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AMSTUD 106A: A.I.: Artificial Intelligence in Fiction (ENGLISH 106)

From self-driving cars to bots that alter democratic elections, artificial intelligence is growing increasingly powerful and prevalent in our everyday lives. Literature has long been speculating about the techno-utopia¿and catastrophe¿that A.I. could usher in. Indeed, literature itself presents us with a kind of A.I. in the many characters that speak and think in its pages. But how do we classify an intelligence as ¿artificial¿ or not? Is there a clear boundary that demarcates bodies from machines? What, if anything, separates the ¿genre¿ of technology from that of literature? What classifies literature as ¿science fiction,¿ ¿scientific,¿ ¿futuristic,¿ ¿psychological,¿ or ¿dystopian¿? And can technology or literature ever overcome the ultimate division between all intelligences¿the problem of other minds? This course consists in curated multi-genre combinations of literature, philosophy, film, and television that explore what makes someone¿or something¿a person in our world today. Special events will include celebrating the current bicentennial of Mary Shelley¿s Frankenstein (1818) in Stanford Special Collections; a possible visit to Stanford¿s A.I. Laboratory; and chatting with the ELIZA chatbot.
Terms: Aut | Units: 5 | Grading: Letter (ABCD/NP)

ANES 208A: Data Science for Digital Health and Precision Medicine

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.
Terms: Aut | Units: 1-2 | Repeatable for credit | Grading: Medical Option (Med-Ltr-CR/NC)

ANTHRO 128A: The Boundaries of Humanity: Humans, Animals and Machines in the Age of Biotechnology

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.
Terms: not given this year | Units: 3-4 | Grading: Letter or Credit/No Credit

BIO 175: Collective Behavior and Distributed Intelligence (SYMSYS 275)

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.
Terms: not given this year | Units: 3 | Grading: Letter or Credit/No Credit

CEE 329: Artificial Intelligence Applications in the AEC Industry

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.
Terms: Spr | Units: 2 | Grading: Letter (ABCD/NP)
Instructors: ; Fischer, M. (PI)

CEE 329S: Seminar on Artificial Intelligence Applications in the AEC Industry

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.
Terms: Spr | Units: 1 | Grading: Satisfactory/No Credit
Instructors: ; Fischer, M. (PI)

CME 500: Departmental Seminar

Special topic - Artificial Intelligence (AI) in Real Life: Senior industry technical leaders will share insights about how they are actively using artificial intelligence, applications of machine learning and deep learning in their organizations; will raise awareness about the challenges and unintended consequences of AI and address the interdisciplinary skills required to successfully implement AI solutions in their organizations. Speakers will share their vision for an AI future, and address student questions. Weekly speakers will come from many different fields and AI application areas.
Terms: Aut, Win, Spr | Units: 1 | Repeatable for credit | Grading: Satisfactory/No Credit

CS 22A: The Social & Economic Impact of Artificial Intelligence (INTLPOL 200)

(Formerly IPS 200.) 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.
Terms: Win | Units: 1 | Grading: Satisfactory/No Credit
Instructors: ; Kaplan, J. (PI)

CS 202: Law for Computer Science Professionals

An overview of intellectual property law as it relates to computer science and other disciplines, including discussions of patents, trademarks, copyrights, trade secrets, computer fraud litigation and interesting historical tidbits. Emphasis on topics of current interest such as software and business method patents, copyright issues concerning software, music, art and artificial intelligence, and current disputes of note including the recently-settled Waymo v. Uber lawsuit and the ongoing Oracle v. Google, Apple v. Samsung and hiQ v. LinkedIn sagas. Guest lectures typically have covered open source and the free software movement, practical issues for business founders (including corporate formation issues and non-disclosure, non-compete, work-made-for-hire and license agreements), and other pertinent topics. Classes are presented in an open discussion format broadly directed to students with both technical and non-technical backgrounds.
Terms: Aut | Units: 1 | Grading: Satisfactory/No Credit
Instructors: ; Hansen, D. (PI)

CS 208E: Great Ideas in Computer Science

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.
Terms: Aut | Units: 3 | Grading: Letter (ABCD/NP)
Instructors: ; Gregg, C. (PI); Allen, B. (GP)

CS 221: Artificial Intelligence: Principles and Techniques

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).
Terms: Aut, Spr | Units: 3-4 | Grading: Letter or Credit/No Credit

CS 227B: General Game Playing

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.
Terms: Spr | Units: 3 | Grading: Letter or Credit/No Credit
Instructors: ; Genesereth, M. (PI)

CS 257: Logic and Artificial Intelligence (PHIL 356C)

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/
Terms: not given this year | Units: 2-4 | Grading: Letter or Credit/No Credit

CS 294A: Research Project in Artificial Intelligence

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.
Terms: not given this year | Units: 3 | Repeatable for credit | Grading: Letter or Credit/No Credit

CS 329: Topics in Artificial Intelligence

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.
Terms: offered occasionally | Units: 3 | Repeatable for credit | Grading: Letter or Credit/No Credit

CS 329M: Topics in Artificial Intelligence: Algorithms of Advanced Machine Learning

This advanced graduate course explores in depth several important classes of algorithms in modern machine learning. We will focus on understanding the mathematical properties of these algorithms in order to gain deeper insights on when and why they perform well. We will also study applications of each algorithm on interesting, real-world settings. Topics include: spectral clustering, tensor decomposition, Hamiltonian Monte Carlo, adversarial training, and variational approximation. Students will learn mathematical techniques for analyzing these algorithms and hands-on experience in using them. We will supplement the lectures with latest papers and there will be a significant research project component to the class. Prerequisites: Probability (CS 109), linear algebra (Math 113), machine learning (CS 229), and some coding experience.
Terms: not given this year | Units: 3 | Grading: Letter or Credit/No Credit

CS 521: Seminar on AI Safety

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.
Terms: not given this year | Units: 1 | Grading: Satisfactory/No Credit

CS 522: Seminar in Artificial Intelligence in Healthcare

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.
Terms: Aut | Units: 1 | Grading: Satisfactory/No Credit

EALC 402A: Topics in International Technology Management (EASTASN 402A, EE 402A)

Theme for Autumn 2018 is "AI in Smart Physical Systems: Is Asia Ahead of the U.S.?" Distinguished guest speakers from industry present and discuss practical innovations from Asia related to the use of artificial intelligence in smart physical systems, e.g. smart buildings, autonomous vehicles, drone fleets, smart manufacturing, etc. See syllabus for specific requirements, which may differ from those of other seminars at Stanford.
Terms: Aut | Units: 1 | Repeatable for credit | Grading: Satisfactory/No Credit

EASTASN 402A: Topics in International Technology Management (EALC 402A, EE 402A)

Theme for Autumn 2018 is "AI in Smart Physical Systems: Is Asia Ahead of the U.S.?" Distinguished guest speakers from industry present and discuss practical innovations from Asia related to the use of artificial intelligence in smart physical systems, e.g. smart buildings, autonomous vehicles, drone fleets, smart manufacturing, etc. See syllabus for specific requirements, which may differ from those of other seminars at Stanford.
Terms: Aut | Units: 1 | Repeatable for credit | Grading: Satisfactory/No Credit
Instructors: ; Dasher, R. (PI)

ECON 152: The Future of Finance (ECON 252, PUBLPOL 364, STATS 238)

(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.
Terms: not given this year | Units: 2 | Grading: Letter or Credit/No Credit

ECON 252: The Future of Finance (ECON 152, PUBLPOL 364, STATS 238)

(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.
Terms: not given this year | Units: 2 | Grading: Letter or Credit/No Credit

EE 402A: Topics in International Technology Management (EALC 402A, EASTASN 402A)

Theme for Autumn 2018 is "AI in Smart Physical Systems: Is Asia Ahead of the U.S.?" Distinguished guest speakers from industry present and discuss practical innovations from Asia related to the use of artificial intelligence in smart physical systems, e.g. smart buildings, autonomous vehicles, drone fleets, smart manufacturing, etc. See syllabus for specific requirements, which may differ from those of other seminars at Stanford.
Terms: Aut | Units: 1 | Repeatable for credit | Grading: Satisfactory/No Credit
Instructors: ; Dasher, R. (PI)

ENGLISH 13Q: Imaginative Realms

This class looks at the tradition of the imagined universe in fiction and poetry. Special topics include magical realism, artificial intelligence, and dystopias. Primary focus on giving students a skill set to tap into their own creativity. Opportunities for students to explore their creative strengths, develop a vocabulary with which to discuss their own creativity, and experiment with the craft and adventure of their own writing. For undergrads only.
Terms: Aut | Units: 3 | UG Reqs: WAY-CE | Grading: Letter (ABCD/NP)
Instructors: ; Ekiss, K. (PI)

ENGLISH 106: A.I.: Artificial Intelligence in Fiction (AMSTUD 106A)

From self-driving cars to bots that alter democratic elections, artificial intelligence is growing increasingly powerful and prevalent in our everyday lives. Literature has long been speculating about the techno-utopia¿and catastrophe¿that A.I. could usher in. Indeed, literature itself presents us with a kind of A.I. in the many characters that speak and think in its pages. But how do we classify an intelligence as ¿artificial¿ or not? Is there a clear boundary that demarcates bodies from machines? What, if anything, separates the ¿genre¿ of technology from that of literature? What classifies literature as ¿science fiction,¿ ¿scientific,¿ ¿futuristic,¿ ¿psychological,¿ or ¿dystopian¿? And can technology or literature ever overcome the ultimate division between all intelligences¿the problem of other minds? This course consists in curated multi-genre combinations of literature, philosophy, film, and television that explore what makes someone¿or something¿a person in our world today. Special events will include celebrating the current bicentennial of Mary Shelley¿s Frankenstein (1818) in Stanford Special Collections; a possible visit to Stanford¿s A.I. Laboratory; and chatting with the ELIZA chatbot.
Terms: Aut | Units: 5 | Grading: Letter (ABCD/NP)
Instructors: ; Tackett, J. (PI)

ENGLISH 131C: A.I.: Artificial Intelligence in Fiction

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.
Terms: not given this year | Units: 3-5 | UG Reqs: WAY-A-II | Grading: Letter or Credit/No Credit

ETHICSOC 174B: Universal Basic Income: the philosophy behind the proposal (ETHICSOC 274B, PHIL 174B, PHIL 274B, POLISCI 338)

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.
Terms: Spr | Units: 4 | UG Reqs: WAY-ER | Repeatable for credit | Grading: Letter or Credit/No Credit
Instructors: ; Bidadanure, J. (PI)

ETHICSOC 274B: Universal Basic Income: the philosophy behind the proposal (ETHICSOC 174B, PHIL 174B, PHIL 274B, POLISCI 338)

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.
Terms: Spr | Units: 4 | Repeatable for credit | Grading: Letter or Credit/No Credit
Instructors: ; Bidadanure, J. (PI)

GSBGEN 503: The Business of Healthcare

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.
Units: 2 | Grading: GSB Student Option LTR/PF
Instructors: ; Krubert, C. (PI)

HRMGT 503: People Analytics

How can we use big data, machine learning and artificial intelligence to inform design, hiring, promotion and human resource management processes in organizations? We will discuss the theoretical and practical challenges that these issues present, and the ways by which data can help resolve them. In doing so, we will explore various data analytic methods and different data types, as well as the pitfalls and ethical issues their use introduces.
Units: 2 | Grading: GSB Letter Graded
Instructors: ; Goldberg, A. (PI)

HUMBIO 96SI: Big problems, big solutions? tackling difficult issues in today's healthcare system.

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.
Terms: given next year | Units: 1-2 | Grading: Satisfactory/No Credit

INTLPOL 200: The Social & Economic Impact of Artificial Intelligence (CS 22A)

(Formerly IPS 200.) 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.
Terms: Win | Units: 1 | Grading: Satisfactory/No Credit
Instructors: ; Kaplan, J. (PI)

LAW 682B: Discussion: Beyond Neoliberalism

Scholars' and policy makers' thinking about political economy evolves as one understanding of the role of government ceases to reflect people's 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 what has come to be called "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 the greatest 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 when and how to correct them persisted, but the general premises were widely embraced by policymakers and politicians (reflected, for example, in the so-called Washington Consensus). Today, this consensus is breaking down. Neoliberal policies have contributed to generating profound wealth inequality and have little to offer to address the perceived negative consequences 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.
Terms: Win | Units: 1 | Grading: Law Mandatory P/R/F

LAW 682L: Discussion: The Ethical Robot

We will consider the developing legal and ethical problems of robots and artificial intelligence (AI), particularly self-directed and learning AIs. How do self-driving cars value human lives? How do we trade off accuracy against other values in predictive algorithms? And how can courts and legislatures set legal rules robots can understand and obey? Spring Quarter. Meeting 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: Attendance, Class Participation.
Terms: Spr | Units: 1 | Grading: Law Mandatory P/R/F
Instructors: ; Lemley, M. (PI)

LAW 806N: Policy Practicum: The Future of Algorithms: Navigating Legal, Social and Policy Challenges

Clients: (1) Stanford Machine Learning Group (https://stanfordmlgroup.github.io/); (2) the Center for Automotive Research at Stanford (https://cars.stanford.edu/); and (3) the Computational Policy Lab (https://policylab.stanford.edu/). Although much of the media attention surrounding artificial intelligence (AI) tends to focus on the advances being made in industry, major breakthroughs in the field often begin at the university level. Stanford is among the global leaders in this regard. All across campus, teams led by preeminent researchers are deploying projects that apply cutting-edge AI systems to complex and highly challenging social, technical and policy problems. Stanford has pioneered projects ranging from systems aimed at improving palliative care outcomes, to those aimed at improving the ethical decision-making of autonomous vehicles, to those that shape critical decisions in the criminal justice system. Yet the successful deployment of these projects in the real-world is deeply intertwined with questions of regulation and legal liability that push existing doctrinal boundaries---from IP to health regulation to due process and civil rights---to their limits. This policy lab seeks to engage with some of the most challenging legal questions and opportunities presented by these emerging technologies. We will work closely with some of Stanford's leading research teams to help them navigate the murky---oftentimes uncharted---legal, regulatory, ethical, and policy waters surrounding the deployment of novel AI applications. In doing so, we will provide extensive legal research support, collaboratively strategize and design deployments, help innovators evaluate and pilot new applications, and ultimately expand access to transformative technologies for populations in serious need. Students will work primarily with clients from Stanford departments at the forefront of studying, developing and deploying AI systems: (1) the Computer Science Department's Stanford Machine Learning Group, led by Andrew Ng (https://stanfordmlgroup.github.io/); (2) the Mechanical Engineering Department's Center for Automotive Research at Stanford, led by Chris Gerdes and Stephen Zoepf (https://cars.stanford.edu/); and (3) the Management, Science, and Engineering Department's Computational Policy Lab, led by Sharad Goel (https://policylab.stanford.edu/). We seek to build a collaborative team of diverse backgrounds and skill sets to learn from each other and enhance the overall capacity of the research. We encourage students who are interested in tech policy, entrepreneurship, AI, access to justice, and social impact to join us, including upper-division and graduate students from Law, Computer Science, Electrical Engineering, Mechanical Engineering, MS&E, Public Policy, and the social sciences. Students interested in this policy lab should submit a consent form with a resume and statement of interest to be reviewed by Professor Malone. Law students wishing to undertake R credit will perform additional research for a white paper analyzing the issues and results of the collective research. R credit is possible only by consent of the instructor. After the term begins, and with the consent of the instructor, students accepted into the course may transfer from section (01) into section (02), which meets the R requirement. NOTE: Students may not count more than a combined total of eight units of directed research projects and policy lab practica toward graduation unless the additional counted units are approved in advance by the Petitions Committee. Such approval will be granted only for good cause shown. Even in the case of a successful petition for additional units, a student cannot receive a letter grade for more than eight units of independent research (Policy Lab practicum, Directed Research, Senior Thesis, and/or Research Track). Any units taken in excess of eight will be graded on a mandatory pass basis. For detailed information, see "Directed Research/Policy Labs" in the SLS Student Handbook. CONSENT APPLICATION: To apply for this course, students must complete and submit a Consent Application 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: Attendance, Performance, Class Participation, Written Assignments, Final Paper.
Terms: Win | Units: 2-3 | Repeatable for credit | Grading: Law Honors/Pass/Restrd Cr/Fail

LAW 806O: Policy Practicum: Administering by Algorithm: Artificial Intelligence in the Regulatory State

Client: Administrative Conference of the United States, https://www.acus.gov/. This policy lab will explore the growing role that artificial intelligence (AI) and related technologies are playing in the federal administrative state. Already, a wide range of federal agencies are utilizing software that uses machine learning and related techniques associated with AI to make and support decisions. Examples include efforts by the Social Security Administration to improve decisional quality in the adjudication of benefits claims, by the EPA to model the toxicity of chemical compounds, and by the IRS to predict tax non-compliance and identify audit targets. Other uses of AI are in the pipeline or already in place but remain outside public view. Such use will almost certainly increase as AI becomes more sophisticated and cheaper and as the private sector's increasing reliance on AI to make decisions presses agencies to keep pace in order to regulate effectively. As agency use of AI proliferates, administrators, lawyers, and judges will have to ask how well agency deployment of machine learning systems conforms to well-established principles of constitutional and administrative law. Students enrolled in this policy lab will have a unique opportunity to help set the terms of that debate via a first-of-its-kind report to be submitted to the Administrative Conference of the United States (linked here), an independent federal agency charged with recommending improvements to administrative process and procedure. Students will spearhead completion of a report designed to explore the use of AI in the administrative state at multiple levels. The first part of the project will be a mapping exercise, with descriptive and predictive components. The chief descriptive task will be to canvass the hundreds of agencies that make up the federal administrative state and document agency use of AI across a wide range of substantive policy areas. A related, more predictive part of the project will draw on Stanford University's distinctive concentration of technical knowledge in AI and related fields to assess where AI may be most likely to be deployed by agencies in the near- and medium-term. The final part of the project will turn to normative issues, by contributing to a framework for thinking about the many legal, policy-analytic, and philosophical questions raised by agency use of AI to perform regulatory tasks. To the extent possible, we will consider how agency use of AI may affect the administrative state in general terms, and will explore some of the implications of core administrative law doctrines --- such as the nondelegation doctrine, arbitrary and capricious review, due process, and rules governing reliance on subordinates for decisions --- for agency use of AI. Students enrolled in the lab will work in teams, with each allocated a cluster of agencies. As the project unfolds, teams will drill down on practices, both actual and predicted, in specific agencies that exemplify the legal and normative tensions that will arise as agencies increasingly deploy AI technologies. Students will be encouraged to deploy a range of methodologies, including careful secondary and legal research, case studies, survey work, and stakeholder interviews, among others. Some of this work may also require travel to Washington, D.C. or agency regional offices in order to fully understand agency practices or, upon the project's completion, to present findings. The policy lab is open to all graduate students at Stanford University, and will ideally attract both law students and also non-law students from technical fields who can contribute a sophisticated understanding of the current trajectory of AI technology. For law students, past coursework or a strong background or interest in administrative law is highly recommended. Students from all parts of the University who wish to enroll in the policy lab may also consider taking Justice Cuéllar's fall quarter course at the Law School, "Regulating Artificial Intelligence." Law students wishing to undertake R credit will perform additional research or take on additional tasks analyzing the issues and results of the collective research. R credit is possible only by consent of the instructor. After the term begins, and with the consent of the instructor, students accepted into the course may transfer from section (01) into section (02), which meets the R requirement. NOTE: Students may not count more than a combined total of eight units of directed research projects and policy lab practica toward graduation unless the additional counted units are approved in advance by the Petitions Committee. Such approval will be granted only for good cause shown. Even in the case of a successful petition for additional units, a student cannot receive a letter grade for more than eight units of independent research (Policy Lab practicum, Directed Research, Senior Thesis, and/or Research Track). Any units taken in excess of eight will be graded on a mandatory pass basis. For detailed information, see "Directed Research/Policy Labs" in the SLS Student Handbook. CONSENT APPLICATION: To apply for this course, students must complete and submit a Consent Application 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: Attendance, Performance, Class Participation, Written Assignments, Final Paper.
Terms: Win | Units: 2-3 | Repeatable for credit | Grading: Law Honors/Pass/Restrd Cr/Fail

LAW 1038: The Future of Finance

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).
Terms: Win | Units: 2 | Grading: Law Honors/Pass/Restrd Cr/Fail

LAW 4031: Disruptive Technologies: Their Impact on Our Laws, and the Laws' Impact on the Technology

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.
Terms: not given this year | Units: 2 | Grading: Law Honors/Pass/Restrd Cr/Fail

LAW 4039: Regulating Artificial Intelligence

Less than a generation ago,"artificial intelligence" (AI) was largely an esoteric topic in academic computer science and philosophy --- and perhaps a more familiar one in science fiction. Today the term is widely understood to describe fast-evolving technologies (such as elaborate convolutional neural networks leveraging vast amounts of data) increasingly used in finance, transportation, health care, national security, and a variety of other fields. This highly interactive new course surveys current and emerging legal and policy problems related to how law structures humanity's relationship to artificially-constructed intelligence. To deepen future lawyers' understanding of current and medium-term problems in this area, the course explores definitions and foundational concepts associated with "artificial intelligence," likely directions in which AI will evolve, and different types of legally-relevant concerns raised by those developments and by the use of existing versions of AI. We will consider distinct settings where regulation of AI is emerging as a challenge or topic of interest, including autonomous vehicles, autonomous weapons, AI in social media/communications platforms, and systemic AI safety problems; doctrines and legal provisions relevant to the development, control, and deployment of AI such as the European Union's General Data Protection Regulation; the connection between the legal treatment of manufactured intelligence and related bodies of existing law, such as administrative law, torts, constitutional principles, criminal justice, and international law; and new legal arrangements that could affect the development and use of AI. Cross-cutting themes will include: how law affects the way important societal decisions are justified, the balance of power and responsibility between humans and machines in different settings, the incorporation of multiple values into AI decisionmaking frameworks, the interplay of norms and formal law, and similarities and differences to other domains of human activity raising regulatory trade-offs and affected by technological change (such as environmental protection, aviation, and the food economy). Note: The course is designed both for students who want a survey of the field and lack any technical knowledge, as well as for students who want to gain tools and ideas to deepen their existing interest or background in the topic. Students with longer-term interest in or experience with the subject are welcome to do a more technically-oriented paper or project in connection with this class. But technical knowledge or familiarity with AI is not a prerequisite, as various optional readings and some in-class material will help provide necessary background. Requirements: The course will involves a mix of lectures, in-class activities, and student-led discussion and presentations. Requirements include attendance, participation in planning and conducting at least one student-led group presentation or discussion, two short 3-5 pp. response papers for other class sessions, and either an exam or a 25-30 pp. research paper. CONSENT APPLICATION: We will try to accommodate as many people as possible with interest in the course. But to facilitate planning and confirm your level of interest, if you are interested in the course please submit a short email with the subject line "application" to Pat Adan (padan@stanford.edu) by September 7, 2018. Please describe in a few sentences (as soon as possible) why you want to take this class, your level of interest in the subject, any topic or topics in which you are especially interested, and whether you prefer the paper or exam option. Emails received after September 7 will be reviewed on a rolling basis.
Terms: Aut | Units: 3 | Grading: Law Honors/Pass/Restrd Cr/Fail
Instructors: ; Cuellar, M. (PI)

LAW 4041: Lawyering for Innovation: Artificial Intelligence

In recent years, artificial intelligence (AI) has made the jump from science fiction to technical viability to product reality. Industries as far flung as finance, transportation, defense, and healthcare invest billions in the field. Patent filings for robotics and machine learning applications have surged. And policymakers are beginning to grapple with technologies once confined to the realm of computer science, such as predictive analytics and neural networks. AI's rise to prominence came thanks to a confluence of factors. Increased computing power, large-scale data collection, and advancements in machine learning---all accompanied by dramatic decreases in costs---have resulted in machines that now have the ability to exhibit complex "intelligent" behaviors. They can navigate in real-world environments, process natural language, diagnose illnesses, predict future events, and even conquer strategy games. These abilities, in turn, have allowed companies and governments to entrust machines with responsibilities once exclusively reserved for humans---including influencing hiring decisions, bail release conditions, loan considerations, medical treatment and police deployment. But with these great new powers, of course, come great new responsibilities. The first public deployments of AI have seen ample evidence of the technology's disruptive---and destructive---capabilities. AI-powered systems have killed and maimed, filled social networks with hate, and been accused of shaping the course of elections. And as the technology proliferates, its governance will increasingly fall upon lawyers involved in the design and development of new products, oversight bodies and government agencies. AI is the biggest addition to technology law and policy since the rise of the internet, and its influence spreads far beyond the tech sector. As such, those entering practice in a wide variety of fields need to understand AI from the ground up in order to competently assess and influence its policy, legal and product implications as deployments scale across industries in the coming years. This course is designed to teach precisely that. It seeks to equip students with an understanding of the basics of AI and machine learning systems by studying the implications of the technology along the design/deployment continuum, moving from (1) system inputs (data collection) to (2) system design (engineering) and finally to (3) system outputs (product features). This input/design/output framework will be used throughout the course to survey substantive engineering, policy and legal issues arising at each of those key stages. In doing so, the course will span topics including privacy, bias, discrimination, intellectual property, torts, transparency and accountability. The course will also feature leading experts from a variety of AI disciplines and professional backgrounds. An important aspect of the course is gaining an understanding of the technical underpinnings of AI, which will be packaged in an easy-to-understand, introductory manner with no prior technical background required. In addition to reflection paper assignments, students will have the opportunity to write blogposts providing legal, regulatory and policy analysis of current issues involving AI. Student contributions will have the option of being posted on the newly created Stanford AI & Law Blog, which will be designed to compile and synthesize AI-related developments for policymakers, startups and product counsels trying to understand and stay up to date with this fast moving technology. The course will be offered for two units of credit (H/P/R/F). Grading will be determined by attendance, class participation and written assignments. Given the course's multi-disciplinary focus, students outside of the law school, particularly those studying computer science, engineering or business, are welcome. CONSENT APPLICATION: To apply for this course, students must complete and submit a Consent Application 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.
Terms: Spr | Units: 2 | Grading: Law Honors/Pass/Restrd Cr/Fail
Instructors: ; Rubin, T. (PI)

LAW 4043: The Social & Economic Impact of Artificial Intelligence

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. Elements used in grading: TBA. Cross-listed with Computer Science (CS 22A) and International Policy (INTLPOL 200).
Terms: Win | Units: 1 | Grading: Law Mandatory P/R/F
Instructors: ; Kaplan, J. (PI)

LAW 6005: Technological, Economic and Business Forces Transforming the Private Practice of Law

(Formerly Law 388) The private practice of law is undergoing fundamental change. Technological, economic and business forces are placing extreme pressure on the traditional "Big Law" firm model. These forces will transform, eliminate or replace virtually every aspect of the legal services provided by traditional firms. Foundations of the law firm model such as bespoke client services, "billable" hours, large staffs (e.g., paralegals and secretaries), high associate-to-partner ratios and summer associate programs are becoming (or have already become) relics of a bygone era. Sophisticated clients today are utilizing a wide range of internal and external service providers and technologies such as artificial intelligence for their legal work. The diversity of how legal services are delivered and priced to clients is rapidly increasing. This rapid increase is dramatically altering the supply and demand side of the legal economy and is altering the types of skills and prerequisites required for attorneys to be successful private practice. The course is composed of two parts. In part one, the technological, economic and business practices transforming the legal profession are identified and their impact on the traditional approaches to private practice law firms will be examined. In part two, the course focuses on how individual lawyers can adapt to or embrace the forces transforming law to improve their practice and succeed in the new environment. Part two of the course will additionally focus on how specific skills such as project management, social networking and information management will be crucial to a successful legal career. Part two of the course will also discuss how the changing legal environment creates new ethical and professional challenges for attorneys. Elements used in grading: Attendance, class participation and a research paper for the written assignment.
Terms: Win | Units: 2 | Grading: Law Honors/Pass/Restrd Cr/Fail
Instructors: ; Yoon, J. (PI)

LAW 6015: Innovations in the Delivery of Legal Services

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.
Terms: not given this year | Units: 2 | Grading: Law Honors/Pass/Restrd Cr/Fail

LAW 7020: Ethics On the Edge: Business, Non-Profit Organizations, Government, and Individuals

(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).
Terms: Spr | Units: 2 | Grading: Law Honors/Pass/Restrd Cr/Fail

ME 268: Robotics, AI and Design of Future Education

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
Terms: Win | Units: 1 | Repeatable for credit | Grading: Satisfactory/No Credit
Instructors: ; Jiang, L. (PI)

MED 285: Global Leaders and Innovators in Human and Planetary Health

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.
Terms: Aut | Units: 1 | Grading: Medical School MD Grades

MS&E 177: Creativity Rules

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.
Terms: Aut, Spr | Units: 4 | Grading: Letter (ABCD/NP)

MS&E 238: Leading Trends in Information Technology

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.
Terms: not given this year | Units: 3 | Grading: Letter or Credit/No Credit

MS&E 238A: Leading Trends in Information Technology

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.
Terms: not given this year | Units: 1 | Grading: Satisfactory/No Credit

OIT 249: MSx: Data and Decisions

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.
Units: 2 | Grading: GSB Letter Graded

PHIL 20N: Philosophy of 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.
Terms: Win | Units: 3 | Grading: Letter (ABCD/NP)
Instructors: ; Crimmins, M. (PI)

PHIL 153L: Computing Machines and Intelligence (PHIL 253L)

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.
Terms: Aut | Units: 4 | Grading: Letter or Credit/No Credit

PHIL 174B: Universal Basic Income: the philosophy behind the proposal (ETHICSOC 174B, ETHICSOC 274B, PHIL 274B, POLISCI 338)

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.
Terms: Spr | Units: 4 | UG Reqs: WAY-ER | Repeatable for credit | Grading: Letter or Credit/No Credit
Instructors: ; Bidadanure, J. (PI)

PHIL 253L: Computing Machines and Intelligence (PHIL 153L)

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.
Terms: Aut | Units: 4 | Grading: Letter or Credit/No Credit

PHIL 274B: Universal Basic Income: the philosophy behind the proposal (ETHICSOC 174B, ETHICSOC 274B, PHIL 174B, POLISCI 338)

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.
Terms: Spr | Units: 4 | Repeatable for credit | Grading: Letter or Credit/No Credit
Instructors: ; Bidadanure, J. (PI)

PHIL 356C: Logic and Artificial Intelligence (CS 257)

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/
Terms: not given this year | Units: 2-4 | Grading: Letter or Credit/No Credit

POLISCI 338: Universal Basic Income: the philosophy behind the proposal (ETHICSOC 174B, ETHICSOC 274B, PHIL 174B, PHIL 274B)

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.
Terms: Spr | Units: 4 | Repeatable for credit | Grading: Letter or Credit/No Credit
Instructors: ; Bidadanure, J. (PI)

PSYCH 247: Topics in Natural and Artificial Intelligence

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.
Terms: Aut | Units: 3 | Grading: Letter or Credit/No Credit
Instructors: ; Goodman, N. (PI)

PSYCH 291: Causal Cognition

Causality is central to our understanding of the world and of each other. We think causally when we predict what will happen in the future, infer what happened in the past, and interpret other people's actions and emotions. Causality is intimately linked to explanation -- to answering questions about why something happened. In this discussion-based seminar class, we will first read foundational work in philosophy that introduces the main frameworks for thinking about causation. We will then read some work on formal and computational theories of causation that was inspired by these philosophical frameworks. Equipped with this background, we will study the psychology of causal learning, reasoning, and judgment. We will tackle questions such as: How can we learn about the causal structure of the world through observation and active intervention? What is the relationship between causal reasoning and mental simulation? Why do we select to talk about some causes over others when several causes led to an outcome? Toward the end of the course, we will discuss how what we have learned in psychology about causation may be useful for other fields of inquiry, such as legal science as well as machine learning and artificial intelligence.
Terms: Spr | Units: 3 | Grading: Letter or Credit/No Credit
Instructors: ; Gerstenberg, T. (PI)

PUBLPOL 364: The Future of Finance (ECON 152, ECON 252, STATS 238)

(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.
Terms: not given this year | Units: 2 | Grading: Letter or Credit/No Credit

STATS 238: The Future of Finance (ECON 152, ECON 252, PUBLPOL 364)

(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.
Terms: not given this year | Units: 2 | Grading: Letter or Credit/No Credit

STATS 315B: Modern Applied Statistics: Data Mining

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.
Terms: Spr | Units: 2-3 | Grading: Letter or Credit/No Credit
Instructors: ; Friedman, J. (PI)

SYMBSYS 2: Sym Sys: Many Parts, Cohesive Whole

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.
Terms: not given this year | Units: 1 | Grading: Satisfactory/No Credit

SYMSYS 112: Challenges for Language Systems (SYMSYS 212)

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.
Terms: not given this year | Units: 3-4 | Grading: Letter or Credit/No Credit

SYMSYS 115: Critique of Technology

What is the character of technology? How does technology reveal aspects of human nature and social practices? How does it shape human experience and values? We will survey the history of philosophy of technology -- from ancient and enlightenment ideas, to positivist and phenomenological conceptions -- to develop a deeper understanding of diverse technological worldviews. This will prepare us to consider contemporary questions about the "ethos" of technology. Specific questions will vary depending upon the interests of participants, but may include: ethical and existential challenges posed by artificial intelligence; responsible product design in the "attention economy"; industry regulation and policy issues for information privacy; and the like. PREREQUISITES: PHIL80
Terms: not given this year | Units: 3-4 | Grading: Letter or Credit/No Credit

SYMSYS 122: Artificial Intelligence: Philosophy, Ethics, & Impact

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 this course is to equip students with the intellectual tools, ethical foundation, and psychological framework to successfully navigate the coming age of intelligent machines.
Terms: not given this year | Units: 3-4 | UG Reqs: WAY-ER | Grading: Letter or Credit/No Credit

SYMSYS 161: Applied Symbolic Systems: Venture Capital, Artificial Intelligence, and The Future (SYMSYS 261)

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.
Terms: Spr | Units: 2 | Repeatable for credit | Grading: Satisfactory/No Credit
Instructors: ; Dar, Z. (PI); Li, N. (PI)

SYMSYS 208: Computer Machines and Intelligence

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.
Terms: Win | Units: 3 | Grading: Letter or Credit/No Credit

SYMSYS 212: Challenges for Language Systems (SYMSYS 112)

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.
Terms: not given this year | Units: 3-4 | Grading: Letter or Credit/No Credit

SYMSYS 261: Applied Symbolic Systems: Venture Capital, Artificial Intelligence, and The Future (SYMSYS 161)

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.
Terms: Spr | Units: 2 | Repeatable for credit | Grading: Satisfactory/No Credit
Instructors: ; Dar, Z. (PI); Li, N. (PI)

SYMSYS 275: Collective Behavior and Distributed Intelligence (BIO 175)

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.
Terms: not given this year | Units: 3 | Grading: Letter or Credit/No Credit
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