Jeff Hawkins invented the Palm Pilot and the Treo smartphone, and now he’s trying to invent the future of artificial intelligence. In this interview, he explains why he’s so adamant about his company’s approach and how the company is settling in after some early hiccups.
Jeff Hawkins is best known for bringing us the Palm Pilot, but he’s working on something that could be much, much bigger.
For the past several years, Hawkins has been studying how the human brain functions with the hope of replicating it in software. In 2004, he published a book about his findings. In 2012, Numenta, the company he founded to commercialize his work, finally showed itself to the world after roughly seven years operating in stealth mode.
I recently spoke with Hawkins to get his take on why his approach to artificial intelligence will ultimately overtake other approaches, including the white-hot field of deep learning. We also discussed how Numenta has survived some early business hiccups and how he plans to keep the lights on and the money flowing in.
An edited version of the interview follows. Hawkins kicks if off with a description of Numenta’s technology — which it calls hierarchical temporal memory — and how it came to be.
Derrick Harris: Please explain Numenta’s approach to brain-inspired artificial intelligence technology.
Jeff Hawkins: We’re going through a transition right now in the world of machine intelligence that’s similar to the transition from analog to digital computing back in the 1940s. Today, if you look in the world of machine learning and machine intelligence, you see varied types of things going on. There are different types of algorithms that people are using — specific and universal — and people debate which approach is better.
We’re very confident that by the end of the 2020s, we’re going to be settled on a dominant paradigm. It’s going to be quite different than the one we’re currently in today, where specific algorithms that excel at one task dominate. We believe it’s going to be based instead on the universal algorithms that work on many problems. They’re going to be memory-based, not mathematically based. They’re going to be based primarily on time-based patterns, and they’re going to be online learning paradigms.
Our belief in this comes really from studying the brain. This is what the neocortex does. The neocortex uses one framework for vision, audition, language, motor, planning, robotics, everything. Everything you do, writing, thoughts, planning conferences, it’s all the same thing. It’s one memory framework.
We’ve invented a term called hierarchical temporal memory, which describes the basic theory about what’s going on here. Very importantly, nothing in HTM is task-specific, just like in your brain. The way you see and the way you hear and the way you feel, the neural tissue that does that is exactly the same. You can actually swap them around, and you’ll still work.
That’s the key part of the whole bet here, that there are going to be, as we know in the brain, universal learning algorithms that may not be the best at everything, but they’re really good at everything....MUCH MORE
