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Soon even artists will be unemployed.

by ryacko 9y ago

pcurve 9y ago
I would think it's actually engineering job that is more at risk. Artists and animators will visually create these games and AI will figure out the code.
- memco 9y ago
  One thing that bugged me about the 2009 Star Trek film was why Checkov runs over to take manual control of the teleporter to beam up Kirk as he’s freefalling towards the planet: surely a a machine at their level of technological sophistication would be able to calculate the trajectory and beam them up in real time without too much trouble.
wpietri 9y ago
Just the opposite, I think. The promise of computers has always been to take over boring work so that humans can focus on the more interesting bits. Artists will always have something to do.
- cheez 9y ago
  You might need less artists or be able to create more games with the same number.
- jdietrich 9y ago
  Entirely plausible hypothetical: what happens if Deepmind train a massive neural network on the entire Spotify catalog, weighted for Billboard chart position and Grammy nominations? What if that algorithm turns out to be as good at songwriting as AlphaGo is at the game of Go? Will anyone listen to human-written songs if AlphaSong starts producing superhumanly beautiful music? Will any of us listen to the same music if AlphaSong can analyse our personal playlists and produce an infinite number of songs that perfectly match our musical tastes?
  - YeGoblynQueenne 9y ago
    In practical terms, the problem with learning songwriting, vs learning Go, is that the number of board positions in Go is finite whereas the number of songs that can be written is infinite. With Go, then, the task of an AI player (like AlphaGo) is to search a finite space for those board states that allow it to win. With songwriting- the task is not even clearly defined. Obviously, you're trying to generate songs people will want to listen to, so you have to search an infinite space for a set of songs that satisfy some criteria of "listenability", but what criteria are these? Popular songs range over wildly different music styles, from Black Metal to Medieval folk revivalist music through RnB and Arabic belly dancing music. What examples will we train our AI songwriter on? All music, ever? Specific kinds of music? Specific songs as exemplars of specific styles? Or in other words, what exactly will our AI songwriter be trying to learn? It'd be looking into an infinite haystack filled with almost identical needles for a particular needle it's never even seen well enough to recognise.
    - jdietrich 9y ago
      >In practical terms, the problem with learning songwriting, vs learning Go, is that the number of board positions in Go is finite whereas the number of songs that can be written is infinite. There are about 10^170 legal positions in Go. We think that there are about 10^82 particles in the observable universe. The space isn't infinitely large, but it's close enough. The space of enjoyable songs is remarkably small. There are only 12 notes in the chromatic scale and 8 in the diatonic scale. The human range of hearing only covers about nine octaves. There are a relatively small pool of rhythms and harmonies that sound pleasing to most people. Accidental plagiarism is a very common problem in popular music - it's easy to unintentionally write a song that's almost identical to another song. The most common defence in music plagiarism cases is simply to find lots of prior art; the song that you've allegedly plagiarised is probably extremely similar to a lot of other songs. Jazz musicians can improvise over unfamiliar chord progressions precisely because most music isn't particularly original. If you understand music theory, you can make fairly accurate guesses about what's coming next. Practically all music follows a similar set of understandable patterns, even across cultures. https://www.theatlantic.com/science/archive/2016/09/music-plagiarism/499985/ https://www.theatlantic.com/science/archive/2016/09/music-pl...
nawtacawp 9y ago
For sure: "The new, modified version, Creative Adversarial Networks (CAN), is designed to generate work that does not fit the known artistic styles, thus “maximizing deviation from established styles and minimizing deviation from art distribution,” according to the paper. For the training, they used 81,449 paintings by 1,119 artists in the publicly available WikiArt data set." https://news.artnet.com/art-world/rutgers-artificial-intelligence-art-1019066 https://news.artnet.com/art-world/rutgers-artificial-intelli...
markshead 9y ago
As I understand it, this would allow you to create a level of a game like Super Mario Brothers as usually and then it could use that to generate additional levels that work in the same way with the same set of rules.

Soon even artists will be unemployed.

by ryacko 9y ago

Soon even artists will be unemployed.

I would think it's actually engineering job that is more at risk. Artists and animators will visually create these games and AI will figure out the code.

memco 9y ago
One thing that bugged me about the 2009 Star Trek film was why Checkov runs over to take manual control of the teleporter to beam up Kirk as he’s freefalling towards the planet: surely a a machine at their level of technological sophistication would be able to calculate the trajectory and beam them up in real time without too much trouble.

wpietri 9y ago

Just the opposite, I think. The promise of computers has always been to take over boring work so that humans can focus on the more interesting bits. Artists will always have something to do.

cheez 9y ago
You might need less artists or be able to create more games with the same number.
jdietrich 9y ago
Entirely plausible hypothetical: what happens if Deepmind train a massive neural network on the entire Spotify catalog, weighted for Billboard chart position and Grammy nominations? What if that algorithm turns out to be as good at songwriting as AlphaGo is at the game of Go? Will anyone listen to human-written songs if AlphaSong starts producing superhumanly beautiful music? Will any of us listen to the same music if AlphaSong can analyse our personal playlists and produce an infinite number of songs that perfectly match our musical tastes?
- YeGoblynQueenne 9y ago
  In practical terms, the problem with learning songwriting, vs learning Go, is that the number of board positions in Go is finite whereas the number of songs that can be written is infinite. With Go, then, the task of an AI player (like AlphaGo) is to search a finite space for those board states that allow it to win. With songwriting- the task is not even clearly defined. Obviously, you're trying to generate songs people will want to listen to, so you have to search an infinite space for a set of songs that satisfy some criteria of "listenability", but what criteria are these? Popular songs range over wildly different music styles, from Black Metal to Medieval folk revivalist music through RnB and Arabic belly dancing music. What examples will we train our AI songwriter on? All music, ever? Specific kinds of music? Specific songs as exemplars of specific styles? Or in other words, what exactly will our AI songwriter be trying to learn? It'd be looking into an infinite haystack filled with almost identical needles for a particular needle it's never even seen well enough to recognise.
  - jdietrich 9y ago
    >In practical terms, the problem with learning songwriting, vs learning Go, is that the number of board positions in Go is finite whereas the number of songs that can be written is infinite. There are about 10^170 legal positions in Go. We think that there are about 10^82 particles in the observable universe. The space isn't infinitely large, but it's close enough. The space of enjoyable songs is remarkably small. There are only 12 notes in the chromatic scale and 8 in the diatonic scale. The human range of hearing only covers about nine octaves. There are a relatively small pool of rhythms and harmonies that sound pleasing to most people. Accidental plagiarism is a very common problem in popular music - it's easy to unintentionally write a song that's almost identical to another song. The most common defence in music plagiarism cases is simply to find lots of prior art; the song that you've allegedly plagiarised is probably extremely similar to a lot of other songs. Jazz musicians can improvise over unfamiliar chord progressions precisely because most music isn't particularly original. If you understand music theory, you can make fairly accurate guesses about what's coming next. Practically all music follows a similar set of understandable patterns, even across cultures. https://www.theatlantic.com/science/archive/2016/09/music-plagiarism/499985/ https://www.theatlantic.com/science/archive/2016/09/music-pl...

nawtacawp 9y ago

For sure: "The new, modified version, Creative Adversarial Networks (CAN), is designed to generate work that does not fit the known artistic styles, thus “maximizing deviation from established styles and minimizing deviation from art distribution,” according to the paper. For the training, they used 81,449 paintings by 1,119 artists in the publicly available WikiArt data set." https://news.artnet.com/art-world/rutgers-artificial-intelligence-art-1019066 https://news.artnet.com/art-world/rutgers-artificial-intelli...

markshead 9y ago

As I understand it, this would allow you to create a level of a game like Super Mario Brothers as usually and then it could use that to generate additional levels that work in the same way with the same set of rules.