Closing the loop is a phrase used in robotics. Open-loop systems are when you take an action and you can't measure the results—there's no feedback. Closed-loop systems are when you take an action, you measure the results, and you change your action accordingly. Systems with closed loops have feedback loops, they self-adjust and quickly stabilize in optimal conditions. Systems with open loops overshoot; they miss it entirely.
CHRIS ANDERSON is the CEO of 3D Robotics and founder of DIY Drones. He is the former editor-in-chief of Wired magazine. Chris Anderson's Edge Bio Page
I worked on coming up with a method of defining intelligence that would necessarily have a solution, as opposed to being necessarily unsolvable. That was this idea of bounded optimality, which, roughly speaking, says that you have a machine and the machine is finite—it has finite speed and finite memory. That means that there is only a finite set of programs that can run on that machine, and out of that finite set one or some small equivalent class of programs does better than all the others; that’s the program that we should aim for.
That’s what we call the bounded optimal program for that machine and also for some class of environments that you’re intending to work in. We can make progress there because we can start with very restricted types of machines and restricted kinds of environments and solve the problem. We can say, "Here is, for that machine and this environment, the best possible program that takes into account the fact that the machine doesn’t run infinitely fast. It can only do a certain amount of computation before the world changes."
STUART RUSSELL is a professor of computer science at UC Berkeley and coauthor (with Peter Norvig) of Artificial Intelligence: A Modern Approach. Stuart Russell's Edge Bio Page
Coming very soon is going to be augmented reality technology, where you see the physical world, but also virtual objects and entities that you perceive in the middle of them. We’ll put on augmented reality glasses and we’ll have augmented entities out there. My face recognition is not so great, but my augmented glasses will tell me, "Ah, that’s John Brockman." A bit of AI inside my augmented reality glasses will recognize people for me.
At that level, artificial intelligence will start to become an extension of my mind. I suspect before long we’re all going to become very reliant on this. I’m already very reliant on my smartphone and my computers. These things are going to become more and more ubiquitous parts of our lives. The mind starts bleeding into the world. So many parts of the world are becoming parts of our mind, and eventually we start moving towards this increasingly digital reality. And this raises the question I started with: How real is all of this?
DAVID CHALMERS is University Professor of Philosophy and Neural Science and Co-Director of the Center for Mind, Brain, and Consciousness at New York University, and also Distinguished Professor of Philosophy at the Australian National University. David Chalmers's Edge Bio Page
This is another example where AI, in this case, machine-learning methods, intersects with these ethical and civic questions in an ultimately promising and potentially productive way. As a society we have these values in maxim form, like equal opportunity, justice, fairness, and in many ways they’re deliberately vague. This deliberate flexibility and ambiguity are what allows things to be a living document that stays relevant. But here we are in this world where we have to say of some machine-learning model, is this racially fair? We have to define these terms, computationally or numerically.
It’s problematic in the short term because we have no idea what we’re doing; we don’t have a way to approach that problem yet. In the slightly longer term—five or ten years—there’s a profound opportunity to come together as a polis and get precise about what we mean by justice or fairness with respect to certain protected classes. Does that mean it’s got an equal false positive rate? Does that mean it has an equal false negative rate? What is the tradeoff that we’re willing to make? What are the constraints that we want to put on this model-building process? That’s a profound question, and we haven’t needed to address it until now. There’s going to be a civic conversation in the next few years about how to make these concepts explicit.
BRIAN CHRISTIAN is the author of The Most Human Human: What Artificial Intelligence Teaches Us About Being Alive, and coauthor (with Tom Griffiths) of Algorithms to Live By: The Computer Science of Human Decisions. Brian Christian's Edge Bio Page
A new thinking came about in the early '80s when we changed from rule-based systems to a Bayesian network. Bayesian networks are probabilistic reasoning systems. An expert will put in his or her perception of the domain. A domain can be a disease, or an oil field—the same target that we had for expert systems.
The idea was to model the domain rather than the procedures that were applied to it. In other words, you would put in local chunks of probabilistic knowledge about a disease and its various manifestations and, if you observe some evidence, the computer will take those chunks, activate activate them when needed and compute for you the revised probabilities warranted by the new evidence.
It's an engine for evidence. It is fed a probabilistic description of the domain and, when new evidence arrives, the system just shuffles things around and gives you your revised belief in all the propositions, revised to reflect the new evidence.
JUDEA PEARL, professor of computer science at UCLA, has been at the center of not one but two scientific revolutions. First, in the 1980s, he introduced a new tool to artificial intelligence called Bayesian networks. This probability-based model of machine reasoning enabled machines to function in a complex, ambiguous, and uncertain world. Within a few years, Bayesian networks completely overshadowed the previous rule-based approaches to artificial intelligence.
Leveraging the computational benefits of Bayesian networks, Pearl realized that the combination of simple graphical models and probability (as in Bayesian networks) could also be used to reason about cause-effect relationships. The significance of this discovery far transcends its roots in artificial intelligence. His principled, mathematical approach to causality has already benefited virtually every field of science and social science, and promises to do more when popularized.
He is the author of Heuristics, Probabilistic Reasoning in Intelligent Systems, and Causality: Models, Reasoning, and Inference, and a winner of the Alan Turing Award. Judea Pearl's Edge Bio Page
In artificial intelligence we need to turn back to psychology. Brute force is great. We're using it in a lot of ways like in speech recognition, license plate recognition, and for categorization, but there are still some things that people do a lot better. We should be studying human beings to understand how they do it better.
People are still much better at understanding sentences, paragraphs, books, and discourse, where there's connected prose. It's one thing to do a keyword search. You can find any sentence you want that's out there on the web by just having the right keywords, but if you want a system that could summarize an article for you in a way that you trust, we're nowhere near that. The closest thing we have to that might be Google Translate, which can translate your news story into another language, but not at a level that you trust. Again, trust is a big part of it. You would never put a legal document into Google Translate and think that the answer is correct.
GARY MARCUS, CEO and founder, Geometric Intelligence; professor of psychology, New York University; author, Guitar Zero: The New Musician and the Science of Learning. Gary Marcus's Edge Bio Page
The question is, what makes us different from all these things? What makes us different is the particulars of our history, which gives us our notions of purpose and goals. That's a long way of saying when we have the box on the desk that thinks as well as any brain does, the thing it doesn't have, intrinsically, is the goals and purposes that we have. Those are defined by our particulars—our particular biology, our particular psychology, our particular cultural history.
The thing we have to think about as we think about the future of these things is the goals. That's what humans contribute, that's what our civilization contributes—execution of those goals; that's what we can increasingly automate. We've been automating it for thousands of years. We will succeed in having very good automation of those goals. I've spent some significant part of my life building technology to essentially go from a human concept of a goal to something that gets done in the world.
There are many questions that come from this. For example, we've got these great AIs and they're able to execute goals, how do we tell them what to do?...
STEPHEN WOLFRAM, distinguished scientist, inventor, author, and business leader, is Founder & CEO, Wolfram Research; Creator, Mathematica, Wolfram|Alpha & the Wolfram Language; Author, A New Kind of Science. Stephen Wolfram's Edge Bio Page
This can't be the end of human evolution. We have to go someplace else.
It's quite remarkable. It's moved people off of personal computers. Microsoft's business, while it's a huge monopoly, has stopped growing. There was this platform change. I'm fascinated to see what the next platform is going to be. It's totally up in the air, and I think that some form of augmented reality is possible and real. Is it going to be a science-fiction utopia or a science-fiction nightmare? It's going to be a little bit of both.
JOHN MARKOFF is a Pulitzer Prize-winning journalist who covers science and technology for The New York Times. His most recent book is the forthcoming Machines of Loving Grace: The Quest for Common Ground Between Humans and Robots. John Markoff's Edge Bio Page
The reasons why I'm engaged in trying to lower the existential risks has to do with the fact that I'm a convinced consequentialist. We have to take responsibility for modeling the consequences of our actions, and then pick the actions that yield the best outcomes. Moreover, when you start thinking about—in the pallet of actions that you have—what are the things that you should pay special attention to, one argument that can be made is that you should pay attention to areas where you expect your marginal impact to be the highest. There are clearly very important issues about inequality in the world, or global warming, but I couldn't make a significant difference in these areas.
JAAN TALLINN is a co-founder of The Centre for the Study of Existential Risk at University of Cambridge, UK as well as The Future of Life Institute in Cambridge, MA. He is also a founding engineer of Kazaa and Skype. Jaan Tallinn's Edge Bio Page
...Today, you can send a design to a fab lab and you need ten different machines to turn the data into something. Twenty years from now, all of that will be in one machine that fits in your pocket. This is the sense in which it doesn't matter. You can do it today. How it works today isn't how it's going to work in the future but you don't need to wait twenty years for it. Anybody can make almost anything almost anywhere.
...Finally, when I could own all these machines I got that the Renaissance was when the liberal arts emerged—liberal for liberation, humanism, the trivium and the quadrivium—and those were a path to liberation, they were the means of expression. That's the moment when art diverged from artisans. And there were the illiberal arts that were for commercial gain. ... We've been living with this notion that making stuff is an illiberal art for commercial gain and it's not part of means of expression. But, in fact, today, 3D printing, micromachining, and microcontroller programming are as expressive as painting paintings or writing sonnets but they're not means of expression from the Renaissance. We can finally fix that boundary between art and artisans.
...I'm happy to take claim for saying computer science is one of the worst things to happen to computers or to science because, unlike physics, it has arbitrarily segregated the notion that computing happens in an alien world.
NEIL GERSHENFELD is a Physicist and the Director of MIT's Center for Bits and Atoms. He is the author of FAB. Neil Gershenfeld's Edge Bio Page