Jeff Hawkins: Thousand Brains Theory of Intelligence

**Lex Fridman** (0:00)
The following is a conversation with Jeff Hawkins. He's the founder of the Redwood Center for Theoretical Neuroscience in 2002, and Numenta in 2005 In his 2004 book titled On Intelligence, and in the research before and after, he and his team have worked to reverse engineer the neural cortex and propose artificial intelligence, architectures, approaches, and ideas that are inspired by the human brain. These ideas include Hierarchical Temporal Memory, HTM, from 2004, and new work, The Thousand Brains Theory of Intelligence, from 2017, 18, and 19 Jeff's ideas have been an inspiration to many who have looked for progress beyond the current machine learning approaches, but they have also received criticism for lacking a body of empirical evidence supporting the models. This is always a challenge when seeking more than small incremental steps forward in AI. Jeff has a brilliant mind, and many of the ideas he has developed and aggregated from neuroscience are worth understanding and thinking about. There are limits to deep learning as it is currently defined. Forward progress in AI is shrouded in mystery. My hope is that conversations like this can help provide an inspiring spark for new ideas. This is the Artificial Intelligence Podcast. If you enjoy it, subscribe on YouTube, iTunes, or simply connect with me on Twitter at Lex Friedman, spelled F-R-I-D. And now here's my conversation with Jeff Hawkins.
Are you more interested in understanding the human brain or in creating artificial systems that have many of the same qualities but don't necessarily require that you actually understand the underpinning workings of our mind?

**Jeff Hawkins** (1:58)
So, there's a clear answer to that question. My primary interest is understanding the human brain. No question about it. But, I also firmly believe that we will not be able to create fully intelligent machines until we understand how the human brain works. So, I don't see those as separate problems. I think there's limits to what can be done with machine intelligence if you don't understand the principles by which the brain works. And so, I actually believe that studying the brain is actually the fastest way to get to machine intelligence.

**Lex Fridman** (2:28)
And within that, let me ask the impossible question. How do you not define, but at least think about what it means to be intelligent?

**Jeff Hawkins** (2:35)
So, I didn't try to answer that question first. We said, let's just talk about how the brain works. Let's figure out how certain parts of the brain, mostly the neocortex, but some other parts too. The parts of the brain most associated with intelligence. And let's discover the principles by how they work. Because intelligence isn't just like some mechanism and it's not just some capabilities. It's like, okay, we don't even know where to begin on this stuff.
And so now that we've made a lot of progress on this, after we've made a lot of progress on how the neocortex works and we can talk about that, I now have a very good idea what's going to be required to make intelligent machines. I can tell you today, but some of the things are going to be necessary, I believe, to create intelligent machines.

**Lex Fridman** (3:19)
Well, so we'll get there. We'll get to the neocortex and some of the theories of how the whole thing works. You're saying, as we understand more and more about the neocortex, about our own human mind, we'll be able to start to more specifically define what it means to be intelligent. It's not useful to really talk about that until...

**Jeff Hawkins** (3:38)
I don't know if it's not useful. Look, there's a long history of AI, as you know, and there's been different approaches taken to it. And who knows, maybe they're all useful. So the good old-fashioned AI, the expert systems, the current convolutional neural networks, they all have their utility. They all have a value in the world. But I would think almost everyone agrees that none of them are really intelligent in a sort of a deep way that humans are. And so it's just the question of how do you get from where those systems were or are today to where a lot of people think we're going to go? And there's a big, big gap there, a huge gap. And I think the quickest way of bridging that gap is to figure out how the brain does that. And then we can sit back and look and say, oh, what are these principles that the brain works on are necessary and which ones are not? Clearly, we don't have to build this in, and telegenic machines aren't going to be built out of, you know, organic living cells. But there's a lot of stuff that goes on the brain that's going to be necessary.