scientists

"In my TED talk, I argued that it’s not about building trustworthy tech. It’s about creating tools so that people can make choices for themselves — tools that let us shift the conversation on what technology should be"

"“I always say I’m not born of tech. I did not start my career in tech — I began working at nonprofits, public policy organizations, and even as an economist. That breadth of experience gives me a perspective on technology that goes beyond the usual tech world.”"

"With generative AI, the public can now simply ask, ‘Why is it that when I ask for five scientists, I only get men?’ and that’s where change begins"

"“The shortest answer to a better AI future is choice. People need the ability to decide which algorithms affect their lives — whether they’re users or creators"

"“Quantitative social scientists belong on these teams — not just to philosophize, but to translate abstract policy mandates into measurable, technical specifications."

"“When I joined Accenture for responsible AI, I saw the problem as a series of quantitative social science challenges. It was an amazing opportunity to think big, think globally, and start solving big problems"

"“The journey toward responsible AI is aspirational. It isn’t about reaching a final state of unbiased perfection once and for all; it’s about continuously evolving and building systems that empower choice"

"“I am a big open source advocate. Open source used to be the backbone of the tech industry — it made tech accessible by enabling people to learn from free code on GitHub. Of course, with that openness come security challenges, but its role in fostering innovation is undeniable"

"“AI is borderless like air. We need global regulation to tackle it, yet recent trends suggest a future of ‘sovereign AI’ with fragmented approaches across nations"

"“When I founded Humane Intelligence, I purposely chose a nonprofit model. It was crucial for me to align with funders who cared about long-term, mission-oriented impact, rather than just chasing quarterly profits"

"It is a great honour to receive the LEO award, and I humbly would like to accept this on behalf of the South African information systems community. I would also like to acknowledge all who have made it possible for me to achieve my objectives during my academic career: my family - in particular, Annemarie, my dear wife of 45 years, my South African academic colleagues, and, especially, my students."

"Reading from text in general is a hard problem, because it involves all of common sense knowledge. But reading from text in structured domans I don’t think is as hard. It is a critical problem that needs to be solved."

"The dream of the intelligent machine is the vision of creating something that does not depend on having people preprogram its problem‐solving behaviour. Put another way, artificial intelligence (AI) should not seek to merely solve problems but should rather seek to solve the problem of how to solve problems. This chapter seeks to provide a focused explication of particular methods that indeed allow machines to improve themselves by learning from experience and to explain the fundamental theoretical and practical considerations of applying them to problems of machine learning. To begin this explication, the discussion first goes back to the Turing Test. The acceptance of the Turing Test focused attention on mimicking human behaviour. A human may be described as an intelligent problem‐solving machine. The idea of constructing an artificial brain or neural network has been proposed many times."

"There are certain major mysteries that are magnificent open questions of the greatest import. Some of the things computer scientists study are not. If you’re studying the structure of data-bases—well, sorry to say, that’s not one of the big magnificent questions."

"I think the only way is the way human culture has gotten there. We transmit our knowledge via cultural artifacts called texts. It used to be manuscripts, then it was printed text, now it’s electronic text. We put our young people through a lot of reading to absorb the knowledge of our culture. You don’t go out and experience chemistry, you study chemistry."

"We should keep our "eye on the prize." Actually, two related prizes. One is that when we finish our job, whether it is 100 years from now or 200 years from now, we will have invented the ultra-intelligent computer. The other is that we will have a very complete model of how the human mind works. I don’t mean the human brain, I mean the mind: the symbolic processing system."

"So at the beginning of 2000, I retired. Since then I have been leading a wonderful life doing whatever I please. Now that I have a lot more time than I had before, I’m getting geekier and geekier. It feels like I’m 10 years old again, getting back involved with details of computing."

"Our group, the Heuristic Programming Project, did path-breaking work in the large, unexplored wilderness of all the great scientific theories we could possibly have. But most of that beautiful wilderness today remains largely unexplored. Am I am happy with where we have gotten in induction research? Absolutely not, although I am proud of the few key steps we took that people will remember."

"In my view the science that we call AI, maybe better called computational intelligence, is the manifest destiny of computer science."

"That convinced me to get very serious about archives, including my own. If you’re interested in discoveries and the history of ideas, and how to manufacture ideas by computer, you’ve got to treat this historical material as fundamental data. How did people think? What alternatives were being considered? Why was the movement from one idea to another preposterous at one time and then accepted"

"We need to have a way for computers to read books on chemistry and learn chemistry. Or read books on physics and learn physics. Or biology. Or whatever. We just don’t do that today. Our AI programs are handcrafted and knowledge engineered. We will be forever doing that unless we can find out how to build programs that read text, understand text, and learn from text."

"I’m talking about mysteries like the initiation and development of life. Equally mysterious is the emergence of intelligence. Stephen Hawking once asked, "Why does the universe even bother to exist?" You can ask the same question about intelligence. Why does intelligence even bother to exist?"

"In 1994 an amazing thing happened. The phone rings and it is Professor Sheila Widnall of the Department of Aeronautics and Astronautics of MIT. She said, "Do you know anyone who wants to be Chief Scientist of the Air Force? And by the way, if you are interested let me know." She had been chosen to be Secretary of the Air Force, and she was looking for her Chief Scientist. I thought about it briefly, told her yes, and stayed for three years."

"I am also working on some ideas for new learning architectures for deep-learning networks, inspired in part by the Cascade Correlation architecture that I developed in 1990 with Chris Lebiere."

"My job was to be a window on science for the Chief of Staff of the Air Force. I was the first person to be asked to be Chief Scientist who was not an Aero-Astro person, a weapons person, or from the physical sciences. There had not been any computer scientists before me."

"When I was younger, I was too busy for history and not cognizant of the importance of it. As I got older and began to see my own career unfolding, I began to realize the impact of the ideas of others on my ideas. I became more and more of a history buff."

"For the people who will be out there years from now, the question will be: will we have fully explicated the theory of thinking in your lifetime? It would be very interesting to see what you people of a hundred years from now know about all of this."

"The great thing about being retired is not that you work less hard, but that what you do is inner-directed. The world has so many things you want to know before you’re out of here that you have a lot to do."

"It was a rather unsettling experience to come back to Stanford. After playing a role on a big stage, all of a sudden you come back and your colleagues ask,"

"The other was on software development. The military up to that point believed in, and could only imagine, a structured-programming top-down world. You set up requirements, you get a contractor to break down the requirements into blocks, another contractor breaks them down into mini-blocks, and down at the bottom there are some people writing the code. It takes years to do. When it all comes back up to the top, (a) it’s not right, and (b) it’s not what you want anymore. They just didn’t know how to contract for cyclical development. Well, I think we were able to help them figure out how to do that."

"I did two big things. One was consciousness-raising in the Air Force about software. The one big report I wrote, at the end of my term, was a report called, It’s a Software-First World. The Air Force had not realized that. They probably still do not think that. They think it is an airframe-based world."

"As a researcher, I am primarily interested in Artificial Intelligence and its applications. I have worked in many areas of AI: planning, knowledge representation and reasoning, image processing, natural language processing, document classification, artificial neural networks, and the use of massively parallel machines to solve AI problems. I am also interested in the use of AI techniques to build better user interfaces and context-aware systems."

"I am a Professor Emeritus in Carnegie Mellon’s School of Computer Science (SCS). That means I am formally retired, but still active in research, advising, and departmental activities. My home department is the Language Technologies Institute (LTI). I am also emeritus faculty in the Computer Science Department (CSD)."

"I don’t believe there is a general pattern recognition problem. I believe that pattern recognition, like most of human reasoning, is domain specific. Cognitive acts are surrounded by knowledge of the domain, and that includes acts of inductive behavior. So I don’t really put much hope in "general anything" for AI. In that sense I have been very much aligned with Marvin Minsky’s view of a "society of mind." I’m very much oriented toward a knowledge-based model of mind."

"Currently, I am working on Scone, a practical Knowledge Base System (KBS) that can represent a large body of real-world knowledge and that can efficiently perform the kinds of search and inference that seem so effortless for us humans. This work is based in part on the NETL system that I developed for my Ph.D. thesis in the late 1970s, but the Scone system is designed to run on standard laptops, desktop machines, and servers rather than on special parallel hardware."

"My research group has worked on a number of applications of Scone, with a special focus on using Scone to support knowledge-based natural language understanding and generation. I believe that Scone-like knowledge base systems will be important tools in the future, perhaps used in even more ways than database systems are used today."

"I was one of the core developers of the Common Lisp language, and my research group developed the CMU Common Lisp implementation which formed the basis for many commercial Common Lisp systems, and now is maintained as open-source software, along with a split-off version, Steel Bank Common Lisp."

"In 1982, I proposed the use of :-) and :-( in posts and Email messages. These are generally regarded as the first internet emoticons, and the text-only ancestors of today’s graphical emojis."

"Many scientists suspect that the universe can ultimately be described by a simple (perhaps even deterministic) formalism; all that is real unfolds mechanically according to that formalism. But how, then, is it possible for us to be conscious, or to make genuine choices? And how can there be an ethical dimension to such choices? Drescher sketches computational models of consciousness, choice, and subjunctive reasoning—what would happen if this or that were to occur?—to show how such phenomena are compatible with a mechanical, even deterministic universe. Analyses of Newcomb’s Problem (a paradox about choice) and the Prisoner’s Dilemma (a paradox about self-interest vs. altruism, arguably reducible to Newcomb’s Problem) help bring the problems and proposed solutions into focus. Regarding quantum mechanics, Drescher builds on Everett’s relative-state formulation—but presents a simplified formalism, accessible to laypersons—to argue that, contrary to some popular impressions, quantum mechanics is compatible with an objective, deterministic physical reality, and that there is no special connection between quantum phenomena and consciousness."

"The schema mechanism learns from its experiences, expressing discoveries in its existing representational vocabulary and extending that vocabulary with new concepts. A novel empirical learning technique, marginal attribution, can find results of an action that are obscure because each occurs rarely in general, although reliably under certain conditions. Drescher shows that several early milestones in the Piagetian infant’s invention of the concept of persistent object can be replicated by the schema mechanism."

"Made-Up Minds addresses fundamental questions of learning and concept invention by means of an innovative computer program that is based on the cognitive-developmental theory of psychologist Jean Piaget. Drescher uses Piaget’s theory as a source of inspiration for the design of an artificial cognitive system called the schema mechanism, and then uses the system to elaborate and test Piaget’s theory. The approach is original enough that readers need not have extensive knowledge of artificial intelligence, and a chapter summarizing Piaget assists readers who lack a background in developmental psychology."

"It is you who judges who you are. In this way, the judgment is accurate."

".. When an individual, e.g. higher forms of animals, thinks, it is always for his own advantage whether the resulting action or expression is favorable or not to the onlookers or observers."

"Our problem is : What are the underlying desires or wishes, that lead some scientists to insist upon mechanistic conceptions, and others equally eminent, to espouse some form of scientific vitalism ? For in psychology, as in other sciences, a materialistic or vitalistic bias may be found at the root of nearly all factional schools, or contentious groups. Sometimes, of course, the underlying desire relates solely to the advancement of the personal fortunes of the workers concerned ; and such purely egoistic motives probably play a considerable part in the evolution of every scientific doctrine. In addition to this, however, originators and promulgators of conceptual systems of thought, nearly always possess hidden desires to push science in this direction or that, " for science's own sake ". The goal selected is the one that accords most closely with the basic emotional set of the scientific agitator. And the emotional sets of scientists may be classified, broadly, into two elementary groups, materialistic and vitalistic."

"Science for its part speaks against the special importance of any object of science, including human beings. … Science as opposed to religion recognizes nothing sacred either outside man or within him. But collectively, science is the assertion of man over non-man, surely an unembarrassed claim to importance and rule. Yet as individuals, scientists are anonymous factors in the scientific enterprise, each one substitutable for another. For all science cares, scientists could as well be numbered as named."

"So many people today — and even professional scientists — seem to me like someone who has seen thousands of trees but has never seen a forest."

"Scientists are supposed to live in ivory towers. Their darkrooms and their vibration-proof benches are supposed to isolate their activities from the disturbances of common life. What they tell us is supposed to be for the ages, not for the next election. But the reality may be otherwise."

"I think all the scientists that I've worked with have been pretty remarkable people in terms of their scientific ability. They have not always been the most gentle people, kindliest people in dealing with their colleagues or their staffs, and so on. And some of them, I guess, have gotten to the top of the ladder by stepping over bodies. But you find that in any field. There are rascals in every profession, and scientist are no different in this respect of fighting their way to the top and even using their claws if necessary."

"Scientists are just as vulnerable to wishful thinking, just as likely to be tempted by base motives, just as venal and gullible and forgetful as the rest of humankind. Scientists don't consider themselves to be saints; they don't even pretend to be priests (who according to tradition are supposed to do a better job than the rest of us at fighting off human temptation and frailty). Scientists take themselves to be just as weak and fallible as anybody else, but recognizing those very sources of error in themselves and in the groups to which they belong, they have devised elaborate systems to tie their own hands, forcibly preventing their frailties and prejudices from infecting their results."

"There is a noticeable general difference between the sciences and mathematics on the one hand, and the humanities and social sciences on the other. It's a first approximation, but one that is real. In the former, the factors of integrity tend to dominate more over the factors of ideology. It's not that scientists are more honest people. It's just that nature is a harsh taskmaster. You can lie or distort the story of the French Revolution as long as you like, and nothing will happen. Propose a false theory in chemistry, and it'll be refuted tomorrow."