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Janelle  Shane

Janelle Shane

Artificial Intelligence Speaker & Humorist

Janelle Shane

Artificial Intelligence Speaker & Humorist


Janelle Shane’s AI humor blog, AIweirdness.com, looks at the strange side of artificial intelligence. She has been featured in the New York Times, The Atlantic, WIRED, Popular Science, All Things Considered, and Slate.

In 2019 she was named one of Fast Company’s 100 most creative people in business. Her soon-to-be-released TED talk is a funny and insightful look at the nature of machine learning algorithms.

Her upcoming book You Look Like a Thing and I Love You: How AI Works and Why It’s Making the World a Weirder Place uses cartoons and humorous pop-culture experiments to look inside the minds of the algorithms that run our world, making artificial intelligence and machine learning accessible and entertaining.

She received her BS in Electrical Engineering from Michigan State University, her MPhil in Physics from the University of St. Andrews, and her PhD from the University of California San Diego.

Speaker Videos

You Look Like a Thing and I Love You | Talks at Google

TED: The Danger of AI Is Weirder Than You Think

Speech Topics

What AI Can Do And Can't

AI is making headlines and disrupting entire industries. But what IS artificial intelligence, and what is it good at? At her AI humor blog, AiWeirdness.com, Shane watches machine learning algorithms struggle to deal with the complexities of the human world. From AIs that struggle to invent plausible paint colors, to AIs that struggle to recognize which pictures don’t actually contain giraffes or sheep, the funny failures of AI tell us a lot about their real-world shortcomings as well. Shane talks about the kinds of problems where AI will succeed, fail, or succeed at solving the wrong problem entirely.

Lessons Learned From Machine Learning Gone Wrong

Through Shane’s weird and hilarious anecdotes, learn about things that give machine learning algorithms trouble (recognizing sheep, counting giraffes, telling jokes) – and the implications these have on deploying machine learning algorithms in the real world.