Results for artificial intelligence

How will digital health, low-cost patient-generated and genomic data enable precision medicine to transform health care? This Everyone Included¿ course from Stanford Medicine X and SHC Clinical Inference will provide an overview of data science principles and showcase real world solutions being created to advance precision medicine through implementation of digital health tools, machine learning and artificial intelligence approaches. This class will feature thought leaders and luminaries who are patients, technologists, providers, researchers and leading innovators from academia and industry. This course is open to undergraduate and graduate students. Lunch will be provided.

Advances in research and technology are blurring the boundaries between humans, animals, and machines, challenging conventional notions of human nature. Seminar explores the question of what it now means to be human and the personal, social, and ethical implications of our advancing technologies through the lens of various disciplines, including anthropology, cognitive psychology, neuroscience, genetics, evolutionary biology, biotechnology, and artificial intelligence. Includes guest speakers from fields and industries where important questions are being raised.

This course will explore possibilities for student research projects based on presentations of faculty research. We will cover a broad range of topics within the general area of collective behavior, both natural and artificial. Students will build on faculty presentations to develop proposals for future projects.

Through weekly lectures given by prominent researchers, practicing professionals, and entrepreneurs, this class will examine important industry problems and critically assess corresponding AI directions in both academia and industry. Students will gain an understanding of how AI can be used to provide solutions in the architecture, engineering, and construction industry and asses the technology, feasibility, and corresponding implementation effort. Students are expected to participate actively in the lectures and discussions, submit triweekly reflection writings, and present their own evaluation of existing solutions. Enrollment limited to 12 students.

Through weekly lectures given by prominent researchers, practicing professionals, and entrepreneurs, this class will examine important industry problems and critically assess corresponding AI directions in both academia and industry. Students will gain an understanding of how AI can be used to provide solutions in the architecture, engineering, and construction industry and asses the technology, feasibility, and corresponding implementation effort. Students are expected to actively prepare for and participate in all lectures and corresponding discussions.

Students will learn about and apply cutting-edge artificial intelligence techniques to real-world social good spaces (such as healthcare, government, education, and environment). Taught jointly by CS+Social Good and the Stanford AI Group, the aim of the class is to empower students to apply these techniques outside of the classroom. The class will focus on techniques from machine learning and deep learning, including regression, support vector machines (SVMs), neural networks, convolutional neural networks (CNNs), and recurrent neural networks (RNNs). The course alternates between lectures on machine learning theory and discussions with invited speakers, who will challenge students to apply techniques in their social good domains. Students complete weekly coding assignments reinforcing machine learning concepts and applications. Prerequisites: programming experience at the level of CS107, mathematical fluency at the level of CS103, comfort with probability at the level of CS109 (or equivalent). Application required for enrollment.

Recent advances in computing may place us at the threshold of a unique turning point in human history. Soon we are likely to entrust management of our environment, economy, security, infrastructure, food production, healthcare, and to a large degree even our personal activities, to artificially intelligent computer systems. The prospect of "turning over the keys" to increasingly autonomous systems raises many complex and troubling questions. How will society respond as versatile robots and machine-learning systems displace an ever-expanding spectrum of blue- and white-collar workers? Will the benefits of this technological revolution be broadly distributed or accrue to a lucky few? How can we ensure that these systems respect our ethical principles when they make decisions at speeds and for rationales that exceed our ability to comprehend? What, if any, legal rights and responsibilities should we grant them? And should we regard them merely as sophisticated tools or as a newly emerging form of life? The goal of CS22 is to equip students with the intellectual tools, ethical foundation, and psychological framework to successfully navigate the coming age of intelligent machines.

Stanford Introductory Seminar. Preference to freshmen. Covers the intellectual tradition of computer science emphasizing ideas that reflect the most important milestones in the history of the discipline. No prior experience with programming is assumed. Topics include programming and problem solving; implementing computation in hardware; algorithmic efficiency; the theoretical limits of computation; cryptography and security; and the philosophy behind artificial intelligence.

Great Ideas in Computer Science Covers the intellectual tradition of computer science emphasizing ideas that reflect the most important milestones in the history of the discipline. Topics include programming and problem solving; implementing computation in hardware; algorithmic efficiency; the theoretical limits of computation; cryptography and security; computer networks; machine learning; and the philosophy behind artificial intelligence. Readings will include classic papers along with additional explanatory material. Enrollment limited to students in the Master's program in Computer Science Education.

Artificial intelligence (AI) has had a huge impact in many areas, including medical diagnosis, speech recognition, robotics, web search, advertising, and scheduling. This course focuses on the foundational concepts that drive these applications. In short, AI is the mathematics of making good decisions given incomplete information (hence the need for probability) and limited computation (hence the need for algorithms). Specific topics include search, constraint satisfaction, game playing, Markov decision processes, graphical models, machine learning, and logic. Prerequisites: CS 103 or CS 103B/X, CS 106B or CS 106X, CS 107, and CS 109 (algorithms, probability, and programming experience).

A general game playing system accepts a formal description of a game to play it without human intervention or algorithms designed for specific games. Hands-on introduction to these systems and artificial intelligence techniques such as knowledge representation, reasoning, learning, and rational behavior. Students create GGP systems to compete with each other and in external competitions. Prerequisite: programming experience. Recommended: 103 or equivalent.

This is a course at the intersection of philosophical logic and artificial intelligence. After reviewing recent work in AI that has leveraged ideas from logic, we will slow down and study in more detail various components of high-level intelligence and the tools that have been designed to capture those components. Specific areas will include: reasoning about belief and action, causality and counterfactuals, legal and normative reasoning, natural language inference, and Turing-complete logical formalisms including (probabilistic) logic programming and lambda calculus. Our main concern will be understanding the logical tools themselves, including their formal properties and how they relate to other tools such as probability and statistics. At the end, students should expect to have learned a lot more about logic, and also to have a sense for how logic has been and can be used in AI applications. Prerequisites: A background in logic, at least at the level of Phil 151, will be expected. In case a student is willing to put in the extra work to catch up, it may be possible to take the course with background equivalent to Phil 150 or CS 157. A background in AI, at the level of CS 221, would also be very helpful and will at times be expected. 2 unit option only for PhD students past the second year. Course website: http://web.stanford.edu/class/cs257/

Student teams under faculty supervision work on research and implementation of a large project in AI. State-of-the-art methods related to the problem domain. Prerequisites: AI course from 220 series, and consent of instructor.

Computing object motions in computer graphics, geometrical computing, robotics, or artificial intelligence for applications such as design, manufacturing, robotics, animated graphics, surgical planning, drug design, assembly planning, graphic animation of human figures, humanoid robots, inspection and surveillance, simulation of crowds, and biology. Path planning methods to generate collision-free paths among static obstacles. Extensions include uncertainty, mobile obstacles, manipulating moveable objects, maneuvering with kinematic constraints, and making and breaking contacts. Configuration space, geometric arrangements, and random sampling. Theoretical methods.

Advanced material is often taught for the first time as a topics course, perhaps by a faculty member visiting from another institution. May be repeated for credit.

In this seminar, we will focus on the challenges in the design of safe and verified AI-based systems. We will explore some of the major problems in this area from the viewpoint of industry and academia. We plan to have a weekly seminar speaker to discuss issues such as verification of AI systems, reward misalignment and hacking, secure and attack-resilient AI systems, diagnosis and repair, issues regarding policy and ethics, as well as the implications of AI safety in automotive industry. Prerequisites: There are no official prerequisites but an introductory course in artificial intelligence is recommended.

Artificial intelligence is poised to make radical changes in healthcare, transforming areas such as diagnosis, genomics, surgical robotics, and drug discovery. In the coming years, artificial intelligence has the potential to lower healthcare costs, identify more effective treatments, and facilitate prevention and early detection of diseases. This class is a seminar series featuring prominent researchers, physicians, entrepreneurs, and venture capitalists, all sharing their thoughts on the future of healthcare. We highly encourage students of all backgrounds to enroll (no AI/healthcare background necessary). Speakers and more at shift.stanford.edu/healthai.

(Same as Law 1038) If you are interested in a career in finance or that touches finance (computational science, economics, public policy, legal, regulatory, corporate, other), this course will give you a useful perspective. We will take on hot topics in the current landscape of global financial markets such as how the world has evolved post-financial crisis, how it is being disrupted by FinTech, RegTech, artificial intelligence, crowd financing, blockchain, machine learning & robotics (to name a few), how it is being challenged by IoT, cyber, financial warfare & crypto currency risks (to name a few) and how it is seizing new opportunities in fast-growing areas such as ETFs, new instruments/payment platforms, robo advising, big data & algorithmic trading (to name a few). The course will include guest-lecturer perspectives on how sweeping changes are transforming business models and where the greatest opportunities exist for students entering or touching the world of finance today including existing, new and disruptive players. While derivatives and other quantitative concepts will be handled in a non-technical way, some knowledge of finance and the capital markets is presumed. Elements used in grading: Class Participation, Attendance, Final Paper. Consent Application: To apply for this course, students must complete and email to the instructors the Consent Application Form, which is available on the Public Policy Program's website at https://publicpolicy.stanford.edu/academics/undergraduate/forms. See Consent Application Form for submission deadline.

(Same as Law 1038) If you are interested in a career in finance or that touches finance (computational science, economics, public policy, legal, regulatory, corporate, other), this course will give you a useful perspective. We will take on hot topics in the current landscape of global financial markets such as how the world has evolved post-financial crisis, how it is being disrupted by FinTech, RegTech, artificial intelligence, crowd financing, blockchain, machine learning & robotics (to name a few), how it is being challenged by IoT, cyber, financial warfare & crypto currency risks (to name a few) and how it is seizing new opportunities in fast-growing areas such as ETFs, new instruments/payment platforms, robo advising, big data & algorithmic trading (to name a few). The course will include guest-lecturer perspectives on how sweeping changes are transforming business models and where the greatest opportunities exist for students entering or touching the world of finance today including existing, new and disruptive players. While derivatives and other quantitative concepts will be handled in a non-technical way, some knowledge of finance and the capital markets is presumed. Elements used in grading: Class Participation, Attendance, Final Paper. Consent Application: To apply for this course, students must complete and email to the instructors the Consent Application Form, which is available on the Public Policy Program's website at https://publicpolicy.stanford.edu/academics/undergraduate/forms. See Consent Application Form for submission deadline.

From self-driving cars to bots that alter democratic elections, artificial intelligence is growing increasingly powerful and prevalent in our everyday lives. Fiction has long been speculating about the techno-utopia¿and catastrophe¿that A.I. could usher in. Indeed, fiction itself presents us with a kind of A.I. in the many characters that speak and think in its pages. So what constitutes an ¿intelligence¿ within literature or technology? In either field, is it ever possible to overcome the problem of other minds? Is there an ultimate boundary that demarcates bodies from machines? This course will begin with Mary Shelley¿s Frankenstein (1818) and Edgar Allan Poe¿s ¿Maelzel¿s Chess Player¿ (1836), then proceed through works such as Samuel Butler¿s Erewhon (1872), Isaac Asimov¿s I, Robot (1950), Stanley Kubrick¿s 2001: A Space Odyssey (1968), and Stanford lecturer Scott Hutchins¿s A Working Theory of Love (2012), including a possible visit from Hutchins. Throughout, we will be asking ourselves what makes someone¿or something¿a person in our world today.

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.nnThe 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.

(Same as LAW 7020) The objective of the course is to explore the increasing ethical challenges in a world in which technology, global risks, and societal developments are accelerating faster than our understanding can keep pace. We will unravel the factors contributing to the seemingly pervasive failure of ethics today among organizations and leaders across all sectors: business, government and non-profit. A framework for ethical decision-making underpins the course. The relationship between ethics and culture, global risks (poverty, cyber-terrorism, climate change, etc.) leadership, law and policy will inform discussion. Prominent guest speakers will attend certain sessions interactively. A broad range of international case studies might include: the Rohingya crisis in Myanmar; civilian space travel (Elon Musk's Mars plans); designer genetics; social media ethics (e.g. Facebook and Russia and on-line sex trafficking); free speech on University campuses (and Gawker type cases); artificial intelligence; Brexit; corporate and financial sector scandals (Epi pen pricing, hedge funds, Wells Fargo, Volkswagen emissions testing manipulation); and non-profit sector ethics challenges (e.g. should NGOs engage with ISIS). Final project in lieu of exam on a topic of student's choice. Attendance required. Class participation important (with multiple opportunities to earn participation credit beyond speaking in class). Strong emphasis on rigorous analysis, critical thinking and testing ideas in real-world contexts. Students wishing to take the course who are unable to sign up within the enrollment limit should contact Dr. Susan Liautaud at susanl1@stanford.edu. The course offers credit toward Ethics in Society, Public Policy core requirements (if taken in combination with PUBLPOL 103E or PUBLPOL 103F), and Science, Technology and Society majors and satisfies the undergraduate Ways of Thinking requirement. The course is open to undergraduate and graduate students. Undergraduates will not be at a disadvantage. Everyone will be challenged. Distinguished Career Institute Fellows are welcome and should contact Dr. Susan Liautaud directly at susanl1@stanford.edu. *Public Policy majors taking the course to complete the core requirements must obtain a letter grade. Other students may take the course for a letter grade or C/NC.

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.nnThe 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.

Healthcare spending is now nearly 18% of the entire GDP of the U.S. economy. The S&P healthcare sector has been one of the best producing segments of the market for the last five years, and growth of healthcare expenditures continue to escalate at a rapid pace. This has triggered an abundance of opportunities for those interested in a career in healthcare management, investing, or entrepreneurialism. The Business of Healthcare-2017-18 will present the current market framework from the eyes of a clinician and with the perspective of the consumer-patient, but with the experience of a successful business builder and investor. Course will begin with the discussion of the channels of distribution of healthcare delivery, from providers, to practitioners, to consumer-facing "healthcare lite" sectors of the market. Impact of the regulatory environment, with specific focus on the Affordable Care Act and the impending plans to Repeal/Replace, will be evaluated. High-level exploration of international health care markets and how they compare to the American market will be included. Overview of venture and private equity investing will be deeply probed, with many specific market examples of how investors develop an investment thesis, identify specific targets, diligence companies, and close an investment. Discussion around building financial modeling for target acquisitions will be presented, and the course will delve into the burgeoning area of healthcare analytics and outcomes management, including Artificial Intelligence, and its future impact on positioning, reimbursement and clinical outcomes. Sectors that will be discussed include: Healthcare services, Healthcare IT, Life Sciences, Pharma and Biotechnology, and Managed Care. The topic of the emerging importance of consumerism will be probed and consumer-directed healthcare related products and services will be explored, e.g. nutraceuticals, wellness, fitness, etc. Course will include preparatory readings, presentations from successful and powerful industry leaders, and robust in-class discussion and case studies requiring student engagement. Final grade will consist of class participation, one minor in-class presentation, and a final paper developing either a new healthcare business start-up proposition or presenting an identified investment target in the healthcare industry. Course will be especially valuable for those interested in a career in starting a healthcare company, healthcare investing, healthcare administration, or other healthcare-related management and goal of class will be provide an in-depth overview of how to get started or advance a professional interest in the industry.

It is impossible to innovate in healthcare without first understanding the context in which these innovations take place. The course aims to allow students an intimate setting to debate issues that plague healthcare today, and work with guest speakers (from Stanford Medicine, Stanford Biodesign, RockHealth to Apple Health and more!) to gain insight into what's actually being done about it. Some controversial topics highlighted include: Healthcare Legislation (especially in the context of the last tow administrations), Artificial Intelligence in Healthcare, Gene Therapy, and in-depth analysis of Failed Medical Devices and Innovations.

Recent advances in computing may place us at the threshold of a unique turning point in human history. Soon we are likely to entrust management of our environment, economy, security, infrastructure, food production, healthcare, and to a large degree even our personal activities, to artificially intelligent computer systems. The prospect of "turning over the keys" to increasingly autonomous systems raises many complex and troubling questions. How will society respond as versatile robots and machine-learning systems displace an ever-expanding spectrum of blue- and white-collar workers? Will the benefits of this technological revolution be broadly distributed or accrue to a lucky few? How can we ensure that these systems respect our ethical principles when they make decisions at speeds and for rationales that exceed our ability to comprehend? What, if any, legal rights and responsibilities should we grant them? And should we regard them merely as sophisticated tools or as a newly emerging form of life? The goal of CS22 is to equip students with the intellectual tools, ethical foundation, and psychological framework to successfully navigate the coming age of intelligent machines.

Scholars' and policy makers' thinking about political economy evolves as one understanding of the role of government ceases to reflect people's aspirations and views of social reality and is superseded by another. The laissez faire thinking of the 19th century was replaced by Keynesian management in response to the Great Depression. After WWII, Keynesian thinking was challenged, by 'neoliberalism'---a challenge that began to achieve success in the 1970s in response to perceived failures of government, high inflation, and other economic and social woes. By the mid-1980s, neoliberalism had become the new conventional wisdom, and liberals as well as conservatives accepted its core premises: that society consists of atomized individuals competing rationally to advance their own interests; that this behavior, in aggregate, produces good social outcomes and economic growth; that free markets are therefore the best way to allocate societal resources and government should intervene only to remedy market failures. Disagreements about what constitutes such failures and about corrective interventions persisted, but the general premises were widely embraced by policymakers and politicians---the so-called Washington Consensus. Today, that consensus is breaking down. Neoliberal policies have generated profound wealth inequality and have little to offer to address the perceived threats of globalization and emerging technologies like artificial intelligence and robotics. But what should come next? Our readings in the course will explore a variety of themes related to these debates. How did neoliberalism come to dominate political discourse? What are its core tenets? What kinds of challenges are being presented to them, and what might an alternative approach to political economy for the 21st century look like? Winter Quarter. Five Monday Evenings from 6:30 - 8:30 (precise dates TBD). DISCUSSIONS IN ETHICAL & PROFESSIONAL VALUES COURSES RANKING FORM: To apply for this course, 2L, 3L and Advanced Degree students must complete and submit a Ranking Form available on the SLS website (Click Courses at the bottom of the homepage and then click Consent of Instructor Forms). See Consent Application Form for instructions and submission deadline. Elements used in grading: Class attendance at all sessions and class participation.

If you are interested in a career in finance or that touches finance (computational science, economics, public policy, legal, regulatory, corporate, other), this course will give you a useful perspective. We will take on hot topics in the current landscape of global financial markets such as how the world has evolved post-financial crisis, how it is being disrupted by FinTech, RegTech, artificial intelligence, crowd financing, blockchain, machine learning & robotics (to name a few), how it is being challenged by IoT, cyber, financial warfare & crypto currency risks (to name a few) and how it is seizing new opportunities in fast-growing areas such as ETFs, new instruments/payment platforms, robo advising, big data & algorithmic trading (to name a few). The course will include guest-lecturer perspectives on how sweeping changes are transforming business models and where the greatest opportunities exist for students entering or touching the world of finance today including existing, new and disruptive players. While derivatives and other quantitative concepts will be handled in a non-technical way, some knowledge of finance and the capital markets is presumed. Elements used in grading: Class Participation, Attendance, Final Paper. Consent Application: To apply for this course, students must complete and e-mail the Consent Application Form available on the SLS Registrar's Office website (see Registration) to the instructor(s). Elements used in grading: Class Participation, Attendance, Final Paper. Consent Application: To apply for this course, students must complete and e-mail the Consent Application Form available on the SLS Registrar's Office website (see Registration) to the instructor(s). See Consent Application Form for submission deadline. Cross-listed with Economics (ECON 152/252), Public Policy (PUBLPOL 364), Statistics (STATS 238).

The advent of a highly disruptive technology necessarily butts up against existing laws, regulations and policies designed for the status quo as well as established businesses. This course takes the examples of driverless cars and artificial intelligence and examines the new and challenging legal questions and opportunities presented by these technologies. We will also discuss how business leaders, lawyers and technologists in these areas can navigate and create legal, regulatory and policy environments designed to help their businesses not only survive but thrive. Through a combination of readings, classroom discussions, expert guest speakers from the relevant technology and policy fields and student presentations, this course explores the promise of these technologies, the legal and regulatory challenges presented and the levers in-house counsel and business leaders in these fields can invoke to better navigate the inevitable obstacles facing these highly disruptive technologies. There are no formal prerequisites in engineering or law required, but students should be committed to pursuing novel questions in an interdisciplinary context. Elements used in grading: class preparation and short reflection papers. This course is open to School of Engineering and graduate students with consent of the instructor.

This is an era of groundbreaking change in the legal profession. Twenty years ago, email was unheard of at most law firms. Today, artificial intelligence, machine learning, and online services are creating a fundamental shift in how law is practiced. Beyond technology, massive challenges to the code of professional responsibility, from multi-disciplinary practices to law firms filing for IPOs, are reshaping the legal landscape. This course focuses on the opportunities and challenges these disruptions create for the new lawyer. Students will gain hands-on experience with some of the most innovative organizations in the legal community. Significant time will also be spent analyzing changes anticipated to impact the legal industry in the next decade. Elements used in grading: Attendance, Class Participation, Final Paper.

(Formerly Law 724) The objective of the course is to explore the increasing ethical challenges in a world in which technology, global risks, and societal developments are accelerating faster than our understanding can keep pace. We will unravel the factors contributing to the seemingly pervasive failure of ethics today among organizations and leaders across all sectors: business, government and non-profit. A framework for ethical decision-making underpins the course. The relationship between ethics and culture, global risks (poverty, cyber-terrorism, climate change, etc.) leadership, law and policy will inform discussion. Prominent guest speakers will attend certain sessions interactively. A broad range of international case studies might include: the Rohingya crisis in Myanmar; civilian space travel (Elon Musk's Mars plans); designer genetics; social media ethics (e.g. Facebook and Russia and on-line sex trafficking); free speech on University campuses (and Gawker type cases); artificial intelligence; Brexit; corporate and financial sector scandals (Epi pen pricing, hedge funds, Wells Fargo, Volkswagen emissions testing manipulation); and non-profit sector ethics challenges (e.g. should NGOs engage with ISIS). Final project in lieu of exam on a topic of student's choice. Attendance required. Class participation important (with multiple opportunities to earn participation credit beyond speaking in class). Strong emphasis on rigorous analysis, critical thinking and testing ideas in real-world contexts. Students wishing to take the course who are unable to sign up within the enrollment limit should contact Kylie De La O-Ménard at kyliedm@stanford.edu. The course is open to undergraduate and graduate students. Undergraduates will not be at a disadvantage. Everyone will be challenged. Distinguished Career Institute Fellows are welcome and should contact Dr. Susan Liautaud directly at susanl1@stanford.edu. NOTE: This course does not meet the SLS Ethics requirement. Elements used in grading: Class Participation, Attendance, Written Assignments, and Final Paper. Cross-listed with Ethics in Society ( ETHICSOC 234R), Public Policy ( PUBLPOL 134, PUBLPOL 234).

The seminar will feature guest lectures from industry and academia to discuss the state of the affairs in the field of Robotics, Artificial Intelligence (AI), and how that will impact the future Education. The time of robotics/AI are upon us. Within the next 10 to 20 years, many jobs will be replaced by robots/AI. We will cover hot topics in Robotics, AI, how we prepare students for the rise of Robotics/AI, how we Re-design and Re-invent our education to adapt to the new era

Focuses on new trends and disruptive technologies in IT. Emphasis on the way technologies create a competitive edge and generate business value. Broad range of views presented by guest speakers, including top level executives of technology companies, and IT executives (e.g. CIOs) of Fortune 1000 companies. Special emphasis in technologies such as Cloud Computing, Artificial Intelligence, Security, Mobility, and Big Data.

Focuses on new trends and disruptive technologies in IT. Emphasis on the way technologies create a competitive edge and generate business value. Broad range of views presented by guest speakers, including top level executives of technology companies, and IT executives (e.g. CIOs) of Fortune 1000 companies. Special emphasis in technologies such as Cloud Computing, Artificial Intelligence, Security, Mobility, and Big Data.

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.

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.nnThe 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.

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.nnThe 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.

This is a course at the intersection of philosophical logic and artificial intelligence. After reviewing recent work in AI that has leveraged ideas from logic, we will slow down and study in more detail various components of high-level intelligence and the tools that have been designed to capture those components. Specific areas will include: reasoning about belief and action, causality and counterfactuals, legal and normative reasoning, natural language inference, and Turing-complete logical formalisms including (probabilistic) logic programming and lambda calculus. Our main concern will be understanding the logical tools themselves, including their formal properties and how they relate to other tools such as probability and statistics. At the end, students should expect to have learned a lot more about logic, and also to have a sense for how logic has been and can be used in AI applications. Prerequisites: A background in logic, at least at the level of Phil 151, will be expected. In case a student is willing to put in the extra work to catch up, it may be possible to take the course with background equivalent to Phil 150 or CS 157. A background in AI, at the level of CS 221, would also be very helpful and will at times be expected. 2 unit option only for PhD students past the second year. Course website: http://web.stanford.edu/class/cs257/

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.nnThe 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.

We will read a selection of recent papers from psychology, computer science, and other fields. We will aim to understand: How human-like are state of the art artificial intelligence systems? Where can AI be better informed by recent advances in cognitive science? Which ideas from modern AI inspire new approaches to human intelligence? Specific topics will be announced prior to the beginning of term.

(Same as LAW 7020) The objective of the course is to explore the increasing ethical challenges in a world in which technology, global risks, and societal developments are accelerating faster than our understanding can keep pace. We will unravel the factors contributing to the seemingly pervasive failure of ethics today among organizations and leaders across all sectors: business, government and non-profit. A framework for ethical decision-making underpins the course. The relationship between ethics and culture, global risks (poverty, cyber-terrorism, climate change, etc.) leadership, law and policy will inform discussion. Prominent guest speakers will attend certain sessions interactively. A broad range of international case studies might include: the Rohingya crisis in Myanmar; civilian space travel (Elon Musk's Mars plans); designer genetics; social media ethics (e.g. Facebook and Russia and on-line sex trafficking); free speech on University campuses (and Gawker type cases); artificial intelligence; Brexit; corporate and financial sector scandals (Epi pen pricing, hedge funds, Wells Fargo, Volkswagen emissions testing manipulation); and non-profit sector ethics challenges (e.g. should NGOs engage with ISIS). Final project in lieu of exam on a topic of student's choice. Attendance required. Class participation important (with multiple opportunities to earn participation credit beyond speaking in class). Strong emphasis on rigorous analysis, critical thinking and testing ideas in real-world contexts. Students wishing to take the course who are unable to sign up within the enrollment limit should contact Dr. Susan Liautaud at susanl1@stanford.edu. The course offers credit toward Ethics in Society, Public Policy core requirements (if taken in combination with PUBLPOL 103E or PUBLPOL 103F), and Science, Technology and Society majors and satisfies the undergraduate Ways of Thinking requirement. The course is open to undergraduate and graduate students. Undergraduates will not be at a disadvantage. Everyone will be challenged. Distinguished Career Institute Fellows are welcome and should contact Dr. Susan Liautaud directly at susanl1@stanford.edu. *Public Policy majors taking the course to complete the core requirements must obtain a letter grade. Other students may take the course for a letter grade or C/NC.

(Same as LAW 7020) The objective of the course is to explore the increasing ethical challenges in a world in which technology, global risks, and societal developments are accelerating faster than our understanding can keep pace. We will unravel the factors contributing to the seemingly pervasive failure of ethics today among organizations and leaders across all sectors: business, government and non-profit. A framework for ethical decision-making underpins the course. The relationship between ethics and culture, global risks (poverty, cyber-terrorism, climate change, etc.) leadership, law and policy will inform discussion. Prominent guest speakers will attend certain sessions interactively. A broad range of international case studies might include: the Rohingya crisis in Myanmar; civilian space travel (Elon Musk's Mars plans); designer genetics; social media ethics (e.g. Facebook and Russia and on-line sex trafficking); free speech on University campuses (and Gawker type cases); artificial intelligence; Brexit; corporate and financial sector scandals (Epi pen pricing, hedge funds, Wells Fargo, Volkswagen emissions testing manipulation); and non-profit sector ethics challenges (e.g. should NGOs engage with ISIS). Final project in lieu of exam on a topic of student's choice. Attendance required. Class participation important (with multiple opportunities to earn participation credit beyond speaking in class). Strong emphasis on rigorous analysis, critical thinking and testing ideas in real-world contexts. Students wishing to take the course who are unable to sign up within the enrollment limit should contact Dr. Susan Liautaud at susanl1@stanford.edu. The course offers credit toward Ethics in Society, Public Policy core requirements (if taken in combination with PUBLPOL 103E or PUBLPOL 103F), and Science, Technology and Society majors and satisfies the undergraduate Ways of Thinking requirement. The course is open to undergraduate and graduate students. Undergraduates will not be at a disadvantage. Everyone will be challenged. Distinguished Career Institute Fellows are welcome and should contact Dr. Susan Liautaud directly at susanl1@stanford.edu. *Public Policy majors taking the course to complete the core requirements must obtain a letter grade. Other students may take the course for a letter grade or C/NC.

(Same as Law 1038) If you are interested in a career in finance or that touches finance (computational science, economics, public policy, legal, regulatory, corporate, other), this course will give you a useful perspective. We will take on hot topics in the current landscape of global financial markets such as how the world has evolved post-financial crisis, how it is being disrupted by FinTech, RegTech, artificial intelligence, crowd financing, blockchain, machine learning & robotics (to name a few), how it is being challenged by IoT, cyber, financial warfare & crypto currency risks (to name a few) and how it is seizing new opportunities in fast-growing areas such as ETFs, new instruments/payment platforms, robo advising, big data & algorithmic trading (to name a few). The course will include guest-lecturer perspectives on how sweeping changes are transforming business models and where the greatest opportunities exist for students entering or touching the world of finance today including existing, new and disruptive players. While derivatives and other quantitative concepts will be handled in a non-technical way, some knowledge of finance and the capital markets is presumed. Elements used in grading: Class Participation, Attendance, Final Paper. Consent Application: To apply for this course, students must complete and email to the instructors the Consent Application Form, which is available on the Public Policy Program's website at https://publicpolicy.stanford.edu/academics/undergraduate/forms. See Consent Application Form for submission deadline.

(Same as Law 1038) If you are interested in a career in finance or that touches finance (computational science, economics, public policy, legal, regulatory, corporate, other), this course will give you a useful perspective. We will take on hot topics in the current landscape of global financial markets such as how the world has evolved post-financial crisis, how it is being disrupted by FinTech, RegTech, artificial intelligence, crowd financing, blockchain, machine learning & robotics (to name a few), how it is being challenged by IoT, cyber, financial warfare & crypto currency risks (to name a few) and how it is seizing new opportunities in fast-growing areas such as ETFs, new instruments/payment platforms, robo advising, big data & algorithmic trading (to name a few). The course will include guest-lecturer perspectives on how sweeping changes are transforming business models and where the greatest opportunities exist for students entering or touching the world of finance today including existing, new and disruptive players. While derivatives and other quantitative concepts will be handled in a non-technical way, some knowledge of finance and the capital markets is presumed. Elements used in grading: Class Participation, Attendance, Final Paper. Consent Application: To apply for this course, students must complete and email to the instructors the Consent Application Form, which is available on the Public Policy Program's website at https://publicpolicy.stanford.edu/academics/undergraduate/forms. See Consent Application Form for submission deadline.

Two-part sequence. New techniques for predictive and descriptive learning using ideas that bridge gaps among statistics, computer science, and artificial intelligence. Emphasis is on statistical aspects of their application and integration with more standard statistical methodology. Predictive learning refers to estimating models from data with the goal of predicting future outcomes, in particular, regression and classification models. Descriptive learning is used to discover general patterns and relationships in data without a predictive goal, viewed from a statistical perspective as computer automated exploratory analysis of large complex data sets.

The ten branches of the Symbolic Systems major: applied logic, artificial intelligence, cognitive science, computer music, decision making and rationality, human-computer interaction, learning, natural language, neurosciences, and philosophical foundations. Students unfamiliar with the major gain an overview of its branches; students recently involved with the major gain a better idea of which track they should pursue; and students already familiar with the major gain understanding of how the different branches of the program fit together. Sources include films, readings, and presentations by and discussions with Stanford professors and recent alumni.

Parallel exploration of philosophical and computational approaches to modeling the construction of linguistic meaning. In philosophy of language: lexical sense extension, figurative speech, the semantics/pragmatics interface, contextualism debates. In CS: natural language understanding, from formal compositional models of knowledge representation to statistical and deep learning approaches. We will develop an appreciation of the complexities of language understanding and communication; this will inform discussion of the broader prospects for Artificial Intelligence. Special attention will be paid to epistemological questions on the nature of linguistic explanation, and the relationship between theory and practice. PREREQUISITES: PHIL80; some exposure to philosophy of language and/or computational language processing is recommended.

A weekly seminar allowing students the opportunity to discuss and explore applied Symbolic Systems in technology, entrepreneurship, and venture capital. We will explore popular conventions and trends through the lens of numerous deductive and applied Symbolic Systems.

It has become common for us to see in the media news about computer winning a masters in chess, or answering questions on the Jeopardy TV show, or the impact of AI on health, transportation, education, in the labor market and even as an existential threat to mankind. This interest in AI gives rise questions such as: Is it possible for a computer to think? What is thought? Are we computers? Could machines feel emotions or be conscious? Curiously, there is no single, universally accepted definition of Artificial Intelligence. However in view of the rapid dissemination of AI these questions are important not only for experts, but also for all other members of society. This course is intended for students from different majors Interested in learn how the concept of intelligent machine is understood by the researchers in AI. We will study the evolution of AI research, its different approaches, with focus on the tests developed to verify if a machine is intelligent or not. In addition, we will examine the philosophical problems associated with the concept of intelligent machine. The topics covered will include: Turing test, symbolic AI, connectionist AI, sub- symbolic Ai, Strong AI and Weak AI, Ai singularity, unconventional computing, rationality, intentionality, representation, machine learning, and the possibility of conscious machines.

Parallel exploration of philosophical and computational approaches to modeling the construction of linguistic meaning. In philosophy of language: lexical sense extension, figurative speech, the semantics/pragmatics interface, contextualism debates. In CS: natural language understanding, from formal compositional models of knowledge representation to statistical and deep learning approaches. We will develop an appreciation of the complexities of language understanding and communication; this will inform discussion of the broader prospects for Artificial Intelligence. Special attention will be paid to epistemological questions on the nature of linguistic explanation, and the relationship between theory and practice. PREREQUISITES: PHIL80; some exposure to philosophy of language and/or computational language processing is recommended.

A weekly seminar allowing students the opportunity to discuss and explore applied Symbolic Systems in technology, entrepreneurship, and venture capital. We will explore popular conventions and trends through the lens of numerous deductive and applied Symbolic Systems.

This course will explore possibilities for student research projects based on presentations of faculty research. We will cover a broad range of topics within the general area of collective behavior, both natural and artificial. Students will build on faculty presentations to develop proposals for future projects.