Results for artificial intelligence

This is a team-based course where students will work on a project to design and carry out an experiment intended to drive social impact in collaboration with a partner organization. The first few weeks will include lectures, hands-on tutorials, and labs designed to guide students through the process of experimental design in the digital context. Special topics include designing and selecting outcome measures that capture the impact of interventions; multi-stage experiments with applications to chatbots; learning how treatment effects vary across subgroups; adaptive experiments using bandits and artificial intelligence; and estimation of policies that target treatments based on subject characteristics. Experiments may be conducted on the customer base of a partner organization through their digital applications or on recruited subjects, such as subjects recruited to interactive chatbots. The teaching team will provide templates and technical assistance for designing and running the experiments. Students from different disciplinary backgrounds will be assigned roles to work in teams on the project. This course is part of the GSB's Action Learning Program, in which you will work on real business challenges under the guidance of faculty. In this intensive project-based course, you will learn research-validated foundations, tools, and practices; apply these tools and learnings to a real project for an external organization; create value for the organization by providing insights and deliverables; and be an ambassador to the organization by exposing them to the talent, values, and expertise of the GSB. You will also have the opportunity to gain practical industry experience and exposure to the organization, its industry, and the space in which it operates; build relationships in the organization and industry; and gain an understanding of related career paths. Prerequisites: Some experience with statistical analysis and the R statistical package. Students with less experience will have an opportunity to catch up through tutorials provided through the course. Non-GSB students are expected to have an advanced understanding of tools and methods from data science and machine learning as well as a strong familiarity with R, Python, SQL, and other similar high-level programming languages. Cardinal Course certified by the Haas Center.

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.

Lecture/small group course exploring intersections of STEM, arts and humanities scholarship and practice that engages with, and generated by, exponential technologies. Our course explores the social ethical and artistic implications of artificial intelligence systems with an emphasis on aesthetics, civic society and racial justice, including scholarship on decolonial AI, indigenous AI, disability activism AI, feminist AI and the future of work for creative industries.

(Graduate students register for 220. COMM 120W is offered for 5 units, COMM 220 is offered for 4 units.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.

What does it mean to claim we are all cyborgs ¿ a hybrid of human and machine? Cyborgs have long captured the popular imagination of people around the world, appearing in various forms of media including films, books, and video games. In these instances, cyborgs are typically imagined as futuristic entities, portrayed as products of anticipated technological advancements yet to come. This course takes a different approach, employing the cyborg as a framework to understand human existence and experience across space and time, and explore the relationship between the body, culture, and technology. Drawing from anthropology and other relevant fields, this course emphasizes how humans and tools co-construct each other, blurring the boundaries between natural and artificial, human and machine. The first section of the course will present different theoretical perspectives for understanding human-machine interactions and relationships. In the second section, we will spend each week examining various types of technological embodiments. Specific technologies explored include smartphones and wearables; biohacking and prostheses; virtual reality; and artificial intelligence. And in the last section, we explore the tensions between narratives of technological pessimism and optimism, comparing the ways different individuals and communities perceive and evaluate emergent technologies' consequences for society, now and in the future. This course will provide students with the opportunity to conduct limited, small-scale ethnographic fieldwork on human-machine interactions. The data collected during these ethnographic exercises will inform the in-class presentation and final paper for the course.

Lecture/small group course exploring intersections of STEM, arts and humanities scholarship and practice that engages with, and generated by, exponential technologies. Our course explores the social ethical and artistic implications of artificial intelligence systems with an emphasis on aesthetics, civic society and racial justice, including scholarship on decolonial AI, indigenous AI, disability activism AI, feminist AI and the future of work for creative industries.

This course delves into the rapidly evolving landscape of contemporary art as it intertwines with the advancements in artificial intelligence. Students will explore how artists from Asia and its diaspora are harnessing the capabilities of AI to redefine artistic expressions, appropriate traditional media and aesthetics, and interrogate the boundaries between human creativity and machine intelligence. Drawing upon case studies, hands-on experiments, and critical discussions, students will gain a deeper understanding of the sociocultural implications of AI-infused artistry and its impact on society. This course contextualizes its content in a global narrative, discussing challenging themes and existential inquiries AI has evoked worldwide. Situating AI in the long history of machines, automation, and human engagement with technologies, the class encourages students to think critically about the "transformations" AI made to society. Central to our exploration will be the fundamental questions of what it means to be "human" in a world where machines can mimic, and even surpass, human cognition in certain domains. Drawing parallels between diverse cultures and technologies, we will dissect how human-machine collaborations shape our perceptions of reality, authenticity, emotion, and creativity. Through examination of both Asian philosophies and theories of posthumanism, students will reflect upon the broader philosophical implications of a world where artificial and human intelligence coexist, intertwining and reshaping the very fabric of society, culture, and personal experience. Instructor: Gerui Wang.

This course delves into the rapidly evolving landscape of contemporary art as it intertwines with the advancements in artificial intelligence. Students will explore how artists from Asia and its diaspora are harnessing the capabilities of AI to redefine artistic expressions, appropriate traditional media and aesthetics, and interrogate the boundaries between human creativity and machine intelligence. Drawing upon case studies, hands-on experiments, and critical discussions, students will gain a deeper understanding of the sociocultural implications of AI-infused artistry and its impact on society. This course contextualizes its content in a global narrative, discussing challenging themes and existential inquiries AI has evoked worldwide. Situating AI in the long history of machines, automation, and human engagement with technologies, the class encourages students to think critically about the "transformations" AI made to society. Central to our exploration will be the fundamental questions of what it means to be "human" in a world where machines can mimic, and even surpass, human cognition in certain domains. Drawing parallels between diverse cultures and technologies, we will dissect how human-machine collaborations shape our perceptions of reality, authenticity, emotion, and creativity. Through examination of both Asian philosophies and theories of posthumanism, students will reflect upon the broader philosophical implications of a world where artificial and human intelligence coexist, intertwining and reshaping the very fabric of society, culture, and personal experience. Instructor: Gerui Wang.

This course delves into the rapidly evolving landscape of contemporary art as it intertwines with the advancements in artificial intelligence. Students will explore how artists from Asia and its diaspora are harnessing the capabilities of AI to redefine artistic expressions, appropriate traditional media and aesthetics, and interrogate the boundaries between human creativity and machine intelligence. Drawing upon case studies, hands-on experiments, and critical discussions, students will gain a deeper understanding of the sociocultural implications of AI-infused artistry and its impact on society. This course contextualizes its content in a global narrative, discussing challenging themes and existential inquiries AI has evoked worldwide. Situating AI in the long history of machines, automation, and human engagement with technologies, the class encourages students to think critically about the "transformations" AI made to society. Central to our exploration will be the fundamental questions of what it means to be "human" in a world where machines can mimic, and even surpass, human cognition in certain domains. Drawing parallels between diverse cultures and technologies, we will dissect how human-machine collaborations shape our perceptions of reality, authenticity, emotion, and creativity. Through examination of both Asian philosophies and theories of posthumanism, students will reflect upon the broader philosophical implications of a world where artificial and human intelligence coexist, intertwining and reshaping the very fabric of society, culture, and personal experience. Instructor: Gerui Wang.

This project-based course delves into the cutting-edge of Generative Artificial Intelligence (AI) and its transformative applications in the healthcare domain. As technology continues to evolve, so does the potential for AI to revolutionize healthcare practices, from diagnostics to personalized treatment plans. Participants will learn about the latest advances in Generative AI, exploring state-of-the-art models and techniques tailored for healthcare challenges. Students will be introduced to Human-Centered Design methodology -- involving empathy and needs finding, prototyping and iteration. Class projects will focus on deployment of Generative AI using datasets such as population biobanks, and training models from these population-scale datasets. Key topics covered include the utilization of Generative AI in medical image synthesis, enhancing diagnostic capabilities, and using genomes and protein language models for variant effect prediction. The course also navigates the ethical considerations surrounding the use of generative models in healthcare, addressing issues of privacy, bias, and interpretability. Through a combination of theoretical insights and hands-on practical sessions, participants will gain a deep understanding of how Generative AI is reshaping the healthcare landscape, and how they could have a positive impact. Guest speakers from venture capital and industry with real-world examples will illustrate successful applications of generative models in medical imaging, drug discovery, and patient care, and discuss the challenges they see in translation from research to implementation.Students will need to visit the link and fill out an application before they get a reg. code to register for course. https://dschool.stanford.edu/classes/generative-ai-for-healthcare

Advancements of machine learning and AI into all areas of medicine are now a reality and they hold the potential to transform healthcare and open up a world of incredible promise for everyone. But we will never realize the potential for these technologies unless all stakeholders have basic competencies in both healthcare and machine learning concepts and principles - this will allow successful, responsible development and deployment of these systems into the healthcare domain. The focus of this course is on the key concepts and principles rather than programming or engineering implementation. Those with backgrounds in healthcare, health policy, healthcare system leadership, pharmaceutical, and clinicians as well as those with data science backgrounds who are new to healthcare applications will be empowered with the knowledge to responsibly and ethically evaluate, critically review, and even use these technologies in healthcare. We will cover machine learning approaches, medical use cases in depth, unique metrics to healthcare, important challenges and pitfalls, and best practices for designing, building, and evaluating machine learning in healthcare applications.

The widespread adoption of electronic health records (EHRs) has created a new source of big data namely, the record of routine clinical practice as a by-product of care. This class will teach you how to use EHRs and other patient data in conjunction with recent advances in artificial intelligence (AI) and evolving business models to improve healthcare. Upon completing this course, you should be able to: differentiate between and give examples of categories of care questions that AI can help answer, describe common healthcare data sources and their relative advantages, limitations, and biases in enabling care transformation, understand the challenges in using various kinds of clinical data to create fair algorithmic interventions, design an analysis of a clinical dataset, evaluate and criticize published research to separate hype from reality. Prerequisites: enrollment in the MCiM program. This course is designed to prepare you to pose and answer meaningful clinical questions using healthcare data as well as understand how AI can be brought into clinical use safely, ethically and cost-effectively.

Advancements of machine learning and AI into all areas of medicine are now a reality and they hold the potential to transform healthcare and open up a world of incredible promise for everyone. But we will never realize the potential for these technologies unless all stakeholders have basic competencies in both healthcare and machine learning concepts and principles - this will allow successful, responsible development and deployment of these systems into the healthcare domain. The focus of this course is on the key concepts and principles rather than programming or engineering implementation. Those with backgrounds in healthcare, health policy, healthcare system leadership, pharmaceutical, and clinicians as well as those with data science backgrounds who are new to healthcare applications will be empowered with the knowledge to responsibly and ethically evaluate, critically review, and even use these technologies in healthcare. We will cover machine learning approaches, medical use cases in depth, unique metrics to healthcare, important challenges and pitfalls, and best practices for designing, building, and evaluating machine learning in healthcare applications.

This interdisciplinary course explores the convergence of Web3 technologies, artificial intelligence (AI), and their transformative impact on the field of digital health. Students will examine the potential of decentralized systems, blockchain, and smart contracts to enhance health data privacy, security, and interoperability. Through case studies and hands-on projects, they will gain insights into AI-driven solutions for personalized healthcare, remote patient monitoring, medical image analysis, and clinical decision support. Additionally, students will critically analyze ethical and regulatory considerations in the context of Web3 and AI applications, fostering a deeper understanding of the future of digital health innovation.

Artificial Intelligence (AI) platforms are now widely available, and often require little training or technical expertise. This mini-course focuses on responsible development and use of AI in healthcare. Focus is on the critical analysis of AI systems, and the evolving policy and regulatory landscape. Week one covers modern AI capabilities, including computer vision, natural language processing, and reinforcement learning. Weeks two and three focus on assessing AI systems (including robustness, bias, privacy, and interpretability) and applications (including radiology, suicide prevention, and end-of-life care). Throughout this course students will develop and evaluate a hypothetical AI system. No programming experience is required.

This course delves into the fundamental principles of Deep Learning within the medical field, designed to offer a thorough yet accessible introduction to how these advanced models function, are developed, and are currently transforming healthcare practices. The curriculum covers key areas including neural network architecture, computer vision, natural language processing, convolutional neural networks, alongside classification and regression techniques, aiming to provide students with a solid foundation and intuitive insight into the workings of deep learning applications in medicine.In addition to the core content, participants will have the opportunity to engage with expert-led discussions on the latest advancements and future directions at the intersection of artificial intelligence and medicine.

The next wave of technological innovation and globalization will affect our countries, our societies, and ourselves. This interdisciplinary course provides an introduction to emerging, frontier technologies. Topics covered include artificial intelligence, additive manufacturing and advanced robotics, smart cities and urban mobility, telecommunications with 5G/6G, and other key emerging technologies in society. These technologies have vast potential to address the largest global challenges of the 21st century, ushering in a new era of progress and change.

The next wave of technological innovation and globalization will affect our countries, our societies, and ourselves. This interdisciplinary course provides an introduction to emerging, frontier technologies. Topics covered include artificial intelligence, additive manufacturing and advanced robotics, smart cities and urban mobility, telecommunications with 5G/6G, and other key emerging technologies in society. These technologies have vast potential to address the largest global challenges of the 21st century, ushering in a new era of progress and change.

This course focuses on a geometric learning approach to derive, test, and validate a wide range of artificial intelligence enabled models for engineering (meta-materials, composites, alloys) and natural materials (soil, rock, clay). Students will learn how to incorporate a wide range of data stored in graphs, manifold and point sets to train neural networks to design optimal experiments, embed high-dimensional data, enforce mechanics and physical principles, denoise data with geometry, and enable model-free simulations and discover causality of mechanisms that leads to the failures of materials. Prerequisite: Linear algebra and CEE291

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

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

(Graduate students register for 220. COMM 120W is offered for 5 units, COMM 220 is offered for 4 units.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.

Examination of recent developments in computing technology and platforms through the lenses of philosophy, public policy, social science, and engineering.  Course is organized around five main units: algorithmic decision-making and bias; data privacy and civil liberties; artificial intelligence and autonomous systems; the power of private computing platforms; and issues of diversity, equity, and inclusion in the technology sector.  Each unit considers the promise, perils, rights, and responsibilities at play in technological developments. Prerequisite: CS106A.

(Graduate students register for 220. COMM 120W is offered for 5 units, COMM 220 is offered for 4 units.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.

At the time of the European Enlightenment, the talk about the end of the world was taken to be a remnant of religious beliefs or the domain of insane people. The rational mind knew how to eliminate those obstacles to continuous scientific and technological progress. Today the situation has radically changed. Science and technology are the places where the end of the world is predicted. Apocalypse is looming. This seminar will explore various fields where this transformation is taking place. The following menaces will be considered: nuclear war, climate change, gene editing, synthetic biology, advanced artificial intelligence. Among the philosophies that will be summoned: the post-Heideggerian critique of technoscience (Hannah Arendt and Günther Anders), Hans Jonas' Ethics of the Future, the concept of existential risk (Nick Bostrom) and the instructor's concept of Enlightened Doomsaying. Appeal to literary works and films will be part of the program

At the time of the European Enlightenment, the talk about the end of the world was taken to be a remnant of religious beliefs or the domain of insane people. The rational mind knew how to eliminate those obstacles to continuous scientific and technological progress. Today the situation has radically changed. Science and technology are the places where the end of the world is predicted. Apocalypse is looming. This seminar will explore various fields where this transformation is taking place. The following menaces will be considered: nuclear war, climate change, gene editing, synthetic biology, advanced artificial intelligence. Among the philosophies that will be summoned: the post-Heideggerian critique of technoscience (Hannah Arendt and G¿nther Anders), Hans Jonas' Ethics of the Future, the concept of existential risk (Nick Bostrom) and the instructor's concept of Enlightened Doomsaying. Appeal to literary works and films will be part of the program.

Students will learn about and apply cutting-edge artificial intelligence (AI) techniques to real-world social good spaces (such as healthcare, government, and environmental conservation). The class will balance high-level machine learning techniques? from the fields of deep learning, natural language processing, computer vision, and reinforcement learning? with real world case studies, inviting students to think critically about technical and ethical issues in the development and deployment of AI. The course structure alternates between instructional lectures and bi-weekly guest speakers at the forefront of technology for social good. Students will be given the chance to engage in a flexible combination of AI model building, discussion, and individual exploration. Special topics may include: tech ethics, human-centered AI, AI safety, education technology, mental health applications, AI in policy, assistive robotics. Prerequisites: programming experience at the level of CS106A. Application required for enrollment: http://tinyurl.com/cs21si2024. We encourage students from all disciplines and backgrounds to apply!

Recent advances in Generative Artificial Intelligence place us at the threshold of a unique turning point in human history. For the first time, we face the prospect that we are not the only generally intelligent entities, and indeed that we may be less capable than our own creations. As this remarkable new technology continues to advance, 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 and unpredictable machines raises many complex and troubling questions. How will society respond as they 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 bias and align with 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? Are we merely a stepping-stone to a new form of non-biological life, or are we just getting better at building useful gadgets? 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 superintelligent machines. (Note: This course is pre-approved for credit at SLS and GSB. No programming or technical knowledge is required.)

Since their introduction in 2017, Transformers have taken the world by storm, and are finding applications all over Deep Learning. They have enabled the creation of powerful language models like ChatGPT and Gemini, and are a critical component in other ML applications such as text-to-image and video generation (e.g. DALL-E and Sora). They have significantly elevated the capabilities and impact of Artificial Intelligence. In CS 25, which has become one of Stanford's hottest and most exciting seminars, we examine the details of how Transformers work, and dive deep into the different kinds of Transformers and how they're applied in various fields and applications. We do this through a combination of instructor lectures, guest lectures, and classroom discussions. Potential topics include LLM architectures, creative use cases (e.g. art and music), healthcare/biology and neuroscience applications, robotics and RL (e.g. physical tasks, simulations, or games), and so forth. We invite folks at the forefront of Transformers research for talks, which will also be livestreamed and recorded through YouTube/Zoom. Past speakers have included Andrej Karpathy, Geoffrey Hinton, Jim Fan, Ashish Vaswani, and folks from OpenAI, Google DeepMind, NVIDIA, etc. Our class includes social events and networking sessions and has a popular reception within and outside Stanford, with around 1 million total views on YouTube. This is a 1-unit S/NC course, where attendance is the only homework! Please enroll on Axess or audit by joining the livestream (or in person if seats are available). Prerequisites: basic knowledge of Deep Learning (should understand attention) or CS224N/CS231N/CS230. Course website: https://web.stanford.edu/class/cs25/

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.

This course, designed for the non-computer scientist, will provide students with a solid foundation in the concepts and terminology behind computers, the Internet, and software development. It will give you better understanding and insight when working with technology. It will be particularly useful to future managers and PMs who will work with or who will lead programmers and other tech workers. But it will be useful to anyone who wants a better understanding of tech concepts and terms. We'll start by covering the foundations of Computer Hardware, the CPU, Operating Systems, Computer Networks, and the Web. We will then use our foundation to explore a variety of tech-related topics including Computer Security (how computers are attacked and defensive measures that can be taken); Cloud Computing, Artificial Intelligence, Software Development, Human-Computer Interaction, and Computer Theory.nnPrerequisites: Some programming experience at the High School level of above will help students get the most out of the class, but the course can be successfully completed with no prerequisites.

As we delegate more to artificial intelligence (AI) and integrate AI more in societal decision-making processes, we must find answers to how we can ensure AI systems are safe, follow ethical principles, and align with the creator's intent. Increasingly, many AI experts across academia and industry believe there is an urgent need for both technical and societal progress across AI alignment, ethics, and governance to understand and mitigate risks from increasingly capable AI systems and ensure that their contributions benefit society as a whole. Intro to AI Safety explores these questions in lectures with targeted readings, weekly quizzes, and group discussions. We are looking at the capabilities and limitations of current and future AI systems to understand why it is hard to ensure the reliability of existing AI systems. We will cover ongoing research efforts that tackle these questions, ranging from studies in reinforcement learning and computer vision to natural language processing. We will study work in interpretability, robustness, and governance of AI systems - to name a few. Basic knowledge about machine learning helps but is not required. View the full syllabus at http://tinyurl.com/42rb2sfv. Enrollment is by application only. Apply online at https://forms.gle/v8msM8nJ5FgeEHx1A by 9:00 PM PDT on Saturday, March 16, 2024.

Artificial Intelligence technology can and must be guided by human concerns. The course examines how mental models and user models of AI systems are formed, and how that leads to user expectations. This informs a set of design guidelines for building AI systems that are trustworthy, understandable, fair, and beneficial. The course covers the impact of AI systems on the economy and everyday life, and ethical issues of collecting data and running systems, including respect for persons, beneficence, fairness and justice.

Examination of recent developments in computing technology and platforms through the lenses of philosophy, public policy, social science, and engineering.  Course is organized around five main units: algorithmic decision-making and bias; data privacy and civil liberties; artificial intelligence and autonomous systems; the power of private computing platforms; and issues of diversity, equity, and inclusion in the technology sector.  Each unit considers the promise, perils, rights, and responsibilities at play in technological developments. Prerequisite: CS106A.

Businesses are built on ideas. Today's successful companies are those that most effectively generate, protect, and exploit new and valuable business ideas. Over the past 40 years, intellectual capital has emerged as the leading assets class. Ocean Tomo® estimates that over 80% of the market value of S&P 500 corporations now stems from intangible assets, which consist largely of intellectual property (IP) assets (e.g., the company and product names, logos and designs; patentable inventions; proprietary software and databases, and other proprietary product, manufacturing and marketing information). It is therefore vital for entrepreneurs and other business professionals to have a basic understanding of IP and how it is procured, protected, and exploited. This course provides an overview of the many and varied IP issues that students will confront during their careers. It is intended to be both informative and fun. Classes will cover the basics of patent, trademark, copyright, and trade secret law. Current issues in these areas will be covered, including patent protection for software and business methods, copyrightability of computer programs and APIs, issues relating to artificial intelligence, and the evolving protection for trademarks and trade secrets. Emerging issues concerning the federal Computer Fraud & Abuse Act (CFAA) and hacking will be covered, as will employment issues, including employee proprietary information and invention assignment agreements, work made for hire agreements, confidentiality agreements, non-compete agreements and other potential post-employment restrictions. Recent notable lawsuits will be discussed, including Apple v. Samsung (patents), Alice Corp. v. CLS Bank (software and business method patents), Oracle v. Google (software/APIs), Waymo v. Uber (civil and criminal trade secret theft), and hiQ v. LinkedIn (CFAA). IP law evolves constantly and new headline cases that arise during the term are added to the class discussion. Guest lectures typically include experts on open source software; legal and practical issues confronted by business founders; and, consulting and testifying as an expert in IP litigation. Although many of the issues discussed will involve technology disputes, the course also covers IP issues relating to art, music, photography, and literature. Classes are presented in an open discussion format and they are designed to be enjoyed by students of all backgrounds and areas of expertise.

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. Course same as LAW 7073

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.

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,n Markov decision processes, graphical models, machine learning, and logic. Prerequisites: CS 103 or CS 103B/X, CS 106B or CS 106X, CS 109, and CS 161 (algorithms, probability, and object-oriented programming in Python). We highly recommend comfort with these concepts before taking the course, as we will be building on them with little review.

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

This 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.

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? How do you create responsible, ethical, human centered experiences? Let us 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: CS147 or equivalent background in design thinking. In the event of a waitlist, acceptance to class based on an application provided on the first day of class.

Machine learning has become an indispensable tool for creating intelligent applications, accelerating scientific discoveries, and making better data-driven decisions. Yet, the automation and scaling of such tasks can have troubling negative societal impacts. Through practical case studies, you will identify issues of fairness, justice and truth in AI applications. You will then apply recent techniques to detect and mitigate such algorithmic biases, along with methods to provide more transparency and explainability to state-of-the-art ML models. Finally, you will derive fundamental formal results on the limits of such techniques, along with tradeoffs that must be made for their practical application. CS229 or equivalent classes or experience.

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.

This course will cover both classic findings and the latest research progress on the intersection of cognitive science, neuroscience, and artificial intelligence: How does the study of minds and machines inform and guide each other? What are the assumptions, representations, or learning mechanisms that are shared (across multiple disciplines, and what are different? How can we build a synergistic partnership between cognitive psychology, neuroscience, and artificial intelligence? We will focus on object perception and social cognition (human capacities, especially in infancy and early childhood) and the ways in which these capacities are formalized and reverse-engineered (computer vision, reinforcement learning). Through paper reading and review, discussion, and the final project, students will learn the common foundations shared behind neuroscience, cognitive science, and AI research and leverage them to develop their own research project in these areas. Recommended prerequisites: PSYCH 1, PSYCH 24/SYMSYS 1/CS 24, CS 221, CS 231N

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.

Language models serve as the cornerstone of modern natural language processing (NLP) applications and open up a new paradigm of having a single general purpose system address a range of downstream tasks. As the field of artificial intelligence (AI), machine learning (ML), and NLP continues to grow, possessing a deep understanding of language models becomes essential for scientists and engineers alike. This course is designed to provide students with a comprehensive understanding of language models by walking them through the entire process of developing their own. Drawing inspiration from operating systems courses that create an entire operating system from scratch, we will lead students through every aspect of language model creation, including data collection and cleansing for pre-training, transformer model construction, model training, and evaluation before deployment. Application required, apply at https://docs.google.com/forms/d/e/1FAIpQLSdW0HgT8MHzdM8cgapLWqX2ZPP1yHSX52R_r5JzF52poqXsHg/viewform

Artificial intelligence, specifically deep learning, stands out as one of the most transformative technologies of the past decade. AI can already outperform humans in several computer vision and natural language processing tasks. However, we still face some of the same limitations and obstacles that led to the demise of the first AI boom phase five decades ago. This research-oriented course will first review and reveal the limitations (e.g., iid assumption on training and testing data, voluminous training data requirement, and lacking interpretability) of some widely used AI algorithms, including convolutional neural networks (CNNs), transformers, reinforcement learning, and generative adversarial networks (GANs). To address these limitations, we will then explore topics including transfer learning for remedying data scarcity, knowledge-guided multimodal learning for improving data diversity, out of distribution generalization, attention mechanisms for enabling Interpretability, meta learning, and privacy-preserving training data management. The course will be taught through a combination of lecture and project sessions. Lectures on specialized AI applications (e.g., cancer/depression diagnosis and treatment, AI/VR for surgery, and health education) will feature guest speakers from academia and industry. Students will be assigned to work on an extensive project that is relevant to their fields of study (e.g., CS, Medicine, and Data Science). Projects may involve conducting literature surveys, formulating ideas, and implementing these ideas. Example project topics are but not limited to 1) knowledge guided GANs for improving training data diversity, 2) disease diagnosis via multimodal symptom checking, and 3) fake and biased news/information detection.

The last ten years has seen a watershed in the development of large-scale neural networks in artificial intelligence. At the same time, computational neuroscientists have discovered a surprisingly robust mapping between the internal components of these networks and real neural structures in the human brain. In this class we will discuss a panoply of examples of such "convergent man-machine evolution", including: feedforward models of sensory systems (vision, audition, somatosensation); recurrent neural networks for dynamics and motor control; integrated models of attention, memory, and navigation; transformer models of language areas; self-supervised models of learning; and deep RL models of decision and planning. We will also delve into the methods and metrics for comparing such models to real-world neural data, and address how unsolved open problems in AI (that you can work on!) will drive forward novel neural models. Some meaningful background in modern neural networks is highly advised (e.g. CS229, CS230, CS231n, CS234, CS236, CS 330), but formal preparation in cognitive science or neuroscience is not needed (we will provide this).

Artificial Intelligence (AI) has the potential to drive us towards a better future for all of humanity, but it also comes with significant risks and challenges. At its best, AI can help humans mitigate climate change, diagnose and treat diseases more effectively, enhance learning, and improve access to capital throughout the world. But it also has the potential to exacerbate human biases, destroy trust in information flow, displace entire industries, and amplify inequality throughout the world. We have arrived at a pivotal moment in the development of the technology in which we must establish a foundation for how we will design AI to capture the positive potential and mitigate the negative risks. To do this, building AI must be an inclusive, interactive, and introspective process guided by an affirmative vision of a beneficial AI-future. The goal of this interdisciplinary class is to bridge the gap between technological and societal objectives: How do we design AI to promote human well-being? The ultimate aim is to provide tools and frameworks to build a more harmonious human society based on cooperation toward a shared vision. Thus, students are trained in basic science to understand what brings about the conditions for human flourishing and will create meaningful AI technologies that aligns with the PACE framework: 1) has a clear and meaningful purpose, 2) augments human dignity and autonomy, 3) creates a feeling of inclusivity and collaboration, 4) creates shared prosperity and a sense of forward movement (excellence). Toward this end, students work in interdisciplinary teams on a final project and propose a solution that tackles a significant societal challenge by leveraging technology and frameworks on human thriving.

Most successful machine learning algorithms of today use either carefully curated, human-labeled datasets, or large amounts of experience aimed at achieving well-defined goals within specific environments. In contrast, people learn through their agency: they interact with their environments, exploring and building complex mental models of their world so as to be able to flexibly adapt to a wide variety of tasks. One crucial next direction in artificial intelligence is to create artificial agents that learn in this flexible and robust way. Students will read and take turns presenting current works, and they will produce a proposal of a feasible next research direction. Prerequisites: CS229, CS231N, CS234 (or equivalent).

Digital photography and basic image processing, convolutional neural networks for image processing, denoising, deconvolution, single pixel imaging, inverse problems in imaging, proximal gradient methods, introduction to wave optics, time-of-flight imaging, end-to-end optimization of optics and imaging processing. Emphasis is on applied image processing and solving inverse problems using classic algorithms, formal optimization, and modern artificial intelligence techniques. Students learn to apply material by implementing and investigating image processing algorithms in Python. Term project. Recommended: EE261, EE263, EE278.

How do we make music with artificial intelligence? What does it mean to do so (and is it even a good idea)? How might we design systems that balance machine automation and human interaction? More broadly, how do we want to live with our technologies? Are there - and ought there be - limits to using AI for art? (And what is Art, anyway?) In this "critical making" course, students will learn practical tools and techniques for AI-mediated music creation, engineer software systems incorporating AI, HCI and Music, and critically reflect on the aesthetic and ethical dimensions of technology.

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

Artificial intelligence is poised to make radical changes in healthcare, transforming areas such as diagnosis, genomics, surgical robotics, and drug discovery. In the coming years, artificial intelligence has the potential to lower healthcare costs, identify more effective treatments, and facilitate prevention and early detection of diseases. This class is a seminar series featuring prominent researchers, physicians, entrepreneurs, and venture capitalists, all sharing their thoughts on the future of healthcare. We highly encourage students of all backgrounds to enroll (no AI/healthcare background necessary). Speakers and more at https://tinyurl.com/cs522-stanford

Lecture/small group course exploring intersections of STEM, arts and humanities scholarship and practice that engages with, and generated by, exponential technologies. Our course explores the social ethical and artistic implications of artificial intelligence systems with an emphasis on aesthetics, civic society and racial justice, including scholarship on decolonial AI, indigenous AI, disability activism AI, feminist AI and the future of work for creative industries.

This project-based course delves into the cutting-edge of Generative Artificial Intelligence (AI) and its transformative applications in the healthcare domain. As technology continues to evolve, so does the potential for AI to revolutionize healthcare practices, from diagnostics to personalized treatment plans. Participants will learn about the latest advances in Generative AI, exploring state-of-the-art models and techniques tailored for healthcare challenges. Students will be introduced to Human-Centered Design methodology -- involving empathy and needs finding, prototyping and iteration. Class projects will focus on deployment of Generative AI using datasets such as population biobanks, and training models from these population-scale datasets. Key topics covered include the utilization of Generative AI in medical image synthesis, enhancing diagnostic capabilities, and using genomes and protein language models for variant effect prediction. The course also navigates the ethical considerations surrounding the use of generative models in healthcare, addressing issues of privacy, bias, and interpretability. Through a combination of theoretical insights and hands-on practical sessions, participants will gain a deep understanding of how Generative AI is reshaping the healthcare landscape, and how they could have a positive impact. Guest speakers from venture capital and industry with real-world examples will illustrate successful applications of generative models in medical imaging, drug discovery, and patient care, and discuss the challenges they see in translation from research to implementation.Students will need to visit the link and fill out an application before they get a reg. code to register for course. https://dschool.stanford.edu/classes/generative-ai-for-healthcare

This course delves into the rapidly evolving landscape of contemporary art as it intertwines with the advancements in artificial intelligence. Students will explore how artists from Asia and its diaspora are harnessing the capabilities of AI to redefine artistic expressions, appropriate traditional media and aesthetics, and interrogate the boundaries between human creativity and machine intelligence. Drawing upon case studies, hands-on experiments, and critical discussions, students will gain a deeper understanding of the sociocultural implications of AI-infused artistry and its impact on society. This course contextualizes its content in a global narrative, discussing challenging themes and existential inquiries AI has evoked worldwide. Situating AI in the long history of machines, automation, and human engagement with technologies, the class encourages students to think critically about the "transformations" AI made to society. Central to our exploration will be the fundamental questions of what it means to be "human" in a world where machines can mimic, and even surpass, human cognition in certain domains. Drawing parallels between diverse cultures and technologies, we will dissect how human-machine collaborations shape our perceptions of reality, authenticity, emotion, and creativity. Through examination of both Asian philosophies and theories of posthumanism, students will reflect upon the broader philosophical implications of a world where artificial and human intelligence coexist, intertwining and reshaping the very fabric of society, culture, and personal experience. Instructor: Gerui Wang.

This course will teach from a textbook written by a prominent economist with leading expertise in data science and machine learning. Students will be presented with statistical techniques to process big data for making business and economics decisions. Topics may include statistical uncertainty, regression, classification and factor analysis, experimentations and controls, frameworks for causal inference. We will also explore the relations between nonparametric econometrics, machine learning and artificial intelligence. The statistical package R will be used to illustrate concepts and theory. Prerequisites: Econ 102A or equivalent and Econ 102B.

This 2-credit course will examine vast changes driven by innovation both from within traditional finance and from new ecosystems in fintech among others. Breathtaking advances in financial theory, big data, machine learning, artificial intelligence, computational capability, IoT, payment systems (e.g. blockchain, crypto currencies), new products (e.g. robo advising, digital lending, crowd funding, smart contracts), new trading processes (e.g. algorithmic trading, AI-driven sales & trading), and new markets (e.g. ETFs, zero-cost products), among others are changing not only how financial and non-financial firms conduct business but also how investors and supervisors view the players and the markets. nWe will discuss critical strategy, policy and legal issues, some resolved and others yet to be (e.g. failed business models, cyber challenges, financial warfare, fake news, bias problems, legal standing for cryptos). The course will feature perspectives from guest speakers including top finance executives and Silicon Valley entrepreneurs on up-to-the-minute challenges and opportunities in finance. nWe will discuss slowing global growth against the backdrop of ongoing intervention and wildcards in the capital markets of the U.S., Europe, Hong Kong, Singapore, China, India, Japan, the Middle East and Latin America. We will look forward at strategic opportunities and power players appearing and being dethroned in the markets to discuss who is likely to thrive ¿ and not survive ¿ in the new global financial landscape. nnPrerequisites: If you are an undergraduate wishing to take this course, apply by completing the course application and provide a brief bio here: https://forms.gle/9BGYr8brdYwPS8Cu8

This 2-credit course will examine vast changes driven by innovation both from within traditional finance and from new ecosystems in fintech among others. Breathtaking advances in financial theory, big data, machine learning, artificial intelligence, computational capability, IoT, payment systems (e.g. blockchain, crypto currencies), new products (e.g. robo advising, digital lending, crowd funding, smart contracts), new trading processes (e.g. algorithmic trading, AI-driven sales & trading), and new markets (e.g. ETFs, zero-cost products), among others are changing not only how financial and non-financial firms conduct business but also how investors and supervisors view the players and the markets. nWe will discuss critical strategy, policy and legal issues, some resolved and others yet to be (e.g. failed business models, cyber challenges, financial warfare, fake news, bias problems, legal standing for cryptos). The course will feature perspectives from guest speakers including top finance executives and Silicon Valley entrepreneurs on up-to-the-minute challenges and opportunities in finance. nWe will discuss slowing global growth against the backdrop of ongoing intervention and wildcards in the capital markets of the U.S., Europe, Hong Kong, Singapore, China, India, Japan, the Middle East and Latin America. We will look forward at strategic opportunities and power players appearing and being dethroned in the markets to discuss who is likely to thrive ¿ and not survive ¿ in the new global financial landscape. nnPrerequisites: If you are an undergraduate wishing to take this course, apply by completing the course application and provide a brief bio here: https://forms.gle/9BGYr8brdYwPS8Cu8

This is a team-based course where students will work on a project to design and carry out an experiment intended to drive social impact in collaboration with a partner organization. The first few weeks will include lectures, hands-on tutorials, and labs designed to guide students through the process of experimental design in the digital context. Special topics include designing and selecting outcome measures that capture the impact of interventions; multi-stage experiments with applications to chatbots; learning how treatment effects vary across subgroups; adaptive experiments using bandits and artificial intelligence; and estimation of policies that target treatments based on subject characteristics. Experiments may be conducted on the customer base of a partner organization through their digital applications or on recruited subjects, such as subjects recruited to interactive chatbots. The teaching team will provide templates and technical assistance for designing and running the experiments. Students from different disciplinary backgrounds will be assigned roles to work in teams on the project. This course is part of the GSB's Action Learning Program, in which you will work on real business challenges under the guidance of faculty. In this intensive project-based course, you will learn research-validated foundations, tools, and practices; apply these tools and learnings to a real project for an external organization; create value for the organization by providing insights and deliverables; and be an ambassador to the organization by exposing them to the talent, values, and expertise of the GSB. You will also have the opportunity to gain practical industry experience and exposure to the organization, its industry, and the space in which it operates; build relationships in the organization and industry; and gain an understanding of related career paths. Prerequisites: Some experience with statistical analysis and the R statistical package. Students with less experience will have an opportunity to catch up through tutorials provided through the course. Non-GSB students are expected to have an advanced understanding of tools and methods from data science and machine learning as well as a strong familiarity with R, Python, SQL, and other similar high-level programming languages. Prerequisite: Econ 102B or equivalent. Students complete applications and enrollment will be with instructors consent. ECON 281 is for non-GSB students.

How do we design artificial systems that learn as we do early in life -- as "scientists in the crib" who explore and experiment with our surroundings? How do we make AI "curious" so that it explores without explicit external feedback? Topics draw from cognitive science (intuitive physics and psychology, developmental differences), computational theory (active learning, optimal experiment design), and AI practice (self-supervised learning, deep reinforcement learning). Students present readings and complete both an introductory computational project (e.g. train a neural network on a self-supervised task) and a deeper-dive project in either cognitive science (e.g. design a novel human subject experiment) or AI (e.g. implement and test a curiosity variant in an RL environment). Prerequisites: python familiarity and practical data science (e.g. sklearn or R).

Most successful machine learning algorithms of today use either carefully curated, human-labeled datasets, or large amounts of experience aimed at achieving well-defined goals within specific environments. In contrast, people learn through their agency: they interact with their environments, exploring and building complex mental models of their world so as to be able to flexibly adapt to a wide variety of tasks. One crucial next direction in artificial intelligence is to create artificial agents that learn in this flexible and robust way. Students will read and take turns presenting current works, and they will produce a proposal of a feasible next research direction. Prerequisites: CS229, CS231N, CS234 (or equivalent).

Is Artificial Intelligence really disrupting the EdTech industry or do its business impacts remain elusive? Despite booming interest around AI, concrete examples of how this is actually changing the way EdTech businesses operate remain scant. In this course, students will have the opportunity to engage with EdTech entrepreneurs, board members and venture capitalists around real business cases that illustrate the opportunities and challenges linked to incorporating AI into their product when it comes to driving sales, market share, and profitability.

Epistemology and education are each concerned with knowledge. Epistemology has both positive and normative aspects: it asks what knowledge is and why it is valued. Education is concerned with methods and conditions for conveying knowledge. This course will focus on current topics in epistemology with a view toward their implications for education and pedagogy. We will explore contemporary work in social epistemology and virtue epistemology; multicultural and feminist perspectives; epistemic development, and the significance of artificial intelligence and digital technology for theories of knowledge.

(Same as GSBGEN 591) The last few years have created significant educational challenges and opportunities, especially given the emergence of Artificial Intelligence (AI); there has never been a more pressing and urgent need in our history to foster entrepreneurship in education by leveraging new technologies. This course will help you develop the skills and strategies necessary to effectively create and evaluate educational services and education technology startups, much like educators, entrepreneurs, philanthropists, and venture capital investors do. Some questions we will discuss include: How do entrepreneurs, educators, and VCs evaluate and grow successful education and edtech startups? Why do most startups in edtech fail, and what are the critical ingredients for success, especially in today's challenging times? What does it take to get venture capital financing in edtech? Why now? Each week will feature a different entrepreneur as a guest speaker; these leaders hail from a variety of innovative for-profit and non-profit startups. As we hear from the speakers, we'll evaluate all aspects of their invention, particularly in the context of AI, distance learning and hybrid learning ecosystems. A fundamental question we'll explore in this course is how educators and technologists can better collaborate to leverage the scale and impact of technology to improve educational equity and access. This course will be taught in person; attendance at each session is required. The maximum capacity is 60 students. Juniors, Seniors and graduate students of all Stanford schools are welcome. Syllabus can be viewed here: https://monsalve.people.stanford.edu/courses-and-seminars

The time of robotics/AI is upon us. Within the next 10 to 20 years, many jobs will be replaced by robots/AI (artificial intelligence). This seminar features guest lecturers from industry and academia discussing the current state of the field of robotics/AI, preparing students for the rise of robotics/AI, and redesigning and reinventing education to adapt to the new era.

The subject of reinforcement learning addresses the design of agents that improve decisions over time while operating within complex and uncertain environments. This first course of the sequence restricts attention to the special case of bandit learning, which focuses on environments in which all consequences of an action are realized immediately. This course covers desired agent behaviors and principled scalable approaches to realizing such behavior. Topics include learning from trial and error, exploration, contextualization, generalization, and representation learning. Motivating examples will be drawn from recommendation systems, crowdsourcing, education, and generative artificial intelligence. Homework assignments primarily involve programming exercises carried out in Colab, using the python programming language and standard libraries for numerical computation and machine learning. Prerequisites: programming (e.g., CS106B), probability (e.g., MS&E 121, EE 178 or CS 109), machine learning (e.g., EE 104/ CME 107, MS&E 226 or CS 229).

Digital photography and basic image processing, convolutional neural networks for image processing, denoising, deconvolution, single pixel imaging, inverse problems in imaging, proximal gradient methods, introduction to wave optics, time-of-flight imaging, end-to-end optimization of optics and imaging processing. Emphasis is on applied image processing and solving inverse problems using classic algorithms, formal optimization, and modern artificial intelligence techniques. Students learn to apply material by implementing and investigating image processing algorithms in Python. Term project. Recommended: EE261, EE263, EE278.

This course treats reinforcement learning, which addresses the design of agents to operate in environments where actions induce delayed consequences. Concepts generalize those arising in bandit learning, which is covered in EE277/MS&E 237A. The course covers principled and scalable approaches to realizing a range of intelligent learning behaviors. Topics include planning, credit assignment, and learning of models, value functions, and policies. Motivating examples will be drawn from generative artificial intelligence, web services, control, and finance. Prerequisites: EE277.

EE 379B follows 379A and focuses on state-of-the-art data communication system theory and design, particularly systems with multiple users and dimensions (MIMO over parallel antennas or wires). The focus is on multi-user physical-layer channels like multiple access, broadcast, and interference channels, their capacity regions and designs to achieve any points therein. Examples include the latest cellular, Wi-Fi, wireline, cable, and other systems that stress fundamental transmission limits. Topics include system design, particularly physical-layer modulation/coding analysis and optimization through various artificial intelligence and optimization methods for multi-dimensional channels. Included are methods to design and adapt both transmitter and receiver to variable channels. Prerequisites: EE 278, linear algebra, EE 279 or EE 379A (or 379), or instructor consent. Instructor: Cioffi

EE 392AA focuses on state-of-the-art data communication system theory and design, particularly systems with multiple users and dimensions (MIMO over parallel antennas or wires). The focus is on multi-user physical-layer channels like multiple access, broadcast, and interference channels, their capacity regions and designs to achieve any points therein. Examples include the latest cellular, Wi-Fi, wireline, cable, and other systems that stress fundamental transmission limits. Topics include system design, particularly physical-layer modulation/coding analysis and optimization through various artificial intelligence optimization methods for multi-dimensional channels. Included are methods to design and adapt both transmitter and receiver to variable channels. Prerequisites: EE 278, linear algebra, EE 279 or EE 379, or instructor consent.

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.

Lecture/small group course exploring intersections of STEM, arts and humanities scholarship and practice that engages with, and generated by, exponential technologies. Our course explores the social ethical and artistic implications of artificial intelligence systems with an emphasis on aesthetics, civic society and racial justice, including scholarship on decolonial AI, indigenous AI, disability activism AI, feminist AI and the future of work for creative industries.

Examination of recent developments in computing technology and platforms through the lenses of philosophy, public policy, social science, and engineering.  Course is organized around five main units: algorithmic decision-making and bias; data privacy and civil liberties; artificial intelligence and autonomous systems; the power of private computing platforms; and issues of diversity, equity, and inclusion in the technology sector.  Each unit considers the promise, perils, rights, and responsibilities at play in technological developments. Prerequisite: CS106A.

Business activity has been an inalienable part of human life; as a result, there is always a pressing demand to address the ethical issues that arise in the business context and consider the ethical implications of their impact on people, society, and the world. This course introduces students to philosophical inquiry into ethical issues surrounding business and offers an opportunity to combine ethical theory and practice by engaging with essential and timely questions. Why is ethics important for business? Should corporations mainly be responsible for the interests of shareholders, or should they also take other factors into account? How should we understand the conflict of interests between employees and managers? What does the value of diversity imply in hiring and corporate culture? What marks the difference between ethical and unethical advertisements? What are some ethical concerns regarding emerging technologies and business models, such as attention economy, sharing economy, and artificial intelligence? Throughout this course, students will learn how philosophers have tried to address these questions and use them as frameworks to develop their views on the relationships between business, ethics, and society. This course meets the requirement for Ethical Reasoning (ER) and is open to students in all majors across the university. No prior knowledge of philosophy is required.

At the time of the European Enlightenment, the talk about the end of the world was taken to be a remnant of religious beliefs or the domain of insane people. The rational mind knew how to eliminate those obstacles to continuous scientific and technological progress. Today the situation has radically changed. Science and technology are the places where the end of the world is predicted. Apocalypse is looming. This seminar will explore various fields where this transformation is taking place. The following menaces will be considered: nuclear war, climate change, gene editing, synthetic biology, advanced artificial intelligence. Among the philosophies that will be summoned: the post-Heideggerian critique of technoscience (Hannah Arendt and G¿nther Anders), Hans Jonas' Ethics of the Future, the concept of existential risk (Nick Bostrom) and the instructor's concept of Enlightened Doomsaying. Appeal to literary works and films will be part of the program.

Is aging another disease that can be ultimately cured? We will look at the biology of aging, transitioning from the molecular level through to the cellular and systems level. What are age-related diseases, can lifespan be extended and are centenarians different? Additionally how can artificial intelligence create robotic and software assistants as we get older and is living forever is possible in any form ? Topics will include: molecular theories of aging, impact of oxidative stress, age-related diseases, artificial intelligence for longevity, and innovations to improve the quality of life as we age.

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 decade, and growth of healthcare expenditures continue to escalate at a rapid pace. Given the experiences of COVID, every single company must now at least understand how healthcare affects their business. This has triggered an abundance of opportunities for those interested in a career in healthcare management, investing, or entrepreneurialism. The Business of Healthcare-2022-23 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, as well as value-based care. 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.

The last few years have created significant educational challenges and opportunities, especially given the emergence of Artificial Intelligence (AI); there has never been a more pressing and urgent need in our history to foster entrepreneurship in education by leveraging new technologies. This course will help you develop the skills and strategies necessary to effectively create and evaluate educational services and education technology startups, much like educators, entrepreneurs, philanthropists, and venture capital investors do. Some questions we will discuss include: How do entrepreneurs, educators, and VCs evaluate and grow successful education and edtech startups? Why do most startups in edtech fail, and what are the critical ingredients for success, especially in today's challenging times? What does it take to get venture capital financing in edtech? Why now? Each week will feature a different entrepreneur as a guest speaker; these leaders hail from a variety of innovative for-profit and non-profit startups. As we hear from the speakers, we'll evaluate all aspects of their invention, particularly in the context of AI, distance learning and hybrid learning ecosystems. A fundamental question we'll explore in this course is how educators and technologists can better collaborate to leverage the scale and impact of technology to improve educational equity and access. This course will be taught in person; attendance at each session is required. The maximum capacity is 50 students. Juniors, Seniors and graduate students of all Stanford schools are welcome. Syllabus can be viewed here: https://monsalve.people.stanford.edu/courses-and-seminars

Artificial Intelligence (AI) has the potential to drive us towards a better future for all of humanity, but it also comes with significant risks and challenges. At its best, AI can help humans mitigate climate change, diagnose and treat diseases more effectively, enhance learning, and improve access to capital throughout the world. But it also has the potential to exacerbate human biases, destroy trust in information flow, displace entire industries, and amplify inequality throughout the world. We have arrived at a pivotal moment in the development of the technology in which we must establish a foundation for how we will design AI to capture the positive potential and mitigate the negative risks. To do this, we must be intentional about human-centered design because, 'Only once we have thought hard about what sort of future we want, will we be able to begin steering a course toward a desirable future. If we don't know what we want, we're unlikely to get it.' Thus, building AI must be an inclusive, interactive, and introspective process guided by an affirmative vision of a beneficial AI-future. The goal of this interdisciplinary class is to bridge the gap between technological and societal objectives: How do we design AI to promote human well-being? The ultimate aim is to provide tools and frameworks to build a more harmonious human society based on cooperation toward a shared vision. Thus, students are trained in basic science to understand what brings about the conditions for human flourishing and will create meaningful AI technologies that aligns with the PACE framework:·has a clear and meaningful purpose ·augments human dignity and autonomy ·creates a feeling of inclusivity and collaboration· creates shared prosperity and a sense of forward movement (excellence)Toward this end, students work in interdisciplinary teams on a final project and propose a solution that tackles a significant societal challenge by leveraging technology and frameworks on human thriving.

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.

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.

Recent advances in Generative Artificial Intelligence place us at the threshold of a unique turning point in human history. For the first time, we face the prospect that we are not the only generally intelligent entities, and indeed that we may be less capable than our own creations. As this remarkable new technology continues to advance, 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 and unpredictable machines raises many complex and troubling questions. How will society respond as they 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 bias and align with 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? Are we merely a stepping-stone to a new form of non-biological life, or are we just getting better at building useful gadgets? 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 superintelligent machines. (Note: This course is pre-approved for credit at SLS and GSB. No programming or technical knowledge is required.)

The introduction of artificial intelligence and autonomy into warfare will have profound and unforeseen consequences for national security and human society. This course prepares future policymakers and industry leaders for the complex debate surrounding the developmental, legal, ethical, and operational considerations of creating machines with the ability to apply lethal force. Students will gain a detailed and multi-perspective understanding of the associated opportunities and risk by lectures and discussions with expert guest speakers and a cohort of students from a variety of disciplines and backgrounds. There will be two days of class each week. One day is a lecture session and the other is a discussion session. The lecture session will occasionally have guest lecturers with recent real-world experience in the topic and is intended to expose students to current knowledge and perspectives. The following discussion session will be an opportunity to digest, and reflect on, the ideas from lecture, but will also be a chance for group work on graded assignments. No experience in the content is necessary. Varying perspectives are essential in any conversation on this topic. Undergrads also welcome.

This course will examine contemporary challenges and trade-offs for tech law and policy decision-making presented by false information online. Topics will include private sector content policy approaches, governmental regulatory responses (both U.S. and European), and contemporary litigation challenges in the context of election misinformation; medical misinformation; the spread of misinformation in armed conflict and situations of widespread human rights violations; climate misinformation; and the effects of misinformation on news integrity. In exploring these topics, we will also consider the implications of artificial intelligence for the challenge of managing online misinformation. Along with the faculty, guest speakers from academia and industry thought leaders will present on these topics, followed by a discussion. In addition, students will analyze real-world dilemmas confronting policymakers through practical case studies and will assume the role of a policymaker from either the private sector, the government, or a non-governmental organization as part of each class. Finally, this course will explore regulatory, policy, technological, and other solutions to enhance the integrity of the online information ecosystem and address the growing problem of false information online. Special Instructions: Up to five Law students, with the consent of the instructors, will have the option to write an independent research paper for Law School Research (R) credit. For students in this section (02), the research paper will replace the Final Policy Memo. All other elements used in grading will apply. Students taking the course for R credit can take the course for either 2 or 3 units, depending on the paper length. Elements used in grading: Attendance, Class Participation, Written Assignments; Final Policy Memo or Final Research Paper. CONSENT APPLICATION: To apply for this course, students must complete and submit a Consent Application Form available at https://forms.gle/phWuWfCJzCDNnCfR9. See Consent Application Form for instructions and submission deadline. Cross-listed with LAW 4053.

We will consider the developing legal and ethical problems of robots and artificial intelligence (AI), particularly self-directed and learning AIs. How do self-driving cars (or autonomous weapons systems) value human lives? How do we trade off accuracy against other values in predictive algorithms? At what point should we consider AIs autonomous entities with their own rights and responsibilities? And how can courts and legislatures set legal rules robots can understand and obey? This discussion seminar will meet four times during the Fall quarter. Meeting dates and times to be arranged by instructor. Elements used in grading: Attendance and class participation.

People like to think of technology as value neutral, as essentially objective tools that can be used for good or evil, particularly when questions of race and racial justice are involved. But the technologies we develop and deploy are frequently shaped by historical prejudices, biases, and inequalities and thus may be no less biased and racist than the underlying society in which they exist. In this discussion group, we will consider whether and how racial and other biases are present in a wide range of technologies, particularly artificial intelligence tools like risk assessment algorithms for bail, sentencing, predictive policing, and other decisions in the criminal justice system; algorithms for medical diagnosis and treatment decisions; AI that screens tenant or credit applications or job applications; facial recognition systems; surveillance tools; and many more. Building on these various case studies, we will seek to articulate a framework for recognizing both explicit and subtle anti-black and other biases in technology and understanding them in the broader context of racism and inequality in our society. Finally, we will discuss how these problems might be addressed, including by regulators, legislators, and courts as well as by significant changes in mindset and practical engagement by technology developers, companies, and educators. Class meets 4:30 PM-6:30 PM on Sept. 21, Oct. 5, Oct. 19, Nov. 2, 2023. Elements used in grading: Full attendance, reading of assigned materials, and active participation.

Scholars' and policy makers' thinking about political economy evolves as one understanding of the role of government ceases to reflect people's aspirations and views of social reality and is superseded by another. The laissez faire thinking of the 19th century was replaced by Keynesian management in response to the Great Depression. After WWII, Keynesian thinking was challenged, by 'neoliberalism'---a challenge that began to achieve success in the 1970s in response to perceived failures of government, high inflation, and other economic and social woes. By the mid-1980s, neoliberalism had become the new conventional wisdom, and liberals as well as conservatives accepted its core premises: that society consists of atomized individuals competing rationally to advance their own interests; that this behavior, in aggregate, produces good social outcomes and economic growth; that free markets are therefore the best way to allocate societal resources and government should intervene only to remedy market failures. Disagreements about what constitutes such failures and about corrective interventions persisted, but the general premises were widely embraced by policymakers and politicians. Today, that consensus is breaking down. Neoliberal policies and the particular systems of capitalism that accompany them have generated profound wealth inequality and have little to offer to address the perceived threats of globalization, climate change, and emerging technologies like artificial intelligence and robotics. The coronavirus pandemic has only served to highlight these and other problems.But what should come next? Our readings in the course will explore a variety of themes related to these issues. How did neoliberalism come to dominate political discourse? What are its core tenets? What kinds of challenges are being presented to them, and what might alternative approaches to political economy for the 21st century look like? This discussion seminar will meet four times during the Fall quarter. Meeting dates and times to be arranged by instructor. Elements used in grading: Attendance and class participation.

In the early 2010s, the Bay Area spawned a movement of thinkers obsessed with cognitive biases and "Bayesian reasoning," a way of using statistics and probability to inform beliefs. This group--that later came to be known as "rationalists"--insists on subjecting all spheres of life to scientific scrutiny and probabilistic reasoning. Rationalist takes are often contrarian and challenge mainstream ways of thinking about topics that include everything from science and medicine to philosophy and politics to the rise of artificial intelligence. Rationalist writings in blogs and books can be controversial. For example, some rationalists have discussed the genetics of depression or intelligence. Since 2015, however, rationalism has become a brand in Silicon Valley and hugely influential among pundits and executives. Members of the rationalist movement also overlapped with the growing community of "effective altruism," an effort to remake charity donations by focusing on, and calculating, the actual impact of every dollar on human lives. And the movement is full of quirks and, well, weirdness: a fear of AI armaggedon, polyamory, and group living near Berkeley is common. Politically, rationalists are mostly center-left, but they range from "communist to anarcho-capitalist." What brings them together, however, is that they are careful thinkers, quantitatively-oriented, and contrarians. In this seminar we will explore what the Rationalist movement is all about--what and how they think. We will take both a critical but also inquisitive view. What is to be gained from rationalists? Can their way of thinking improve political debates? We will read several books, including James Scott's "Seeing like a State," Julia Galef's "Scout Mindset," Philip Tetlock's "Superforcasters," blog posts from a website called "Less Wrong," and a series of blog-posts by the psychiatrist-cum-polymath Scott Alexander on drug arrests, crime spikes, and medical regulations, among others. Class meets 5:30 PM-7:30 PM on Sept. 28, Oct. 12, Oct. 26, Nov. 9.

In this discussion seminar we will examine the growth of 'artificial intelligence' (natural language processing, machine learning, and predictive analytics) from an interdisciplinary perspective. The principal objects of focus will be theories of innovation, the proliferation of algorithmic systems that subject human behavior and judgment to algorithmic control, the dependence of algorithmic systems on data surveillance, problems of error, 'leakage,' and bias (including racial bias), the promise of automation, the displacement of ordinary law and professional expertise by algorithmic code, and the tensions between liberal democratic concepts of the rule of law and the operation of code as law. No computer science training or previous training in critical theory is necessary. Class meets 6:00 PM-8:00 PM on Tuesdays (Specific dates are TBA, but we will most likely hold one session in September, two in October, and one in early November). Elements used in grading: Full attendance, reading of assigned materials, and active participation.

Atrocities continue to ravage our planet¿in Syria, Iraq, Myanmar/Burma, North Korea, Xinjiang China, and Yemen, to name a few. And yet, the international community is increasingly divided when it comes to advancing the project of international justice. Whereas earlier armed conflicts have inspired the establishment of international or hybrid tribunals (such as the International Criminal Tribunals for the Former Yugoslavia and the Special Court for Sierra Leone) or were referred to the International Criminal Court (such as the situations in Sudan and Libya), today¿s conflicts have been met with a pervasive tribunal fatigue and geopolitical impasses. The U.N. Security Council in particular has been hamstrung by the propensity of Russia, sometimes with China in tow, to veto (or threaten to veto) robust accountability proposals that have been put forward. As a result, advocates have looked to other organs and institutions within and without the United Nations to respond to the commission of international crimes. The General Assembly, the Human Rights Council, and even the Organization for the Prohibition of Chemical Weapons have thus all become engines of accountability, in part because they are not subject to the veto. In addition, civil society actors (such as the Commission on International Justice & Accountability and the Afghanistan Human Rights and Democracy Organization) have stepped up to undertake investigative functions that would ordinarily be performed by sovereign states or international prosecutors. This lab will support several of these institutions and organizations in their efforts to move justice processes forward. A number of civil society and non-governmental organizations are conducting thorough criminal investigations and forming detailed dossiers on potential perpetrators in an effort to jumpstart national proceedings, including those proceeding under extraordinary bases of jurisdiction, and to lay the groundwork for international prosecutions when¿and if¿an opening appears. Such organizations are also working to support multilateral and unilateral sanctions regimes, such as the United States¿ Global Magnitsky Act. New digital technologies and techniques¿such as big data analytics, artificial intelligence, digital forensics, and blockchain¿are enabling and supporting more searching open source investigations into these atrocities situations. With this lab, students will conduct both factual and legal research on behalf of partner organizations and participate in advocacy efforts to build a more robust international justice architecture. They will also contribute to efforts at other academic institutions to connect students to this work. Students enrolled in Section 01 may receive EL credit and students enrolled in Section 02 may receive PW credit. Elements used in grading: Attendance, Performance, 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.

This 2-credit course will examine vast changes driven by innovation both from within traditional finance and from new ecosystems in fintech among others. Breathtaking advances in financial theory, big data, machine learning, artificial intelligence, computational capability, IoT, payment systems (e.g. blockchain, crypto currencies), new products (e.g. robo advising, digital lending, crowd funding, smart contracts), new trading processes (e.g. algorithmic trading, AI-driven sales & trading), and new markets (e.g. ETFs, zero-cost products), among others are changing not only how financial and non-financial firms conduct business but also how investors and supervisors view the players and the markets. We will discuss critical strategy, policy and legal issues, some resolved and others yet to be (e.g. failed business models, cyber challenges, financial warfare, fake news, bias problems, legal standing for cryptos). The course will feature perspectives from guest speakers including top finance executives and Silicon Valley entrepreneurs on up-to-the-minute challenges and opportunities in finance. Elements used in grading: Class Participation, Attendance, Final Paper. Cross-listed with Economics (ECON 152/252), Public Policy (PUBLPOL 364), Statistics (STATS 238).

Copyright law is the engine that drives not only such traditional entertainment and information industries as music, book publishing, news and motion pictures, but also software, video games and other digital products. This course examines in depth all aspects of copyright law and practice, as well as the business and policy challenges and opportunities that the internet and other new technologies such as artificial intelligence present for the exploitation of copyrighted works. There are no prerequisites for this class. Elements used in grading: Final Exam (open book). A detailed description of how the class will be conducted, including reading assignments and modes of student participation, appears in the course syllabus on Canvas.

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.

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).

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.

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).

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).

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).

This course will examine contemporary challenges and trade-offs for tech law and policy decision-making presented by false information online. Topics will include private sector content policy approaches, governmental regulatory responses (both U.S. and European), and contemporary litigation challenges in the context of election misinformation; medical misinformation; the spread of misinformation in armed conflict and situations of widespread human rights violations; climate misinformation; and the effects of misinformation on news integrity. In exploring these topics, we will also consider the implications of artificial intelligence for the challenge of managing online misinformation. Along with the faculty, guest speakers from academia and industry thought leaders will present on these topics, followed by a discussion. In addition, students will analyze real-world dilemmas confronting policymakers through practical case studies and will assume the role of a policymaker from either the private sector, the government, or a non-governmental organization as part of each class. Finally, this course will explore regulatory, policy, technological, and other solutions to enhance the integrity of the online information ecosystem and address the growing problem of false information online. Special Instructions: Up to five Law students, with the consent of the instructors, will have the option to write an independent research paper for Law School Research (R) credit. For students in this section (02), the research paper will replace the Final Policy Memo. All other elements used in grading will apply. Students taking the course for R credit can take the course for either 2 or 3 units, depending on the paper length. Elements used in grading: Attendance, Class Participation, Written Assignments; Final Policy Memo or Final Research Paper. CONSENT APPLICATION: To apply for this course, students must complete and submit a Consent Application Form available at https://forms.gle/phWuWfCJzCDNnCfR9. See Consent Application Form for instructions and submission deadline. Cross-listed with International Policy (INTLPOL 363).

The growing tension between China and the rest of the world after the COVID-19 outbreak has made it more important than ever for businesses and their advisers to understand the legal framework in China and related compliance issues. Given their need to survive the current economic crisis, which will likely last for some time, foreign businesses--however guarded they are--must keep a watchful eye and be ready to seize opportunities arising from an economy that is too big to give up. Designed to prepare students for different opportunities that are likely to touch on China and its regulatory framework, this introductory course examines Chinese legal rules and principles in select business-related areas, including intellectual property, dispute resolution (e.g., arbitration and litigation), foreign investment law, antimonopoly law, environmental protection, and artificial intelligence. Drawing on her 25 years of experience handling issues related to U.S.--China relations, politics, and legal reforms, the instructor will, wherever appropriate, conduct discussions that help shed light on the role of China in the new world order. 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 or legislation 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.

The private practice of law has and will continue to undergo fundamental change. Technological, economic and business forces are placing extreme pressure on not only the traditional "Big Law" firm model but also role of in-house counsel. These forces will transform, eliminate or replace virtually every aspect of the current practices of firms and in-house legal departments. 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. This diversity in the delivery of legal services is dramatically altering the supply and demand characteristics of the legal economy and markets. The breadth of available technologies and options 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 course focuses on the technological, economic and business practices transforming the legal profession are identified and their impact on the traditional approaches to law 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 also examine 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.

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.

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.

This course introduces the mathematics knowledge involved in machine learning and artificial intelligence on two levels. In the first half of the quarter, we introduce math needed to understand machine learning practices, i.e. data, models, and algorithms. Topics include advanced notions in linear algebra, probability, statistics, and optimization theories. In the second half of the quarter, we focus on math used to study and analyze machine learning in scientific research. Topics include approximation theory, concentration inequalities, functional analysis, and optimization. This course focuses on the mathematical tools for studying machine learning, rather than implementations of machine learning methods. May be repeated for credit. NOTE: Undergraduates require instructor permission to enroll. Undergraduates interested in taking the course should contact the instructor for permission, providing information about relevant background such as performance in prior coursework, reading, etc.

The time of robotics/AI is upon us. Within the next 10 to 20 years, many jobs will be replaced by robots/AI (artificial intelligence). This seminar features guest lecturers from industry and academia discussing the current state of the field of robotics/AI, preparing students for the rise of robotics/AI, and redesigning and reinventing education to adapt to the new era.

High performance computing (HPC) is a field at the forefront of a range of high tech applications such as computational fluid dynamics, image processing, and financial risk management. With the demands of machine learning outstripping conventional computing, HPC is also at the forefront of artificial intelligence. This course will discuss how HPC clusters are used in large-scale problems in academia and industry alike. Students will learn about HPC clusters from the ground up and gain a solid foundation in parallel computer architectures, cluster operating systems, resource management, and containers. They will build their own systems via remote installation of physical hardware, configuration and optimization of a high-speed network, and integration of other technologies used throughout the HPC world. Classes consist of lectures reinforced with assignments on HPC systems located in a teaching laboratory, where discussion and collaboration will be key components of the course. Students will come away with a solid skill set in a field of computing that has broad implications for science and technology.

Get ready to explore the future of high-performance computing (HPC) and artificial intelligence (AI) and its influence on the way we live, work and learn, with the HPC-AI Summer Seminar Series by Stanford High Performance Computing Center and the HPC-AI Advisory Council. This 1-unit course is designed to provide practical insights and thought leadership and discuss topics of great societal importance. One such theme this year is the impact of Generative AI. You will have the opportunity to hear from renowned industry experts and influencers who are shaping our HPC-AI future and even ask them your questions. This engaging course is open to students with any academic background looking to upskill themselves. So don't hesitate, register now! No prerequisites required.

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

The next wave of technological innovation and globalization will affect our countries, our societies, and ourselves. This interdisciplinary course provides an introduction to emerging, frontier technologies. Topics covered include artificial intelligence, additive manufacturing and advanced robotics, smart cities and urban mobility, telecommunications with 5G/6G, and other key emerging technologies in society. These technologies have vast potential to address the largest global challenges of the 21st century, ushering in a new era of progress and change.

The face of healthcare is changing - innovative technologies, based on recent advances in artificial intelligence (AI), are radically altering how care is delivered. Startups are offering entirely new ways to diagnose, manage, treat, and operate. However, few ever reach the patient - those with much more than an idea and an algorithm; they have an intimate understanding of the healthcare landscape and the technical know-how to integrate AI solutions into the medical system successfully. 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 (https://t.ly/PpM2). We encourage students of all academic backgrounds to enroll; the only prerequisite is a strong passion for technology in healthcare. Course may be taken for one unit (lecture only, 11:30AM-12:30PM); or two units, which entails attending discussion section (12:30PM-1:20PM) and completing a project. The second half of each session will involve a discussion about team building, AI/Healthcare business ideas, and idea presentations. Grading criteria for 1-credit students will be based on attendance and weekly reports regarding the summary of each week's lectures (assignments). In addition to these criteria, 2-credit students will submit a business idea report and will deliver a pitch presentation in the last session in front of an invited panel.

The next wave of technological innovation and globalization will affect our countries, our societies, and ourselves. This interdisciplinary course provides an introduction to emerging, frontier technologies. Topics covered include artificial intelligence, additive manufacturing and advanced robotics, smart cities and urban mobility, telecommunications with 5G/6G, and other key emerging technologies in society. These technologies have vast potential to address the largest global challenges of the 21st century, ushering in a new era of progress and change.

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. Ironically, the COVID-19 pandemic became Exhibit 1 in the challenges the global health community faces in scaling innovative interventions. 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. A subset of these examples will be drawn from the current pandemic. Students will think critically to consider conditions under which technologies reach scale and have a positive impact on the global health field. Students will also have an opportunity to work on real-world projects, each of which will focus on the potential opportunity for health technology in a low-resource setting and consider approaches to ensure its impact at scale. This course will be taught by Dr. Anurag Mairal, Adjunct Professor of Medicine and the Director, Global Outreach Programs at Stanford Byers Center for Biodesign, Dr. Krista Donaldson, Director of Innovation to Impact at Stanford Byers Center for Biodesign, and Dr. Michele Barry, Senior Associate Dean for Global Health and Director of the Center for Innovation in Global Health. This course is open to undergraduate students, graduate students, and medical students. Students can take the course for two or three units. Students enrolling in the course for a third unit will work on the group project described above. Students enrolled in the class for three units will also have additional assignments, including an outline, presentation, and paper related to the group project. Cardinal Course certified by the Haas Center. Questions can be directed to Course Manager, Yosefa Gilon, ygilon@stanford.edu. Students must submit an application and be selected to receive an enrollment code. Application - https://forms.gle/WfToKFonCXWc6wZL7

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 come with the greater integration of AI into society.

Deliberative democracy is a political theory that holds that democracy should be based on informed, respectful, and inclusive public deliberation. In this SoCo course, we explore the relationship between artificial intelligence (AI) and deliberative democracy, and examine how AI can be used to support and enhance the democratic process through deliberative democracy. This course will focus on the use of AI in the Stanford Online Deliberation Platform (a collaboration between the Crowdsourced Democracy Team and Deliberative Democracy Lab, both at Stanford), the ethics of AI and democracy, and the potential for AI to support deliberation and participation. The course will also explore the challenges and limitations of using AI in a democratic context and the need for effective regulation and governance of AI.

With the recent developments in generative AI, the value in human creativity is increasingly a focus. Course draws from lessons from creativity in the arts to teach engineering students methods for creativity derived from musicians and artists. For our engineering students to learn creativity as a skill that is distinguishable and differentiated from generative artificial intelligence, this course explores, for instance the anatomy of a Hollywood pop song and the process behind the creation of globally impactful art. Students learn how to transfer these skills into the creation of engaging entrepreneurial solutions, for effective storytelling, and in developing their unique personal and professional stories. Students learn skills to unlock creative power which they will apply in the course as a design vehicle for a wide range of applications in engineering, self-expression, technological exploration, and the development of solutions that are centered around human connection and emotional engagement with the user. Sessions are practical, drawing tools and lessons from interdisciplinary individuals with wide-ranging careers. No artistic or entrepreneurial experience necessary.

The course will explore applied topics at the intersection of game theory, data science and artificial intelligence. The first part of the course will focus on computational approaches to solving complex games, with applications in developing successful algorithmic agents and explore recent successes in the games of Go, Stratego, Poker and Diplomacy. The lectures will provide the foundations of the methods that underlie these computational game theory methods (rooted in the theory of learning in games) and the assignments will explore implementation of simple variants. The second part of the course will explore the interplay between data science and mechanism design. We will overview topics such as optimizing auctions and mechanisms from data and explore applications in optimizing online auction markets. We will also overview methodologies for learning structural parameters in games and econometrics in games and how these can be used to analyze data that stem from strategic interactions, such as auction data. The third part of the course will explore topics that relate to deploying machine learning and data science pipelines in the presence of strategic behavior. Topics will include A/B testing in markets, with applications to A/B testing on digital platforms such as Uber, Amazon and other matching platforms.

The subject of reinforcement learning addresses the design of agents that improve decisions over time while operating within complex and uncertain environments. This first course of the sequence restricts attention to the special case of bandit learning, which focuses on environments in which all consequences of an action are realized immediately. This course covers desired agent behaviors and principled scalable approaches to realizing such behavior. Topics include learning from trial and error, exploration, contextualization, generalization, and representation learning. Motivating examples will be drawn from recommendation systems, crowdsourcing, education, and generative artificial intelligence. Homework assignments primarily involve programming exercises carried out in Colab, using the python programming language and standard libraries for numerical computation and machine learning. Prerequisites: programming (e.g., CS106B), probability (e.g., MS&E 121, EE 178 or CS 109), machine learning (e.g., EE 104/ CME 107, MS&E 226 or CS 229).

This course treats reinforcement learning, which addresses the design of agents to operate in environments where actions induce delayed consequences. Concepts generalize those arising in bandit learning, which is covered in EE277/MS&E 237A. The course covers principled and scalable approaches to realizing a range of intelligent learning behaviors. Topics include planning, credit assignment, and learning of models, value functions, and policies. Motivating examples will be drawn from generative artificial intelligence, web services, control, and finance. Prerequisites: EE277.

How do we make music with artificial intelligence? What does it mean to do so (and is it even a good idea)? How might we design systems that balance machine automation and human interaction? More broadly, how do we want to live with our technologies? Are there - and ought there be - limits to using AI for art? (And what is Art, anyway?) In this "critical making" course, students will learn practical tools and techniques for AI-mediated music creation, engineer software systems incorporating AI, HCI and Music, and critically reflect on the aesthetic and ethical dimensions of technology.

Dawn of the Machines: Behavioral Approaches to Artificial Intelligence. In Spring 2022, this seminar will explore how psychologists and micro-OB scholars can engage with the emergence of AI. Noting that the treatment of AI varies widely in behavioral research (as a social phenomenon, a target of judgment, a statistical tool, a model for humans, etc.), we will discuss recent papers and identify opportunities for research. No lecturing. Instruction is based entirely on reading and discussing published papers. Prerequisites: Requires no technical expertise in AI or programming, but familiarity with social psychological concepts and methods is needed. Enrollment in a PhD Program required. Cannot be audited or taken Pass/Fail.

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.

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.

AI has attracted significant attention in the last year, initially due to the release of ChatGPT, followed by backlash and efforts at creating effective regulation. Questions of ethics underlie every aspect of AI, beginning with the question of whether it is even coherent to speak of an intelligence other than humans. This course presents current ethical issues in the development and application of artificial intelligence through a series of recent case studies. We will spend the first part of the course studying major ethical frameworks (consequentialism, deontology, virtue ethics) and closely-linked research areas within AI and machine learning. In the second part of the course, we will apply these principles to case studies from major areas of debate in AI, with a focus on the translation of ethical principles into practical decisions.The first examples from AI we will cover are existential risks in the context of utilitarianism, the "hidden" labour force of AI in the context of deontology, and the problem of replacing humans in the context of virtue ethics. For the case studies, we will first study fairness and bias in the training and deployment of machine learning models. We will ask what it means for an AI system to be "fair", and how to regulate models which are not interpretable. This is followed by the problems of copyright and large scale training datasets for generative AI models, where we will ask what constitutes unfair use of existing material when it is only being used to train. We continue in a more hypothetical lens with a discussion of whether or not an AI system could be a moral agent or patient, and what rights a non-human intelligence might have. Finally, we conclude with the alignment problem, where we focus on the practical challenges of value alignment and the plausibility of finding a set of values which could be universally accepted. In the last week of the course, students apply their learnings with group presentations on published academic research, unpacking the ethical questions underlying technical developments

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.

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.

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.

This course is an introduction to philosophical issues raised by the growing field of artificial intelligence. What does the rise of increasingly complex artificial intelligence models (OpenAI; ChatGPT, AlphaGo, text-to-image generators) tell us about the nature of mind, rationality, and human creativity? What are ethical issues raised by the increasingly sophisticated use of algorithms in our daily lives - whether it be spotting credit card fraud, targeted advertising, curating our social media content, or prison sentencing? How do notions such as 'moral agency', 'practical reason', and 'responsibility' pertain, if at all, to applications of artificial intelligence, e.g., automated cars and weapons? What does the future of human work look like in light of developments in artificial intelligence? No philosophical background is presupposed. The aim of this class will be to help students engage with the philosophical issues raised by emerging technologies. Individual and group assignments will enable students to develop their critical skills in both written and discussion¿based work.

Examination of recent developments in computing technology and platforms through the lenses of philosophy, public policy, social science, and engineering.  Course is organized around five main units: algorithmic decision-making and bias; data privacy and civil liberties; artificial intelligence and autonomous systems; the power of private computing platforms; and issues of diversity, equity, and inclusion in the technology sector.  Each unit considers the promise, perils, rights, and responsibilities at play in technological developments. Prerequisite: CS106A.

(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.

(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.

Examination of recent developments in computing technology and platforms through the lenses of philosophy, public policy, social science, and engineering.  Course is organized around five main units: algorithmic decision-making and bias; data privacy and civil liberties; artificial intelligence and autonomous systems; the power of private computing platforms; and issues of diversity, equity, and inclusion in the technology sector.  Each unit considers the promise, perils, rights, and responsibilities at play in technological developments. Prerequisite: CS106A.

At the time of the European Enlightenment, the talk about the end of the world was taken to be a remnant of religious beliefs or the domain of insane people. The rational mind knew how to eliminate those obstacles to continuous scientific and technological progress. Today the situation has radically changed. Science and technology are the places where the end of the world is predicted. Apocalypse is looming. This seminar will explore various fields where this transformation is taking place. The following menaces will be considered: nuclear war, climate change, gene editing, synthetic biology, advanced artificial intelligence. Among the philosophies that will be summoned: the post-Heideggerian critique of technoscience (Hannah Arendt and G¿nther Anders), Hans Jonas' Ethics of the Future, the concept of existential risk (Nick Bostrom) and the instructor's concept of Enlightened Doomsaying. Appeal to literary works and films will be part of the program.

At the time of the European Enlightenment, the talk about the end of the world was taken to be a remnant of religious beliefs or the domain of insane people. The rational mind knew how to eliminate those obstacles to continuous scientific and technological progress. Today the situation has radically changed. Science and technology are the places where the end of the world is predicted. Apocalypse is looming. This seminar will explore various fields where this transformation is taking place. The following menaces will be considered: nuclear war, climate change, gene editing, synthetic biology, advanced artificial intelligence. Among the philosophies that will be summoned: the post-Heideggerian critique of technoscience (Hannah Arendt and G¿nther Anders), Hans Jonas' Ethics of the Future, the concept of existential risk (Nick Bostrom) and the instructor's concept of Enlightened Doomsaying. Appeal to literary works and films will be part of the program.

This course will explore the promise and limits of artificial intelligence (AI) through the lens of human cognition. Amid whispers of robots one day taking over the world, it is tempting to imagine that AI is (or soon will be) all-powerful. But few of us understand how AI works, which may lead us to overestimate its current (and even its future) capabilities. As it turns out, intelligence is complicated to build, and while computers outperform humans in many ways, they also fail to replicate key features of human intelligence at least for now. We will take a conceptual, non-technical approach (think: reading essays, not writing code). Drawing upon readings from philosophy of science, computer science, and cognitive psychology, we will examine the organizing principles of AI versus human intelligence, and the capabilities and limitations that follow. Computers vastly outperform humans in tasks that require large amounts of computational power (for example, solving complex mathematical equations). However, you may be surprised to learn the ways in which humans outperform computers. What is it about the human brain that allows us to understand and appreciate humor, sarcasm, and art? How do we manage to drive a car without hitting pedestrians? Is it only a matter of time before computers catch up to these abilities? Or are there differences of kind (rather than degree) that distinguish human intelligence from AI? Will robots always be constrained to the tasks that humans program them to do? Or could they, one day, take over the world? By the end of this course, you will be able to discuss the current capabilities, future potential, and fundamental limitations of AI. You may also arrive at a newfound appreciation for human intelligence, and for the power of your own brain.

Over 900 million individuals worldwide suffer from a mental health disorder. Human and financial costs associated with the management of individuals with mental health disorder are substantial and constitute a growing public health challenge. Yet there are presently no objective markers used to determine which individuals have a mental health disorder and predict the progression of the disorder. Furthermore, there are presently a limited number of effective treatments for mental health disorders, as well as considerable heterogeneity in treatment response. The lack of access to mental health care is yet another challenge in developed as well as developing countries. Newly available technologies such as Artificial Intelligence offer an unprecedented opportunity for developing solutions that address the aforementioned challenges and problems. In this interdisciplinary seminar, students will learn about (i) psychopathology, (ii) state-of-the-art in diagnosis and treatments of mental health disorders, (iii) unaddressed challenges and problems related to mental health, (iv) artificial intelligence and its potential through real-world examples, (v) recent real-world applications of artificial intelligence that address the challenges and problems related to mental health, and (vi) ethical issues associated with the application of artificial intelligence to mental health. Diverse viewpoints and a deeper understanding of these topics will be offered by a mix of hands-on educational sessions and panel discussions with psychiatrists, computer scientists, lawyers, and entrepreneurs. Students will also spend guided time working in small teams to develop innovative (artificial intelligence based) solutions to challenges/problems related to mental health.

The next wave of technological innovation and globalization will affect our countries, our societies, and ourselves. This interdisciplinary course provides an introduction to emerging, frontier technologies. Topics covered include artificial intelligence, additive manufacturing and advanced robotics, smart cities and urban mobility, telecommunications with 5G/6G, and other key emerging technologies in society. These technologies have vast potential to address the largest global challenges of the 21st century, ushering in a new era of progress and change.

The face of healthcare is changing - innovative technologies, based on recent advances in artificial intelligence (AI), are radically altering how care is delivered. Startups are offering entirely new ways to diagnose, manage, treat, and operate. However, few ever reach the patient - those with much more than an idea and an algorithm; they have an intimate understanding of the healthcare landscape and the technical know-how to integrate AI solutions into the medical system successfully. 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 (https://t.ly/PpM2). We encourage students of all academic backgrounds to enroll; the only prerequisite is a strong passion for technology in healthcare. Course may be taken for one unit (lecture only, 11:30AM-12:30PM); or two units, which entails attending discussion section (12:30PM-1:20PM) and completing a project. The second half of each session will involve a discussion about team building, AI/Healthcare business ideas, and idea presentations. Grading criteria for 1-credit students will be based on attendance and weekly reports regarding the summary of each week's lectures (assignments). In addition to these criteria, 2-credit students will submit a business idea report and will deliver a pitch presentation in the last session in front of an invited panel.

This application-only course is the optional lab portion of PSYC 240. This is a higher intensity experience for students who want to apply the knowledge they gain in 240 to building a solution to some of the most pressing problems in mental health. Students will collaborate in multidisciplinary cross-campus teams to design and launch a mental health intervention with the guidance of expert faculty and mentors and using the Stanford Brainstorm Framework for Mental Health Innovation. Interventions may range from diagnosing disease early using artificial intelligence, to designing a wearable device for addiction recovery management, to creating a school-based resiliency program, to establishing more just policies and transitions of care for the incarcerated population.

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.

This course will cover both classic findings and the latest research progress on the intersection of cognitive science, neuroscience, and artificial intelligence: How does the study of minds and machines inform and guide each other? What are the assumptions, representations, or learning mechanisms that are shared (across multiple disciplines, and what are different? How can we build a synergistic partnership between cognitive psychology, neuroscience, and artificial intelligence? We will focus on object perception and social cognition (human capacities, especially in infancy and early childhood) and the ways in which these capacities are formalized and reverse-engineered (computer vision, reinforcement learning). Through paper reading and review, discussion, and the final project, students will learn the common foundations shared behind neuroscience, cognitive science, and AI research and leverage them to develop their own research project in these areas. Recommended prerequisites: PSYCH 1, PSYCH 24/SYMSYS 1/CS 24, CS 221, CS 231N

How do we design artificial systems that learn as we do early in life -- as "scientists in the crib" who explore and experiment with our surroundings? How do we make AI "curious" so that it explores without explicit external feedback? Topics draw from cognitive science (intuitive physics and psychology, developmental differences), computational theory (active learning, optimal experiment design), and AI practice (self-supervised learning, deep reinforcement learning). Students present readings and complete both an introductory computational project (e.g. train a neural network on a self-supervised task) and a deeper-dive project in either cognitive science (e.g. design a novel human subject experiment) or AI (e.g. implement and test a curiosity variant in an RL environment). Prerequisites: python familiarity and practical data science (e.g. sklearn or R).

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. "Registration is limited to graduate students except by instructor consent. Please write to mcfrank@stanford.edu with a one-paragraph justification if you are an undergraduate interested in registering"

The last ten years has seen a watershed in the development of large-scale neural networks in artificial intelligence. At the same time, computational neuroscientists have discovered a surprisingly robust mapping between the internal components of these networks and real neural structures in the human brain. In this class we will discuss a panoply of examples of such "convergent man-machine evolution", including: feedforward models of sensory systems (vision, audition, somatosensation); recurrent neural networks for dynamics and motor control; integrated models of attention, memory, and navigation; transformer models of language areas; self-supervised models of learning; and deep RL models of decision and planning. We will also delve into the methods and metrics for comparing such models to real-world neural data, and address how unsolved open problems in AI (that you can work on!) will drive forward novel neural models. Some meaningful background in modern neural networks is highly advised (e.g. CS229, CS230, CS231n, CS234, CS236, CS 330), but formal preparation in cognitive science or neuroscience is not needed (we will provide this).

Traditional psychological experiments have been performed at small scale: with relatively few participants, reporting on relatively restricted sets of conditions, designed to adjudicate a small number of situation-specific hypotheses. However a confluence of important developments -- from the replication crisis, to the advent of online experimental platforms, to the flowering of modern artificial intelligence -- has made it increasingly evident that psychological science can (and probably should) be done at a larger scale, and in a more systematic fashion, than ever before. In this seminar, we will discuss the meaning and purpose of scale, as it pertains not only to experiment size, but also ecological realness and validity of stimuli and conditions, and the richness of measurement instruments. We will survey recent works of "psychology at scale" from a wide variety of domains, including perception, memory, decision making, language, and social interaction. We will also discuss and develop design principles and best practices for modern psychological experiments ? principles that enable both learning from, and contributing back to, psychology-adjacent areas such as artificial intelligence, neuroscience, and statistical science.

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.

This course will explore changing notions of truth in a world in which technology, global risks, and societal developments are blurring the boundaries of humanity and boring through traditional notions of nation states, institutions, and human identity. It will also offer a parallel journey to consider truth in your own life and how truth contributes to your own resilience in the face of life challenges. We will ask one over-arching question: Does truth matter anymore? If so, why and how? If not, why not? Either way, how does truth relate to ethical decision-making by individuals and institutions and to an ethical society? How does truth relate to a life well lived? Seven themes will organize our exploration of more specific topics: science and subjectivity; identity; memory; authenticity; artificial intelligence; imagination; and a life well-lived. Examples of topics to be explored include, among others: truth and technology (from ChatGPT to home devices); white supremacy; DNA testing and the 'identify as' movement, and identity; University history (Rhodes, Georgetown slavery, Yale Calhoun College, Junipero Serra...); the connections among truth, memory, and history; new questions in gender and racial identity; Chinese beautifying app Meitu and other social media "truth modifiers"; the sharing economy; the impact of AI and DNA testing sites on legal truth. We will consider how we determine and verify the truth; how we "do" truth; the role of truth in ethical decision-making; the importance of truth to effective ethical policy; and the relationship of the truth to a life well lived. An analytically rigorous short final paper in lieu of exam. This three-credit seminar may be taken as a stand-alone course or may accompany PUBLPOL 134 Ethics on the Edge to fulfill the Public Policy major ethics requirement. The course is open to undergraduate and graduate students. Undergraduates will not be at a disadvantage. Everyone will be challenged. Distinguished Career Institute Fellows are welcome and should contact Dr. Susan Liautaud directly at susanliautaud@googlemail.com. Students wishing to take the course who are unable to sign up within the enrollment limit should contact Dr. Susan Liautaud at susanliautaud@googlemail.com. *Public Policy majors taking the course to complete the core requirements and students taking the course for Ways credit must obtain a letter grade. Other students may take the course for a letter grade or C/NC. To satisfy a Ways requirement, this course must be taken for at least 3 units.

The introduction of artificial intelligence and autonomy into warfare will have profound and unforeseen consequences for national security and human society. This course prepares future policymakers and industry leaders for the complex debate surrounding the developmental, legal, ethical, and operational considerations of creating machines with the ability to apply lethal force. Students will gain a detailed and multi-perspective understanding of the associated opportunities and risk by lectures and discussions with expert guest speakers and a cohort of students from a variety of disciplines and backgrounds. There will be two days of class each week. One day is a lecture session and the other is a discussion session. The lecture session will occasionally have guest lecturers with recent real-world experience in the topic and is intended to expose students to current knowledge and perspectives. The following discussion session will be an opportunity to digest, and reflect on, the ideas from lecture, but will also be a chance for group work on graded assignments. No experience in the content is necessary. Varying perspectives are essential in any conversation on this topic. Undergrads also welcome.

Examination of recent developments in computing technology and platforms through the lenses of philosophy, public policy, social science, and engineering.  Course is organized around five main units: algorithmic decision-making and bias; data privacy and civil liberties; artificial intelligence and autonomous systems; the power of private computing platforms; and issues of diversity, equity, and inclusion in the technology sector.  Each unit considers the promise, perils, rights, and responsibilities at play in technological developments. Prerequisite: CS106A.

This course will explore changing notions of truth in a world in which technology, global risks, and societal developments are blurring the boundaries of humanity and boring through traditional notions of nation states, institutions, and human identity. It will also offer a parallel journey to consider truth in your own life and how truth contributes to your own resilience in the face of life challenges. We will ask one over-arching question: Does truth matter anymore? If so, why and how? If not, why not? Either way, how does truth relate to ethical decision-making by individuals and institutions and to an ethical society? How does truth relate to a life well lived? Seven themes will organize our exploration of more specific topics: science and subjectivity; identity; memory; authenticity; artificial intelligence; imagination; and a life well-lived. Examples of topics to be explored include, among others: truth and technology (from ChatGPT to home devices); white supremacy; DNA testing and the 'identify as' movement, and identity; University history (Rhodes, Georgetown slavery, Yale Calhoun College, Junipero Serra...); the connections among truth, memory, and history; new questions in gender and racial identity; Chinese beautifying app Meitu and other social media "truth modifiers"; the sharing economy; the impact of AI and DNA testing sites on legal truth. We will consider how we determine and verify the truth; how we "do" truth; the role of truth in ethical decision-making; the importance of truth to effective ethical policy; and the relationship of the truth to a life well lived. An analytically rigorous short final paper in lieu of exam. This three-credit seminar may be taken as a stand-alone course or may accompany PUBLPOL 134 Ethics on the Edge to fulfill the Public Policy major ethics requirement. The course is open to undergraduate and graduate students. Undergraduates will not be at a disadvantage. Everyone will be challenged. Distinguished Career Institute Fellows are welcome and should contact Dr. Susan Liautaud directly at susanliautaud@googlemail.com. Students wishing to take the course who are unable to sign up within the enrollment limit should contact Dr. Susan Liautaud at susanliautaud@googlemail.com. *Public Policy majors taking the course to complete the core requirements and students taking the course for Ways credit must obtain a letter grade. Other students may take the course for a letter grade or C/NC. To satisfy a Ways requirement, this course must be taken for at least 3 units.

The introduction of artificial intelligence and autonomy into warfare will have profound and unforeseen consequences for national security and human society. This course prepares future policymakers and industry leaders for the complex debate surrounding the developmental, legal, ethical, and operational considerations of creating machines with the ability to apply lethal force. Students will gain a detailed and multi-perspective understanding of the associated opportunities and risk by lectures and discussions with expert guest speakers and a cohort of students from a variety of disciplines and backgrounds. There will be two days of class each week. One day is a lecture session and the other is a discussion session. The lecture session will occasionally have guest lecturers with recent real-world experience in the topic and is intended to expose students to current knowledge and perspectives. The following discussion session will be an opportunity to digest, and reflect on, the ideas from lecture, but will also be a chance for group work on graded assignments. No experience in the content is necessary. Varying perspectives are essential in any conversation on this topic. Undergrads also welcome.

This 2-credit course will examine vast changes driven by innovation both from within traditional finance and from new ecosystems in fintech among others. Breathtaking advances in financial theory, big data, machine learning, artificial intelligence, computational capability, IoT, payment systems (e.g. blockchain, crypto currencies), new products (e.g. robo advising, digital lending, crowd funding, smart contracts), new trading processes (e.g. algorithmic trading, AI-driven sales & trading), and new markets (e.g. ETFs, zero-cost products), among others are changing not only how financial and non-financial firms conduct business but also how investors and supervisors view the players and the markets. nWe will discuss critical strategy, policy and legal issues, some resolved and others yet to be (e.g. failed business models, cyber challenges, financial warfare, fake news, bias problems, legal standing for cryptos). The course will feature perspectives from guest speakers including top finance executives and Silicon Valley entrepreneurs on up-to-the-minute challenges and opportunities in finance. nWe will discuss slowing global growth against the backdrop of ongoing intervention and wildcards in the capital markets of the U.S., Europe, Hong Kong, Singapore, China, India, Japan, the Middle East and Latin America. We will look forward at strategic opportunities and power players appearing and being dethroned in the markets to discuss who is likely to thrive ¿ and not survive ¿ in the new global financial landscape. nnPrerequisites: If you are an undergraduate wishing to take this course, apply by completing the course application and provide a brief bio here: https://forms.gle/9BGYr8brdYwPS8Cu8

PWR 1 courses focus on developing writing and revision strategies for rhetorical analysis and research-based arguments that draw on multiple sources. This course takes as its theme robots and AI. What is the impact of automation on particular kinds of work, including writing? What will human beings do with themselves when machines do more of the work? How will the introduction of increasingly satisfying robot or AI companions alter how we relate to each other in a variety of settings? A full course description and video can be found here: pwrcourses.stanford.edu/pwr1/pwr1sbb For the PWR course catalog please visit https://pwrcourses.stanford.edu/. Enrollment is handled by the PWR office.

As we delegate more to artificial intelligence (AI) and integrate AI more in societal decision-making processes, we must find answers to how we can ensure AI systems are safe, follow ethical principles, and align with the creator's intent. Increasingly, many AI experts across academia and industry believe there is an urgent need for both technical and societal progress across AI alignment, ethics, and governance to understand and mitigate risks from increasingly capable AI systems and ensure that their contributions benefit society as a whole. Intro to AI Safety explores these questions in lectures with targeted readings, weekly quizzes, and group discussions. We are looking at the capabilities and limitations of current and future AI systems to understand why it is hard to ensure the reliability of existing AI systems. We will cover ongoing research efforts that tackle these questions, ranging from studies in reinforcement learning and computer vision to natural language processing. We will study work in interpretability, robustness, and governance of AI systems - to name a few. Basic knowledge about machine learning helps but is not required. View the full syllabus at http://tinyurl.com/42rb2sfv. Enrollment is by application only. Apply online at https://forms.gle/v8msM8nJ5FgeEHx1A by 9:00 PM PDT on Saturday, March 16, 2024.

As we delegate more and more societal responsibilities to Artificial Intelligence, we raise pressing ethical questions about what will happen if these systems aren't aligned with our values. Increasingly many AI experts across academia and industry believe there is an urgent need for both technical and societal progress across AI alignment, ethics, and governance to understand and mitigate risks from advanced AI systems and ensure that their contributions benefit humanity and the world. Intro to AI Alignment explores these questions in lectures and small discussion-based environments led by student facilitators with targeted readings, weekly quizzes and group discussions, and a small final project. After recapping recent advancements in AI development, we will start by exploring two sides of the AI alignment problem that prevent us from building AI systems that reliably understand and follow human-compatible values. Next, we'll discuss current harms from AI as well as risks that future systems could pose and arguments for and against the importance of various AI safety work. Finally, we will learn about existing AI safety technical research, efforts to implement policy and governance measures that reduce AI risk, and how you can personally contribute to AI safety. Basic knowledge about machine learning helps but is not required. Enrollment is by application only. View the full syllabus and apply online at https://linktr.ee/stanfordaialignment by Sunday, Dec 17, 2023 at 9:00 PM PST.

How does Artificial Intelligence construct and reinforce social orders? How do human biases, values, and cultures shape AI? Starting with a descriptive introduction to different types and kinds of algorithms, we will first establish what AI is and what it does, on a technical level. With this shared framework in mind, we will then investigate how AI shapes, and is shaped by social interactions and imaginaries. Through scholarly works in the digital humanities, philosophy, internet studies, engineering, and popular culture, AI's influence on public perception, privacy, morality, popularity, equity, and justice will be critically examined. This course will feature guest lectures from controls engineers and others involved in using AI to protect science, technology, and society. Performance in this course will be evaluated through a data journalism project that asks students to peek behind the shiny User Interfaces of popular websites and identify how the code exerts power over various actors in the network.

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.

Recent advances in Generative Artificial Intelligence place us at the threshold of a unique turning point in human history. For the first time, we face the prospect that we are not the only generally intelligent entities, and indeed that we may be less capable than our own creations. As this remarkable new technology continues to advance, 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 and unpredictable machines raises many complex and troubling questions. How will society respond as they 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 bias and align with 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? Are we merely a stepping-stone to a new form of non-biological life, or are we just getting better at building useful gadgets? 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 superintelligent machines. (Note: This course is pre-approved for credit at SLS and GSB. No programming or technical knowledge is required.)

Lecture/small group course exploring intersections of STEM, arts and humanities scholarship and practice that engages with, and generated by, exponential technologies. Our course explores the social ethical and artistic implications of artificial intelligence systems with an emphasis on aesthetics, civic society and racial justice, including scholarship on decolonial AI, indigenous AI, disability activism AI, feminist AI and the future of work for creative industries.

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? How do you create responsible, ethical, human centered experiences? Let us 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: CS147 or equivalent background in design thinking. In the event of a waitlist, acceptance to class based on an application provided on the first day of class.

Do you really trust AI to understand your words and intentions? In this course, we will challenge the hype surrounding AI language processing and dive into what it truly gets right and wrong about language. You will learn not only about the staggering achievements of AI language models, but also about the limitations and biases that threaten their reliability. Through hands-on exercises and real-world case studies, you will explore how AI can struggle with understanding complex sentence structures, cultural nuances, and even basic language usage. You will also examine the ethical implications of relying on AI for language processing, including the potential for perpetuating existing biases and discrimination in society. This course will equip you with the critical thinking skills needed to navigate the complex and rapidly evolving world of AI language technology. Prior experience with linguistics and/or artificial intelligence is encouraged but not required.

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. "Registration is limited to graduate students except by instructor consent. Please write to mcfrank@stanford.edu with a one-paragraph justification if you are an undergraduate interested in registering"

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

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