Jun 12, 2019

I spoke last week at re:MARS, Amazon's conference focusing on machine learning, automation, robotics, and space. I heard great talks from Jeff Bezos, Kate Darling, Ken Goldberg, Marc Raibert, and others.

The most widely used image-recognition systems are better at identifying items from wealthy households than from poor ones.

Generative adversarial networks can synthesize images to help train computer vision systems. But GANs are compute-hungry and don’t always produce realistic output. Now there’s a more efficient and true-to-life alternative.

Here’s a conundrum: Deep learning could help address a variety of intractable problems, climate change among them. Yet neural networks can consume gargantuan quantities of energy, potentially dumping large amounts of heat-trapping gas into the atmosphere.

Another week, another way to make deepfakes. A team at Samsung recently proposed a model that generates talking-head videos by imposing facial landmarks over still images. Now a different team offers one that makes an onscreen speaker say anything you can type.