Women Academics From The United States

"This Article identifies the government interest in enacting laws governing surveillance by private parties. Using social psychologist Irwin Altman’s framework of “boundary management” as a jumping-off point, I conceptualize privacy harm as interference in an individual’s ability to dynamically manage disclosure and social boundaries. Stemming from this understanding of privacy, the government has two related interests in enacting laws prohibiting surveillance: an interest in providing notice so that an individual can adjust her behavior; and an interest in prohibiting surveillance to prevent undesirable behavioral shifts."

"The machine like the djinnee, which can learn and can make decisions on the basis of its learning, will in no way be obliged to make such decisions as we should have made, or will be acceptable to us.” —Norbert Wiener, mathematician and philosopher"

"While there may be similarities here and there, my child doesn’t sense, act, or learn the way a machine does."

"Where my beasts of their own wrong without my will and knowledge break into another’s close, I shall be punished, for I am the trespasser with my beasts.” —Anonymous case during the reign of Henry VII"

"It may seem preposterous today to hold a robot morally accountable for its actions, but there are already glimpses of this in how we talk about robot-caused harm—in ways that risk assigning them more agency than appropriate."

"Man had always assumed that he was more intelligent than dolphins because he had achieved so much—the wheel, New York, wars and so on—whilst all the dolphins had ever done was muck about in the water having a good time. But conversely, the dolphins had always believed that they were far more intelligent than man—for precisely the same reasons.” —Douglas Adams, The Hitchhiker’s Guide to the Galaxy"

"I sometimes think that, in the desperate straits of humanity today, we would be grateful to have nonhuman friends, even if they are only the friends we build ourselves." —Isaac Asimov, Robot Visions"

"I wanted to do my work from within computing. I wanted to do it as an insider, not as an outside critic, to provide leadership for the field."

"It’s easy to think of privacy and publicity as opposing concepts, and a lot of technology is built on the assumption that you have to choose to be private or public. Yet in practice, both privacy and publicity are blurred. Rather than eschewing privacy when they encounter public spaces, many teens are looking for new ways to achieve privacy within networked publics. As such, when teens develop innovative strategies to achieve privacy, they often reclaim power by doing so. Privacy doesn’t just depend on agency; being able to achieve privacy is an expression of agency"

"Maybe any entity significantly smarter than a human being would be crippled by existential despair."

"Framing the government interest, or interests, this way has several advantages. First, it descriptively maps on to existing laws: These laws either help individuals manage their desired level of disclosure by requiring notice, or prevent individuals from resorting to undesirable behavioral shifts by banning surveillance. Second, the framework helps us assess the strength and legitimacy of the legislative interest in these laws. Third, it allows courts to understand how First Amendment interests are in fact internalized in privacy laws. And fourth, it provides guidance to legislators for the enactment of new laws governing a range of new surveillance technologies — from automated license plate readers (ALPRs) to robots to drones."

"Some We Love, Some We Hate, Some We Eat, is a powerful illumination of how we really behave toward animals."

"The military wasn’t equipped to deal with people’s demand for information about their beloved pups."

"It’s really [three- and four-dimensional shapes] that are exciting for me, but the study of these things is deeply linked with knot theory."

"I didn’t allow myself to work on it during the day,” she said, “because I didn’t consider it to be real math. I thought it was, like, my homework."

"“I think Brown is amazing in that people can put together different courses of study that reflect the kinds of things they’re interested in,Follow your instincts – you never know what you’re going to find.”."

"I noticed that they were talking about these cases the same way I talked about some examples."

"At some point, I wanted a break from being a student."

"It is really, really wonderful to have you all here in person on this beautiful – I hardly ever get to say this in Ithaca – this beautiful sunny day."

"Because, of course, the event was virtual,” . “It was me and two camera people. And that’s it. Rows of empty bleachers. And I’ve got to tell you, even if it’s hot, it is so much nicer to be here with all of you."

"It’s not enough to be the first; it’s just really important not to be the last."

"These are two pieces of advice that go right to the heart of who we are at Cornell, right to the heart of how we’ve kept our community together and moving forward during this extraordinary era."

"Knowledge gives us a compass, But kindness is what gets us down the road. And to quote an African proverb that one of my mentors was fond of sharing, ‘If you want to go fast, go alone; if you want to go far, go together."

"People don’t like to understand how systems work, particularly, I hope that in decision-making, people will use them as decision aids but not decision surrogates. This will require that our systems get much better at explanation."

"I became kind of hooked on CS, but at that point I wasn’t contemplating jumping sideways into a CS career."

"So the dimensionality of the space is kind of unthinkably high. It's also not exactly clear what constitutes an object here. If you were going to behave in this world, it would be a very long sequence of primitive actions that you would take in order to clean this kitchen. And also there's just a fundamental amount of uncertainty in this problem, right? So you don't know what's in the blue bowl or what will happen if you try to pull out a certain thing. You don't know when the people are coming home or what they want for dinner all sorts of stuff you don't know."

"And so any approach that works effectively in a domain like this is going to have to handle very large spaces, very long horizons, and really lots of uncertainty. So we have kind of a standard structural decomposition to this problem. We call this belief space hierarchical planning in the now. I'll decode what that means a little bit."

"Fundamentally, the way we think about it is that we decompose the computation that's in the robot's head now into two parts. The first part is in charge of taking the sequence, the history of actions and observations, and trying to synthesize them into some representation of a belief for a probability distribution about the way the world might be and then another module that takes that belief and decides how to behave."

"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."

"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."

"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."

"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."

"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."

"And so then the question is can I somehow take pieces and parts of all these ideas, some human programming, some robot learning in the wild, some kind of search or evolution offline, some inspiration from humans. Can I take all those things and put them together and see if I can find a way to engineer intelligent robots? So that's basically what I'm up to."

"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."

"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."

"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."

"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."

"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."

"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."

"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."

"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 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."

"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."

"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."

"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 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."