4 ms·

I feel this can potentially revolutionize creative processes, for example in the clothing industry. You just draw up a purse or a shoe, let the machines generat

by bflesch 10y ago

I feel this can potentially revolutionize creative processes, for example in the clothing industry. You just draw up a purse or a shoe, let the machines generate dozens of variants (with pictures), and then you only have to filter and rank them. You can pipe these product sketches directly into focus groups who tell you which product is most likely to sell. You don't need massive staff to come up with product variants any more.

ragebol 10y ago
It has the potential to redefine what we think of as 'creativity', as happened with what we consider intelligence and what we think of as "AI Hard" problems. Perhaps what these networks are generating can be labeled better as "Guided/constrained imitation" rather than real creativity.
- visarga 10y ago
  > real creativity What is real creativity? Creativity is just random noise converted into patterns. Is the computer variety of creativity not real enough?
  - dpark 10y ago
    > Creativity is just random noise converted into patterns. This is not a consensus definition. Creativity doesn't actually seem to be very random at all according to the people who study it.
    - TPCrow 10y ago
      Creativity isn't magic. Humans are not magically creative as much as they'd like to be. If i ask you to think of a random number, you don't just pull it out of thin air, It can be based on tens to hundres of things: -Should i do a relaly low or high number? -People always use round numbers that end in 0 or 5, maybe i shouldn't do that, or should i to make it seem truer -what other large "random" numbers have a heard? -i remember seeing a number recently, maybe try a modification of that -you used {x} as a random number last time, go similar to that? All this adds up in that under a second thought you have when i asked you to think of a random number. the literal same thing goes into all creative works, the output is a function of the input.
      - pmyjavec 10y ago
        What's your point actually? Honest question. You're acting like the brain is some kind of simple algorithm, more goes into a painting or a composition than just a bit of simple logic. A composer is not sitting there at 2am on her Piano going "Hm, I like round numbers, so I might make this note a F because it's the fourth note in the C major scale". I might be wrong but from my understanding, we don't even really understand how neural nets are able to make certain decisions or generate certain pictures yet, correct?
        dpark 10y ago
        > You're acting like the brain is some kind of binary computer It's not binary but it is a computer. The alternative is to believe in magic. > we don't even really understand how a computer is able to make certain decisions or generate certain pictures yet, correct? I don't think that's correct, no. We understand how the process works. We may not understand the weights a specific neural net ends up with, but that's an issue with just having so much data to deal with. Similarly we don't "understand" how a web page ends up with a specific PageRank. We understand the process, but we can't manually reproduce the result because it's just too much data.
      - visarga 10y ago
        Yes, but this function you defined, that applies tens of judgements to select a "random" number, is based on a random number input itself. The random part is just the seed, it then passes through various neural nets that expand on it and turn it into a plausible answer. Randomness is injected into all brain processes on account that biological neurons are stochastic. So there is an amount of randomness mixed into everything the brain does. Some neural nets can map real images into a Gaussian, and back. That means they disentangle the factors of the image into a mix of independent factors that map into the standard deviation. Any set of random numbers could be converted back into an image, by the reverse process.
        dpark 10y ago
        > Yes, but this function you defined, that applies tens of judgements to select a "random" number, is based on a random number input itself. The random part is just the seed, it then passes through various neural nets that expand on it and turn it into a plausible answer. On what basis do you make this claim? Humans are empirically terrible random number generators. If you ask someone to pick a random number, the result is very not random. Our biases are large and obvious, so it seems faulty to claim that our "seed" number is in any way truly random. > Randomness is injected into all brain processes on account that biological neurons are stochastic. So there is an amount of randomness mixed into everything the brain does. There's also some amount of randomness in what happens if you drop a rock but the net result is largely the same: it falls down. The fact that there is some randomness to a process does not mean that the randomness is actually driving the process. > Some neural nets can map real images into a Gaussian, and back. That means they disentangle the factors of the image into a mix of independent factors that map into the standard deviation. Any set of random numbers could be converted back into an image, by the reverse process. I don't see how this is relevant.
  - intoverflow2 10y ago
    Why are some people better at it than others then if it's purely noise and patterns?
    - MichaelBurge 10y ago
      They do a worse job converting the noise into patterns, relative to some common judge of how good the solution is. See any GAN in its early stages of training for an example.
  - radarsat1 10y ago
    Whenever this topic comes up someone with a very engineering-centric attitude jumps on the opportunity to downplay the complexity of the concept of "creativity". Often using the word "just".. I would argue that something like creativity is far too difficult to even define, let alone model and synthesize, to ever be adorned with the modifier "just". While this is a highly philosophical topic, I can say, on the difficulty of defining creativity from a scientific point of view, is that more and more what I think is considered "true creativity" is fundamentally a social concept, and as such, even though computers are good at reproducing patters and variations on patterns, they will never be "truly creative" until they become independent members of society, whatever that means. So as cool as machine learning is getting these days, this would require a leap in AI that is still rather far away (imho). That's why I think it's a little optimistic to label something as complex as creativity as being "just" anything. Creativity is social, insofar as it is defined and recognized by human society, and machines are.. not. Yet.
  - Cybiote 10y ago
    While I do not accept ragebol's notion of real creativity, I very much doubt your definition is correct. The use of 'just' is too strong, excluding the possibility of influence from other inputs. When humans learn, they do so by learning a large set of arbitrary relationships which further trigger complex associations (this is blue, it is used for X, it is like Y, which is like Z). These relationships are further broken down in terms of human meaningful components that are updateable with new knowledge with almost surgical precision. It is the interaction between the relations and components that drives the kind of analogical reasoning humans exhibit: traversing networks of relations and thinking up new ways of combining various components. Without a large base of knowledge to draw from, a creator simply does not have enough data to do anything more than copying. They would not have learned the rich relations nor a diverse enough set of components and operations on them. They would also be unable to know which combinations are truly novel nor have enough experience to predict what might be meaningful or impactful. This is why good creators must continually sample a large set of works. The limit to this is that humans will generally struggle to wander far from experience. Computers on the other hand, can cover more of a space to make connections humans would find difficult. And the less random the decisions, the more interesting the founds structures are likely to be. Where computers struggle and humans shine is the selection process. Ashby defines selection as: "problem solving" is largely, perhaps entirely, a matter of appropriate selection. Take, for instance, any popular book of problems and puzzles. Almost every one can be reduced to the form: out of a certain set, indicate one element". In this work for example, the excellent results are a better grasp of structure induced by having the GAN's networks condition on an input image. This architecture does have the downside of essentially limiting the generative capacity. Humans can generate many variations conditioned on an input but these networks: "Despite the dropout noise, we observe very minor stochasticity in the output of our nets. ...GANs that produce stochastic output, and... capture the full entropy of the conditional distributions they model, is an important question left open by the present work." AGI more and more looks like it will be about striking the right balance between generation and selection. For an artist, selection ability also plays a large part in taste. Having not just a good enough understanding to create novel combinations, but also a good enough model of people to predict what they are likely to find somehow compelling. Computers are good at generating, humans at selection. It is for this reason I disagree with those who believe tools like these herald the end of creativity nor can I agree with those who believe this heralds the end of creators. The quality floor of average art will rise but there will be less evidence of selection talent as the ceiling of what counts as great art must also rise. Joule for Joule, it is unlikely that anything for the forseeable future will beat teams of machine and man.
nathancahill 10y ago
I feel like we would end up here: http://www.gianlucagimini.it/prototypes/velocipedia.html http://www.gianlucagimini.it/prototypes/velocipedia.html
- zelpa 10y ago
  I wonder if you were to average the design of the bicycles whether it would actually produce something that works?
  - dTal 10y ago
    I would have thought that, if you are smart enough to find a "bicycle vector space" in which averaging sketches of a bicycle produces another valid sketch of a bicycle, then you probably already know enough about bicycles to design one without the input of imperfect sketches.

by bflesch 10y ago

ragebol 10y ago

It has the potential to redefine what we think of as 'creativity', as happened with what we consider intelligence and what we think of as "AI Hard" problems. Perhaps what these networks are generating can be labeled better as "Guided/constrained imitation" rather than real creativity.

visarga 10y ago
> real creativity What is real creativity? Creativity is just random noise converted into patterns. Is the computer variety of creativity not real enough?
- dpark 10y ago
  > Creativity is just random noise converted into patterns. This is not a consensus definition. Creativity doesn't actually seem to be very random at all according to the people who study it.
  - TPCrow 10y ago
    Creativity isn't magic. Humans are not magically creative as much as they'd like to be. If i ask you to think of a random number, you don't just pull it out of thin air, It can be based on tens to hundres of things: -Should i do a relaly low or high number? -People always use round numbers that end in 0 or 5, maybe i shouldn't do that, or should i to make it seem truer -what other large "random" numbers have a heard? -i remember seeing a number recently, maybe try a modification of that -you used {x} as a random number last time, go similar to that? All this adds up in that under a second thought you have when i asked you to think of a random number. the literal same thing goes into all creative works, the output is a function of the input.
    - pmyjavec 10y ago
      What's your point actually? Honest question. You're acting like the brain is some kind of simple algorithm, more goes into a painting or a composition than just a bit of simple logic. A composer is not sitting there at 2am on her Piano going "Hm, I like round numbers, so I might make this note a F because it's the fourth note in the C major scale". I might be wrong but from my understanding, we don't even really understand how neural nets are able to make certain decisions or generate certain pictures yet, correct?
      - dpark 10y ago
        > You're acting like the brain is some kind of binary computer It's not binary but it is a computer. The alternative is to believe in magic. > we don't even really understand how a computer is able to make certain decisions or generate certain pictures yet, correct? I don't think that's correct, no. We understand how the process works. We may not understand the weights a specific neural net ends up with, but that's an issue with just having so much data to deal with. Similarly we don't "understand" how a web page ends up with a specific PageRank. We understand the process, but we can't manually reproduce the result because it's just too much data.
    - visarga 10y ago
      Yes, but this function you defined, that applies tens of judgements to select a "random" number, is based on a random number input itself. The random part is just the seed, it then passes through various neural nets that expand on it and turn it into a plausible answer. Randomness is injected into all brain processes on account that biological neurons are stochastic. So there is an amount of randomness mixed into everything the brain does. Some neural nets can map real images into a Gaussian, and back. That means they disentangle the factors of the image into a mix of independent factors that map into the standard deviation. Any set of random numbers could be converted back into an image, by the reverse process.
      - dpark 10y ago
        > Yes, but this function you defined, that applies tens of judgements to select a "random" number, is based on a random number input itself. The random part is just the seed, it then passes through various neural nets that expand on it and turn it into a plausible answer. On what basis do you make this claim? Humans are empirically terrible random number generators. If you ask someone to pick a random number, the result is very not random. Our biases are large and obvious, so it seems faulty to claim that our "seed" number is in any way truly random. > Randomness is injected into all brain processes on account that biological neurons are stochastic. So there is an amount of randomness mixed into everything the brain does. There's also some amount of randomness in what happens if you drop a rock but the net result is largely the same: it falls down. The fact that there is some randomness to a process does not mean that the randomness is actually driving the process. > Some neural nets can map real images into a Gaussian, and back. That means they disentangle the factors of the image into a mix of independent factors that map into the standard deviation. Any set of random numbers could be converted back into an image, by the reverse process. I don't see how this is relevant.
- intoverflow2 10y ago
  Why are some people better at it than others then if it's purely noise and patterns?
  - MichaelBurge 10y ago
    They do a worse job converting the noise into patterns, relative to some common judge of how good the solution is. See any GAN in its early stages of training for an example.
- radarsat1 10y ago
  Whenever this topic comes up someone with a very engineering-centric attitude jumps on the opportunity to downplay the complexity of the concept of "creativity". Often using the word "just".. I would argue that something like creativity is far too difficult to even define, let alone model and synthesize, to ever be adorned with the modifier "just". While this is a highly philosophical topic, I can say, on the difficulty of defining creativity from a scientific point of view, is that more and more what I think is considered "true creativity" is fundamentally a social concept, and as such, even though computers are good at reproducing patters and variations on patterns, they will never be "truly creative" until they become independent members of society, whatever that means. So as cool as machine learning is getting these days, this would require a leap in AI that is still rather far away (imho). That's why I think it's a little optimistic to label something as complex as creativity as being "just" anything. Creativity is social, insofar as it is defined and recognized by human society, and machines are.. not. Yet.
- Cybiote 10y ago
  While I do not accept ragebol's notion of real creativity, I very much doubt your definition is correct. The use of 'just' is too strong, excluding the possibility of influence from other inputs. When humans learn, they do so by learning a large set of arbitrary relationships which further trigger complex associations (this is blue, it is used for X, it is like Y, which is like Z). These relationships are further broken down in terms of human meaningful components that are updateable with new knowledge with almost surgical precision. It is the interaction between the relations and components that drives the kind of analogical reasoning humans exhibit: traversing networks of relations and thinking up new ways of combining various components. Without a large base of knowledge to draw from, a creator simply does not have enough data to do anything more than copying. They would not have learned the rich relations nor a diverse enough set of components and operations on them. They would also be unable to know which combinations are truly novel nor have enough experience to predict what might be meaningful or impactful. This is why good creators must continually sample a large set of works. The limit to this is that humans will generally struggle to wander far from experience. Computers on the other hand, can cover more of a space to make connections humans would find difficult. And the less random the decisions, the more interesting the founds structures are likely to be. Where computers struggle and humans shine is the selection process. Ashby defines selection as: "problem solving" is largely, perhaps entirely, a matter of appropriate selection. Take, for instance, any popular book of problems and puzzles. Almost every one can be reduced to the form: out of a certain set, indicate one element". In this work for example, the excellent results are a better grasp of structure induced by having the GAN's networks condition on an input image. This architecture does have the downside of essentially limiting the generative capacity. Humans can generate many variations conditioned on an input but these networks: "Despite the dropout noise, we observe very minor stochasticity in the output of our nets. ...GANs that produce stochastic output, and... capture the full entropy of the conditional distributions they model, is an important question left open by the present work." AGI more and more looks like it will be about striking the right balance between generation and selection. For an artist, selection ability also plays a large part in taste. Having not just a good enough understanding to create novel combinations, but also a good enough model of people to predict what they are likely to find somehow compelling. Computers are good at generating, humans at selection. It is for this reason I disagree with those who believe tools like these herald the end of creativity nor can I agree with those who believe this heralds the end of creators. The quality floor of average art will rise but there will be less evidence of selection talent as the ceiling of what counts as great art must also rise. Joule for Joule, it is unlikely that anything for the forseeable future will beat teams of machine and man.

nathancahill 10y ago

I feel like we would end up here: http://www.gianlucagimini.it/prototypes/velocipedia.html http://www.gianlucagimini.it/prototypes/velocipedia.html

zelpa 10y ago
I wonder if you were to average the design of the bicycles whether it would actually produce something that works?
- dTal 10y ago
  I would have thought that, if you are smart enough to find a "bicycle vector space" in which averaging sketches of a bicycle produces another valid sketch of a bicycle, then you probably already know enough about bicycles to design one without the input of imperfect sketches.