The Human Strategy
[10.30.17]The idea of a credit assignment function, reinforcing “neurons” that work, is the core of current AI. And if you make those little neurons that get reinforced smarter, the AI gets smarter. So, what would happen if the neurons were people? People have lots of capabilities; they know lots of things about the world; they can perceive things in a human way. What would happen if you had a network of people where you could reinforce the ones that were helping and maybe discourage the ones that weren't?
That begins to sound like a society or a company. We all live in a human social network. We're reinforced for things that seem to help everybody and discouraged from things that are not appreciated. Culture is something that comes from a sort of human AI, the function of reinforcing the good and penalizing the bad, but applied to humans and human problems. Once you realize that you can take this general framework of AI and create a human AI, the question becomes, what's the right way to do that? Is it a safe idea? Is it completely crazy?
ALEX "SANDY" PENTLAND is a professor at MIT, and director of the MIT Connection Science and Human Dynamics labs. He is a founding member of advisory boards for Google, AT&T, Nissan, and the UN Secretary General. He is the author of Social Physics, and Honest Signal. Sandy Pentland's Edge Bio page
THE HUMAN STRATEGY
The big question that I'm asking myself these days is how can we make a human artificial intelligence? Something that is not a machine, but rather a cyber culture that we can all live in as humans, with a human feel to it. I don't want to think small—people talk about robots and stuff—I want this to be global. Think Skynet. But how would you make Skynet something that's really about the human fabric?
The first thing you have to ask is what's the magic of the current AI? Where is it wrong and where is it right?
The good magic is that it has something called the credit assignment function. What that lets you do is take stupid neurons, these little linear functions, and figure out, in a big network, which ones are doing the work and encourage them more. It's a way of taking a random bunch of things that are all hooked together in a network and making them smart by giving them feedback about what works and what doesn't. It sounds pretty simple, but it's got some complicated math around it. That's the magic that makes AI work.
The bad part of that is, because those little neurons are stupid, the things that they learn don't generalize very well. If it sees something that it hasn't seen before, or if the world changes a little bit, it's likely to make a horrible mistake. It has absolutely no sense of context. In some ways, it's as far from Wiener's original notion of cybernetics as you can get because it's not contextualized: it's this little idiot savant.
But imagine that you took away these limitations of current AI. Instead of using dumb neurons, you used things that embedded some knowledge. Maybe instead of linear neurons, you used neurons that were functions in physics, and you tried to fit physics data. Or maybe you put in a lot of stuff about humans and how they interact with each other, the statistics and characteristics of that. When you do that and you add this credit assignment function, you take your set of things you know about—either physics or humans, and a bunch of data—in order to reinforce the functions that are working, then you get an AI that works extremely well and can generalize.
In physics, you can take a couple of noisy data points and get something that's a beautiful description of a phenomenon because you're putting in knowledge about how physics works. That's in huge contrast to normal AI, which takes millions of training examples and is very sensitive to noise. Or the things that we've done with humans, where you can put in things about how people come together and how fads happen. Suddenly, you find you can detect fads and predict trends in spectacularly accurate and efficient ways.
Human behavior is determined as much by the patterns of our culture as by rational, individual thinking. These patterns can be described mathematically, and used to make accurate predictions. We’ve taken this new science of “social physics” and expanded upon it, making it accessible and actionable by developing a predictive platform that uses big data to build a predictive, computational theory of human behavior.
The idea of a credit assignment function, reinforcing “neurons” that work, is the core of current AI. And if you make those little neurons that get reinforced smarter, the AI gets smarter. So, what would happen if the neurons were people? People have lots of capabilities; they know lots of things about the world; they can perceive things in a human way. What would happen if you had a network of people where you could reinforce the ones that were helping and maybe discourage the ones that weren't?
That begins to sound like a society or a company. We all live in a human social network. We're reinforced for things that seem to help everybody and discouraged from things that are not appreciated. Culture is something that comes from a sort of human AI, the function of reinforcing the good and penalizing the bad, but applied to humans and human problems. Once you realize that you can take this general framework of AI and create a human AI, the question becomes, what's the right way to do that? Is it a safe idea? Is it completely crazy?
What we've done with my students, particularly Peter Krafft, and with Josh Tenenbaum, another faculty member, is look at how people make decisions on huge databases of financial decisions, and also other sorts of decisions. What we find is that there's an interesting way that humans make decisions that solve this credit assignment problem and make the community smarter. The part that's most interesting is that it addresses a classic problem in evolution.
Where does culture come from? How can we select for culture in evolution when it's the individuals that reproduce? What you need is something that selects for the best cultures and the best groups, but also selects for the best individuals because they're the things that transmit the genes.
When you put it this way and you go through the mathematical literature, you discover that there's one best way to do this. That way is something you probably haven't heard of. It's called “distributed Thompson sampling,” a mathematical algorithm used in choosing the action that maximizes the expected reward over a set of possible actions....MUCH MORE, including video and/or audio
It's a way of combining evidence, of exploring and exploiting at the same time. It has a unique property in that it's the best strategy both for the individual and for the group. If you select on the basis of the group, and then the group gets wiped out or reinforced, you're also selecting for the individual. If you select for the individual, and the individual does what's good for them, then it's automatically the best thing for the group. That's an amazing alignment of interests and utilities. It addresses this huge question in evolution: Where does culture fit into natural selection?...
