Major Major Major Major

AI’s are also lazy, lazy cheaters–or rather, they optimize for exactly what you tell them to optimize for. One time, researchers wrote an evolutionary algorithm tasked with designing robots that could move, from a pile of parts. The training judged how good a robot was at moving by how quickly it could reach a goal at the other end of a virtual room. So the AI ended up making robots that were just big towers of parts, which then fell over in the direction of the goal. In another experiment, researchers asked an AI to design a circuit that could produce oscillating waves. The AI instead evolved a radio that picked up and reproduced oscillating waves from nearby computers.

Well don’t get mad–they did what you asked.

We all deal with AI’s every day, and surely we are encountering errors like this all the time, whether we can recognize them or not. And these errors can have significant repercussions. Let’s look at some examples of biased training data (like in the above hypothetical about playing cards). Facebook and Apple (and many, many others) have made facial-recognition algorithms that didn’t work on black people and/or women because the training data didn’t have very many of them. A self-driving Tesla that had never encountered a stopped perpendicular trailer thought it was a billboard, ignored it, and then crashed into it, killing the driver.

If you’d like to learn more stuff like this, along with many entertaining and illustrative examples (like telling an AI trained on Harry Potter fanfiction to write recipes), I highly recommend checking out this book! It’s great for everyone from beginners to semipros, and possibly beyond.