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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: not given this year | Units: 5 | Grading: Letter (ABCD/NP)

AMSTUD 120: The Rise of Digital Culture (COMM 120W, COMM 220)

From Snapchat to artificial intelligence, digital systems are reshaping our jobs, our democracies, our love lives, and even what it means to be human. But where did these media come from? And what kind of culture are they creating? To answer these questions, this course explores the entwined development of digital technologies and post-industrial ways of living and working from the Cold War to the present. Topics will include the historical origins of digital media, cultural contexts of their deployment and use, and the influence of digital media on conceptions of self, community, and state. Priority to juniors, seniors, and graduate students.
Terms: Spr | Units: 4-5 | UG Reqs: GER:DB-SocSci, WAY-SI | 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: not given this year | 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

BIODS 220: Artificial Intelligence in Healthcare

Healthcare is one of the most exciting application domains of artificial intelligence, with transformative potential in areas ranging from medical image analysis to electronic health records-based prediction and precision medicine. This course will involve a deep dive into recent advances in AI in healthcare, focusing in particular on deep learning approaches for healthcare problems. We will start from foundations of neural networks, and then study cutting-edge deep learning models in the context of a variety of healthcare data including image, text, multimodal and time-series data. In the latter part of the course, we will cover advanced topics on open challenges of integrating AI in a societal application such as healthcare, including interpretability, robustness, privacy and fairness. The course aims to provide students from diverse backgrounds with both conceptual understanding and practical grounding of cutting-edge research on AI in healthcare.
Terms: Win | Units: 3-4 | Grading: Medical Option (Med-Ltr-CR/NC)

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

COMM 100S: Introduction to Digital Labor

Digital technologies have had a profound influence on our economy, the ways we communicate, and the ways in which we work. This course will provide a lens through which to understand digital labor and digital work today. We will explore the ideological and cultural values of Silicon Valley and their role in shaping the new business models of the Internet Age (such as crowdsourcing, the sharing economy, and humans-as-a-service). We will examine the past, present, and future of mechanisms of workplace control (from clocks to algorithmic management) and the implications of the digital turn on spatial and material dimensions of labor. Finally, we will turn our attention toward possible futures of work, given the increasing presence of automation and artificial intelligence in the workplace. By engaging with social scientific analyses and popular media, students will leave the course with a greater appreciation of worker perspectives and challenges in the digital era.
Terms: Sum | Units: 3 | UG Reqs: WAY-SI | Grading: Letter or Credit/No Credit

COMM 120W: The Rise of Digital Culture (AMSTUD 120, COMM 220)

From Snapchat to artificial intelligence, digital systems are reshaping our jobs, our democracies, our love lives, and even what it means to be human. But where did these media come from? And what kind of culture are they creating? To answer these questions, this course explores the entwined development of digital technologies and post-industrial ways of living and working from the Cold War to the present. Topics will include the historical origins of digital media, cultural contexts of their deployment and use, and the influence of digital media on conceptions of self, community, and state. Priority to juniors, seniors, and graduate students.
Terms: Spr | Units: 4-5 | UG Reqs: GER:DB-SocSci, WAY-SI | Grading: Letter (ABCD/NP)
Instructors: ; Turner, F. (PI)

COMM 180: Ethics, Public Policy, and Technological Change (CS 182, ETHICSOC 182, PHIL 82, POLISCI 182, PUBLPOL 182)

Examination of recent developments in computing technology and platforms through the lenses of philosophy, public policy, social science, and engineering. Course is organized around four main units: algorithmic decision-making and bias; data privacy and civil liberties; artificial intelligence and autonomous systems; and the power of private computing platforms. Each unit considers the promise, perils, rights, and responsibilities at play in technological developments. Prerequisite: CS106A.
Terms: Win | Units: 5 | UG Reqs: WAY-ER | Grading: Letter or Credit/No Credit

COMM 220: The Rise of Digital Culture (AMSTUD 120, COMM 120W)

From Snapchat to artificial intelligence, digital systems are reshaping our jobs, our democracies, our love lives, and even what it means to be human. But where did these media come from? And what kind of culture are they creating? To answer these questions, this course explores the entwined development of digital technologies and post-industrial ways of living and working from the Cold War to the present. Topics will include the historical origins of digital media, cultural contexts of their deployment and use, and the influence of digital media on conceptions of self, community, and state. Priority to juniors, seniors, and graduate students.
Terms: Spr | Units: 4-5 | Grading: Letter (ABCD/NP)
Instructors: ; Turner, F. (PI)

CS 22A: The Social & Economic Impact of Artificial Intelligence (INTLPOL 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 are free of algorithmic bias and respect human ethical principles? What role will they play in our system of justice and the practice of law? How will they be used or abused in democratic societies and autocratic regimes? Will they alter the geopolitical balance of power, and change the nature of warfare? The goal of CS22a 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 28: Artificial Intelligence, Entrepreneurship and Society in the 21st Century and Beyond

Technical developments in artificial intelligence (AI) have opened up new opportunities for entrepreneurship, as well as raised profound longer term questions about how human societal and economic systems may be re­organized to accommodate the rise of intelligent machines. In this course, closely co­taught by a Stanford professor and a leading Silicon Valley venture capitalist, we will examine the current state of the art capabilities of existing artificial intelligence systems, as well as economic challenges and opportunities in early stage startups and large companies that could leverage AI. We will focus on gaps between business needs and current technical capabilities to identify high impact directions for the development of future AI technology. Simultaneously, we will explore the longer term societal impact of AI driven by inexorable trends in technology and entrepreneurship. The course includes guest lectures from leading technologists and entrepreneurs who employ AI in a variety of fields, including healthcare, education, self­driving cars, computer security, natural language interfaces, computer vision systems, and hardware acceleration.
Terms: Aut | Units: 2 | Grading: Satisfactory/No Credit
Instructors: ; Ganguli, S. (PI)

CS 182: Ethics, Public Policy, and Technological Change (COMM 180, ETHICSOC 182, PHIL 82, POLISCI 182, PUBLPOL 182)

Examination of recent developments in computing technology and platforms through the lenses of philosophy, public policy, social science, and engineering. Course is organized around four main units: algorithmic decision-making and bias; data privacy and civil liberties; artificial intelligence and autonomous systems; and the power of private computing platforms. Each unit considers the promise, perils, rights, and responsibilities at play in technological developments. Prerequisite: CS106A.
Terms: Win | Units: 5 | UG Reqs: WAY-ER | Grading: Letter or Credit/No Credit

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)

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 231C: Computer Vision and Image Analysis of Art

This course presents the application of rigorous image processing, computer vision, machine learning, computer graphics and artificial intelligence techniques to problems in the history and interpretation of fine art paintings, drawings, murals and other two-dimensional works, including abstract art. The course focuses on the aspects of these problems that are unlike those addressed widely elsewhere in computer image analysis applied to physics-constrained images in photographs, videos, and medical images, such as the analysis of brushstrokes and marks, medium, inferring artists¿ working methods, compositional principles, stylometry (quantification of style), the tracing of artistic influence, and art attribution and authentication. The course revisits classic problems, such as image-based object recognition, but in highly non-realistic, stylized artworks. Recommended: One of CS 131 or EE 168 or equivalent; ARTHIST 1B. Prerequisites: Programming proficiency in at least one of C, C++, Python, Matlab or Mathematica and tools/frameworks such as OpenCV or Matlab's Image Processing toolbox.
Terms: Spr | Units: 3 | Grading: Letter or Credit/No Credit
Instructors: ; Stork, D. (PI)

CS 247A: Design for Artificial Intelligence

A project-based course that builds on the introduction to design in CS147 by focusing on advanced methods and tools for research, prototyping, and user interface design. Studio based format with intensive coaching and iteration to prepare students for tackling real world design problems. This course takes place entirely in studios; you must plan on attending every studio to take this class. The focus of CS247A is design for human-centered artificial intelligence experiences. What does it mean to design for AI? What is HAI? Let¿s explore what AI actually is and the constraints, opportunities and specialized processes necessary to create AI systems that work effectively for the humans involved. Prerequisites: 147 or equivalent background in design thinking.
Terms: Spr | Units: 3-4 | Grading: Letter (ABCD/NP)
Instructors: ; Stanford, J. (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 421: Designing AI to Cultivate Human Well-Being

Overview: This is a multi-disciplinary cross-listed course focused on the goal of helping to build AI technology that promotes human flourishing. This course aims to expose (a) GSB students to deep learning and AI techniques focused on human well-being, and (b) CS students to behavioral science and design thinking, as well as frameworks and research to better understand human well-being and human-centered designs. Students will form cross-disciplinary teams and work on a final project that delves into an industry and proposes a detailed 5-year road map on how that industry might evolve with AI algorithms that focused on human well-being. Course Description: The past decade of machine learning has given us self-driving cars, practical speech recognition, video game playing robots, effective web search, and revolutionary drug treatments. While Artificial Intelligence has been impressive in achieving these specific tasks, this does not always correspond to the broader goal of cultivating human well-being. The goal of this class is to bridge the gap between technology and societal objectives: How do we design AI to promote human flourishing? On Day 1, we draw on behavioral research to discuss what makes humans thrive. Behavioral research shows that for people to flourish, they need meaning, which involves an ability to understand and value others, a sense of belonging, and knowledge that they are making a contribution bigger than themselves. The conditions for this occur when people feel they have the resources and insight to establish a sense of meaning for themselves. Students will draw on this research to focus on building AI technology that effectively understands, communicates with, collaborates with and augment people. On days 2-5, leaders across industries (e.g., healthcare, transportation) that fundamentally affect human wellbeing will participate in lightning round exchanges to delve deeply into the challenge of building technology focused on human well-being, followed by interactive discussion with students. On the last day, the four-person cross-disciplinary teams will present their 2 page white paper proposals to invited guests. Of note: this course is entirely about high-level "programming" and provides no technical insight on machine learning, data-mining or statistical pattern recognition.
Terms: Win | Units: 2 | Grading: Satisfactory/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: not given this year | Units: 1 | Grading: Satisfactory/No Credit

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: not given this year | Units: 3 | UG Reqs: WAY-CE | Grading: Letter (ABCD/NP)

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: not given this year | Units: 5 | Grading: Letter (ABCD/NP)

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 182: Ethics, Public Policy, and Technological Change (COMM 180, CS 182, PHIL 82, POLISCI 182, PUBLPOL 182)

Examination of recent developments in computing technology and platforms through the lenses of philosophy, public policy, social science, and engineering. Course is organized around four main units: algorithmic decision-making and bias; data privacy and civil liberties; artificial intelligence and autonomous systems; and the power of private computing platforms. Each unit considers the promise, perils, rights, and responsibilities at play in technological developments. Prerequisite: CS106A.
Terms: Win | Units: 5 | UG Reqs: WAY-ER | Grading: Letter or Credit/No Credit

ETHICSOC 185M: Contemporary Moral Problems (PHIL 72, POLISCI 134P)

This course is an introduction to contemporary ethical thought with a focus on the morality of harming others and saving others from harm. It aims to develop students' ability to think carefully and rationally about moral issues, to acquaint them with modern moral theory, and to encourage them to develop their own considered positions about important real-world issues. In the first part of the course, we will explore fundamental topics in the ethics of harm. Among other questions, we will ask: How extensive are one's moral duties to improve the lives of the less fortunate? When is it permissible to inflict harm on others for the sake of the greater good? Does the moral permissibility of a person's action depend on her intentions? Can a person be harmed by being brought into existence? In the second part of the course, we will turn to practical questions. Some of these will be familiar; for example: Is abortion morally permissible? What obligations do we have to protect the planet for the sake of future generations? Other questions we will ask are newer and less well-trodden. These will include: How does the availability of new technology, in particular artificial intelligence, change the moral landscape of the ethics of war? What moral principles should govern the programming and operation of autonomous vehicles?
Terms: Win | Units: 4-5 | UG Reqs: GER:EC-EthicReas, WAY-ER | Grading: Letter (ABCD/NP)
Instructors: ; Gillespie, L. (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)

GSBGEN 596: Designing AI to Cultivate Human Well-Being

Overview: This is a multi-disciplinary cross-listed course focused on the goal of helping to build AI technology that promotes human flourishing. This course aims to expose (a) GSB students to deep learning and AI techniques focused on human well-being, and (b) CS students to behavioral science and design thinking, as well as frameworks and research to better understand human well-being and human-centered designs. Students will form cross-disciplinary teams and work on a final project that delves into an industry and proposes a detailed 5-year road map on how that industry might evolve with AI algorithms that focused on human well-being. Course Description: The past decade of machine learning has given us self-driving cars, practical speech recognition, video game playing robots, effective web search, and revolutionary drug treatments. While Artificial Intelligence has been impressive in achieving these specific tasks, this does not always correspond to the broader goal of cultivating human well-being. The goal of this class is to bridge the gap between technology and societal objectives: How do we design AI to promote human flourishing? On Day 1, we draw on behavioral research to discuss what makes humans thrive. Behavioral research shows that for people to flourish, they need meaning, which involves an ability to understand and value others, a sense of belonging, and knowledge that they are making a contribution bigger than themselves. The conditions for this occur when people feel they have the resources and insight to establish a sense of meaning for themselves. Students will draw on this research to focus on building AI technology that effectively understands, communicates with, collaborates with and augment people. On days 2-5, leaders across industries (e.g., healthcare, transportation) that fundamentally affect human wellbeing will participate in lightning round exchanges to delve deeply into the challenge of building technology focused on human well-being, followed by interactive discussion with students. On the last day, the four-person cross-disciplinary teams will present their 2 page white paper proposals to invited guests. Of note: this course is entirely about high-level "programming" and provides no technical insight on machine learning, data-mining or statistical pattern recognition.
Units: 2 | Grading: GSB Pass/Fail
Instructors: ; Aaker, J. (PI); Li, F. (SI)

HRMGT 203: 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

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)

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 are free of algorithmic bias and respect human ethical principles? What role will they play in our system of justice and the practice of law? How will they be used or abused in democratic societies and autocratic regimes? Will they alter the geopolitical balance of power, and change the nature of warfare? The goal of CS22a 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 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: not given this year | 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

Even just a generation ago, interest in "artificial intelligence" (AI) was largely confined to academic computer science, philosophy, engineering research and development efforts, and science fiction. Today the term is widely understood to encompass not only long-term efforts to simulate the kind of general intelligence humans reflect, but also fast-evolving technologies (such as elaborate convolutional neural networks leveraging vast amounts of data) increasingly affecting finance, transportation, health care, national security, advertising and social media, and a variety of other fields. Conceived for students with interest in law, business, public policy, design, and ethics, this highly interactive course surveys current and emerging legal and policy problems related to how law structures humanity's relationship to artificially-constructed intelligence. To deepen students' understanding of current and medium-term problems in this area, the course explores definitions and foundational concepts associated with "artificial intelligence," likely directions for the evolution of AI, 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. We will also cover topics associated with the development and design of AI as they relate to the legal system, such as measuring algorithmic bias and explainability of AI models. 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 decision making frameworks, the interplay of norms and formal law, the technical complexities that may arise as society scales deployment of AI systems, and similarities and differences to other domains of human activity raising regulatory trade-offs and affected by technological change. 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 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, please fill out an application (available at https://bit.ly/2MJIem9) by September 4, 2019. Applications received after September 4, 2019 will be considered on a rolling basis if space is available. The application is also available on the SLS website (Click Courses at the bottom of the homepage and then click Consent of Instructor Forms).
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. The writing assignments will center on reflection papers on legal, regulatory and policy analysis of current issues involving AI. 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: not given this year | Units: 2 | Grading: Law Honors/Pass/Restrd Cr/Fail

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: Attendance. Cross-listed with Computer Science (CS 22A) and International Policy (INTLPOL 200).
Terms: not given this year | Units: 1 | Grading: Law Mandatory P/R/F

LAW 4047: Ethics, Public Policy, and Technological Change

Examination of recent developments in computing technology and platforms through the lenses of philosophy, public policy, social science, and engineering. Course is organized around four main units: algorithmic decision-making and bias; data privacy and civil liberties; artificial intelligence and autonomous systems; and the power of private computing platforms. Each unit considers the promise, perils, rights, and responsibilities at play in technological developments. Prerequisite: CS106A. Elements used in grading: Attendance, class participation, written assignments, coding assignments, and final exam. Cross-listed with Communication (COMM 180), Computer Science (CS 182), Ethics in Society (ETHICSOC 182), Philosophy (PHIL 82), Political Science (POLISCI 182), Public Policy (PUBLPOL 182).
Terms: Win | Units: 4 | Grading: Law Honors/Pass/Restrd Cr/Fail

LAW 5001: China Law and Business

(Formerly Law 245) China's adoption of its open door policy in 1978 to welcome foreign investment started the country's forty-year trajectory of legal reforms in different areas, including foreign investment, intellectual property, dispute resolution, and antimonopoly law. The launch of the Belt and Road Initiative in 2013, China's ambitious global economic plan, has taken legal reforms in the country to another level, as numerous measures are being undertaken to ensure the success of this initiative, which is associated with tremendous legal challenges. This introductory course is designed to provide an overview of the Chinese legal system and to discuss legal and business issues related to the above-mentioned economic evolution spearheaded by China but having an impact around the world. The course will specifically examine Chinese legal rules and principles in select business-related areas, including intellectual property, dispute resolution, foreign investment vehicles, mergers and acquisitions, antimonopoly law, and artificial intelligence. Through active class participation and analysis of legal and business cases, students will learn both the law on the books and the law in action, as well as strategies that Chinese and international businesses alike can use to overcome limitations in the Chinese legal system. Leaders from the law and business communities will be invited to share their experiences and insights. This course is particularly suitable for law students, MBA students, and students enrolled in the East Asian Studies Program. Undergraduates who have permission from the instructor may also take this course. A Stanford Non-Law Student Course Registration Form is available on the SLS Registrar's Office website. Elements used in grading: class participation (20%), team project (40%), and extended take-home exam (40%). For the team project component, students will work with another student enrolled in the class to produce an analysis of a judicial case in China and discuss, for example, the implications of the related Chinese legal principles for businesses and/or major differences between these principles and similar U.S. legal principles. Quality team projects may have the opportunity to be included in the professional journal published by the China Guiding Cases Project ("CGCP"), which is led by Dr. Mei Gechlik, the instructor, and her global team of nearly 200 members. Team projects selected for publication will receive editorial input from the CGCP and authors may have a chance to present their papers at CGCP events.
Terms: Spr | Units: 3 | Grading: Law Honors/Pass/Restrd Cr/Fail

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

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 7073: Law, Bias, and Algorithms

Human decision making is increasingly being displaced by algorithms. Judges sentence defendants based on "risk scores;" regulators take enforcement actions based on predicted violations; advertisers target materials based on demographic attributes; and employers evaluate applicants and employees based on machine-learned models. A predominant concern with the rise of such algorithmic decision making (machine learning or artificial intelligence) is that it may replicate or exacerbate human bias. Algorithms might discriminate, for instance, based on race or gender. This course surveys the legal principles for assessing bias of algorithms, the engineering techniques for how to design and assess bias of algorithms, and assesses how antidiscrimination law and the design of algorithms may need to evolve to account for the potential emergence of machine bias. The course will meet jointly with MS&E 330 [https://explorecourses.stanford.edu/search?view=catalog&filter-coursestatus-Active=on&page=0&catalog=&academicYear=&q=MS%26E+330%3A+Law%2C+Bias%2C+%26+Algorithms+%29&collapse=]. Minimal coding background is assumed, but students will learn through interactive coding sessions in class. Admission is by consent of instructor and is limited to 20 students. Student assessment is based on response papers and a final project. Elements used in grading: Attendance, Class Participation, Written Assignments. 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: not given this year | Units: 3 | 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)

ME 344S: HPC-AI Summer Seminar Series

How will high performance computing and artificial intelligence change the way you live, work and learn? What skill sets will you need in the future?nThis summer, we kick off a new seminar series, the HPC-AI Summer Seminar Series, presented by the Stanford High Performance Computing Center and the HPC-AI Advisory Council.nEach week combines thought leadership and practical insights with topics of great societal importance and responsibility - from applications, tools and techniques to delving into emerging trends and technologies.nThese experts and influencers who are shaping our HPC and AI future will share their vision and will address audience questions. Students of all academic backgrounds and interests are encouraged to register for this 1-unit course. No prerequisites required. Register early.
Terms: Sum | Units: 1 | Grading: Letter or Credit/No Credit

MED 232: Global Health: Scaling Health Technology Innovations in Low Resource Settings

Recent advances in health technologies - incorporating innovations like robotics, cloud computing, artificial intelligence, and smart sensors - have raised expectations of a dramatic impact on health outcomes across the world. However, bringing innovative technologies to low resource settings has proven challenging, limiting their impact. This course explores critical questions regarding the implementation and impact of technological innovations in low resource settings. The course will feature thought leaders from the health technology community, who will explore examples of technologies that have been successful in low resource communities, as well as those that have failed. Students will think critically to consider conditions under which technologies reach scale and have positive impact in the global health field. This course is open to undergraduate students, graduate students, and medical students. Undergraduates can take this course for a letter grade and 3 units. Graduate students and MD students can enroll for 1-2 units, but the course will require 2 units worth of work. Students enrolling in the course for a third unit will also work on group projects, each of which will focus on the potential opportunity for a health technology in a low resource setting and consider approaches to ensure its impact at scale. Students enrolled in the class for three units will also have additional assignments, including weekly blog posts. Students will fill out an application after the first day of class to determine enrollment.
Terms: Win | Units: 1-3 | Repeatable for credit | Grading: Medical Option (Med-Ltr-CR/NC)

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

MUSIC 223C: Tradition, Experimentation, and Technology in String Quartet Composition and Performance

This course will explore string quartet composition and performance by focusing in on the act of composer-performer collaboration. It will investigate this relationship and its facets through the composition of a work for the Saint Lawrence String Quartet by Patricia Alessandrini based on the SLSQ's relationship with the Opus 76 quartets of Haydn employing Artificial Intelligence (AI) techniques, in addition to workshopping of student exercises and compositions. Students will have the opportunity to participate in the class as performers, composers, technologists, or musicologically, through analysis of the collaborative process informed by concepts such as agency, representation, interpretation, expression, and experimentation.
Terms: Spr | Units: 1-3 | Grading: Letter or Credit/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

OSPKYOTO 221K: Artificial Intelligence: Principles and Techniques

Terms: Aut | Units: 3-4 | Grading: Letter or Credit/No Credit

PHIL 20N: Philosophy of Artificial Intelligence

Terms: Aut | Units: 3 | Grading: Letter (ABCD/NP)
Instructors: ; Etchemendy, J. (PI)

PHIL 24D: Current Ethical Issues in Artificial Intelligence and Machine Learning

This tutorial examines philosophical issues in artificial intelligence and machine learning. The focus will be on ethical questions raised by current and forthcoming engineering applications, rather than on classic foundational issues like whether machines can be conscious. Hands¿on knowledge of current AI / ML technologies is not required, but students with such experience will be enthusiastically welcomed. Students will be encouraged to shape the direction of the class based on their own interests or experience in industry.
Terms: not given this year | Units: 2 | Grading: Satisfactory/No Credit

PHIL 72: Contemporary Moral Problems (ETHICSOC 185M, POLISCI 134P)

This course is an introduction to contemporary ethical thought with a focus on the morality of harming others and saving others from harm. It aims to develop students' ability to think carefully and rationally about moral issues, to acquaint them with modern moral theory, and to encourage them to develop their own considered positions about important real-world issues. In the first part of the course, we will explore fundamental topics in the ethics of harm. Among other questions, we will ask: How extensive are one's moral duties to improve the lives of the less fortunate? When is it permissible to inflict harm on others for the sake of the greater good? Does the moral permissibility of a person's action depend on her intentions? Can a person be harmed by being brought into existence? In the second part of the course, we will turn to practical questions. Some of these will be familiar; for example: Is abortion morally permissible? What obligations do we have to protect the planet for the sake of future generations? Other questions we will ask are newer and less well-trodden. These will include: How does the availability of new technology, in particular artificial intelligence, change the moral landscape of the ethics of war? What moral principles should govern the programming and operation of autonomous vehicles?
Terms: Win | Units: 4-5 | UG Reqs: GER:EC-EthicReas, WAY-ER | Grading: Letter (ABCD/NP)
Instructors: ; Gillespie, L. (PI)

PHIL 82: Ethics, Public Policy, and Technological Change (COMM 180, CS 182, ETHICSOC 182, POLISCI 182, PUBLPOL 182)

Examination of recent developments in computing technology and platforms through the lenses of philosophy, public policy, social science, and engineering. Course is organized around four main units: algorithmic decision-making and bias; data privacy and civil liberties; artificial intelligence and autonomous systems; and the power of private computing platforms. Each unit considers the promise, perils, rights, and responsibilities at play in technological developments. Prerequisite: CS106A.
Terms: Win | Units: 5 | UG Reqs: WAY-ER | Grading: Letter or Credit/No Credit

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

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

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 134P: Contemporary Moral Problems (ETHICSOC 185M, PHIL 72)

This course is an introduction to contemporary ethical thought with a focus on the morality of harming others and saving others from harm. It aims to develop students' ability to think carefully and rationally about moral issues, to acquaint them with modern moral theory, and to encourage them to develop their own considered positions about important real-world issues. In the first part of the course, we will explore fundamental topics in the ethics of harm. Among other questions, we will ask: How extensive are one's moral duties to improve the lives of the less fortunate? When is it permissible to inflict harm on others for the sake of the greater good? Does the moral permissibility of a person's action depend on her intentions? Can a person be harmed by being brought into existence? In the second part of the course, we will turn to practical questions. Some of these will be familiar; for example: Is abortion morally permissible? What obligations do we have to protect the planet for the sake of future generations? Other questions we will ask are newer and less well-trodden. These will include: How does the availability of new technology, in particular artificial intelligence, change the moral landscape of the ethics of war? What moral principles should govern the programming and operation of autonomous vehicles?
Terms: Win | Units: 4-5 | UG Reqs: GER:EC-EthicReas, WAY-ER | Grading: Letter (ABCD/NP)
Instructors: ; Gillespie, L. (PI)

POLISCI 182: Ethics, Public Policy, and Technological Change (COMM 180, CS 182, ETHICSOC 182, PHIL 82, PUBLPOL 182)

Examination of recent developments in computing technology and platforms through the lenses of philosophy, public policy, social science, and engineering. Course is organized around four main units: algorithmic decision-making and bias; data privacy and civil liberties; artificial intelligence and autonomous systems; and the power of private computing platforms. Each unit considers the promise, perils, rights, and responsibilities at play in technological developments. Prerequisite: CS106A.
Terms: Win | Units: 5 | UG Reqs: WAY-ER | Grading: Letter or Credit/No Credit

PSYCH 147S: Introduction to the Psychology of Emotion

What are emotions? What purpose do they serve? How do we measure them? Can we control them? In this course, we will explore some of the most interesting questions in psychology: questions about emotion. Emotions shape our perceptions of the world, influence critical life decisions, and allow us to connect with others. This seminar will provide a selective review of the scientific study of emotion in Affective Science. The first unit of the course will focus on the theoretical foundations, the basic science of emotion, and methods for measuring emotions. In the second unit of the course, we will discuss topics at the intersection of motivation and emotion, such as decision-making and self-control. In the third unit, we will delve into the social function of emotions. In the fourth unit of the course, we will study the ways people succeed and fail at controlling their emotions. In the fifth unit, we will discuss a variety of additional topics such as how emotions change across the lifespan, how emotions can be harnessed to engineer behavior change, as well as emotions and artificial intelligence. My goal is that you will leave this course with a scientifically-informed understanding of your own and others' emotions as well as strategies for how to effectively use and manage your feelings in daily life.
Terms: Sum | Units: 3 | Grading: Letter or Credit/No Credit

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

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

PUBLPOL 182: Ethics, Public Policy, and Technological Change (COMM 180, CS 182, ETHICSOC 182, PHIL 82, POLISCI 182)

Examination of recent developments in computing technology and platforms through the lenses of philosophy, public policy, social science, and engineering. Course is organized around four main units: algorithmic decision-making and bias; data privacy and civil liberties; artificial intelligence and autonomous systems; and the power of private computing platforms. Each unit considers the promise, perils, rights, and responsibilities at play in technological developments. Prerequisite: CS106A.
Terms: Win | Units: 5 | UG Reqs: WAY-ER | 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: 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 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 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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