DARPA Director: Automated Reasoning Within Reach
0 Comments Published by John Dillard on Friday, February 23, 2007 at 1:42 PM.
We've had a great response to the post below on why optimizing decision-making is the most important thing an organization can do. In particular, we've had a number of folks ask about decision automation. I read this interview with Tony Tether, Chief of the Defense Advanced Research Projects Agency (DARPA) on Wired's blog today that touches on advanced research related to decision automation. It's pretty far-out science--more thought-provoking than practical, but nonetheless demonstrates the imperative for organizations to focus on this issue. Here's an excerpt:
TT: Since the '90s to now, our ability to create algorithms that can reason -- can more abstractly reason -- about a problem and come up with answers, and also remember what they did using Bayesian techniques and changing values, has really advanced. I mean, it tremendously advanced in the past -- from the '90s to, say, the early 2000s. At the same time, computers became more powerful. We're on the verge of having computers with densities approaching a monkey's brain, and it won't be long before we'll have a computer with the density of transistors, or equivalent to neurons and almost human. What we're missing is the architecture. So it seemed like it was time. We had great advances in algorithms for reasoning and in algorithms that learned in general. At the same time, the computers, the actual intrinsic hardware, was really approaching the density of a human brain. And so it seemed like it was time to try again. We've had some great success. This cognitive program I told you about is actually showing that it is learning, and it is learning in a very difficult environment. This is the program Stanford Research runs for us.
Interesting stuff. We'll post Part II of our thought on the importance of optimizing decisions soon.
TT: Since the '90s to now, our ability to create algorithms that can reason -- can more abstractly reason -- about a problem and come up with answers, and also remember what they did using Bayesian techniques and changing values, has really advanced. I mean, it tremendously advanced in the past -- from the '90s to, say, the early 2000s. At the same time, computers became more powerful. We're on the verge of having computers with densities approaching a monkey's brain, and it won't be long before we'll have a computer with the density of transistors, or equivalent to neurons and almost human. What we're missing is the architecture. So it seemed like it was time. We had great advances in algorithms for reasoning and in algorithms that learned in general. At the same time, the computers, the actual intrinsic hardware, was really approaching the density of a human brain. And so it seemed like it was time to try again. We've had some great success. This cognitive program I told you about is actually showing that it is learning, and it is learning in a very difficult environment. This is the program Stanford Research runs for us.
NS: Which program is this?
TT: It's PAL [Perceptive Assistant that Learns].Interesting stuff. We'll post Part II of our thought on the importance of optimizing decisions soon.
0 Responses to “DARPA Director: Automated Reasoning Within Reach”
Post a CommentLinks to this post
Create a Link