Asking the Right Questions About AI

Super interesting write up about the realities of AI, what it can and cant do! Via, Medium!

In the past few years, we’ve been deluged with discussions of how artificial intelligence (AI) will either save or destroy the world. Self-driving cars will keep us alive; social media bubbles will destroy democracy; robot toasters will rob us of our ability to heat bread.

It’s probably pretty clear to you that some of this is nonsense, and that some of this is real. But if you aren’t deeply immersed in the field, it can be hard to guess which is which. And while there are endless primers on the Internet for people who want to learn to program AI, there aren’t many explanations of the ideas, and the social and ethical challenges they imply, for people who aren’t and don’t want to be software engineers or statisticians.

And if we want to be able to have real discussions about this as a society, we need to fix that. So today, we’re going to talk about the realities of AI: what it can and can’t actually do, what it might be able to do in the future, and what some of the social, cultural, and ethical challenges it poses are. I won’t cover every possible challenge; some of them, like filter bubbles and disinformation, are so big that they need entire articles of their own. But I want to give you enough examples of the real problems that we face that you’ll be situated to start to ask hard questions on your own.

I’ll give you one spoiler to start with: most of the hardest challenges aren’t technological at all. The biggest challenges of AI often start when writing it makes us have to be very explicit about our goals, in a way that almost nothing else does — and sometimes, we don’t want to be that honest with ourselves.

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Artificial Intelligence and Machine Learning

As I write this, I’m going to use the terms “artificial intelligence” (AI) and “machine learning” (ML) more or less interchangeably. There’s a stupid reason these terms mean almost the same thing: it’s that “artificial intelligence” has historically been defined as “whatever computers can’t do yet.” For years, people argued that it would take true artificial intelligence to play chess, or simulate conversations, or recognize images; every time one of those things actually happened, the goalposts got moved. The phrase “artificial intelligence” was just too frightening: it cut too close, perhaps, to the way we define ourselves, and what makes us different as humans. So at some point, professionals started using the term “machine learning” to avoid the entire conversation, and it stuck. But it never really stuck, and if I only talked about “machine learning” I’d sound strangely mechanical — because even professionals talk about AI all the time.

So let’s start by talking about what machine learning, or artificial intelligence, is. In the strictest sense, machine learning is part of the field of “predictive statistics:” it’s all about building systems which can take information about things which happened in the past, and make out of those some kind of model of the world around them which they can then use to predict what might happen under other circumstances. This can be as simple as “when I turn the wheel left, the car tends to turn left, too,” or as complicated as trying to understand a person’s entire life and tastes.

Read more and listen to the full story on Medium!