Computer Scientists From The United States

"“A lot of the progress in machine learning – and this is an unpopular opinion in academia – is driven by an increase in both computing power and data. An analogy is to building a space rocket: You need a huge rocket engine, and you need a lot of fuel.”"

"I think of this as maybe similar to the fact that the complex numbers are an algebraic closure of the real numbers. And quite similarly again, the problems in the quantum model often have richer connections between them; they somehow ring ‘right,’ mathematically."

"I think there are more algorithms down that road — very interesting ones — but it will probably be difficult to come up with them."

"There are so many different universal models of quantum computers, each one based on a completely different algorithmic approach — like adiabatic computation, quantum walks, measurement-based quantum computation, topological quantum computation, and more — and they are all essentially equivalent,” she said. “You don’t really know which direction the next quantum algorithm will come from."

"If computers were human, they’d present themselves as autistic, schizophrenic, or otherwise brittle. It would be unwise or dangerous for that person to take care of children and cook meals, but it’s on the horizon for home robots. That’s like saying, ‘We have an important job to do, but we’re going to hire dogs and cats to do it.'"

"Sometimes the veneer of intelligence is not enough."

""What we needed, is nothing less than an “AI Manhattan Project”, a full frontal assault on common sense: the challenge is to create an Encyclopédia of Common sense", Michio Kaku citing Lenat."

"The time may come when a greatly expanded Cyc will underlie countless software applications. But reaching that goal could easily take another two decades."

"Once you have a truly massive amount of information integrated as knowledge, then the human-software system will be superhuman, in the same sense that mankind with writing is superhuman compared to mankind before writing."

"Today’s quantum ecosystem is extremely vibrant, and there is a lot of energy and focus on particular questions"

"I think there are advantages and disadvantages to that. I would like to see more stamina from the community when it comes to looking into the more difficult questions that have less immediate reward — like, for example, deeply understanding quantum algorithms."

"The thing is that people have tried a lot with few successes, but I don’t think that too many people have tried enough"

"One very, very interesting thing about quantum computation is that it touches so many different fields in mathematics"

"It’s not like that in classical computation. It’s really something that is special for quantum computation because it’s somehow ‘complete’ — quantum computation is some kind of completion, mathematically, of classical computation."

"The reef was super healthy and colorful, like being in a National Geographic television show,” . “As soon as I put my face in the water, this whole swarm of fish came towards me and then swerved to the right.."

"I got excited about how nature makes these complicated, distributed, mobile networks. Those multi-robot systems became a new direction of my research."

"“As far as we know, there isn’t a blueprint or an a priori distribution between who’s doing the building and who is not. We know the queen does not set the agenda,” . “These colonies start with hundreds of termites and expand their structure as they grow.”"

"“I have no idea how that works, “I mean, how do you create systems that are so adaptive?”"

"It’s almost like each individual fish acts like a distributed sensor. Instead of me doing all the work, somebody on the left can say, ‘Hey, I saw something.’ When the group divides the labor so that some of us look out for predators while the rest of us eat, it costs less in terms of energy and resources"

"It’s almost like each individual fish acts like a distributed sensor, Instead of me doing all the work, somebody on the left can say, ‘Hey, I saw something.’ When the group divides the labor so that some of us look out for predators while the rest of us eat, it costs less in terms of energy and resources than trying to eat and look out for predators all by yourself."

"The other factor is that Amazon’s robots do a mix of centralized and decentralized decision-making, The robots plan their own paths, but they also use the cloud to know more. That lets us ask: Is it better to know everything about all your neighbors all the time? Or is it better to only know about the neighbors that are closer to you?”"

"There are few others [like Amazon] with hundreds of robots moving around safely in a facility space. And the opportunity to work on algorithms in a deployed system was very exciting."

"Previous papers, and in particular Hastings’ first papers on topological obstructions, manage to show that stoquastic adiabatic computation is capable of understanding something about the global structure of a problem."

"AI has been making tremendous progress in machine translation, self-driving cars, etc. Basically, all the progress I see is in specialised intelligence. It might be hundreds or thousands of years or, if there is an unexpected breakthrough, decades."

"In 2016, I was approached by Huawei to be their Chief Scientist and Vice President for VR/AR/MR consumer products. I proposed building a big research center, in the UIUC Research Park, which would be a joint effort between the University of Illinois and Huawei. Both parties were excited, and after a year of preparation, I joined Huawei while retaining a part-time UIUC position. Like many US universities, UIUC had taken in many thousands of high-paying Chinese students in recent years (presumably to help with rising costs and the lack of tax funding). These students were absolutely shocked and thrilled that I was leading this effort, bridging the gaps between China and the US. Needless to say this was eventually doomed in 2017 amid rising nationalism, the eventual US-China trade war, and Huawei being put on the entity list. I nevertheless had a wonderful time working in Huawei and learned so much about consumer product development. I met many hard working, kind, and intellectually interesting people, and thoroughly enjoyed the culture in Shenzhen, Shanghai, and Hangzhou."

"The unbelievable Oculus success also opened many doors, and I had the opportunity to get to know people I never would have met before, including CEOs, venture capitalists, serial entrepreneurs, billionaires, politicians, and Hollywood people. At the same time, I returned to UIUC to continue my tenured position. I was so excited about the consumer VR revolution that I started a new course and wrote a VR book based on it. The key insight was that understanding human perception and physiology is critical to engineering of VR systems and experiences. Thus, the course and book provide a unique integration of these subjects. A version of the course was recorded by NPTEL when I visited IIT Madras in 2015 and is available online."

"After the industry experiences, I could see startling differences between the academic and business worlds. I was most comfortable in universities, where I was able to freely learn, grow, and openly share whatever I know with people around me. In industry, information is protected, which leads to complicated games and strategies for gaining power based on who knows what. It is a difficult place to be if you enjoy sharing your knowledge and understanding, and generally helping people with their projects. I also found the Dunning-Kruger effect to cause serious problems in industry, especially when people don't know what they don't know, but have massive power over others and company directions. Both worlds clearly play important roles, but I learned that a university research environment makes me the happiest. I feel very lucky to have been able to try both at fairly high levels."

"One "occupational hazard" of being a professor is exposure to various countries, cultures, and people from all over the world. I was very fortunate to visit a few dozen countries, often hosted by people who have the same professional interests. This made me wonder what my life would be like if I had grown up there. I became attracted to Poland, where I have family heritage, and then Nordic countries. I was especially attached to Finland after teaching a short course at the University of Vaasa in 2007; I was invited by Pekka Isto, a Finnish motion planning expert."

"In 2012-2013, I came with my family to Oulu for a sabbatical to write books and get to know Finland better. After living there for nine months, we came to deeply appreciate Finnish culture and lifestyle. It seemed that Finland naturally fit my personality. People are respectful of each other and generally cooperate in maintaining a safe, responsible, and fair society. Modesty and respect for cultural differences around the world are strongly emphasized. Education and educators are highly respected. I thought it would be perfect for raising my children, with safe, playful, and nurturing infrastructure, and where good health care and education are free rights of residents (private universities are even illegal). Relating to my profession, there is strong interest in developing advanced technology while also being respectful of nature, the environment, and wellbeing. These are the values that suit me well, and that I want my family to learn. In 2018, I was invited to become a professor at the University of Oulu, and I could not be happier. I am currently co-leading the Perception Engineering Group, which pursues problems in virtual reality, robotics, and telepresence. The university made a nice video about me, and a Finnish newspaper explains more about why I moved to Oulu. I am also involved in helping the industrial ecosystem in Finland, especially in virtual reality and its various flavors (VR, AR, MR, XR, ...)."

"No one knows what the right algorithm is, but it gives us hope that if we can discover some crude approximation of whatever this algorithm is and implement it on a computer, that can help us make a lot of progress.”"

"Intelligence is ten million rules. This refers to the prior and tacit knowledge that authors presume their readers all possess (such as "if person x knows person y, then x's date of death can't be earlier than y's date of birth") not counting the vastly larger number of "facts" such as one might find in Wikipedia or by Googling."

"Deep-learning will transform every single industry. Healthcare and transportation will be transformed by deep-learning. I want to live in an AI-powered society. When anyone goes to see a doctor, I want AI to help that doctor provide higher quality and lower cost medical service. I want every five-year-old to have a personalised tutor."

"So, ask yourself: If what you're working on succeeds beyond your wildest dreams, would you have significantly helped other people? If not, then keep searching for something else to work on. Otherwise you're not living up to your full potential.”"

"“Despite all the hype and excitement about AI, it’s still extremely limited today relative to what human intelligence is.”"

"Success in creating AI would be the biggest event in human history. Unfortunately, it might also be the last, unless we learn how to avoid the risks."

"“If you show a poetry professor your shiny new multiple choice teaching technology, he will invite you to exit his office.”"

"“If you want to publish data, you should do it to share knowledge.”"

"Fearing a rise of killer robots is like worrying about overpopulation on Mars.”"

"“It seemed really amazing that you could write a few lines of code and have it learn to do interesting things.”"

"“It is difficult to think of a major industry that AI will not transform. This includes healthcare, education, transportation, retail, communications, and agriculture. There are surprisingly clear paths for AI to make a big difference in all of these industries."

"Imagine if we can just talk to our computers and have it understand, ‘Please schedule a meeting with Bob for next week.’ Or if each child could have a personalized tutor. Or if self-driving cars could save all of us hours of driving.”"

"“Education is not about thinning the herd. Education is about helping every student succeed.”"

"Imagine if your success in life is determined only by your guts, your hard work, and your willpower, and not by the wealth of your parents. I think it'll be a much more interesting world. I think this could change the way the planet is run”"

"So just as convolution gives us a great leverage when you apply it to the right part of the problem, then the intuition as well, hopefully there there's a few mechanisms, hopefully not like 100 mechanisms, but maybe 10. And then if we figure out how to use those mechanisms to bias learning and to structure behavior that we could learn robust ways of behaving that are efficient and so on."

"For instance, everyone's used to the idea of convolution now in image space. But if you think about it. And I've had people tell me who work on convolutional neural networks that they don't build any structure into their system. It's just a neural network. But of course, as soon as you build the compositional structure into a neural network, you are taking a position on some regularities that are in the input signal and so on. And you're taking advantage of that so that you don't have to learn a whole fully connected network, but you just learn some compositional kernels."

"So one set of possible kind of general ideas includes convolution in space also and time. Maybe understanding the kinematics of the system that it's connected together in joints and segments, a notion of planning to move through space, being able to do causal reasoning-- if I were to do this, what would happen? -- Abstracting over individual objects, various kinds of state and temporal abstraction and so on. So our view-- I don't want to commit to a particular list-- but is that there's a list of structural principles that are pretty generic and very broadly useful and we should build them in."

"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."

"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 I'm still there, OK? So I'm still in I'm still trying to figure out how we can design an architecture that can learn efficiently. And so the research strategy that I have really adopted, I work closely with a colleague, Tomás Lozano-Pérez. Our strategy has been the following, which is to try to think of some very generic representation and inference mechanisms and build those in and then figure out how to learn the rest of the stuff. And we're all used to I think by now the idea of some representation that inference mechanisms that we would want to build in."

"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."