Courses

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 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.

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.

This course seeks to prepare future policymakers and industry leaders for the complex debate surrounding the development and employment of automated and autonomous weapons for warfare. Exploring the developmental, legal, ethical, and operational considerations of introducing automated and autonomous systems into warfare, the course will seek to create a broad understanding of strategic opportunities and risk. nnThe course will begin by examining the principles and functions of war to provide a baseline of why warfighters pursue automation and autonomy, and we will examine historical examples of how militaries have integrated these concepts in a variety of contexts. The course will then examine the relevant technologies - both those immediately available and those that push the future technological frontier. Those include computers, robotics, and artificial intelligence as well as the processes for turning technology into warfighting capability. In the final phase we will review applicable legal and policy regimes, and consider the ethical dilemmas created by the introduction of new automated and autonomous capability from military, governmental, commercial, and activist perspectives. nnA secondary objective of the course is to prepare students with a practical policymaking toolkit for analyzing and developing policy for a complex issue, with applicability beyond the issue of autonomy and warfare. This course encourages students to digest the information about autonomy and warfare, and to think creatively and practically about how policymakers and private sector leaders should address them, through a series of simulations, briefings and written exercises, and exercises intended to represent the actual policymaking progress.nnThis course does not advocate any policy position but instead seeks to foster a more complete understanding of why and how automated and autonomous systems are being integrated into warfare as well as a circumspect review of the advantages, risks, and opportunities. This is a seminar course with limited enrollment. Each class session will be divided into lecture/discussion format where each lecture will set the stage for a vigorous guided discussion. Students will be required to explore policy options and debate the advantages and risk inherent in each option. The course will also leverage industry, technology, policy, and operational experts to provide differing viewpoints and specialized knowledge and experience.

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 seeks to prepare future policymakers and industry leaders for the complex debate surrounding the development and employment of automated and autonomous weapons for warfare. Exploring the developmental, legal, ethical, and operational considerations of introducing automated and autonomous systems into warfare, the course will seek to create a broad understanding of strategic opportunities and risk. nnThe course will begin by examining the principles and functions of war to provide a baseline of why warfighters pursue automation and autonomy, and we will examine historical examples of how militaries have integrated these concepts in a variety of contexts. The course will then examine the relevant technologies - both those immediately available and those that push the future technological frontier. Those include computers, robotics, and artificial intelligence as well as the processes for turning technology into warfighting capability. In the final phase we will review applicable legal and policy regimes, and consider the ethical dilemmas created by the introduction of new automated and autonomous capability from military, governmental, commercial, and activist perspectives. nnA secondary objective of the course is to prepare students with a practical policymaking toolkit for analyzing and developing policy for a complex issue, with applicability beyond the issue of autonomy and warfare. This course encourages students to digest the information about autonomy and warfare, and to think creatively and practically about how policymakers and private sector leaders should address them, through a series of simulations, briefings and written exercises, and exercises intended to represent the actual policymaking progress.nnThis course does not advocate any policy position but instead seeks to foster a more complete understanding of why and how automated and autonomous systems are being integrated into warfare as well as a circumspect review of the advantages, risks, and opportunities. This is a seminar course with limited enrollment. Each class session will be divided into lecture/discussion format where each lecture will set the stage for a vigorous guided discussion. Students will be required to explore policy options and debate the advantages and risk inherent in each option. The course will also leverage industry, technology, policy, and operational experts to provide differing viewpoints and specialized knowledge and experience.

Social and political dimensions of the regulation of new and innovative forms of science and technology. Examines how science and technology both shape politics and are shaped by politics, with particular attention to the political system of California. Considers the role of scientific advisors in government and society; dilemmas of expert authority and bias; relations between experts and non-experts; techniques for improving the practice of science and technology policy. Presents case studies of the implications of emerging technologies such as bioengineering and biosecurity; cybersecurity and human rights online; regulation of social media; bias and artificial intelligence; and decentralized finance.

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.

How do we use mathematical thinking to design better machine learning methods? This course focuses on developing mathematical tools for answering these questions. This course will cover fundamental concepts and principled algorithms in machine learning, particularly those that are related to modern large-scale non-linear models. The topics include concentration inequalities, generalization bounds via uniform convergence, non-convex optimization, implicit regularization effect in deep learning, and unsupervised learning and domain adaptations. nnPrerequisites: linear algebra ( MATH 51 or CS 205), probability theory (STATS 116, MATH 151 or CS 109), and machine learning ( CS 229, STATS 229, or STATS 315A).

This course describes recent advances in neuroscience-inspired optimization algorithms that infuse natural intelligence into machine (or artificial) intelligence, together with adaptive stochastic approximation and multi-armed bandits, introduced by Herbert Robbins and the instructor in the 1980s and underwent continual development. Applications of these methods and algorithms in biomedicine and healthcare, economics and financial technology, imaging and robotics are also presented. Prerequisite: STATS/CS229 or equivalent.

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

Lecture/studio course exploring arts and humanities scholarship and practice engaging with, and generated by, emerging emerging and exponential technologies. Our course will explore intersections of art and artificial intelligence with an emphasis on social impact and racial justice. Open to all undergraduates.

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.