Asking the Right Questions About AI
&gt;&gt; <i>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.</i><p>That&#x27;s a typically unhistorical explanation of the relationship between machine learning and AI, which is really quite simple: machine learning is a field in the broader reseach subject of AI. &quot;Professionals&quot; did not &quot;at some point&quot; start using machine learning instead of AI. The field got a name when it solidified into a field, just like Natural Language Processing did, or Machine Vision.<p>In fact, to use &quot;machine learning&quot; as a byword for &quot;AI&quot; is not different than using &quot;NLP&quot; or &quot;Machine Vision&quot; as a byword for AI. It is that wrong and makes it that evident that the person using the terms doesn&#x27;t really understand where they come from.