Creation storiesFake news: you ain’t seen nothing yet
- Instead, he took only a few days to create the clip on a desktop computer using a generative adversarial network (GAN), a type of machine-learning algorithm.
- Putting words into the mouth of Mr Trump, say, or of any other public figure, is a matter of feeding recordings of his speeches into the algorithmic hopper and then telling the trained software what you want that person to say.
- Mr Goodfellow observed that, although deep learning allowed machines to discriminate marvellously well between different sorts of data (a picture of a cat v one of a dog, say), software that tried to generate pictures of dogs or cats was nothing like as good.
- The adversary would look at the generated images and judge whether they were “real”, meaning similar to those that already existed in the generative software’s training database.
- By trying to fool the adversary, the generative software would learn to create images that look real, but are not.
EARLIER this year Françoise Hardy, a French musician, appeared in a YouTube video (see link). She is asked, by a presenter off-screen, why President Donald Trump sent his press secretary, Sean Spicer, to lie about the size of the inauguration crowd. First, Ms Hardy argues. Then she says Mr Spicer “gave alternative facts to that”.
@TheEconomist: One machine-learning expert suggests the generation of plausible YouTube fakes may be possible within three years
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