Janelle Shane

AS HEARD ON NPR'S "SCIENCE FRIDAY" Discover the book that Malcolm Gladwell, Susan Cain, Daniel Pink, and Adam Grant want you to read this year, an "accessible, informative, and hilarious" introduction to the weird and wonderful world of artificial intelligence (Ryan North). "You look like a thing and I love you" is one of the best pickup lines ever... according to an artificial intelligence trained by scientist Janelle Shane, creator of the popular blog AI Weirdness. She creates silly AIs that learn how to name paint colors, create the best recipes, and even flirt (badly) with humans--all to understand the technology that governs so much of our daily lives. We rely on AI every day for recommendations, for translations, and to put cat ears on our selfie videos. We also trust AI with matters of life and death, on the road and in our hospitals. But how smart is AI really... and how does it solve problems, understand humans, and even drive self-driving cars? Shane delivers the answers to every AI question you've ever asked, and some you definitely haven't. Like, how can a computer design the perfect sandwich? What does robot-generated Harry Potter fan-fiction look like? And is the world's best Halloween costume really "Vampire Hog Bride"? In this smart, often hilarious introduction to the most interesting science of our time, Shane shows how these programs learn, fail, and adapt--and how they reflect the best and worst of humanity. You Look Like a Thing and I Love You is the perfect book for anyone curious about what the robots in our lives are thinking. "I can't think of a better way to learn about artificial intelligence, and I've never had so much fun along the way." - Adam Grant, New York Times bestselling author of Originals

How Artificial Intelligence Works and Why It's Making the World a Weirder Place

You Look Like a Thing and I Love You

Fred Rivett

And since the Al learned without explicit instruction, sometimes its strategies are very unconventional. Sometimes a little too unconventional. If you don't tell Al which moves are valid, it may find and exploit strange loopholes that completely break your game.

Even without understanding what bias is, Al can still manage to be biased. After all many Als learn by copying humans. The question theyre answering is not “What is the best solution?” but “What would the humans have done?”

Among the community of Al researchers and enthusiasts, Al has a reputation for seeing giraffes everywhere. Given a random photo of an uninteresting bit of landscape — a pond, for example, or some trees — Al will tend to report the presence of giraffes. The effect is so common that internet security expert Melissa Elliott suggested the term giraffing for the phenomenon of Al overreporting relatively rare sights.

When the machine learning algorithm powering Google Translate wasn't sure what the translation as, it may have defaulted to outputting bits of its training data — resulting in the weird religious fragments.

"For a while, when you typed “I'm going to my Grandma's,” GBoard would actually suggest “funeral.”

Why are Als so prone to solving the wrong problem?

1. They develop their own ways of solving a problem rather than relying on step-by-step instructions from a programmer.

2. They lack the contextual knowledge to understand when their solutions are not what humans would have preferred.

It's really tricky to come up with a goal that the Al isn't going to accidentally misinterpret. Especially if its misinterpreted version of the task is easier than what you want it to do.

Another frustrated programmer's attempts to train virtual robot dogs to walk resulted in dogs that twitched across the ground, did weird push-ups with their back legs crossed, and even hacked the simulation's physics so they could hover. As engineer Sterling Crispin wrote on Twitter: 

“I thought I was making progress...but these JERKS just found a flaw in the physics simulation and they're using it to glide across the floor like total cheaters.”

Dolphin trainers have learned that it's handy to get the dolphins to help with keeping their tanks clean. All they have to do is teach the dolphins to fetch trash and bring it to their keepers in exchange for a fish. It doesn't always work well, however. Some dolphins learn that the exchange rate is the same no matter how large the bit of trash is, and they learn to hoard trash instead of returning it, tearing off small pieces to bring to their keepers for a fish apiece.

That's why you'll get algorithms like the navigation app that, during the California wildfires of December 2017, directed cars toward neighborhoods that were on fire. It wasn't trying to kill people: it just saw that those neighborhoods had less traffic. Nobody had told it about fire.

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