Originally published January 20, 1998 at Edge. Published on KurzweilAI.net August 6, 2001.

JB: Let’s start with Firefly and work backward. What are you doing now?

MAES: I started out doing artificial intelligence, basically trying to study intelligence and intelligent behavior by synthesizing intelligent machines, I realized that what I’ve been doing in the last seven years could better be referred to as intelligence augmentation, so it’s IA as opposed to AI. I’m not trying to understand intelligence and build this stand-alone intelligent machine that is as intelligent as a human and that hopefully teaches us something about how intelligence in humans may work, but instead what I’m doing is building integrated forms of man and machine, and even multiple men and multiple machines, that have as a result that one individual can be super-intelligent, so it’s more about making people more intelligent and allowing people to be able to deal with more stuff, more problems, more tasks, more information. Rather than copying ourselves, I’m building machines that can do that.

We’ve been using many techniques in this nascent field of intelligence augmentation. One technique is that you rely on software entities which we’ve termed software agents that are typically long-lived, continuously running, fairly simple, and that can help you keep track of a certain task, or that can help you by automating that task, or semi-automating it so it’s as if you were extending your brain or expanding your brain by having software entities out there that are almost part of you that are looking out for your interests and helping you deal with multiple tasks.

One of the limitations of our minds as they are now, is that we’re good at doing one thing at a time and keeping track of one thing, but the nature of our everyday concerns is very different, and we have to deal with multiple problems and do a lot of multi-tasking and continuously keep track of all these different things. It’s something we’re not good at, something we’re not made for. But we can extend ourselves, or augment ourselves, by having software entities that are an extension of ourselves and act on our behalf. It can be very simple things, like a very simple monitor, for example, that monitors for you whether there’s still milk in your fridge, and that reminds you when the milk is running out, and even reminds you at the right time, when you are driving past the supermarket or when you’re in the supermarket.

JB: Smart refrigerator?

MAES: I have a two-year old who drinks a lot of milk, so it’s one of the concerns that I have to deal with, one of many concerns. Instead of having to check every morning and every evening and try to keep remembering how much milk there is in the fridge, why can’t the fridge do this for me? That’s a simple example, but I would be very happy if that problem were solved and I didn’t have to worry about that. Our lives are full of silly little problems like that but they matter a lot – – we have to deal with them. To a large extent these little extensions of ourselves could deal with such concerns, or could help us deal with them. The digital equivalent of this, is the monitors that keep track of your stocks or something, and tell you if a certain stock that you own has been rising more than usual or down more than usual, things like that. So I have this vision where we could extend or augment our minds by these software entities that help us, that know what we care about, what the problems are that we are trying to solve. Every one of them may deal with a very specific small problem, and they don’t even have to be intelligent, and they’re trivial to build, but I think they would make a huge difference in our efficiency, the efficiency of our lives.

JB: What technology is involved?

MAES: The example I gave of the milk is a very simple one. In other situations it may be something more complicated, like we build agents that monitor your reading habits, or, say, news reading habits. They may pick up a certain regularity in what you read, like maybe you own a lot of Apple stock and you want to make sure you see every article about Apple, and you read every article about Apple in the newspaper. That could easily be automated. We built agents that monitor what you read, keep track of all of that and memorize it, and then discover patterns – that you read every article about Apple Computer – and then offer to automate that for you and to highlight those articles in the newspaper so that you definitely won’t miss them.

JB: How do you read the newspapers?

MAES: This only works for electronic newspapers. We have prototypes of these kinds of systems and, you can just monitor what a person reads and try to infer from that what it is they’re interested in. You can also ask them for more explicit feedback, and ask them, did you like this article, do you want more of this kind of articles in the future, or was this something that you didn’t like even though you read it. That’s going a step further. It involves the use of machine-learning techniques. A lot of that kind of work is finding its way into products like Microsoft’s Office 97 where there is a simple form of an assistant which monitors what you’re doing, which knows about typical patterns of activities that you engage in and which gives you help which is contextualized, based on data it has about sequences of action that people engage in when involved in a particular task.

JB: Are we talking about anthropomorphic assistants?

MAES: Agents are not necessarily personified. It won’t necessarily look like a cute character on your screen – there’s no reason nor need for doing that. For example, the Firefly work doesn’t have any kind of personification, and still there is a system there that helps you in a personalized way. The two are orthogonal issues, and it’s up to a designer of an agent to decide whether it’s appropriate to use personification or not. In most of the work that we’ve done at our lab, the agents are not at all personified. In any event, I’ll be happy to anticipate all of Jaron Lanier’s comments and talk about them in the interview.

JB: Debate with an empty chair?

MAES: Jaron and I already had our debate on Hot Wired’s “brain tennis” pages. We’ve already gone through this whole thing. But let me continue. I talked initially about very simple agents that would be completely programmed, like the milk monitor in your fridge; I talked about agents that can do some machine learning and that can pick up patterns and offer to automate them. A third approach that we have been pursuing the most actively is one where agents are not necessarily smart at all themselves, but what they do is they allow you to benefit from the intelligence of other people that have solved the problem that you are currently dealing with.

Take for example the buying of a car. I went through the process of trying to figure out what car to buy just a couple of months ago. I didn’t know what methods to use, I didn’t have a clue about what car I wanted to buy. I did a lot of research on the Web. The first problem was finding what Web site was worth going to in terms of car information, or new car information. Then I had to learn what the different Web sites could offer me; which ones have good reviews, which ones give you the information about actual cost and prices of new cars.

I dealt with this problem for a month or two, and I accumulated a lot of information about new cars and about car information on the Web, where you should go first and second and third etc., and then once I decided what car to buy I also gathered information about the exact cost of that car to the dealer, what the lowest price was that I could possibly get away with and how to get the best deal. I learned about the different dealerships in and around Boston for the particular Saab I wanted to buy. It’s such a shame that someone else cannot benefit from all that work that I did. Wouldn’t it have been great if something would have recorded some of my experience and some of what I learned so that then that knowledge would be available to another person who is going through exactly the same problem? We are a social species, and we can benefit from each other’s intelligence and each other’s problem solving. Very few of the problems that we deal with, very few of the tasks or activities that we deal with are completely original in the sense that nobody else has ever faced that same problem before. Almost every problem that we deal with is something that hundreds or sometimes even millions of other people have dealt with before.

Continued at: http://www.edge.org/3rd_culture/maes/maes_p3.html

Copyright © 2001 by Edge Foundation, Inc.

www.edge.org