4 ms·

This is really cool tech and very awesome work by deepmind. But man does this scare me. I remember a quote: “You furnish the pictures and I’ll furnish the war.

by wpasc 9y ago

This is really cool tech and very awesome work by deepmind. But man does this scare me. I remember a quote: “You furnish the pictures and I’ll furnish the war.” – William Randolph Hearst, January 25, 1898. This was in the lead up to the spanish american war. Can you imagine tech like this/tech like "deepfakes" being used today? Fake news that was text alone has done and is doing damage in elections around the world now. Imagine that armed with pictures?!?! In a dueling NN architecture, many say the discriminator will be able to detect the fake images. I wonder is there a threshold that a produced image is just too damn close to a real picture that even an equally good NN that is discriminating can no longer differentiate? In the end, both real and fake images are just pixel values... what would we do then? Cool tech, scary possibilities.

deleted 9y ago
[deleted]
MrLeap 9y ago
This has already happened, albeit manually. See this clumsy representation: https://en.wikipedia.org/wiki/Adnan_Hajj_photographs_controversy https://en.wikipedia.org/wiki/Adnan_Hajj_photographs_controv... Will be interesting if it ever reaches the point where it can be automated and scaled. I predict a modernized repeat of the war of the worlds tipping point followed by ratcheted skepticism for all kinds of temporal simulacra. Then 3d imaging will enter widespread consumer usage and prove to be very difficult to convincingly reproduce by neural networks, until it is. Trust will be restored in some kind of media until it's broken. Rinse and repeat.
- opportune 9y ago
  Here's another scary GAN proof of concept [0]. In this case, researchers transferred someone's face in real time to facial expression and mouth movements of public figures. Combined with DeepMind's new tech that seems to be able to produce human voice with believable candor and inflection [1], you could make some very convincing fake footage. [0]https://www.youtube.com/watch?v=ohmajJTcpNk https://www.youtube.com/watch?v=ohmajJTcpNk [1]https://research.googleblog.com/2018/03/expressive-speech-synthesis-with.html https://research.googleblog.com/2018/03/expressive-speech-sy...
gwern 9y ago
This isn't remotely near the state of the art for raw image generation; that would be something like ProGAN or pixel-CNN, which don't involve any reinforcement learning or paintbrushes, and already do photorealistic synthesis. Horse, barn. The point of OP is that you're learning to generate little images in a much more difficult setup: you have to control some complex blackbox system (like a paintbrush robot) to try to generate an image with only crude success/failure feedback at the end of the sequence of actions. The hope is that by going through this intermediate environment, instead of generating an entire image in a single shot via convolutions, it'll be learning more abstract structure about what makes up a face etc, so hypothetically it could do things like rotate faces in 3D (whereas something like ProGAN only sees faces as 2D blobs so it can do things like add/subtract sunglasses or change hair color, but 3D transformations are beyond it). And, with this more abstract deeper understanding, it should be able to speed up learning in settings like robotics etc (instead of paintings, imagine they are videos of humans controlling pick-and-place robot arms); you can see this as one way of approaching unsupervised learning and providing primitives which a higher-level agent can learn faster from (somewhat like the GAIL architecture uses GANs for semi-supervised learning).

by wpasc 9y ago

deleted 9y ago

[deleted]

MrLeap 9y ago

This has already happened, albeit manually. See this clumsy representation: https://en.wikipedia.org/wiki/Adnan_Hajj_photographs_controversy https://en.wikipedia.org/wiki/Adnan_Hajj_photographs_controv... Will be interesting if it ever reaches the point where it can be automated and scaled. I predict a modernized repeat of the war of the worlds tipping point followed by ratcheted skepticism for all kinds of temporal simulacra. Then 3d imaging will enter widespread consumer usage and prove to be very difficult to convincingly reproduce by neural networks, until it is. Trust will be restored in some kind of media until it's broken. Rinse and repeat.

opportune 9y ago
Here's another scary GAN proof of concept [0]. In this case, researchers transferred someone's face in real time to facial expression and mouth movements of public figures. Combined with DeepMind's new tech that seems to be able to produce human voice with believable candor and inflection [1], you could make some very convincing fake footage. [0]https://www.youtube.com/watch?v=ohmajJTcpNk https://www.youtube.com/watch?v=ohmajJTcpNk [1]https://research.googleblog.com/2018/03/expressive-speech-synthesis-with.html https://research.googleblog.com/2018/03/expressive-speech-sy...

gwern 9y ago

This isn't remotely near the state of the art for raw image generation; that would be something like ProGAN or pixel-CNN, which don't involve any reinforcement learning or paintbrushes, and already do photorealistic synthesis. Horse, barn. The point of OP is that you're learning to generate little images in a much more difficult setup: you have to control some complex blackbox system (like a paintbrush robot) to try to generate an image with only crude success/failure feedback at the end of the sequence of actions. The hope is that by going through this intermediate environment, instead of generating an entire image in a single shot via convolutions, it'll be learning more abstract structure about what makes up a face etc, so hypothetically it could do things like rotate faces in 3D (whereas something like ProGAN only sees faces as 2D blobs so it can do things like add/subtract sunglasses or change hair color, but 3D transformations are beyond it). And, with this more abstract deeper understanding, it should be able to speed up learning in settings like robotics etc (instead of paintings, imagine they are videos of humans controlling pick-and-place robot arms); you can see this as one way of approaching unsupervised learning and providing primitives which a higher-level agent can learn faster from (somewhat like the GAIL architecture uses GANs for semi-supervised learning).