Can We Comprehend Deep Learning Systems?

Science fiction readers have long enjoyed the humorous works of Lewis Padgett (who was really a married couple). In the 1943 short story The Proud Robot, they describe a robot with ways of thinking and sensing that, well, don't make sense to humans:

"How’d you know where to reach me?"
"I vastened you," the robot said.
"What?"
"I vastened you were at the Vox-View studios with Patsy Brock."
"What’s vastened?" Gallegher wanted to know.
"It’s a sense I’ve got. You’ve nothing remotely like it, so I can’t describe it to you. It’s rather like a combination of sagrazi and prescience."
"Sagrazi?"
"Oh, you don’t have sagrazi, either, do you? Well, don’t waste my time..."

In an intriguing article in Technology Review, Will Knight talks about how nobody really knows how the most advanced algorithms behind self-driving cars (and other technology) are able to do the things they do.

...deep learning, has proved very powerful at solving problems in recent years, and it has been widely deployed for tasks like image captioning, voice recognition, and language translation. There is now hope that the same techniques will be able to diagnose deadly diseases, make million-dollar trading decisions, and do countless other things to transform whole industries.

But this won’t happen—or shouldn’t happen—unless we find ways of making techniques like deep learning more understandable to their creators and accountable to their users. Otherwise it will be hard to predict when failures might occur—and it’s inevitable they will.

There’s already an argument that being able to interrogate an AI system about how it reached its conclusions is a fundamental legal right. Starting in the summer of 2018, the European Union may require that companies be able to give users an explanation for decisions that automated systems reach. This might be impossible... Even the engineers who build these apps cannot fully explain their behavior.

(Via The Dark Secret at the Heart of AI.)

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