In fact, AI is already everywhere. It shapes your online experience, determining the ads you see and suggesting videos while detecting social media bots and malicious websites. Companies use AI-powered resume scanners to decide which candidates to interview, and they use AI to decide who should be approved for a loan. The AIs in self-driving cars have already driven millions of miles (with the occasional human rescue during moments of confusion). We’ve also put AI to work in our smartphones, recognizing our voice commands, autotagging faces in our photos, and even applying a video filter that makes it look like we have awesome bunny ears.

 

But we also know from experience that everyday AI is not flawless, not by a long shot. Ad delivery haunts our browsers with endless ads for boots we already bought. Spam filters let the occasional obvious scam through or filter out a crucial email at the most inopportune time.

 

As more of our daily lives are governed by algorithms, the quirks of AI are beginning to have consequences far beyond the merely inconvenient. Recommendation algorithms embedded in YouTube point people toward ever more polarizing content, traveling in a few short clicks from mainstream news to videos by hate groups and conspiracy theorists.1 The algorithms that make decisions about parole, loans, and resume screening are not impartial but can be just as prejudiced as the humans they’re supposed to replace—sometimes even more so. AI-powered surveillance can’t be bribed, but it also can’t raise moral objections to anything it’s asked to do. It can also make mistakes when it’s misused—or even when it’s hacked. Researchers have discovered that something as seemingly insignificant as a small sticker can make an image recognition AI think a gun is a toaster, and a low-security fingerprint reader can be fooled more than 77 percent of the time with a single master fingerprint.

 

People often sell AI as more capable than it actually is, claiming that their AI can do things that are solidly in the realm of science fiction. Others advertise their AI as impartial even while its behavior is measurably biased. And often what people claim as AI performance is actually the work of humans behind the curtain. As consumers and citizens of this planet, we need to avoid being duped. We need to understand how our data is being used and understand what the AI we’re using really is—and isn’t.

On AI Weirdness, I spend my time doing fun experiments with AI. Sometimes this means giving AIs unusual things to imitate—like those pickup lines. Other times, I see if I can take them out of their comfort zones—like the time I showed an image recognition algorithm a picture of Darth Vader and simply asked it what it saw: it declared that Darth Vader was a tree and then proceeded to argue with me about it. From my experiments, I’ve found that even the most straightforward task can cause an AI to fail, as if you’d played a practical joke on it. But it turns out that pranking an AI—giving it a task and watching it flail—is a great way to learn about it.
In fact, as we’ll see in this book, the inner workings of AI algorithms are often so strange and tangled that looking at an AI’s output can be one of the only tools we have for discovering what it understood and what it got terribly wrong. When you ask an AI to draw a cat or write a joke, its mistakes are the same sorts of mistakes it makes when processing fingerprints or sorting medical images, except it’s glaringly obvious that something’s gone wrong when the cat has six legs and the joke has no punchline. Plus, it’s really hilarious.

 

In the course of my attempts to take AIs out of their comfort zone and into ours, I’ve asked AIs to write the first line of a novel, recognize sheep in unusual places, write recipes, name guinea pigs, and generally be very weird. But from these experiments, you can learn a lot about what AI’s good at and what it struggles to do—and what it likely won’t be capable of doing in my lifetime or yours.

 

Here’s what I’ve learned:

The Five Principles of AI Weirdness:
• The danger of AI is not that it’s too smart but that it’s not smart enough.
• AI has the approximate brainpower of a worm.
• AI does not really understand the problem you want it to solve.
• But: AI will do exactly what you tell it to. Or at least it will try its best.
• And AI will take the path of least resistance.