It has become a pleasurable challenge to sit opposite Jonathan Flombaum in the Sun’s podcast studio and listen to him think out loud. An assistant professor in the department of psychological and brain sciences at Johns Hopkins University, Flombaum is associated with the Visual Thinking Lab at Homewood. He’s focused on big questions about the brain that seem more philosophical than scientific: What we already know about how it works and, more importantly, what we don’t know, and why so much of the brain’s process is a mystery to us.
“When computer scientists talk about algorithms, they are not talking about instructions in a specific computer language. That’s a program. The algorithm is the basic set of instructions that can be translated into really any computer language. Every computer you interact with from your phone to your laptop uses essentially an algorithm to do addition.”
Artificial intelligence, machine learning and deep learning
“Artificial intelligence and its subfield of machine learning are concerned with building algorithms that are genuinely intelligent, in the way that humans are,” Flombaum writes. “Are there algorithms that can figure out how to do things that people have not explained to them in detail? Are there algorithms that can do things that people have not done yet, perhaps things that humans couldn’t do at all. Are there algorithms that can discover things and acquire new knowledge on their own, maybe even make discoveries that have yet to be made? In a word, are there algorithms that can learn?
“The answer, of course, is yes. Machine learning is in part a field that has married computer science with a little neuroscience and a good amount of psychology. Machine-learning algorithms — the kind that pick Netflix movies for you, that buy and sell stocks at some hedge funds, that serve you ads on the Internet, that pilot autonomous cars — are algorithms that learn, algorithms that obtain knowledge apart from the endowment of their human overlords.
Curveballs and Three-pointers
“Many of the things that deep learning algorithms are especially good for are things that people can do but can’t explain. Psychologists have for a long time distinguished between explicit and implicit knowledge. Explicit knowledge includes all the facts, experiences and opinions we talk about. Implicit is all the stuff we clearly know, but can’t explain.”
Pedro Domingos, author of The Master Algorithm, explains why programs that adopt implicit human knowledge are so important: “As of today, people can write many programs that computers can’t learn. But more surprisingly, computers can learn programs that people can’t write. We know how to drive cars and decipher handwriting, but these skills are subconscious; we’re not able to explain to a computer how to do these things.”
Autonomous Cars and Teenagers