51 - 60 of 72 results for: artificial intelligence

Are you interested in innovative ideas and strategies for addressing urgent challenges in human and planetary health? This invited lecture series, co-convened by faculty, fellows and students collaborating across several Stanford centers, invites the discussion of global problems, perspectives, and solutions in this fast-changing, vital domain. Guest faculty are leaders, innovators, and experts selected from organizations in diverse sectors such as: healthcare/medical innovation, foundations/venture capital, biotechnology/pharmaceuticals, social innovation/entrepreneurship health, tech/media and artificial intelligence (AI), human rights, global poverty/development, sustainable agriculture/hunger/nutrition. Registration open to all Stanford students and fellows. May be repeated for credit.

Highly experiential course focuses on factors that stimulate creativity and innovation in individuals, teams, and organizations. Workshops, case studies, and team projects, supported by guest speakers and readings. Autumn quarter will focus on "inventing the future," including tools for predicting the impact of frontier technologies, such as virtual reality, drones, genomics, artificial intelligence, and autonomous vehicles. Spring quarter will focus on tackling a real-world problem. In both quarters, students will learn how to frame and re-frame problems, challenge assumptions, work on creative teams, and generate innovative ideas. Limited enrollment. Admission by application: dschool.stanford.edu/classes.

Data and Decisions teaches you how to use data and quantitative reasoning to make sound decisions in complex and uncertain environments. The course draws on probability, statistics, and decision theory. Probabilities provide a foundation for understanding uncertainties, such as the risks faced by investors, insurers, and capacity planners. We will discuss the mechanics of probability (manipulating some probabilities to get others) and how to use probabilities to make decisions about uncertain events. Statistics allows managers to use small amounts of information to answer big questions. For example, statistics can help predict whether a new product will succeed or what revenue will be next quarter. The third topic, decision analysis, uses probability and statistics to plan actions, such as whether to test a new drug, buy an option, or explore for oil. In addition to improving your quantitative reasoning skills, this class seeks to prepare you for later classes that draw on this material, including finance, economics, marketing, and operations. At the end we will discuss how this material relates to machine learning and artificial intelligence.

Is it really possible for an artificial system to achieve genuine intelligence: thoughts, consciousness, emotions? What would that mean? How could we know if it had been achieved? Is there a chance that we ourselves are artificial intelligences? Would artificial intelligences, under certain conditions, actually be persons? If so, how would that affect how they ought to be treated and what ought to be expected of them? Emerging technologies with impressive capacities already seem to function in ways we do not fully understand. What are the opportunities and dangers that this presents? How should the promises and hazards of these technologies be managed?nnPhilosophers have studied questions much like these for millennia, in scholarly debates that have increased in fervor with advances in psychology, neuroscience, and computer science. The philosophy of mind provides tools to carefully address whether genuine artificial intelligence and artificial personhood are possible. Epistemology (the philosophy of knowledge) helps us ponder how we might be able to know. Ethics provides concepts and theories to explore how all of this might bear on what ought to be done. So we will read philosophical writings in these areas as well as writings explicitly addressing the questions about artificial intelligence, hoping for a deep and clear understanding of the difficult philosophical challenges the topic presents.nnNo background in any of this is presupposed, and you will emerge from the class having made a good start learning about computational technologies as well as a number of fields of philosophical thinking. It will also be a good opportunity to develop your skills in discussing and writing critically about complex issues.

This tutorial examines philosophical issues in artificial intelligence and machine learning. The focus will be on ethical questions raised by current and forthcoming engineering applications, rather than on classic foundational issues like whether machines can be conscious. Hands¿on knowledge of current AI / ML technologies is not required, but students with such experience will be enthusiastically welcomed. Students will be encouraged to shape the direction of the class based on their own interests or experience in industry.

In this course we will explore the central question of what intelligence is by adopting artificial intelligence research as a point of reference. Starting with ideas proposed by Alan Turing in his 1950 paper, we will see what the contemporary interpretations are for those questions, and learn what new questions new technologies have brought. Among the subtopics are: Is it possible for a computer to think? What is thought? Are we computers? Could machines feel emotions or be conscious? Can AI die? Is there a relation between AI and decidability? What is the relationship between AI and Neuroscience Research? nThis course is intended for students of different majors interested in learning how the researchers in AI understand today the concept of intelligent machine, and examine what are the philosophical problems associated with the concept of artificial intelligence.

The past three decades have seen the elaboration of a vast body of literature on unconditional basic income a radical policy proposal Philippe Van Parijs referred to as a disarmingly simple idea. It consists of a monthly cash allowance given to all citizens, regardless of personal desert and without means test to provide them with a standard of living above the poverty line. The seminar will seek to engage students in normative debates in political theory (feminism, liberalism, republicanism, communism, libertarianism, etc.) by appealing to the concrete example of basic income. It will allow students to learn a great deal about a policy that is gaining tremendous currency in academic and public debates, while discussing and learning about prominent political theorists - many of whom have written against or for basic income at one point in their career.nnnThe seminar is open to undergraduate and graduate students in all departments. There are no pre-requisites. We will ask questions such as: is giving people cash no strings attached desirable and just? Would basic income promote a more gender equal society through the remuneration of care-work, or would it risks further entrenching the position of women as care-givers? Would alternative policies be more successful (such as the job guarantees, stakeholder grants or a negative income tax)? How can we test out basic income? What makes for a reliable and ethical basic income pilot? Students in Politics, Philosophy, Public Policy, Social Work, and Sociology should find most of those questions relevant to their interests. Some discussions on how to fund basic income, on the macro-economic implications of basic income and on the existing pilots projects (in Finland, Namibia, India, Canada and the US) may be of interest to Economists; while our readings on the impact of new technologies and artificial intelligence on the future of work and whether a basic income could be a solution, are likely to be on interest to computer scientists and engineers. By the end of the class, students will have an in depth knowledge of the policy and will have developed skills in the normative analysis of public policy. They will be able to deploy those critical and analytical skills to assess a broad range of other policies.

In this course we will explore the central question of what intelligence is by adopting artificial intelligence research as a point of reference. Starting with ideas proposed by Alan Turing in his 1950 paper, we will see what the contemporary interpretations are for those questions, and learn what new questions new technologies have brought. Among the subtopics are: Is it possible for a computer to think? What is thought? Are we computers? Could machines feel emotions or be conscious? Can AI die? Is there a relation between AI and decidability? What is the relationship between AI and Neuroscience Research? nThis course is intended for students of different majors interested in learning how the researchers in AI understand today the concept of intelligent machine, and examine what are the philosophical problems associated with the concept of artificial intelligence.

The past three decades have seen the elaboration of a vast body of literature on unconditional basic income a radical policy proposal Philippe Van Parijs referred to as a disarmingly simple idea. It consists of a monthly cash allowance given to all citizens, regardless of personal desert and without means test to provide them with a standard of living above the poverty line. The seminar will seek to engage students in normative debates in political theory (feminism, liberalism, republicanism, communism, libertarianism, etc.) by appealing to the concrete example of basic income. It will allow students to learn a great deal about a policy that is gaining tremendous currency in academic and public debates, while discussing and learning about prominent political theorists - many of whom have written against or for basic income at one point in their career.nnnThe seminar is open to undergraduate and graduate students in all departments. There are no pre-requisites. We will ask questions such as: is giving people cash no strings attached desirable and just? Would basic income promote a more gender equal society through the remuneration of care-work, or would it risks further entrenching the position of women as care-givers? Would alternative policies be more successful (such as the job guarantees, stakeholder grants or a negative income tax)? How can we test out basic income? What makes for a reliable and ethical basic income pilot? Students in Politics, Philosophy, Public Policy, Social Work, and Sociology should find most of those questions relevant to their interests. Some discussions on how to fund basic income, on the macro-economic implications of basic income and on the existing pilots projects (in Finland, Namibia, India, Canada and the US) may be of interest to Economists; while our readings on the impact of new technologies and artificial intelligence on the future of work and whether a basic income could be a solution, are likely to be on interest to computer scientists and engineers. By the end of the class, students will have an in depth knowledge of the policy and will have developed skills in the normative analysis of public policy. They will be able to deploy those critical and analytical skills to assess a broad range of other policies.