Women Academics From The United States

"knew little about the disease, commonly known as Lou Gehrig's disease. I asked, "Do people die?" He looked away and said, "Sometimes." I started to cry, knowing that "sometimes" meant "always." How could I have this awful disease? I ate healthy, exercised, was thin and was rarely sick."

"I had hoped the weakness in my leg would be treatable, but my doctor shook his head. The intravenous immunoglobulin treatments he'd been giving me for a possible motor neuropathy weren't working, leading him to conclude I had "probable ALS.""

"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."

"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."

"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."

"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."

"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 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 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."

"In some sense, this is a thing that people have thought about for a long time, right? This would be like running some kind of evolutionary algorithm or some search or simulation inside the factory. And it's very attractive, but I think generally speaking, hard to make work well. So the question is what should I do, right? I could maybe I can set up this whole evolutionary setup somehow. And then I could just snooze for a really long time while some very complicated program tries to figure out the best robot program to put in the head of the robot. But I don't know. I am simultaneously too impatient for that."

"I'm going to-- well, no. OK, let me say something about this. So then the one way to view the research agenda is to say that first of all, I'd like to be inspired by what we know about humans. And in particular, I'm very interested in this bulky core knowledge type stuff because that tells me something about what evolution, in some sense, saw fit to engineer into natural intelligences. And if I understand that natural systems seem to be born with a bias or some built in structure to think in terms of other agents, to understand that they move through 3D space, to talk about, think about objects as clumps of matter that cohere, that's a very helpful engineering bias for building a system."

"I also know just some physics and variance about the worlds that my robot's going to operate in. And maybe humans don't have this built in explicitly, but they almost surely have a built in implicitly. And I also have some other constraints as an engineer who's trying to make intelligent robots, which is that humans are the engineers, right?"

"Yeah, actually. For years there has been. So a more typical formalization would be in terms of predictive models and planning or reasoning. So reinforcement learning. Also, it depends. The phrase unfortunately, the phrase, "reinforcement learning," grows and stretches too. And sometimes for many people and in many discourses, it's come to mean all of intelligent behavior, in which case, I would say, well, no it's all reinforcement learning. But that's vacuous. Other formulations involve reasoning about objects and their relationships and thinking about the long term consequences of taking actions in the world and so on. So there's really different ways of framing and formalizing the problem. And they give you very different computational profiles and different learning strategies. OK, good."

"So I'll just tell you some story because people usually like stories, and it's kind of the afternoon. So and this is related to the question about reinforcement learning, probably, right? So how did I get into this whole thing? When I just finished my undergraduate degree, which actually was in philosophy, weirdly enough, I went to work at a research institute while I was starting my PhD. And they had this robot nobody really knew actually very much about robotics. So And it was my job as the brand new person to try to get the robot to drive down the hallway."

"Then I sort of reinvented reinforcement learning in a not very good way, really. But it was kind of entertaining. And I this is a slide by the way for those young people in the audience. You might know, but back in the day, we used to write with colored depends on pieces of clear plastic. And that's what we used to give talks. So I had this kind of pseudo reinforcement learning thing. And by 1990, I actually had this little robot called Spanky that did actual reinforcement learning during my actual defense. So it didn't learn anything too complicated. But it did do it in real time. So that was kind of fun."

"So OK, I finished my PhD. And I thought, OK, I know something about robot learning now. But I really want to make robots that can do complicated things. And I couldn't figure out how to get basic reinforcement learning methods to really scale up to problems that I cared about. And so this is one last flight. I'll show you from some talk that I gave in 1995. And I kind of complained that the ideal that you could take just a big bunch of what I like to call neural goo now, just a big bunch of generic neural network stuff, and train it to be an intelligent agent all by itself. But that wasn't going to be feasible. And instead, we needed some kind of compositional structure. And that would give us more efficient learning and more robust behavior and so on."

"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."

"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."

"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."

"Tackling waste, fraud, and abuse would mean going to the agencies that administer spending themselves — not the BFS."

"I’m excited about being part and parcel of the experience for students that are thinking through this next stage of their careers."

"These are achievable goals. While the challenges we face are significant, they are not insurmountable."

"Although there were a lot of people who were going to go into corporate law or who were interested in financial institutions, the nature of the classes, because they were situated in the law school, was that you didn’t do a lot of the things that you would do in a core finance course: understanding balance sheets, thinking through whether an investment opportunity makes sense, testing whether a firm experienced abnormal returns when its earnings were announced."

"And all this gets to the question that I have really struggled with — and still can’t quite answer for you now: What exactly are the tariffs for? What is the point of the tariffs? How do we measure success for this new ordering of global trade?"

"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."

"The idea that taking a tariff rate of 1.5 percent and turning it into a tariff rate of 15 percent plus is somehow a win for Americans — I’m just baffled by the concept. Because no one would say that if you took the sales tax on certain goods and you increased it 15-fold, that was a win for Americans. But effectively, that’s what we’ve done."

"OK, I'll keep going. I'll surely be able to offend some people soon. And I'll work harder at that. OK. so if we kind of accept this idea that we're going to build in some structure, then what? And the thing that my colleague and I have done recently. Well, now, maybe not super recently, but recent. In order to test out the idea that there's a set of mechanisms that would work well, what we did is we hand built the rest of the system. So we hand-built some transition models, inference rules, ways of doing search control and so on and connected them up to these general mechanisms and made a system."

"You have this regulatory intervention that may have been well-intentioned to help consumers, but overall consumers are actually being hurt."

"The current system really doesn’t work well for people who are lower income in our society because we have banks that charge them exorbitant fees. Consumers then don’t want to go to banks because of those fees and instead turn to alternatives like payday lenders, which end up charging even higher fees."

"I think I was initially interested and maybe a bit concerned around AI based not on my literature, my rhetoric backgrounds, but because I had done a lot of work building large scale measurement systems."

"And so I was effectively at the heart of the processes for creating data and then making claims about that data as a reliable representation of dynamic reality. And I knew how difficult that was."

"I knew that data was always imperfect. It was always partial. It was never the whole story. And so in the early twenty tens, when I saw first machine learning and then we all decided to use the term AI become the next big things following the Alex Net paper, following the recognition that GPUs could effectively supercharge the compute available for neural network training."

"I became very concerned initially that the data sources that were being used and the claims that were being made about these models created with this data were not accurate, were not sufficient to reflect the things that were being claimed, were certainly not sufficient to create an intelligent machine"

"the types of stories we tell about intelligence, about computational sophistication, were serving to entice people to trust and lend credibility to ultimately what are large companies, large corporate actors who may or may not have as their core objective broad social benefit and who create these technologies and these tools behind shrouds of corporate secrecy."

"Every analysis of increased tariffs, both those we have published at The Budget Lab and from others, finds the same thing: tariffs make Americans poorer. American consumers pay the cost of tariffs in the form of higher prices."

"The parts of law school that I loved most were the relationships I had with my classmates and the relationships I had with faculty, many of whom are my mentors and advisors that I still look up to tremendously in my own career. I’m really excited about being that kind of person, I hope, to this next generation of law students."

"What is most exciting about Penn is that it feels like a community where those sorts of relationships are organically developed."

"We’re going to see an inflation uptick, and we’re going to see a weaker labor market as a result of all that has already been done."

"They still had the last mile to go, but they were directionally there. The labor market was strong. And then President Trump took office."

"My application areas cover a wide range of domain areas, but often in earth and space science informatics and health informatics."

"I am interested in making smart systems that help people and machines function better,"

"Medicine is definitely a huge one. It’s important to remember that there is a global shortage of doctors that is quite large in some countries."

"I thought that was such an awesome application and something I never would have thought of! I want every dairy farmer and people no matter their profession or where they are in the world to be able to use this technology on the problems that they care about."

"For instance, in Nigeria, it would take 300 years to train enough doctors to fill the shortage with their existing pipeline. I know a pediatric radiologist from South Africa who said there are only 14 pediatric radiologists for the entire African continent. My hope is that deep learning will help doctors and community health workers be more effective."