In this episode, Byron and Gregory talk about consciousness, jobs, data science, transfer learning.
Byron Reese: This is “Voices in AI”, brought to you by Gigaom. I’m Byron Reese. Today our guest is Gregory Piatetsky. He’s a leading voice in Business Analytics, Data Mining, and Data Science. Twenty years ago, he founded and continues to operate a site called KDnuggets about knowledge discovery. It’s dedicated to the various topics he’s interested in. Many people think it’s a must-read resource. It has over 400,000 regular monthly readers. He holds an MS and a PhD in computer science from NYU.
Welcome to the show.
Gregory Piatetsky: Thank you, Byron. Glad to be with you.
I always like to start off with definitions, because in a way we’re in such a nascent field in the grand scheme of things that people don’t necessarily start off agreeing on what terms mean. How do you define artificial intelligence?
Artificial intelligence is really machines doing things that people think require intelligence, and by that definition the goalposts of artificial intelligence are constantly moving. It was considered very intelligent to play checkers back in the 1950s, then there was a program. The next boundary was playing chess, and then computers mastered it. Then people thought playing Go would be incredibly difficult, or driving cars. General artificial intelligence is the field that tries to develop intelligent machines. And what is intelligence? I’m sure we will discuss, but it’s usually in the eye of the beholder.
Well, you’re right. I think a lot of the problem with the term artificial intelligence is that there is no consensus definition of what intelligence is. So, are you saying if we’re constantly moving the goalposts, it sounds like you’re saying we don’t have systems today that are intelligent.