All Quotes

"I immediately decided that I would do whatever it took to make it into that 10 percent. My daughters, Laura and Erika, were just 18 and 22, far too young to say goodbye. I know some people make the decision to not use life-prolonging interventions, such as a feeding tube, noninvasive breathing support or a ventilator. Susan Spencer Wendel, author of the book, " Until I Say Good-Bye," has said she will not use any of these interventions. While I respect her decision, it is not right for me."

"I remember the nurse practitioner, a long-time member of the team, said that refusing would be like writing "DNR" - do not resuscitate - on my chart. But I had researched the changes that tracheostomy would bring, and I just wanted to delay them a few months."

"The field of AI in Education is concerned with development of Artificial Intelligence techniques for the study of human teaching and for the engineering of systems that facilitate human learning."

"Three research goals have become apparent. The first is to use AI and cognitive science techniques to model experts who problem-solve in a domain, as well as tutors teaching and students learning in that domain."

"The second research goal involves explaining learning and teaching as parts of the human information-processing system. Since all intelligent beings learn, differences in learning rates might be due to a level of prior knowledge or to the quality of teaching."

"The third research goal is to demonstrate completeness and reliability in the engineering side of the discipline and to show that intelligent instructional systems can be used effectively in training and classroom situations."

"Research activities in this field are important to education, not only because such systems might someday become routine in classrooms, but also because such systems might support students in activities not available in traditional classrooms, such as extensive oneon-one collaboration with a tutor and freedom to explore hypothetical worlds, to make conjectures, and to test hypotheses."

"Knowledge Representation. In constructing intelligent tutors, two aspects of knowledge representation (qv) are important. First, what knowledge do teachers and trainers use to understand the domain, diagnose student behavior, and select new strategic approaches, and second, what are good representational schemes for encoding domain knowledge."

"Computational biology is the art of developing and applying computational methods to answer questions in biology, such as studying how proteins fold, identifying genes that are associated with diseases, or inferring human population histories from genetic data."

"I have interests in both the development of computational methods and in answering specific biology questions, primarily related to the function of RNA, a molecule central to the function of cells."

"While genome sequencing has obviously been useful in revealing the sequences that are involved in coding various aspects of the molecular biology of the cell, it has had a secondary impact that is less obvious at first glance."

"The low cost and high throughput (the ability to process large volumes of material) of genome sequencing allowed for a more "big-data" approach to biology, so that experiments that previously could only be applied to individual genes could suddenly be applied in parallel to all of the genes in the genome."

"A result of the scale of these new experiments is the emergence of very large data sets in biology whose interpretation demands the application of state-of-the-art computer science methods."

"The problems require interdisciplinary dexterity and involve not only management of large data sets but also the development of novel abstract frameworks for understanding their structure."

"If you’re going to build agents that interact with people, you have to think about people’s cognition and the ways they behave. That doesn’t necessarily mean you have to do cognitive modeling — although that is an interesting approach — but you do need to care about how people process information and communicate."

"When I was working on speech understanding systems at SRI in the 1970s, other research team members were responsible for syntax and grammar — determining the structure and building a computer representation of the meaning of an individual sentence. Everyone involved in early speech understanding systems knew that wasn’t enough. When people talk, the context matters. They use pronouns and definite descriptions. They depend on each other to interpret those imprecise expressions appropriately in context. For example, depending on the setting, “the cup” might mean my coffee cup or the cup you received as a gift. We knew that if we were going to have a system that could carry on a dialogue and be able to handle the way people actually spoke, we needed to have a computational model of dialogue that could track context. Many researchers thought if they sat in a chair and thought really hard, they could figure it out. I expected that wouldn’t work and devised a way to capture dialogue about the same topic from many different pairs of people. This was actually the first “Wizard of Oz” experiment in dialogue systems, though that name came later. I placed two people in separate rooms and had one give the other instructions in how to put together a piece of equipment — an air compressor. My analysis of the way they talked led to the first computational model of discourse."

"These systems are major accomplishments, but they don’t come close to human dialogue capabilities. When Siri first came out people said to me, ‘you have nothing left to do, right?’ So, I borrowed a phone with Siri and it took me two questions to break the system. I asked, “Where are the nearest gas stations,” and then I asked, “Which ones are open?” It replied, “Would you like me to search the web for ‘which ones are open?’” It had no context, no discourse. Siri has improved since then, but it’s still pretty easy to break the system with a question that depends on dialogue context. No current system is thinking to the extent Turing imagined computers might be by now."

"To clarify: I suggested that the way we use computers had changed so much, as had our knowledge of human cognition, that Turing himself might ask a different question now. My new question is rooted in our now knowing that collaboration is essential to intelligent behavior and seems to play a fundamental role in the ways infants learn. Can we design systems that behave so well that they pass for human? One big challenge, which my team is addressing in our research, is getting delegation to work well. Delegation of particular responsibilities to different team members is a hallmark of teamwork. To make teamwork work (or as we might say in computer science, to make it tractable), team members have to share information but not overwhelm each other with too much information. An enormous challenge for systems is to be able to determine what information to share with whom when."

"For example, I’m making dinner with Bobby and Susie. Susie is assigned appetizers, Bobby is assigned the main dish and I’m assigned dessert. I don’t ask Bobby how he is making the main course because if he has to tell me everything he’s doing, it’s a huge cognitive load. That said, it’s still crucial to know certain things, such as if we both need the same pan."

"Right. We’re working with a pediatrician at Stanford University Hospital whose patients have complex diseases, many of them seeing 10 to 15 doctors. The cognitive load for coordinating care among 15 people (turning the group into a real team) is enormous — no care giver needs to see everything everyone else is doing but they may need to know something about each other’s work. A key question is when one member of the team learns something new about a patient, who should get that information and when? Our goal is to build the foundations for smart computer care coordination systems to help. To do that, we need to figure how to effectively compute the information to be shared in the absence of detailed models of how people are carrying out their responsibilities. If we do this, we’ll also know how to build computer agents that are good teammates."

"One of the things I want students to learn is the importance of designing artifacts for the people who will use them. A computer system should make us feel smarter, not dumber and work seamlessly with us, like a human partner. I tell students to look for limitations and cracks in a system and think about the unintended consequences of those limitations. If you’re only focused on what you’re building, you’re blind to what a system may do that you hadn’t thought about."

"The fear of AI systems running amok or taking over the world is greatly exaggerated. Some of the predictions are based on lack of understanding of the current state of AI (or even of what’s actually computable). Also, it’s important not to lose sight of who’s in charge: people design AI systems, and they can design any number of plugs to pull. If we design systems to work with people — which has always been my goal — then the probability of them running amok is greatly lowered."

"Even so, as the people who develop these systems, AI scientists and practitioners need to take responsibility for the uses to which AI capabilities are put. We should be clear about the limitations of the technology. Should we think – and talk – about negative or potential unintended consequences? Absolutely! Are these concerns reasons not to develop systems that are smart? Absolutely not."

"I’m energized by this chance to serve the citizens of Illinois and advance the mission of learning, discovery, engagement and economic development”"

"Although I tend to focus on statistical machine learning, my research passion is actually artificial intelligence. I like to build large integrated systems, so I have also tended to spend a great deal of my time doing research on autonomous agents, interactive entertainment, some aspects of HCI, software engineering, and even programming languages."

"I think of my field as interactive artificial intelligence. My fundamental research goal is to understand how to build autonomous agents that must live and interact with large numbers of other intelligent agents, some of whom may be human. Progress towards this goal means that we can build artificial systems that work with humans to accomplish tasks more effectively; can be more robust to changes in environment, relationships, and goals; and can better co-exist with humans as long-lived partners."

"After thinking about this problem for a number of years, I've decided that the central technical issues here are: adaptive modeling, especially activity discovery (as distinct from activity recognition); and scalable interaction, including coordination and influence. Further, I have come to believe that as a practical matter, it is necessary to build development environments that support rapid development, and so I try to think seriously about authorial tools, including adaptive programming languages, domain-specific example-driven development, socially-guided machine learning, and corresponding issues in software engineering."

"All in all, I believe that there are many opportunities in this space, and it should interest anyone who cares about any of the areas I mention above. To that end, I have spent time building The Laboratory for Interactive Artificial Intelligence and the pfunk research group. Our research goal is to develop methodologies for building persistent, adaptive, collaborative, and believable agents that must live with other similar agents, including humans."

"It is also worth pointing out that I have developed a strong interest in (re)defining Computing as a separate and vivid discipline. This has most obviously manifested itself in my efforts at curricular development and reform. Possibly related to these efforts, I was the Associate Dean of Academic Affairs in the College for a number of years before becoming the Senior Associate Dean and eventually the Executive Associate Dean (where I retained my role in Academic Affairs while overseeing a lot of the operations of the College). I was lucky enough to serve as the fourth John P. Imlay, Jr. Dean of the College. After four years doing that, I became the Provost at University of Wisconsin-Madison."

"Definitely related to these efforts, I have put a great deal of energy into thinking about access at all levels, with an eye toward broadening participation in the professoriate. I don't have dozens of papers in this space, but I care about it as deeply as I do my AI research and all the rest that I do. Generally speaking, I split my time among my professor and admin selves because I think efforts around operationalizing and supporting broad access deserve as much intellectual energy and thought as any of our other academic efforts"

"I’m energized by this chance to serve the citizens of Illinois and advance the mission of learning, discovery, engagement and economic development”"

"Although I tend to focus on statistical machine learning, my research passion is actually artificial intelligence. I like to build large integrated systems, so I have also tended to spend a great deal of my time doing research on autonomous agents, interactive entertainment, some aspects of HCI, software engineering, and even programming languages."

"I think of my field as interactive artificial intelligence. My fundamental research goal is to understand how to build autonomous agents that must live and interact with large numbers of other intelligent agents, some of whom may be human. Progress towards this goal means that we can build artificial systems that work with humans to accomplish tasks more effectively; can be more robust to changes in environment, relationships, and goals; and can better co-exist with humans as long-lived partners."

"After thinking about this problem for a number of years, I've decided that the central technical issues here are: adaptive modeling, especially activity discovery (as distinct from activity recognition); and scalable interaction, including coordination and influence. Further, I have come to believe that as a practical matter, it is necessary to build development environments that support rapid development, and so I try to think seriously about authorial tools, including adaptive programming languages, domain-specific example-driven development, socially-guided machine learning, and corresponding issues in software engineering."

"All in all, I believe that there are many opportunities in this space, and it should interest anyone who cares about any of the areas I mention above. To that end, I have spent time building The Laboratory for Interactive Artificial Intelligence and the pfunk research group. Our research goal is to develop methodologies for building persistent, adaptive, collaborative, and believable agents that must live with other similar agents, including humans."

"It is also worth pointing out that I have developed a strong interest in (re)defining Computing as a separate and vivid discipline. This has most obviously manifested itself in my efforts at curricular development and reform. Possibly related to these efforts, I was the Associate Dean of Academic Affairs in the College for a number of years before becoming the Senior Associate Dean and eventually the Executive Associate Dean (where I retained my role in Academic Affairs while overseeing a lot of the operations of the College). I was lucky enough to serve as the fourth John P. Imlay, Jr. Dean of the College. After four years doing that, I became the Provost at University of Wisconsin-Madison."

"Definitely related to these efforts, I have put a great deal of energy into thinking about access at all levels, with an eye toward broadening participation in the professoriate. I don't have dozens of papers in this space, but I care about it as deeply as I do my AI research and all the rest that I do. Generally speaking, I split my time among my professor and admin selves because I think efforts around operationalizing and supporting broad access deserve as much intellectual energy and thought as any of our other academic efforts"

"OK, so what's my research goal? I come from the machines end of this world, roughly. And what I really want to do is figure out how it is that we can make intelligent robots. And I do this mostly because I'm interested in intelligence more than I'm interested actually in robots. But I think that trying to make a physical agent who goes out and interacts in the world is a really good test bed for understanding what kinds of reasoning and perception and control we need in order to make it an intelligent system."

"So the way I think about the problems-- this is kind of a definitely a computer scientist way to think about the problem-- is to think about the robot as a transducer, as some kind of a system that's connected up to the world. And it makes observations of the world. And it takes actions that change the state of the world. And presumably, there's some objective, right? We want to take actions that change the state of the world in some way that we think will be good."

"The reason I want to start by backing all the way up to this like very basic control theory picture is that right now there's an enormous amount of argument about how one should make robots. Should they do planning and reasoning? Should they do reinforcement learning? How should we do it? So there's a huge kind of crisis almost in the field about what the best methods are. And what I want to start out this talk by doing is actually thinking about how we can answer that question in a way that's not political or religious, but technical."

"So the way I want to think about this, the job of this program. So I'm going to make a robot. I'm going to put a program in the head of the robot. So let's say, I'm not going to worry about hardware. I'm just going to read about the software. And so the program that I'm going to put in the head of my robot, it has to do this job that's written in the formula up here. And what this is just shorthand for saying is that it has to represent some kind of mapping from observation and actions that it's had in the past. So o, a star means the whole history of observations and actions that it's ever had. Based on that, it has to pick the next action. So that's not really saying much of anything at all. That's just a description of every single robot control program basically that's been written. You have to take your history of actions and observations, compute the next action."

"And so what we want to do is think about first of all, what's the best-- what would be the best pi to put inside the robot. How can we think about that? And then we have to think about the problem of how is it that we, in my case, as me, as an engineer, I'm going to find that pi that I should put in my robot."

"So one way to think about the whole problem set up then is that I, as the robotics engineer, have to do for my robots the job that nature did for you. That is to say, I have to think about I'm a robot factory. I'm going to make these robots. And the robots are going to go out in the world. Maybe they're going to go and work in people's Kitchens or something. And every kitchen is going to be different. So there's going to be a lot that I don't know about the world. But somehow, I have to figure out the best program, what program, to put in the head of all my robots, so that when they go out in the world to behave, they can do a good job so that's the way I think about the problem that I face. And in order to think about what would be the best program, I kind of think about it this way."

"So I imagine that there's some distribution over possible environments that the robot could find itself in when it actually goes out into the world, right? So maybe it's going to go to houses and the houses are all somewhat different. And once I put that program in the house, maybe it's going to do some estimation or learning. It's going to adapt to the circumstances it's in. My job is to find a program that does a good job of adapting in all the environments that might find itself in."

"So imagine that you have some kind of probability distribution over the worlds that the robot could actually end up operating in. I want to find a program that's going to behave well, let's say get a lot of reward in expectation on average over all the environments that it could possibly find itself in. So that's, I would say, kind of a reasonable formal objective for a robot. And one thing that's good about this as an objective is that we don't have to argue about it, right? It doesn't say whether there should be learning in there or what kind of learning or should it be a genetic algorithm or should it have planning. In some sense, you could say, "I just want to make the program that's going to be the best that can be on average over these environments.""

"But the problem is now I've written down an objective function. I've said, "Oh, if you could tell me a distribution over possible worlds that you'd like this program to work well in, then I know in a certain mathematical sense with the best program is." But now my problem as the engineer, as the person who is in the robot factory, which is again, the kind of maybe analogous to the problem of nature, is I have to figure out how do I how do I find this program that's going to be good and all these situations?"

"So there are a bunch of ways you can think about the problem. I mean, one would be to say, "Oh, I'm really lazy. I don't really want to think very much about working in the factory. It seems awfully hard. I will just make a robot that has roughly an empty head. It doesn't really know very much at all. And then it just has to interact in the world and learn everything by interacting." But of course, you don't really want a robot that comes to your kitchen and begins to learn about physics, right? That would break a lot of dishes."

"Another strategy-- and this is like the classic engineering strategy-- is that, no, I'm like a serious engineer. And I'm going to sit here and think really, really hard. And I'm going to write a program. And it's going to be a great program. And I'm just going to put it straight in the robot's head. And it's going to go off, and it's going to be awesome and do everything it needs to do. And that strategy actually can work very well in certain kinds of problems. It lets that, the Boston dynamics robots do Parkour. But as we try to address bigger and more complicated problems, it becomes harder and harder for engineers to just straight up write the program."

"We could just try to figure out how humans work because humans work pretty well in a variety of domains. And so one program would be to say, "Well, we forget how humans work. And then that's what we do. We make robots that work like that." So first of all, that's a hard biology problem. I think it's very important that people work on it. But it's also not a general engineering methodology because for instance, I might want robots that work in certain kinds of circumstances or problem domains that are really different from the niche that humans are well tuned for. And so I might want to make a robot that isn't really human-like in its intelligence. And then it seems like what we're left with that maybe we could just say, well, we'll somehow recapitulate evolution. Like we just search around in the space of programs and try to find ones that work well and then eventually get ones that are great for our environment. But that seems slow and complicated."

"So if I enumerate my options and they all don't look very good, I don't know what to do. So one thing to think about, though, is this last thing. So the kind of evolution idea. So let's just pursue this a little bit more. So imagine that we want to try to find a program that works well in expectation over all environments. One way to think about that is that inside the factory, we kind of simulate a bunch of environments. We try a bunch of robot programs. And we try to find one that works well in all those environments. And that's like a really interesting strategy. We would have to think of a space of possible programs for the robot, some objective function. We figure out, well, what are we trying to optimize, a distribution over problems to test."