I don’t think I’d make that statement. The definition of artificial intelligence to me is always a bit of a challenge. The artificial part, I think, is easy, we just covered that. The intelligence part, I’ve looked at different definitions of artificial intelligence, and most of them use the word “intelligence” in the definition. That doesn’t seem to get us much further. I could say something like, “it’s artifacts that can acquire and/or apply knowledge,” but then we’re going to have a conversation about what knowledge is. So, what I get out of it is it’s not very satisfying to talk about intelligence at this level of generality because, yes, in answer to your question, artificial intelligence systems do things which human beings do, in different ways and, as you indicated, not with the same fullness or level that human beings do. That doesn’t mean that they’re not intelligent, they have certain capabilities that we regard as intelligent.
You know it’s really interesting because at its core you’re right, there’s no consensus definition on intelligence. There’s no consensus definition on life or death. And I think that’s really interesting that these big ideas aren’t all that simple. I’ll just ask you one more question along these lines then. Alan Turing posed the question in 1950, “Can a machine think?” What would you say to that?
I would say yes, but now we have to wonder what “think” might mean, because “think” is one aspect of intelligent behavior, it indicates some kind of reasoning or reflection. I think that there are software systems that do reason and reflect, so I will say yes, they think.
All right, so now let’s get to SRI International. For the listeners who may not be familiar with the company can you give us the whole background and some of the things you’ve done to date, and why you exist, and when it started and all of that?
Great, just a few words about SRI International. SRI International is a non-profit research and development company, and that that’s a pretty rare category. A lot of companies do research and development—a fewer than used to, but still quite a few—and very few have research and development as their business, but that is our business. We’re also non-profit, which really means that we don’t have shareholders. We still have to make money, but all the money we make has to go into the mission of the organization which is to do R&D for the benefit of mankind. That’s the general thing. It started out as part of Stanford, it was formerly the Stanford Research Institute. It’s been independent since 1970 and it’s one of the largest of these R&D companies in the world, about two thousand people.
Now, the information and computing sciences part, as you said, that’s about two hundred and fifty people, and probably the thing that we’re most famous for nowadays is that we created Siri. Siri was a spinoff of one of my labs, the AI Center. It was a spinoff company of SRI, that’s one of the things we do, and it was acquired by Apple, and has now become world famous. But we’ve been in the field of artificial intelligence for decades. Another famous SRI accomplishment would be Shakey the Robot, which was really the first robot that could move around and reason and interact. That was many years ago. We’ve also, in more recent history, been involved in very large government-sponsored AI projects which we’ve led, and we just have lots of things that we’ve done in AI.
Is it just a coincidence that Siri and SRI are just one letter different, or is that deliberate?
It’s a coincidence. When SRI starts companies we bring in entrepreneurs from the outside almost always, because it would be pretty unusual for an SRI employee to be the right person to be the CEO of the startup company. It does happen, but it’s unusual. Anyway, in this case, we brought in a guy named Dag Kittlaus, and he’s of Norwegian extraction, and he chose the name. Siri is a Norwegian women’s name and that became the name of the company. Actually, somewhat to our surprise, Apple retained that name when they launched Siri.
Let’s go through some of the things that your group works on. Could we start with those sorts of technologies? Are there other things in that family of conversational AI that you work on and are you working on the next generation of that?
Yes, indeed, in fact, we’ve been working on the next generation for a while now. I like to think about conversational systems in different categories. Human beings have conversations for all kinds of reasons. We have social conversations, where there’s not particularly any objective but being friendly and socializing. We have task-oriented kinds of conversations—those are the ones that we are focusing on mostly in the next generation—where you’re conversing with someone in order to perform a task or solve some problem, and what’s really going on is it’s a collaboration. You and the other person, or people, are working together to solve a problem.
I’ll use an example from the world of online banking because we have another spinoff called Kasisto that is using the next-generation kind of conversational interaction technology. So, let’s say that you walk into a bank, and you say to the person behind the counter, “I want to deposit $1,000 in checking.” And the person on the other side, the teller says, “From which account?” And you say, “How much do I have in savings?” And the teller says, “You have $1,500, but if you take $1,000 out you’ll stop earning interest.” So, take that little interaction. That’s a conversational interaction. People do this all the time, but it’s actually very sophisticated and requires knowledge.
If you now think of, not a teller, but a software system, a software agent that you’re conversing with—we’ll go through the same little interaction. The person says, “I want to deposit $1,000 in checking.” And the teller said, “From which account?” The software system has to know something about banking. It has to know that a deposit is a money transfer kind of interaction and it requires a from-account and a to-account. And in this case, the to-account has been specified but the from-account hasn’t been specified. In many cases that person would simply ask for that missing information, so that’s the first part of the interaction. So, again, the teller says, “From which account?” And the person says, “How much do I have in savings?” Well, that’s not an answer to the question. In fact, it’s another question being introduced by the person and it’s actually a balance inquiry question. They want to know how much they have in savings. Now, when I go through this the first time, the reason I do this twice is that when I went through it the first time, almost nobody even notices that that wasn’t an answer to the question, but if you try out a lot of the personal assistant systems that are out there, they tend to crater on that kind of interaction, because they don’t have enough conversational knowledge to be able to handle that kind of thing. And then the interaction goes on where the teller is providing information, beyond what the person asked, about potentially losing interest, or it might be that they would get a fee or something like that.
That illustrates the point that we expect our conversational partners to be proactive, not just to simply answer our questions, but to actually help us solve the problem. That’s the kind of interaction that we’re building systems to support. It’s very different than the personal assistants that are out there like Siri, and Cortana, and Google which are meant to be very general. Siri doesn’t really know anything about banking, which isn’t a criticism it’s not supposed to know anything about banking, but if you want to get your banking done over your mobile phone then you’re going to need a system that knows about banking. That’s one example of sort of next-generation conversational interaction.