Faith in written information is under attack in some quarters. But images and sound recordings retain for many an inherent trustworthiness. [Machine-learning algorithms] are part of a technological wave that threatens this credibility. Audio is easier to fake. Normally, computers generate speech by linking lots of short recorded speech fragments to create a sentence. Generative audio works differently, using neural networks to learn the statistical properties of the audio source in question, then reproducing those properties directly. 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. Generating images is harder. [Generative adversarial networks] were introduced in 2014 by Ian Goodfellow. Mr Goodfellow ... suggests that the generation of YouTube fakes that are very plausible may be possible within three years. Others think it might take longer. But all agree that it is a question of when, not if. We think that AI is going to change the kinds of evidence that we can trust," says Mr Goodfellow.