Honestly, I'd assume that pretty much any of these flaws are more fixable than you'd expect. The first 90% of the project takes 90% of the time, the last 10% of the project takes the other 90% of the time. I don't think it's wise to speculate at this point on what problems are "actually easy" or "actually hard", or how long it'll take to achieve certain milestones. I don't think that even the people at the forefront of this research have any way of knowing for sure.
viraptor 5y ago
It's interesting how some of them are described as limitations. I can't understand the designer photograph prompt either, and not just because of the "operated" part. The mazes look great to me. And the million apples look like what I'd expect from a photo called "a million apples". If it learns from humans and produces results like humans would... What's the definition of "limitation" here?
- aetherson 5y ago
  The maze that looks more or less like a maze at first glance is full of disconnected sections that can't be reached by each other. It's missed a really essential part of being a maze. (The hedge maze doesn't look like a hedge maze at all: I'm kind of surprised that DALL-E isn't able to figure out that consistent width of the passages is a part of hedge mazes). The designer photograph one is complicated and I'm not surprised that DALL-E misunderstand it. But you're cherry-picking for the apples. It didn't consistently give an picture of five apples when asked for five apples. When asked for a bowl of fruit with no apples, it gives a bowl of only apples. That's obviously strongly sub-human comprehension.
  - viraptor 5y ago
    I totally picked the examples - some are definitely deficiencies, but I wanted to concentrate on the debatable ones. A counterpoint to the maze - it depends what we expect. If you wanted a maze designed, it would be of course a bad example. But if you wanted one painted, it's... good? If it was in a gallery, it could easily be a commentary on life with limitations, dead ends and problems we can't solve.
    - 2muchcoffeeman 5y ago
      I’d argue an unsolvable maze is art only if the artist can conceive of and understands the metaphor. Or can at least comprehend that they are doing mazes with no solution. The computer has no understanding of what it’s doing. And I don’t think you can argue that it’s interrupting a brief for a commission. Is it even producing at???
mortehu 5y ago
An important caveat is that the model isn't actually limited to generating full images from a single prompt, even if this perhaps how the author is interacting with it. The model can also be used for editing existing images, so you would be able to stack prompts while choosing the best intermediate results.
- aetherson 5y ago
  The author uses that in one of his examples (the ermine lady).
  - mortehu 5y ago
    It seemed to me that was still just a single prompt, based on a pre-existing image painted by a person. Though it uses the same functionality.
pyentropy 5y ago
I don't get it? Most of the "limitation" examples are on par with humans, both linguistically and artistically. So much so that it feels like a DALL-E ad :)
- aetherson 5y ago
  ...you know a lot of people who can't count past four? Who don't understand the concept of negation? Who don't understand what a modern supercart aisle looks like? Who lose track of elements in a description after four or five? Who can't get that when you ask for a "monk and a skeleton," you don't mean that the monk is also a skeleton? Who literally don't know what letters are?
  - DavidSJ 5y ago
    A lot of people can't subitize past four. Counting is another matter: it's a symbolic, deliberate, step-by-step activity. Arguably most neural networks don't do symbolic, step-by-step reasoning "natively", and maybe human beings don't either. But they can be told to, and then the same network becomes much smarter: https://arxiv.org/abs/2201.11903 https://arxiv.org/abs/2201.11903
  - gus_massa 5y ago
    > Who don't understand the concept of negation? I teach students of the first year of the university, and plenty of them have problems with reading comprehension, in particular to understand sentences with negations.
    - Filligree 5y ago
      This, pretty much. I sometimes feel we might be underestimating AI because most people on HN are well above average.
  - rolisz 5y ago
    What comes to mind is all those cakes with bad inscriptions. First Google result: https://images.app.goo.gl/JZtBVCwmCFfcmV6F8 https://images.app.goo.gl/JZtBVCwmCFfcmV6F8 Yeah, a lot of people seem to have problems interpreting instructions for drawings.
smortaz 5y ago
is there a hosted version of dall-e that people can play with? free or paid. thanks!
- marc_io 5y ago
  Not specifically DALL-E, but you can play with MindsEye beta, which is quite easy to run, considering the complexity of all this stuff: https://multimodal.art/mindseye https://multimodal.art/mindseye It doesn't get close to the quality of the art pictures produced using DALL-E, but it's nice to play with.
  - smortaz 5y ago
    thx will try!
- tmountain 5y ago
  No, it’s something we will keep hearing about and nobody will get to try it. OpenAI is anything but open. Looks like great tech, but it’s irrelevant if it’s totally inaccessible.
  - smortaz 5y ago
    :). i see seemingly random people keep posting their experiments with it. i guess they’re all in the inner circle or something
dqpb 5y ago
Why does this need to be a thread of tweets embedded in tweets?
- Sebb767 5y ago
  Because a normal thread can be made usable by things like threadreader and that obviously undermines the whole point of blogging on Twitter ;-)
rasz 5y ago
I would rather see a research into how much of "learned" material is it regurgitating ala Copilot verbatim Carmacks fast inverse square root.
- cycrutchfield 5y ago
  That’s not too hard, just compare generated images to similar images from the training data.
  - native_samples 5y ago
    It's pretty clear from the outputs that it's been heavily trained on stock photo libraries. Or maybe that's just the material it extracted the most info from in the training set. Stock image libraries have enormous numbers of high quality, high res images annotated with simple labels so they're ideal for this type of training task. I'm actually surprised nobody remarked on this yet because the resemblance is so strong. E.g. look at what it outputs for welcome signs. It can do a great job for that. Many of the images have artistic use of focus, perspective and blur. That's because: https://www.shutterstock.com/search/photo+of+a+welcome+sign https://www.shutterstock.com/search/photo+of+a+welcome+sign Likewise the "omg ai bias" examples are easily explainable as being due to training on stock photo libraries: https://www.shutterstock.com/search/photo+of+a+nurse https://www.shutterstock.com/search/photo+of+a+nurse Every single output they get for "photo of a <job>" looks exactly like what you'd get from stock photos. This might also be why it struggles with counting. https://www.shutterstock.com/search/an+apple https://www.shutterstock.com/search/an+apple Easy. https://www.shutterstock.com/search/5+apples https://www.shutterstock.com/search/5+apples Hmmm ..... not so easy. The search is good: the results that actually have apples in them tend to have five, but, the actual labels don't include the counts. Likewise with negation. How many images are annotated with "photo of a man NOT running"? My guess is, nearly none.
    - cycrutchfield 5y ago
      Hmm my assumption is that they heavily curated the training images and captions with human labelers. I doubt for something like this they would just scrape stock images and use it directly.
dredmorbius 5y ago
https://nitter.kavin.rocks/benjamin_hilton/status/1520032772072607747 https://nitter.kavin.rocks/benjamin_hilton/status/1520032772...
aantix 5y ago
You can see the future of search.. No longer is it a description of what currently exists, but an expression of what's been queried. Which may demonstrate something entirely different.
- grumbel 5y ago
  Yep, that's the truly impressive part with DALL-E2. If you take those sentences and paste them into Google Images, the results will always be much worse. Even if you find a query for which there do exist closely matching images, they will still look worse. Meanwhile if you do it in reverse, take an exist image and try to recreate it in DALL-E2, you can get extremely closer. Over on Reddit they did a experiment: https://old.reddit.com/r/dalle2/comments/ub0sfg/dalle_2_imitation_game_results_check_sticky_for/ https://old.reddit.com/r/dalle2/comments/ub0sfg/dalle_2_imit...
sydthrowaway 5y ago
I wonder what Filip Pieknewski thinks of DALL-E
Jack000 5y ago
Most of these stem from the way DALLE-2 is designed - in particular the composition, counting, negation and concept blending examples. In the paper they explain that these aspects were deliberately sacrificed in order to improve image quality and diversity, and for the ability to generate variations of an existing image. These aspects were much better in the prior version (GLIDE), which conditioned on text tokens instead of just a fixed length vector.
- deleted 5y ago
  [deleted]
Al-Khwarizmi 5y ago
It's very interesting, but for me, the fact that one has to resort to very complicated sentences, rare words or ambiguous words to show limitations (and even then, they're only near misses, not utter failures) is a testimony to how amazing DALL-E is.
makerofthings 5y ago
DALL-E is way better than me. Sure, I can count past four and I know what letters are, but I can’t draw a wizard koala as good as that. Also, there is criticism of how it draws faces, but it’s way better than I am.
- grumbel 5y ago
  Also when it comes to faces, that's intentional. Trying to generate realistic looking humans is explicitly forbidden, just like politics, violence, nudity and other stuff. Images matching those topics are likely filtered out of the training set (it's no public, so no idea what is actually in it). It still has some ideas how humans look like, but that seems to come from classic paintings, not real world photos of humans.
lqet 5y ago
> e.g. I had to try this one (“a medieval painting of a monk and a skeleton drinking together in a tavern”) a few times to get the monk to not be a skeleton! And he still looks a bit skeleton-y. Well, the prompt is actually a bit vague - is it a monk sitting together with a skeleton, or is it a single entity that is both a monk and a skeleton? Seems like DALL-E tried to find some middle ground.
- webmaven 5y ago
  > Well, the prompt is actually a bit vague - is it a monk sitting together with a skeleton, or is it a single entity that is both a monk and a skeleton? Seems like DALL-E tried to find some middle ground. My thought exactly. I'd be interested to know whether any attempts were made at explicit disambiguation (eg. "A monk sitting beside a skeleton", etc.).
siruncledrew 5y ago
All these posts about DALL-E are making me so excited to try it if I ever make it through the waitlist.

213 points by amrrs 5y ago

Great roundup. AI are great at technical optimization problems (for example, reducing the usage of electricity in a data center). For creative and subjective works, it seems like a machine-human hybrid model will be the way to go for the next decade. The AI spits out some rough drafts, then the human artist picks it up from there. All of these last-mile problems would be trivial for an artist to touch up.

lumost 5y ago
I suspect that we'll see a big wave of Large Transformer models getting built into high cost professional tools. Need to write some random background NPC dialogue for a game? Need to generate a draft Cad design for "Airframe supporting a large turbojet engine with X newtons thrust"? These are problems where the high cost of inference won't be significant, and there is an expert available to revise and discard the result if its not working. I'd be curious if there is a good startup model for anything in this space, seems like you would already need to be a vendor of the underlying technical software to make a viable business (CAD,Game Engines,Creative Tools etc.)
- flywithit 5y ago
  Sounds about right based on my experience with GitHub copilot. It does make me fear a little bit. I mean I’m not experienced with cad but I could see you might train on a particular cad program, that program may have undiscovered bug X that allows a model to pass all the synthetic tests while optimizing for something like cost (which goes undiscovered because no human would ever design in such a way)… the reviewing engineers rubber stamp under pressure and then bam, 9/10 airframes fall apart after 1,000 miles. That’s the copilot analogue at least.
maroonblazer 5y ago
Yes, and that could inspire a massive creative explosion. One application I'm pining for: I'd love Apple to introduce AI into Logic Pro X (LPX) such that it could take a 4, 6, 8 or 16 bar set of tracks and generate infinite variations, from which I can pick and choose ones to embellish. Or, similarly, I have innumerable LPX projects that are just a single idea that, in the hands of someone more capable (or with more motivation) than myself, could be turned into full pieces. I'd love to be able to give LPX 2 or more of these projects and it generates transitional pieces between them. All LPX upgrades are free, but I'd happily pay for this feature.

phaedrus 5y ago

Some of the proposed models for human thought can't handle negation. An example of this is map-based models. Theories which propose human thought is a form of a production system (as in logic) get a boost from the fact they can explain negation as well as other things. So to see DALL-E can't handle negation is, to my thinking, an important difference between the way it "thinks" (or what passes for that) and how we do.

astrange 5y ago
Its training data might not have negations in it - I'm not sure anyone has tried to explain what CLIP understands of text. Besides that, we know it doesn't because models don't recurse because no matter how big they are, they still have constant execution times. And we gave up on systematically parsing text in favor of just vibing anyway (technical term). That's just as well since most parsers didn't really work in practice; I think I vaguely remember one as kind of working (https://www.link.cs.cmu.edu/link/ https://www.link.cs.cmu.edu/link/) but it can't handle bad grammar or misspellings like ML can.
- visarga 5y ago
  That's not true, the autoregressive LMs (GPTs) can generate variable length outputs allowing for step by step computation. Recent papers get them to generate the full chain of thought reasoning process to the solution (also called rationales in some papers), improving accuracy. But this version of Dall-E is not autoregressive, they've gone for diffusion models in the generator and for a single vector embedding (no internal spatial grid structure) for the encoder. That explains why it's bad at negations, counting and stacking coloured blocks. I believe these skills were not the top priority of OpenAI this time, they understandably went for pretty images.
  - astrange 5y ago
    I agree GPT as a whole generates variable length outputs, but the model isn't generating them in a single pass, is it? I thought there was some sampling process implemented in traditional code outside the model that made it do that, and it wasn't learned. Have forgotten the details. If Dall-E took variable length sequences as inputs, its execution time would still be O(N) on the length of the sequence and it would still have a fixed amount of memory to work with. Whereas a computer program of length N can do pretty much anything, and a human given a description can go off and think about it.

atty 5y ago

It’s kind of crazy to think that Alex-net was just ~10 years ago, and look at what DALL-E can do now. I can’t imagine what the ML landscape is going to look like in another 10-20 years. Excited to be working in the field to experience it all first hand.

sydthrowaway 5y ago
Is it too late to join?
- kanonieer 5y ago
  Better today than tomorrow.
- natly 5y ago
  You only have one life
- delusional 5y ago
  Now is the perfect time to join. There's tons of open chat-rooms filled with people excited about the space. Computing resources are basically thrown at you. I'm not an ML guy, but I find the terminology and explanations very approachable. If you want to get started I can recommend the EleutherAI[1] discord. It seems to have a good mix of experience and excitement. [1]: https://www.eleuther.ai/ https://www.eleuther.ai/
  - sydthrowaway 5y ago
    Hmm, I’d feel like a johnny come lately
  - webmaven 5y ago
    > Now is the perfect time to join. There's tons of open chat-rooms filled with people excited about the space. Computing resources are basically thrown at you. Any pointers on where one has to stand in order to be targeted by said computing resources?
- 5y ago

cl42 5y ago

So what? I too wouldn't know how to draw anything related to "ophanim" unaided by a dictionary. I'd also argue that its ability to come up with symbols that look oddly like letters but aren't actual letters could be a form of creativity. Don't get me wrong, I'm not a GPT-3/DALL-E fanboy but I don't get why this is interesting. Anyone who has worked with a creative designer/artist/architect/etc. would know that there are so many "wrong answers" when it comes to creating visual imagery. Some of these aren't limitations of DALL-E but rather misinterpretations of our own expectations.

balaji1 5y ago
It would have been cool if there were a few tweets with the caveat of "tho here's what DALL-E is good at". Maybe the twitter account has showcased that separately. otherwise so what if DALL-E has limitations on some weird obscure prompts. As I am hearing about DALL-E the first time, I am still impressed with the images in this tweet thread.
jonas21 5y ago
When something is new and mysterious, knowing what it is not is just as interesting as knowing what it is.
- visarga 5y ago
  True, but the "blue cube over the red cube" limitation is caused by a deliberate choice to maximise other abilities of the model at the expense of exact word for word attribute memory. The reason is that CLIP is encoding the text to a simple fixed length vector, the more text you put in the more gets ignored.
Vetch 5y ago
Such failures are interesting, in the physicist's sense. That it struggles with composition sometimes but not other times might indicate model has learned a particular distribution factorization which is good much of the time but makes it unable to properly account for conditional dependencies in certain queries. The issue of losing track of lots of details may also be related. Its struggles with negation is also fascinating because that has been a thorn since the early days of GOFAI. Negation is trickier and more involved than what one might naively assume. For those taking relief from such weaknesses, it's worth remembering that this is as bad as it is going to be and probably not for long. Model size is surprisingly small when compared to recent behemoths. That it can already do all that it does is certainly cause for concern.

yosito 5y ago

As someone who appreciates the craftsmanship and care that goes into human-created art, I found the abilities of DALL-E to be a little distressing. These limitations really point out how much DALL-E is still very different from humans, in a way that I find reassuring. Human reasoning still seems to be out of the reach of these kinds of models. What they really seem to be is very very good imitators which, after being trained on very large amounts of data, can imitate what they've seen. You could make an argument that that's essentially what humans do too, just in a more complex way. But I think it underscores why humans and machines will always be different. We'll always be trained on different inputs, and have our own unique way of combining those inputs, even as the abilities of AI rival and even surpass our own in many ways.

ravi-delia 5y ago
Honestly, I'd assume that pretty much any of these flaws are more fixable than you'd expect. We probably won't hit upon the right way for a while, since large models are so expensive, but with some trickery on the architectural level and bigger models, they'll spit out better and better art until it's indistinguishable from human level. Most of your brain is doing the same kind of dumb learning these models are, just much better and faster. There are some fundamental architectural differences, especially relating to the higher-level functions we associate with "consciousness" (working and short term memory, attention, maybe the executive?), but for almost every task you stick with the big dumb blob of compute and it gets the job done better than your higher faculties ever could. Don't underestimate the extent to which your brain plows through difficulties by throwing neurons at the problem until it goes away. Our biggest models are still a fraction of the size and a smaller fraction of the complexity of the bits of brain which perform the same function. I won't rule out anything until we see models of that scale.
- Jimmy 5y ago
  >Honestly, I'd assume that pretty much any of these flaws are more fixable than you'd expect. The first 90% of the project takes 90% of the time, the last 10% of the project takes the other 90% of the time. I don't think it's wise to speculate at this point on what problems are "actually easy" or "actually hard", or how long it'll take to achieve certain milestones. I don't think that even the people at the forefront of this research have any way of knowing for sure.

viraptor 5y ago

It's interesting how some of them are described as limitations. I can't understand the designer photograph prompt either, and not just because of the "operated" part. The mazes look great to me. And the million apples look like what I'd expect from a photo called "a million apples". If it learns from humans and produces results like humans would... What's the definition of "limitation" here?

aetherson 5y ago
The maze that looks more or less like a maze at first glance is full of disconnected sections that can't be reached by each other. It's missed a really essential part of being a maze. (The hedge maze doesn't look like a hedge maze at all: I'm kind of surprised that DALL-E isn't able to figure out that consistent width of the passages is a part of hedge mazes). The designer photograph one is complicated and I'm not surprised that DALL-E misunderstand it. But you're cherry-picking for the apples. It didn't consistently give an picture of five apples when asked for five apples. When asked for a bowl of fruit with no apples, it gives a bowl of only apples. That's obviously strongly sub-human comprehension.
- viraptor 5y ago
  I totally picked the examples - some are definitely deficiencies, but I wanted to concentrate on the debatable ones. A counterpoint to the maze - it depends what we expect. If you wanted a maze designed, it would be of course a bad example. But if you wanted one painted, it's... good? If it was in a gallery, it could easily be a commentary on life with limitations, dead ends and problems we can't solve.
  - 2muchcoffeeman 5y ago
    I’d argue an unsolvable maze is art only if the artist can conceive of and understands the metaphor. Or can at least comprehend that they are doing mazes with no solution. The computer has no understanding of what it’s doing. And I don’t think you can argue that it’s interrupting a brief for a commission. Is it even producing at???

mortehu 5y ago

An important caveat is that the model isn't actually limited to generating full images from a single prompt, even if this perhaps how the author is interacting with it. The model can also be used for editing existing images, so you would be able to stack prompts while choosing the best intermediate results.

aetherson 5y ago
The author uses that in one of his examples (the ermine lady).
- mortehu 5y ago
  It seemed to me that was still just a single prompt, based on a pre-existing image painted by a person. Though it uses the same functionality.

pyentropy 5y ago

I don't get it? Most of the "limitation" examples are on par with humans, both linguistically and artistically. So much so that it feels like a DALL-E ad :)

aetherson 5y ago
...you know a lot of people who can't count past four? Who don't understand the concept of negation? Who don't understand what a modern supercart aisle looks like? Who lose track of elements in a description after four or five? Who can't get that when you ask for a "monk and a skeleton," you don't mean that the monk is also a skeleton? Who literally don't know what letters are?
- DavidSJ 5y ago
  A lot of people can't subitize past four. Counting is another matter: it's a symbolic, deliberate, step-by-step activity. Arguably most neural networks don't do symbolic, step-by-step reasoning "natively", and maybe human beings don't either. But they can be told to, and then the same network becomes much smarter: https://arxiv.org/abs/2201.11903 https://arxiv.org/abs/2201.11903
- gus_massa 5y ago
  > Who don't understand the concept of negation? I teach students of the first year of the university, and plenty of them have problems with reading comprehension, in particular to understand sentences with negations.
  - Filligree 5y ago
    This, pretty much. I sometimes feel we might be underestimating AI because most people on HN are well above average.
- rolisz 5y ago
  What comes to mind is all those cakes with bad inscriptions. First Google result: https://images.app.goo.gl/JZtBVCwmCFfcmV6F8 https://images.app.goo.gl/JZtBVCwmCFfcmV6F8 Yeah, a lot of people seem to have problems interpreting instructions for drawings.

smortaz 5y ago

is there a hosted version of dall-e that people can play with? free or paid. thanks!

marc_io 5y ago
Not specifically DALL-E, but you can play with MindsEye beta, which is quite easy to run, considering the complexity of all this stuff: https://multimodal.art/mindseye https://multimodal.art/mindseye It doesn't get close to the quality of the art pictures produced using DALL-E, but it's nice to play with.
- smortaz 5y ago
  thx will try!
tmountain 5y ago
No, it’s something we will keep hearing about and nobody will get to try it. OpenAI is anything but open. Looks like great tech, but it’s irrelevant if it’s totally inaccessible.
- smortaz 5y ago
  :). i see seemingly random people keep posting their experiments with it. i guess they’re all in the inner circle or something

dqpb 5y ago

Why does this need to be a thread of tweets embedded in tweets?

Sebb767 5y ago
Because a normal thread can be made usable by things like threadreader and that obviously undermines the whole point of blogging on Twitter ;-)

rasz 5y ago

I would rather see a research into how much of "learned" material is it regurgitating ala Copilot verbatim Carmacks fast inverse square root.

cycrutchfield 5y ago
That’s not too hard, just compare generated images to similar images from the training data.
- native_samples 5y ago
  It's pretty clear from the outputs that it's been heavily trained on stock photo libraries. Or maybe that's just the material it extracted the most info from in the training set. Stock image libraries have enormous numbers of high quality, high res images annotated with simple labels so they're ideal for this type of training task. I'm actually surprised nobody remarked on this yet because the resemblance is so strong. E.g. look at what it outputs for welcome signs. It can do a great job for that. Many of the images have artistic use of focus, perspective and blur. That's because: https://www.shutterstock.com/search/photo+of+a+welcome+sign https://www.shutterstock.com/search/photo+of+a+welcome+sign Likewise the "omg ai bias" examples are easily explainable as being due to training on stock photo libraries: https://www.shutterstock.com/search/photo+of+a+nurse https://www.shutterstock.com/search/photo+of+a+nurse Every single output they get for "photo of a <job>" looks exactly like what you'd get from stock photos. This might also be why it struggles with counting. https://www.shutterstock.com/search/an+apple https://www.shutterstock.com/search/an+apple Easy. https://www.shutterstock.com/search/5+apples https://www.shutterstock.com/search/5+apples Hmmm ..... not so easy. The search is good: the results that actually have apples in them tend to have five, but, the actual labels don't include the counts. Likewise with negation. How many images are annotated with "photo of a man NOT running"? My guess is, nearly none.
  - cycrutchfield 5y ago
    Hmm my assumption is that they heavily curated the training images and captions with human labelers. I doubt for something like this they would just scrape stock images and use it directly.

dredmorbius 5y ago

https://nitter.kavin.rocks/benjamin_hilton/status/1520032772072607747 https://nitter.kavin.rocks/benjamin_hilton/status/1520032772...

aantix 5y ago

You can see the future of search.. No longer is it a description of what currently exists, but an expression of what's been queried. Which may demonstrate something entirely different.

grumbel 5y ago
Yep, that's the truly impressive part with DALL-E2. If you take those sentences and paste them into Google Images, the results will always be much worse. Even if you find a query for which there do exist closely matching images, they will still look worse. Meanwhile if you do it in reverse, take an exist image and try to recreate it in DALL-E2, you can get extremely closer. Over on Reddit they did a experiment: https://old.reddit.com/r/dalle2/comments/ub0sfg/dalle_2_imitation_game_results_check_sticky_for/ https://old.reddit.com/r/dalle2/comments/ub0sfg/dalle_2_imit...

sydthrowaway 5y ago

I wonder what Filip Pieknewski thinks of DALL-E

Jack000 5y ago

Most of these stem from the way DALLE-2 is designed - in particular the composition, counting, negation and concept blending examples. In the paper they explain that these aspects were deliberately sacrificed in order to improve image quality and diversity, and for the ability to generate variations of an existing image. These aspects were much better in the prior version (GLIDE), which conditioned on text tokens instead of just a fixed length vector.

deleted 5y ago
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Al-Khwarizmi 5y ago

It's very interesting, but for me, the fact that one has to resort to very complicated sentences, rare words or ambiguous words to show limitations (and even then, they're only near misses, not utter failures) is a testimony to how amazing DALL-E is.

makerofthings 5y ago

DALL-E is way better than me. Sure, I can count past four and I know what letters are, but I can’t draw a wizard koala as good as that. Also, there is criticism of how it draws faces, but it’s way better than I am.

grumbel 5y ago
Also when it comes to faces, that's intentional. Trying to generate realistic looking humans is explicitly forbidden, just like politics, violence, nudity and other stuff. Images matching those topics are likely filtered out of the training set (it's no public, so no idea what is actually in it). It still has some ideas how humans look like, but that seems to come from classic paintings, not real world photos of humans.

lqet 5y ago

> e.g. I had to try this one (“a medieval painting of a monk and a skeleton drinking together in a tavern”) a few times to get the monk to not be a skeleton! And he still looks a bit skeleton-y. Well, the prompt is actually a bit vague - is it a monk sitting together with a skeleton, or is it a single entity that is both a monk and a skeleton? Seems like DALL-E tried to find some middle ground.

webmaven 5y ago
> Well, the prompt is actually a bit vague - is it a monk sitting together with a skeleton, or is it a single entity that is both a monk and a skeleton? Seems like DALL-E tried to find some middle ground. My thought exactly. I'd be interested to know whether any attempts were made at explicit disambiguation (eg. "A monk sitting beside a skeleton", etc.).

siruncledrew 5y ago

All these posts about DALL-E are making me so excited to try it if I ever make it through the waitlist.