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Courses

HAI believes in the integration of AI across human-centered systems and applications in a setting that can only be offered by Stanford University. Stanford’s seven leading schools on the same campus enable HAI to offer a multidisciplinary approach to education.

Learn more about AI courses at Stanford below:

Courses and Programs

    • Stanford Students
    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. After the term begins, students accepted into the course can transfer, with consent of the instructor, from section (01) into section (02), which meets the R requirement. 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 TBA. Applications received after the deadline 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).

    When

    N/A

    Subject

    • LAW

    Delivery Method

    N/A

    Time Commitment

    Academic Quarter

    Earned Outcome

    N/A

    Cost

    N/A

    • Stanford Students
    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.

    When

    N/A

    Subject

    • LAW

    Delivery Method

    N/A

    Time Commitment

    Academic Quarter

    Earned Outcome

    N/A

    Cost

    N/A

    • Stanford Students
    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. Elements used in grading: Attendance. Cross-listed with Computer Science (CS 22A) and International Policy (INTLPOL 200).

    When

    • Winter

    Subject

    • LAW

    Delivery Method

    N/A

    Time Commitment

    Academic Quarter

    Earned Outcome

    N/A

    Cost

    N/A

    • Stanford Students
    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).

    When

    N/A

    Subject

    • LAW

    Delivery Method

    N/A

    Time Commitment

    Academic Quarter

    Earned Outcome

    N/A

    Cost

    N/A

    • Stanford Students
    Even just a generation ago, interest in "artificial intelligence" (AI) was largely confined to academic computer science, philosophy, engineering, and science fiction. Today the term is understood to encompass not only long-term efforts to simulate the general intelligence associated with humans, but also fast-evolving technologies (such as elaborate neural networks leveraging vast amounts of data) with the potential to reshape finance, transportation, health care, national security, advertising and social media, and other fields. Taught by a sitting judge, a former EU Parliament member, and a law professor, and conceived to serve students with interest in law, business, public policy, design, and ethics, this interactive course surveys current and emerging legal and governance problems related to humanity's relationship to artificially-constructed intelligence. To deepen students' understanding of legal and governance problems in this area, the course explores definitions and foundational concepts associated with AI, likely pathways of AI's evolution, different types of law and policy concerns raised by existing and future versions of AI, and the distinctive domestic and international political economies of AI governance. We will consider discrete settings where regulation of AI is emerging as a challenge or topic of interest, among them: autonomous vehicles, autonomous weapons, labor market decisions, AI in social media/communications platforms, judicial and governmental decision-making, and systemic AI safety problems; the growing body of legal doctrines and policies relevant to the development and control of AI such as the European Union's General Data Protection Regulation and the California Consumer Privacy Act; the connection between governance of manufactured intelligence and related bodies of law, such as administrative law, torts, constitutional principles, civil rights, criminal justice, and international law; and new legal and governance arrangements that could affect the development and use of AI. We will also cover topics associated with the design and development of AI as they relate to law and governance, such as measuring algorithmic bias and explainability of AI models. Cross-cutting themes will include: how law and policy affect 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; technical complexities that may arise as society scales deployment of AI systems; AI's implications for transnational law and governance and geopolitics; 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 students who want to gain tools and ideas to deepen their existing interest or technical 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 class sessions and readings as well as certain in-class material will help provide necessary background. Requirements: The course involves a mix of lectures, practical exercises, and student-led discussion and presentations. Elements used in grading: Requirements include attendance, participation in a student-led group presentation and a group-based practical exercise, two short 3-5 pp. response papers, and either an exam or research paper. After the term begins, students accepted into the course can transfer, with consent of the instructor, from section (01) into section (02), which meets the R requirement. 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://docs.google.com/forms/d/e/1FAIpQLSfwRxaM1omTsJmK9k0gksdS5jBPRz-YCuYhRUpDlVXXglDHjg/viewform by March 12, 2021. Applications received after the deadline 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). Cross-listed with International Policy (INTLPOL 364).

    When

    N/A

    Subject

    • LAW

    Delivery Method

    N/A

    Time Commitment

    Academic Quarter

    Earned Outcome

    N/A

    Cost

    N/A

    • Stanford Students
    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, examines emerging 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. Admission is by consent of instructor and is limited to 20 students. Student assessment is based on class participation, response papers, and a final project. CONSENT APPLICATION: To apply for this course, students must complete and submit a Consent Application Form available on the SLS website (https://law.stanford.edu/education/courses/consent-of-instructor-forms/). See Consent Application Form for instructions and submission deadline.

    Instructors

    • Daniel Ho

    When

    • Autumn

    Subject

    • LAW

    Delivery Method

    N/A

    Time Commitment

    Academic Quarter

    Earned Outcome

    N/A

    Cost

    N/A

    • Stanford Students
    Since your first week of law school, you have been reading legal opinions written by judges. Who were those judges and did their identities affect their views? From a judge's perspective, what makes a case hard or easy? Did the process by which the judge was selected--or could be removed from office--influence her or his decision? How do judges make choices about the larger legal ecosystem in which you will practice law? After all, judges determine many aspects of the legal environment in which lawyers operate, from whether you can livestream a court hearing from your phone to whether you will take the bar exam in person or online. Taught by a Justice on a California Court of Appeal, this seminar explores judicial decision making about cases and the court system from a variety of perspectives. It draws from accounts by social scientists, lawyers, and judges themselves, analyzing what judges do and critiquing how they do it. The seminar examines systems of judicial selection, evaluation, and removal in both the federal and state court systems and their potential effects on judicial decision making. We will take up questions such as whether the identity of judges matters to their decisions, how heuristics or implicit biases might influence outcomes, how communities try to choose "good" judges and what they do when those choices go wrong, evaluate efforts to diversify the bench, and consider what lessons might be learned from the experiences of various states in evaluating and electing judges. One theme of the seminar involves the interaction of judges with litigants, the public, and other government actors--on twenty-first-century terms. We will ask how courts should manage questions related to transparency, privacy, access to justice, and technology. We will think about how judges might choose or be compelled to rely on emerging automation technologies, whether simple algorithms or advanced machine learning. We also will consider the extent to which judges do and should take into account the views of executive officials, legislators, nongovernmental organizations, and members of the general public when deciding cases and structuring the legal system. In addition, we will look at ethics rules governing what judges can learn and what they can say. For example, can or should a judge run an experiment that tests a litigant's factual assertion, or, in her free time, write an online product review, lead a religious group, or participate in a commission to improve state government? The seminar will pursue these questions from both theoretical and practical perspectives. Sitting judges from a variety of courts will share their insights with seminar participants. Students will write a research paper on a relevant topic of their choice, and will be encouraged to think critically about how judges make decisions and how courts can be improved in realistic ways. We will think together about how judges and courts can best deliver justice in a changing, contested, unequal, and increasingly complex world. Elements used in grading: Attendance, Class Participation, Written Assignments, Research Paper.

    Instructors

    • Allison Danner

    When

    • Autumn

    Subject

    • LAW

    Delivery Method

    N/A

    Time Commitment

    Academic Quarter

    Earned Outcome

    N/A

    Cost

    N/A

    • Stanford Students
    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

    Instructors

    • Li Jiang

    When

    • Winter

    Subject

    • ME

    Delivery Method

    N/A

    Time Commitment

    Academic Quarter

    Earned Outcome

    N/A

    Cost

    N/A

    • Stanford Students
    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? The HPC-AI Summer Seminar Series, presented by the Stanford High Performance Computing Center and the HPC-AI Advisory Council, 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. These experts and influencers who are shaping our HPC and AI future will share their vision and will address audience questions. The overarching theme this year is the potential influence and impact of HPC and AI to battle COVID-19. Students of all academic backgrounds and interests are encouraged to register for this 1-unit course. No prerequisites required. Register early.

    Instructors

    • Steve Jones

    When

    • Summer

    Subject

    • ME

    Delivery Method

    N/A

    Time Commitment

    Academic Quarter

    Earned Outcome

    N/A

    Cost

    N/A

    • Stanford Students
    The face of healthcare is changing - innovative technologies, based on recent advances in artificial intelligence, are radically altering how care is delivered. Startups are offering entirely new ways to diagnose, manage, treat, and operate. Few ever reach the patient - those that do have much more than an idea and an algorithm; they have an intimate understanding of the healthcare landscape and the technical knowhow to successfully integrate AI solutions into the medical system. In this course, we tackle the central question: How can young students find feasible and impactful medical problems, and build, scale, and translate technology solutions into the clinic. Together, we will discover the transformative technologies of tomorrow that we can build today. Please see the syllabus for more information. We encourage students of all backgrounds to enroll- the only prerequisite is a strong passion for technology in healthcare. Syllabus: rebrand.ly/aihealth

    When

    • Spring

    Subject

    • MED

    Delivery Method

    N/A

    Time Commitment

    Academic Quarter

    Earned Outcome

    N/A

    Cost

    N/A

    • Stanford Students
    AI has been advancing quickly, with its impact everywhere. In healthcare, innovation in AI could help transforming of our healthcare system. This course offers a diverse set of research projects focusing on cutting edge computer vision and machine learning technologies to solve some of healthcare's most important problems. The teaching team and teaching assistants will work closely with students on research projects in this area. Research projects include Care for Senior at Senior Home, Surgical Quality Analysis, AI Assisted Parenting, Burn Analysis & Assessment and more. AI areas include Video Understanding, Image Classification, Object Detection, Segmentation, Action Recognition, Deep Learning, Reinforcement Learning, HCI and more. The course is open to students in both school of medicine and school of engineering.

    Instructors

    • Amit Kaushal
    • Ehsan Adeli
    • Kevin Schulman

    When

    • Autumn

    Subject

    • MED

    Delivery Method

    N/A

    Time Commitment

    Academic Quarter

    Earned Outcome

    N/A

    Cost

    N/A

    • Stanford Students
    Explores the relation between technology, war, and national security policy with reference to current events. Course focuses on current U.S. national security challenges and the role that technology plays in shaping our understanding and response to these challenges, including the recent Russia-Ukraine conflict. Topics include: interplay between technology and modes of warfare; dominant and emerging technologies such as nuclear weapons, cyber, sensors, stealth, and biological; security challenges to the U.S.; and the U.S. response and adaptation to new technologies of military significance.

    Instructors

    • Herb Lin
    • Rhea Kumar
    • Niv Rajesh
    • Theo Velaise
    • Antone Cruz
    • Vaidehi Bhaskara
    • Alexis Legrand

    When

    • Autumn

    Subject

    • MS&E

    Delivery Method

    N/A

    Time Commitment

    Academic Quarter

    Earned Outcome

    N/A

    Cost

    N/A

    • Stanford Students
    Explores the relation between technology, war, and national security policy with reference to current events. Course focuses on current U.S. national security challenges and the role that technology plays in shaping our understanding and response to these challenges, including the recent Russia-Ukraine conflict. Topics include: interplay between technology and modes of warfare; dominant and emerging technologies such as nuclear weapons, cyber, sensors, stealth, and biological; security challenges to the U.S.; and the U.S. response and adaptation to new technologies of military significance.

    Instructors

    • Herb Lin
    • Rhea Kumar
    • Niv Rajesh
    • Theo Velaise
    • Antone Cruz
    • Vaidehi Bhaskara
    • Alexis Legrand

    When

    • Autumn

    Subject

    • MS&E

    Delivery Method

    N/A

    Time Commitment

    Academic Quarter

    Earned Outcome

    N/A

    Cost

    N/A

    • Stanford Students
    This course explores how new technologies pose challenges and create opportunities for the United States to compete more effectively with rivals in the international system with a focus on strategic competition with the People's Republic of China. In this experiential policy class, you will address a priority national security challenge employing the "Lean" problem solving methodology to validate the problem and propose a detailed technology informed solution tested against actual experts and stakeholders in the technology and national security ecosystem. The course builds on concepts presented in MS&E 193/293: Technology and National Security and provides a strong foundation for MS&E 297: Hacking for Defense.

    Instructors

    • Joe Felter
    • Raj Shah
    • Steve Blank

    When

    • Autumn

    Subject

    • MS&E

    Delivery Method

    N/A

    Time Commitment

    Academic Quarter

    Earned Outcome

    N/A

    Cost

    N/A

    • Stanford Students
    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.

    When

    N/A

    Subject

    • OIT

    Delivery Method

    N/A

    Time Commitment

    Academic Quarter

    Earned Outcome

    N/A

    Cost

    N/A

    • Stanford Students
    Independent research and analysis of the historical, current, and future potential for high-growth entrepreneurship in Hong Kong, China and its surrounding region. How entrepreneurship in China compares to Silicon Valley and other similar innovation clusters today around the globe. Special emphasis on financial innovations such as bitcoin, blockchain, AI/ML applications in finance and insurance. Role of context with respect to entrepreneurship and innovation through direct contact with entrepreneurs, interviewing potential customers, professional investors, innovation education centers, policy makers, government officials, and any NGOs involved with entrepreneurship.

    When

    • Autumn

    Subject

    • OSPHONGK

    Delivery Method

    N/A

    Time Commitment

    Academic Quarter

    Earned Outcome

    N/A

    Cost

    N/A

    • Stanford Students
    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. Same as CS 221. Prerequisites: CS 103 or CS 103B/X, CS 106B or CS 106X, CS 107, and CS 109 (algorithms, probability, and programming experience)

    When

    • Autumn

    Subject

    • OSPKYOTO

    Delivery Method

    N/A

    Time Commitment

    Academic Quarter

    Earned Outcome

    N/A

    Cost

    N/A

    • Stanford Students
    Artificial Intelligence (AI) has the potential to transform society in a way that has not been seen before. AI can bring many positive benefits, such as allowing ideas to more flexibly cross language barriers, improve medical outcomes, and enhance the safety and efficiency of our transportation systems. However, as with the introduction with other technologies, there is the potential of negative consequences, such as job insecurity and the introduction of vulnerabilities that come with greater levels of automation. We will delve deeply into the core issues at stake that comes with the greater integration of AI into society. The course will be composed of discussion and guest lectures from industry leaders and academics associated with Oxford. Assignments include readings, class presentations, individual research projects, and essays. Field trips will include visits to London and Edinburgh.

    Instructors

    • Mykel Kochenderfer

    When

    • Autumn

    Subject

    • OSPOXFRD

    Delivery Method

    N/A

    Time Commitment

    Academic Quarter

    Earned Outcome

    N/A

    Cost

    N/A

    • Stanford Students
    This course will introduce the neuroscience of human sensory perception (hearing, balance, vision, smell, taste, touch) and explore avenues by which technology and bioscience will enhance and augment these human senses. Employing artificial intelligence, emerging devices with embedded sensors may afford perceptual and cognitive abilities beyond the limits of our biological systems. We will consider emerging multi-functional devices with capabilities beyond their sensory functions via connection within an ecosystem of technologies to characterize activities (e.g., physical, social), enhance safety (e.g., fall alerts, balance improvement), track health (e.g., multi-sensory biometric monitoring), enhance communication (e.g., speech enhancement, language translation, virtual assistant), augment cognition (e.g., memory, understanding), and monitor emotional wellbeing (e.g., sentiment, depression). We will also review simulated multisensory stimuli towards achieving immersive experiences with virtual and augmented reality technologies.

    Instructors

    • Robert Jackler
    • Achintya Bhowmik

    When

    • Winter

    Subject

    • OTOHNS

    Delivery Method

    N/A

    Time Commitment

    Academic Quarter

    Earned Outcome

    N/A

    Cost

    N/A

    • Stanford Students
    (Graduate students register for 234.) Neuroscience, psychology, linguistics, artificial intelligence, and other related fields face fundamental obstacles when they turn to the study of the mind. Can there be a rigorous science of us? German philosopher Edmund Husserl (1859-1938), founder of phenomenology, devised a method intended to disclose the basic structures of minds. In this class, we will read one of Husserl's major later works, Cartesian Meditations, as well as companion essays from both his time and ours. A guiding question for us will be how phenomenology is applied outside of philosophy, specifically, how has it influenced discussions of the mind in the sciences? Prerequisite: one prior course in philosophy, or permission of instructor.

    When

    N/A

    Subject

    • PHIL

    Delivery Method

    N/A

    Time Commitment

    Academic Quarter

    Earned Outcome

    N/A

    Cost

    N/A

    • Stanford Students
    We will pursue questions of metaphysics and epistemology through a focus on the nature of virtual realities and their relationships to non-virtual realities. Readings will be chosen from historical and contemporary sources, including David Chalmers'n book "Reality+."

    When

    • Spring

    Subject

    • PHIL

    Delivery Method

    N/A

    Time Commitment

    Academic Quarter

    Earned Outcome

    N/A

    Cost

    N/A

    • Stanford Students
    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?Philosophers 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. 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.No 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.

    Instructors

    • John Etchemendy

    When

    • Winter

    Subject

    • PHIL

    Delivery Method

    N/A

    Time Commitment

    Academic Quarter

    Earned Outcome

    N/A

    Cost

    N/A

    • Stanford Students
    (Graduate students register for 234.) Neuroscience, psychology, linguistics, artificial intelligence, and other related fields face fundamental obstacles when they turn to the study of the mind. Can there be a rigorous science of us? German philosopher Edmund Husserl (1859-1938), founder of phenomenology, devised a method intended to disclose the basic structures of minds. In this class, we will read one of Husserl's major later works, Cartesian Meditations, as well as companion essays from both his time and ours. A guiding question for us will be how phenomenology is applied outside of philosophy, specifically, how has it influenced discussions of the mind in the sciences? Prerequisite: one prior course in philosophy, or permission of instructor.

    When

    N/A

    Subject

    • PHIL

    Delivery Method

    N/A

    Time Commitment

    Academic Quarter

    Earned Outcome

    N/A

    Cost

    N/A

    • Stanford Students
    Tutorial taught by grad student. Enrollment limited to 10. Robots and artificial intelligence present a new sort of Wild West. AI programs drive cars without a license; robots offer sexual services in exchange for payment; autonomous weapons systems roam around, looking to kill with impunity. With this new frontier comes significant ethical issues. There are several clusters of questions for us to consider, including most pressing: which technologies are permissible to develop and implement? Second, under the heading of what philosophers sometimes call moral 'agenthood': what would make robots themselves count as agents, and to what standards are they responsible? Finally, under the heading of moral 'patienthood': in what ways can robots be benefited or harmed, and how does this impact humanity's ethical obligations? Each week, our discussion will be framed around a pair of assignments: a short story, TV episode, or video; and a philosophical text. As we move through the course, the questions above will be tackled in the context of specific emerging technologies, such as self-driving cars, autonomous weapons, sex robots, and more. This tutorial is graded Satisfactory/Unsatisfactory. In order to receive credit, students must read all of the assigned readings, participate in all class meetings, and submit a short reading response for most weeks.

    Instructors

    • Austen McDougal

    When

    N/A

    Subject

    • PHIL

    Delivery Method

    N/A

    Time Commitment

    Academic Quarter

    Earned Outcome

    N/A

    Cost

    N/A

    • Stanford Students
    This course explores cutting-edge disputes in the ethics, law and politics of artificial intelligence. We will examine the relation between foundational questions about fairness, autonomy, corporate responsibility, and the value of human life; and practical questions about the ethical design and regulation of emerging technologies. Topics include superintelligence and existential risk, explainable intelligent systems, nudging and targeted advertising, and algorithmic fairness.

    When

    • Spring

    Subject

    • PHIL

    Delivery Method

    N/A

    Time Commitment

    Academic Quarter

    Earned Outcome

    N/A

    Cost

    N/A