8 ms·

This DALL-E 2 is absolutely the wildest thing I've seen by far. If we can get it to render 2D images, imagine 2d films, fully textured 3d mesh & environments is

This DALL-E 2 is absolutely the wildest thing I've seen by far. If we can get it to render 2D images, imagine 2d films, fully textured 3d mesh & environments is almost a certainty in the future. The implications of this is tremendous. We could be looking at crowd-directed AI generated consumable content. Imagine that you could watch infinite variations of the ending in Sopranos season 6. Imagine that you could generate a production quality 3D animatable human mesh that can interact with the AI generated 3D environment. Pair this with ultrasound based non-invasive haptic feedback, taste, smell with some type of 3D holographic projector. The possibilities are literally endless. Holy moly!!!! We are on the cusp of a completely new way to generate and consume content. Copyright laws will have to be rewritten. Once this pandora box is open it can't be closed.

truemotive 4y ago
> Imagine that you could watch infinite variations of the ending in Sopranos season 6 This sounds like a special version of hell…
londons_explore 4y ago
ML models used in this way will struggle with scene to scene consistency... For example, when you generate someone riding a horse, and later want the same character to be at home drinking a coffee. It'll be hard to get the model to render the same face, mannerisms and style for the same person in both cases. I think that alone will take years to properly solve.
- dougabug 4y ago
  Not so much. Check out E3GAN from Gordon Wetzstein’s lab at Stanford: https://matthew-a-chan.github.io/EG3D/ https://matthew-a-chan.github.io/EG3D/. In particular, note how they are able to apply a process called GAN Inversion to find a point in the 3D GAN’s representation space to generate a 3D model of a person’s face (one of the collaborators) from a single query image.
  - londons_explore 4y ago
    But it's much trickier to find the point in a GAN's representation space for "the exact same fluffy pink earrings that the actress was wearing in the last scene". Mostly because one can't write code to judge equal-ness of pink earrings. Or the fact the trash can in the background had a lid in the last scene and is now missing the lid. Or the fact the brick wall has turned into a painted wall. etc. Having said that, I'm sure some filmmakers will go for it anyway - after all, human-made films have some consistency issues, and many viewers simply don't care.
    - notahacker 4y ago
      I think the main thing is it becomes a programming problem rather than an AI limitation. From what I've seen it's now possible for the AI to generate EarringsAsset57 and KitchenWall51 and then to ask the AI to draw the actor wearing EaringsAsset57 in front of Kitchenwall51 and for stills at least, it can produce acceptable results
    - dougabug 4y ago
      You can decompose an entire scene into a collection of 3D GANs, each of which is fit by GAN Inversion to the object instance which best explains what is seen in the images. Also, we absolutely can tell the difference between trash cans with and without lids, as well as brick walls vs painted walls. This falls under many areas of ML-based Computer Vision, such as metric, contrastive, and zero shot machine learning, also open world object detection / recognition. These frame level semantic discrepancies can be detected by discriminative DNNs, which we can use to provide a temporal consistency loss to guide the generator to produce temporally consistent videos.
    - webmaven 4y ago
      > But it's much trickier to find the point in a GAN's representation space for "the exact same fluffy pink earrings that the actress was wearing in the last scene". Not so much, since you can supply an image of the desired item and get back a bunch of vectors to reuse.
  - tomatowurst 4y ago
- ben_w 4y ago
  For film-length consistency, I wouldn’t be surprised by it taking years to solve, despite what @dougabug says in a sibling comment. But “years” isn’t very much time. (I have the mental image of it being as easy to make a film as it was for the character of Mariner in that episode of Star Trek Lower Decks where she makes a filmified version of the ship, in the holodeck, in a few seconds; but the more I think about it, the more I realise I think I don’t feel the normal drives for expensive economic signalling in a way that would allow me to actually predict what people would do given the technology).
- l33tman 4y ago
  You need to loop back the generated output to provide context for the next output, just like how GPT-3 works. This at least establishes the needed data to train for consistent outputs. Given a deep well-trained network, the high-level meta-data that needs to be consistent across scenes is just a small set and the network will discover this just as GPT-3 did.
  - dougabug 4y ago
    Right. In the literature this is typically referred to as autoregressive sequence generation, and you can use it generate a whole story consistent with a given prompt (of any length), or even a whole image where you give it the first N seed pixels.
- notahacker 4y ago
  Consistency in stills seems to be solvable with the current generation models. You can now get an AI to draw Brad Pitt on a horse wearing a Stetson and Brad Pitt in Central Perk drinking a coffee with the right source data, or move a subject into a different setting by tweaking text descriptions enough, and it seems they often get details like shadows and background borders right. I think it'll struggle enormously with subtle details of mannerism in film and coherently fleshing out stuff where it hasn't got a lot of data in its database (getting Brad Pitt or New York from multiple angles is a lot easier than getting a consistent thisactordoesnotexist or novel fantasy setting). But I think the bigger barrier to adoption outside CGI studios (for producing content above meme-level quality) is that actually describing enough details to get everything to cohere even in static images will be beyond the average person.
  - techdragon 4y ago
    That’s because Brad Pitt is a named celebrity with lots of photos from many angles that has been included by name in the training data. It can get Brad Pitt like content by name. If I say “5 foot 6 inch tall older Italian woman <insert rest of scene description here>” I’m going to get an absolutely randomised person as the focus each time, the mode has no memory and this has been the BIGGEST limitation in all these transformer based architectures. There’s no memory of each output, they are all discrete and as much as you can kinda fudge it with GPT-3 by including part of the previous paragraph or sentences in your prompt to try and keep it on track, it can, does, and inevitably will drift horribly off track unless you manually coach it over and over micromanaging it’s output to keep it useful and sensible. With GPT it’s each text prompt, with DALL-E it’s each text or image prompt, and with a hypothetical video version it would be the same limit, each prompt has no memory of the previous and so it can’t implicitly reuse anything created by a previous prompt. You have to hack that in sound the core transformer architecture to make a facsimile of memory by things like pasting in extra data or teaching DALL-E 2 to edit a picture you upload and then uploading the picture you just generated using it, video would be no different and consequently this approach will not scale efficiency beyond individual items of content like an arbitrary sized block of text, a single photo or small group of photo edits, or a single “shot” of video content containing a scene or two worth of contiguous narrative flow.
verisimi 4y ago
As a thought experiment, imagine if this functionality already existed but only for a small subset. Perhaps for secretive 3-letter agencies? What would someone with that ability be able to do? How convincing could their imagery, films, etc be? Could they even shift perceptions of the world, get people to do things because of the imagery? How powerful would that ability be - is it an ability that the military etc would have looked into, and investigated possible weaponisation? Is it possible that this has been the case for years?
- SargeZT 4y ago
  It's doubtful. The asymmetry between theory and scale has been shrinking on most fronts when it comes to the government. The government certainly has brilliant scientists on its payroll and in the past has been able to shove resources at problems that no other entity has, but these are no longer the days of the Manhattan Project and this isn't the atom bomb. You can't throw money at this problem beyond buying computing power (which the private sector now holds more of than the government does), and it requires incremental advancements in dozens of fields internationally over the course of years, along with huge collaboratively captioned datasets that simply can't be produced by the government without tipping their hand.
- grumbel 4y ago
  Being able to create fake films doesn't really buy you anything, when people aren't believing them. And for believability, you need connections with reality and trust, that you don't get from fake films alone. People can just look at other sources and see that none of this happened at the given time and place. And random videos that pop up on the Internet are very low in terms of trust anyway. If you want to influence public opinion, there are already far more powerful tools. Just be Google or Twitter and create some strategically placed filter bubbles. Everything people will see, actually happened and can be verified. Nothing is hidden and will show up with a specific enough search query. But every less specific search query will nudge people into whatever direction you want them to move.
  - code51 4y ago
    To counter that, imagine police bodycams with embedded AI touching up live-recording so that the police was always right, suspect always did something wrong. If you build the source device with this touch-up capability, if you are the certificate authority about these, if you are also defining how these recordings would be used in court, then a perfect fake is useful. (None of us see the actual sensor output from our mobile phones) TL;DR In order for a fake to be useful, you have to fake AND be the only reputable source. If other recorders cannot get close enough, then your touched-up "real" video is the only material available.
andrepd 4y ago
I am really quite sceptical about these wild wild claims. There's quite a big difference between "generate an image that at first glance kinda looks like an armchair in the shape of an avocado", and "create actual meaningful content" such as you describe.
- widdershins 4y ago
  Just like several years ago you'd say "there's a big difference between these trippy deep-dream images and an actual meaningful illustration with a style and a coherent theme". But here we are.
  - Hoasi 4y ago
    > Just like several years ago you'd say "there's a big difference between these trippy deep-dream images and an actual meaningful illustration with a style and a coherent theme". But here we are. Exactly, at the very least, that will massively disrupt the fields of illustration and concept art.
  - andrepd 4y ago
    >style and coherent theme Can I play with it then, to verify that myself? Or can I just view cherry-picked examples where it works sort of well? That's one of the major flaws of nn approaches, since you don't have an understanding of what's going on, just an uninterpretable set of billions of numbers, you can't know what are the failure modes and the limitations of your algorithm.
    - sdenton4 4y ago
      Lots of people have written big posts at this point about this topic. DALL-E2 let's you guide the image generation with text prompts, and mask part of the image and regenerate the masked parts. This allows collaboration between the use and the algorithm to iteratively converge on what the user wants. There's also been lots of exploration to find text prompt recipes that produce interesting and more consistent results. In the end, it's a tool! Tools are used by humans to do something that matters to the human user. Humans adapt to the tools they use to maximize the tool's utility. Understanding that is fundamental for thinking about where ML will take us. It's not magic, it's tools.
lagrange77 4y ago
> Sopranos season 6 A new episode of TNG, VOY and the Sopranos every single day!
- layer8 4y ago
  I’m going to try “TNG in the style of DS9 seasons 4-6”. :)
peoplefromibiza 4y ago
> We are on the cusp of a completely new way to generate and consume content. Copyright laws will have to be rewritten. Once this pandora box is open it can't be closed. The question is not if but why We don't need more content, we need more curated and higher quality content. Random by definition has a very low probability of producing quality. Case in point: The walking dead. It's already an infinite variation of the same topic but the good bits ended after season 2...
- dougabug 4y ago
  Sure, but how is this different from all the blogs, vlogs, social media, and the Internet in general (with an assist from smartphones, digital cameras, game engines, camera drones, and real-time ray tracing GPUs) allowing humanity to create an unprecedented global flood of content now? Or the problem of finding something satisfying to watch out literally tens of thousands of shows on Netflix, Amazon Prime, Hulu, Disney+, HBO Max, AppleTV+, etc? That’s why we have critics, reviews, review aggregators, search engines, recommendation engines, community discussion boards… Clearly, the answer to an AI generated problem (pretty much anyone being able to content previously requiring massive budgets) is more AI! We can evolve content critics to help people navigate a world of practically unlimited content to find stuff they enjoy. Also, consider all the potentially great films that never got made because even the best filmmakers can only get a fraction of their ideas actually produced.
  - peoplefromibiza 4y ago
    > Also, consider all the potentially great films that never got made because even the best filmmakers can only get a fraction of their ideas actually produced. and that's a good thing. Consider how many bad movies were not made thanks to this! IMO, content creation should be harder, not easier. If it becomes too easy, the quality goes toward hobbyist level, which is not what I want to pay for. Also, bad content was a thing on its own, take for example Troma films, they are entertainingly bad, not just repetitive and dull bad. > Or the problem of finding something satisfying to watch out literally tens of thousands of shows on Netflix, Amazon Prime, Hulu, Disney+, HBO Max, AppleTV+, etc? the problem is exactly the mass amount of content created to lure people in. But do I really want to watch another iteration of the superhero trope? No, I don't. I wanna watch something that I've never seen before. Imagine a book written to mimic the style of my favourite writers on my favourite topic: Sci-fi. On paper it sounds like a dream, but in reality it would be a terrible, confusing, incoherent and ultimately a trying-too-hard-to-match-my-taste product. Like when Data reads his poems on TNG and when asked why people seemed distracted, Laforge answers "Well, your poems were clever, Data, and your Haiku was clever, and your sonnet was clever. But did it evoke an emotional response? To be honest, no" Of course Data being Data, he knows he's clever and he knows he's an amateur poet and wants to hear the truth about his works. But do we want to pay for content generated by the machines so that they can learn to imitate us better? [1] I sincerely don't want to. https://en.wikipedia.org/wiki/The_Paradox_of_Choice https://en.wikipedia.org/wiki/The_Paradox_of_Choice EDIT: with [1] I mean that I don't want to pay streaming content publishers to improve their algorithm so that they can mass produce more dull content for people to consume (and pay) If they really want to improve things, they should pay people to make less shows and/or movies but better. Shows like "Another life" (6% on Rotten Tomatoes) would not exist if they were shot on real sets that take time, money, permits and a lot people to recreate.
scroot 4y ago
If this kind of thing becomes the norm, what will the DALL-E 200 train itself on? At some point it's going to be sucking its own exhaust.
- dougabug 4y ago
  At some point, it will have to evolve beyond learning from human labeled (or even just collected) examples to learn by its own open ended dynamics and self-directed experience. This is analogous to the original AlphaGo bootstrapping from records of millions of human played games, to AlphaZero learning to play Go purely from self-play.
  - Lambdanaut 4y ago
    That's a beautiful analogy. At some point Alpha Go learnt all it could from the narratives of humans, and eventually it had to become the creator of its own narratives. Applied generally enough, we see the narrative value of humans, at least in the eyes of the machine, to go down down down, as the value of its own richer and more unknown narratives go up up up. In this way it becomes the dominant spectacle on the Earth, leaving humanity with a questionable fate.
    - bachmitre 4y ago
      Exactly, at that point you let the AIs play against each other and go back to watch some TV. (or in the case of DALL-E, we won't be able to understand the concept/description space of the images anymore, let alone the images generated from it).
  - Jeff_Brown 4y ago
    I'm having trouble drawing that analogy, because Alpha Go knew what it was to win before it taught itself to play. The equivalent for DALL-E would be to start off knowing what an elephant looks like, what water looks like, what elephants in water look like ...
    - dougabug 4y ago

truemotive 4y ago

> Imagine that you could watch infinite variations of the ending in Sopranos season 6 This sounds like a special version of hell…

londons_explore 4y ago

ML models used in this way will struggle with scene to scene consistency... For example, when you generate someone riding a horse, and later want the same character to be at home drinking a coffee. It'll be hard to get the model to render the same face, mannerisms and style for the same person in both cases. I think that alone will take years to properly solve.

dougabug 4y ago
Not so much. Check out E3GAN from Gordon Wetzstein’s lab at Stanford: https://matthew-a-chan.github.io/EG3D/ https://matthew-a-chan.github.io/EG3D/. In particular, note how they are able to apply a process called GAN Inversion to find a point in the 3D GAN’s representation space to generate a 3D model of a person’s face (one of the collaborators) from a single query image.
- londons_explore 4y ago
  But it's much trickier to find the point in a GAN's representation space for "the exact same fluffy pink earrings that the actress was wearing in the last scene". Mostly because one can't write code to judge equal-ness of pink earrings. Or the fact the trash can in the background had a lid in the last scene and is now missing the lid. Or the fact the brick wall has turned into a painted wall. etc. Having said that, I'm sure some filmmakers will go for it anyway - after all, human-made films have some consistency issues, and many viewers simply don't care.
  - notahacker 4y ago
    I think the main thing is it becomes a programming problem rather than an AI limitation. From what I've seen it's now possible for the AI to generate EarringsAsset57 and KitchenWall51 and then to ask the AI to draw the actor wearing EaringsAsset57 in front of Kitchenwall51 and for stills at least, it can produce acceptable results
  - dougabug 4y ago
    You can decompose an entire scene into a collection of 3D GANs, each of which is fit by GAN Inversion to the object instance which best explains what is seen in the images. Also, we absolutely can tell the difference between trash cans with and without lids, as well as brick walls vs painted walls. This falls under many areas of ML-based Computer Vision, such as metric, contrastive, and zero shot machine learning, also open world object detection / recognition. These frame level semantic discrepancies can be detected by discriminative DNNs, which we can use to provide a temporal consistency loss to guide the generator to produce temporally consistent videos.
  - webmaven 4y ago
    > But it's much trickier to find the point in a GAN's representation space for "the exact same fluffy pink earrings that the actress was wearing in the last scene". Not so much, since you can supply an image of the desired item and get back a bunch of vectors to reuse.
- tomatowurst 4y ago
ben_w 4y ago
For film-length consistency, I wouldn’t be surprised by it taking years to solve, despite what @dougabug says in a sibling comment. But “years” isn’t very much time. (I have the mental image of it being as easy to make a film as it was for the character of Mariner in that episode of Star Trek Lower Decks where she makes a filmified version of the ship, in the holodeck, in a few seconds; but the more I think about it, the more I realise I think I don’t feel the normal drives for expensive economic signalling in a way that would allow me to actually predict what people would do given the technology).
l33tman 4y ago
You need to loop back the generated output to provide context for the next output, just like how GPT-3 works. This at least establishes the needed data to train for consistent outputs. Given a deep well-trained network, the high-level meta-data that needs to be consistent across scenes is just a small set and the network will discover this just as GPT-3 did.
- dougabug 4y ago
  Right. In the literature this is typically referred to as autoregressive sequence generation, and you can use it generate a whole story consistent with a given prompt (of any length), or even a whole image where you give it the first N seed pixels.
notahacker 4y ago
Consistency in stills seems to be solvable with the current generation models. You can now get an AI to draw Brad Pitt on a horse wearing a Stetson and Brad Pitt in Central Perk drinking a coffee with the right source data, or move a subject into a different setting by tweaking text descriptions enough, and it seems they often get details like shadows and background borders right. I think it'll struggle enormously with subtle details of mannerism in film and coherently fleshing out stuff where it hasn't got a lot of data in its database (getting Brad Pitt or New York from multiple angles is a lot easier than getting a consistent thisactordoesnotexist or novel fantasy setting). But I think the bigger barrier to adoption outside CGI studios (for producing content above meme-level quality) is that actually describing enough details to get everything to cohere even in static images will be beyond the average person.
- techdragon 4y ago
  That’s because Brad Pitt is a named celebrity with lots of photos from many angles that has been included by name in the training data. It can get Brad Pitt like content by name. If I say “5 foot 6 inch tall older Italian woman <insert rest of scene description here>” I’m going to get an absolutely randomised person as the focus each time, the mode has no memory and this has been the BIGGEST limitation in all these transformer based architectures. There’s no memory of each output, they are all discrete and as much as you can kinda fudge it with GPT-3 by including part of the previous paragraph or sentences in your prompt to try and keep it on track, it can, does, and inevitably will drift horribly off track unless you manually coach it over and over micromanaging it’s output to keep it useful and sensible. With GPT it’s each text prompt, with DALL-E it’s each text or image prompt, and with a hypothetical video version it would be the same limit, each prompt has no memory of the previous and so it can’t implicitly reuse anything created by a previous prompt. You have to hack that in sound the core transformer architecture to make a facsimile of memory by things like pasting in extra data or teaching DALL-E 2 to edit a picture you upload and then uploading the picture you just generated using it, video would be no different and consequently this approach will not scale efficiency beyond individual items of content like an arbitrary sized block of text, a single photo or small group of photo edits, or a single “shot” of video content containing a scene or two worth of contiguous narrative flow.

verisimi 4y ago

As a thought experiment, imagine if this functionality already existed but only for a small subset. Perhaps for secretive 3-letter agencies? What would someone with that ability be able to do? How convincing could their imagery, films, etc be? Could they even shift perceptions of the world, get people to do things because of the imagery? How powerful would that ability be - is it an ability that the military etc would have looked into, and investigated possible weaponisation? Is it possible that this has been the case for years?

SargeZT 4y ago
It's doubtful. The asymmetry between theory and scale has been shrinking on most fronts when it comes to the government. The government certainly has brilliant scientists on its payroll and in the past has been able to shove resources at problems that no other entity has, but these are no longer the days of the Manhattan Project and this isn't the atom bomb. You can't throw money at this problem beyond buying computing power (which the private sector now holds more of than the government does), and it requires incremental advancements in dozens of fields internationally over the course of years, along with huge collaboratively captioned datasets that simply can't be produced by the government without tipping their hand.
grumbel 4y ago
Being able to create fake films doesn't really buy you anything, when people aren't believing them. And for believability, you need connections with reality and trust, that you don't get from fake films alone. People can just look at other sources and see that none of this happened at the given time and place. And random videos that pop up on the Internet are very low in terms of trust anyway. If you want to influence public opinion, there are already far more powerful tools. Just be Google or Twitter and create some strategically placed filter bubbles. Everything people will see, actually happened and can be verified. Nothing is hidden and will show up with a specific enough search query. But every less specific search query will nudge people into whatever direction you want them to move.
- code51 4y ago
  To counter that, imagine police bodycams with embedded AI touching up live-recording so that the police was always right, suspect always did something wrong. If you build the source device with this touch-up capability, if you are the certificate authority about these, if you are also defining how these recordings would be used in court, then a perfect fake is useful. (None of us see the actual sensor output from our mobile phones) TL;DR In order for a fake to be useful, you have to fake AND be the only reputable source. If other recorders cannot get close enough, then your touched-up "real" video is the only material available.

andrepd 4y ago

I am really quite sceptical about these wild wild claims. There's quite a big difference between "generate an image that at first glance kinda looks like an armchair in the shape of an avocado", and "create actual meaningful content" such as you describe.

widdershins 4y ago
Just like several years ago you'd say "there's a big difference between these trippy deep-dream images and an actual meaningful illustration with a style and a coherent theme". But here we are.
- Hoasi 4y ago
  > Just like several years ago you'd say "there's a big difference between these trippy deep-dream images and an actual meaningful illustration with a style and a coherent theme". But here we are. Exactly, at the very least, that will massively disrupt the fields of illustration and concept art.
- andrepd 4y ago
  >style and coherent theme Can I play with it then, to verify that myself? Or can I just view cherry-picked examples where it works sort of well? That's one of the major flaws of nn approaches, since you don't have an understanding of what's going on, just an uninterpretable set of billions of numbers, you can't know what are the failure modes and the limitations of your algorithm.
  - sdenton4 4y ago
    Lots of people have written big posts at this point about this topic. DALL-E2 let's you guide the image generation with text prompts, and mask part of the image and regenerate the masked parts. This allows collaboration between the use and the algorithm to iteratively converge on what the user wants. There's also been lots of exploration to find text prompt recipes that produce interesting and more consistent results. In the end, it's a tool! Tools are used by humans to do something that matters to the human user. Humans adapt to the tools they use to maximize the tool's utility. Understanding that is fundamental for thinking about where ML will take us. It's not magic, it's tools.

lagrange77 4y ago

> Sopranos season 6 A new episode of TNG, VOY and the Sopranos every single day!

layer8 4y ago
I’m going to try “TNG in the style of DS9 seasons 4-6”. :)

peoplefromibiza 4y ago

> We are on the cusp of a completely new way to generate and consume content. Copyright laws will have to be rewritten. Once this pandora box is open it can't be closed. The question is not if but why We don't need more content, we need more curated and higher quality content. Random by definition has a very low probability of producing quality. Case in point: The walking dead. It's already an infinite variation of the same topic but the good bits ended after season 2...

dougabug 4y ago
Sure, but how is this different from all the blogs, vlogs, social media, and the Internet in general (with an assist from smartphones, digital cameras, game engines, camera drones, and real-time ray tracing GPUs) allowing humanity to create an unprecedented global flood of content now? Or the problem of finding something satisfying to watch out literally tens of thousands of shows on Netflix, Amazon Prime, Hulu, Disney+, HBO Max, AppleTV+, etc? That’s why we have critics, reviews, review aggregators, search engines, recommendation engines, community discussion boards… Clearly, the answer to an AI generated problem (pretty much anyone being able to content previously requiring massive budgets) is more AI! We can evolve content critics to help people navigate a world of practically unlimited content to find stuff they enjoy. Also, consider all the potentially great films that never got made because even the best filmmakers can only get a fraction of their ideas actually produced.
- peoplefromibiza 4y ago
  > Also, consider all the potentially great films that never got made because even the best filmmakers can only get a fraction of their ideas actually produced. and that's a good thing. Consider how many bad movies were not made thanks to this! IMO, content creation should be harder, not easier. If it becomes too easy, the quality goes toward hobbyist level, which is not what I want to pay for. Also, bad content was a thing on its own, take for example Troma films, they are entertainingly bad, not just repetitive and dull bad. > Or the problem of finding something satisfying to watch out literally tens of thousands of shows on Netflix, Amazon Prime, Hulu, Disney+, HBO Max, AppleTV+, etc? the problem is exactly the mass amount of content created to lure people in. But do I really want to watch another iteration of the superhero trope? No, I don't. I wanna watch something that I've never seen before. Imagine a book written to mimic the style of my favourite writers on my favourite topic: Sci-fi. On paper it sounds like a dream, but in reality it would be a terrible, confusing, incoherent and ultimately a trying-too-hard-to-match-my-taste product. Like when Data reads his poems on TNG and when asked why people seemed distracted, Laforge answers "Well, your poems were clever, Data, and your Haiku was clever, and your sonnet was clever. But did it evoke an emotional response? To be honest, no" Of course Data being Data, he knows he's clever and he knows he's an amateur poet and wants to hear the truth about his works. But do we want to pay for content generated by the machines so that they can learn to imitate us better? [1] I sincerely don't want to. https://en.wikipedia.org/wiki/The_Paradox_of_Choice https://en.wikipedia.org/wiki/The_Paradox_of_Choice EDIT: with [1] I mean that I don't want to pay streaming content publishers to improve their algorithm so that they can mass produce more dull content for people to consume (and pay) If they really want to improve things, they should pay people to make less shows and/or movies but better. Shows like "Another life" (6% on Rotten Tomatoes) would not exist if they were shot on real sets that take time, money, permits and a lot people to recreate.

scroot 4y ago

If this kind of thing becomes the norm, what will the DALL-E 200 train itself on? At some point it's going to be sucking its own exhaust.

dougabug 4y ago
At some point, it will have to evolve beyond learning from human labeled (or even just collected) examples to learn by its own open ended dynamics and self-directed experience. This is analogous to the original AlphaGo bootstrapping from records of millions of human played games, to AlphaZero learning to play Go purely from self-play.
- Lambdanaut 4y ago
  That's a beautiful analogy. At some point Alpha Go learnt all it could from the narratives of humans, and eventually it had to become the creator of its own narratives. Applied generally enough, we see the narrative value of humans, at least in the eyes of the machine, to go down down down, as the value of its own richer and more unknown narratives go up up up. In this way it becomes the dominant spectacle on the Earth, leaving humanity with a questionable fate.
  - bachmitre 4y ago
    Exactly, at that point you let the AIs play against each other and go back to watch some TV. (or in the case of DALL-E, we won't be able to understand the concept/description space of the images anymore, let alone the images generated from it).
- Jeff_Brown 4y ago
  I'm having trouble drawing that analogy, because Alpha Go knew what it was to win before it taught itself to play. The equivalent for DALL-E would be to start off knowing what an elephant looks like, what water looks like, what elephants in water look like ...
  - dougabug 4y ago