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1 - 10 of 100 results for: CS

CS 22A: The Social & Economic Impact of Artificial Intelligence (INTLPOL 200, SYMSYS 122)

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? Wha more »
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.)
Terms: Win | Units: 1
Instructors: Kaplan, J. (PI)

CS 24: Minds and Machines (LINGUIST 35, PHIL 99, PSYCH 35, SYMSYS 1, SYMSYS 200)

(Formerly SYMSYS 100). An overview of the interdisciplinary study of cognition, information, communication, and language, with an emphasis on foundational issues: What are minds? What is computation? What are rationality and intelligence? Can we predict human behavior? Can computers be truly intelligent? How do people and technology interact, and how might they do so in the future? Lectures focus on how the methods of philosophy, mathematics, empirical research, and computational modeling are used to study minds and machines. Students must take this course before being approved to declare Symbolic Systems as a major. All students interested in studying Symbolic Systems are urged to take this course early in their student careers. The course material and presentation will be at an introductory level, without prerequisites. If you have any questions about the course, please email symsys1staff@gmail.com.
Terms: Aut, Win | Units: 4 | UG Reqs: GER:DB-SocSci, WAY-FR

CS 29N: Computational Decision Making

Although we make decisions every day, many people base their decisions on initial reactions or ""gut"" feelings. There are, however, powerful frameworks for making decisions more effectively based on computationally analyzing the choices available and their possible outcomes. In this course we give an introduction to some of these frameworks, including utility theory, decision analysis, and game theory. We also discuss why people sometimes make seemingly reasonable, yet irrational, decisions. We begin the class by presenting some of the basics of probability theory, which serves as the main mathematical foundation for the decision making frameworks we will subsequently present. Although we provide a mathematical/computational basis for the decision making frameworks we examine, we also seek to give intuitive (and sometimes counterintuitive) explanations for actual decision making behavior through in-class demonstrations. No prior experience with probability theory is needed (we'll cover what you need to know in class), but students should be comfortable with mathematical manipulation at the level of Math 20 or Math 41.
Terms: Win | Units: 3

CS 40: Cloud Infrastructure and Scalable Application Deployment

Trying to launch your next viral programming project and anticipating substantial user growth? This course will help you learn to implement your ideas in the cloud in a scalable, cost-effective manner. Topics will include cloud AI/ML pipelines, virtual machines, containers, basic networking, expressing infrastructure as code (IaC), data management, security and observability, and continuous integration and deployment (CI/CD). Through hands-on learning and practical examples, you'll learn to effectively deploy and manage cloud infrastructure. There is no out-of-pocket cost associated with this class and cloud credits will be provided for all students. Prerequisites: Programming maturity up to CS 107. Familiarity with the command line, version control, and basic development tools to the level of CS 45/ CS 104, in particular: Basic Unix command line utilities and administration; Editing code with a TUI editor such as vim, emacs, or nano; Using Git and GitHub for collaborative projects (i.e. branching and pull requests); Basic familiarity with package managers for languages and operating systems (e.g., pip, apt, homebrew); Prior web development or networking experience helpful but not required.
Terms: Win | Units: 3

CS 51: CS + Social Good Studio: Designing Social Impact Projects

Get real-world experience researching and developing your own social impact project! Students work in small teams to develop high-impact projects around problem domains provided by partner organizations, under the guidance and support of design/technical coaches from industry and non-profit domain experts. Main class components are workshops, community discussions, guest speakers and mentorship. Studio provides an outlet for students to create social change through CS while engaging in the full product development cycle on real-world projects. The class culminates in a showcase where students share their project ideas and Minimum Viable Product prototypes with stakeholders and the public. Application required; please see cs51.stanford.edu for more information.
Terms: Win | Units: 2
Instructors: Cain, J. (PI)

CS 83N: Playback Theater

Playback combines elements of theater, community work and storytelling. In a playback show, a group of actors and musicians create an improvised performance based on the audience's personal stories. A playback show brings about a powerful listening and sharing experience. During the course, we will tell, listen, play together, and train in playback techniques. We will write diaries to process our experience in the context of education and research. The course is aimed to strengthen listening abilities, creativity and the collaborative spirit, all integral parts of doing great science. In playback, as in research, we are always moving together, from the known, to the unknown, and back. There is limited enrollment for this class. Application is required.
Terms: Win | Units: 3 | UG Reqs: WAY-CE
Instructors: Reingold, O. (PI)

CS 100ACE: Problem-solving Lab for CS106A

Additional problem solving practice for the introductory CS course CS 106A. Sections are designed to allow students to acquire a deeper understanding of CS and its applications, work collaboratively, and develop a mastery of the material. Limited enrollment, permission of instructor required. Concurrent enrollment in CS 106A required.
Terms: Aut, Win, Spr | Units: 1
Instructors: King, E. (PI)

CS 100BACE: Problem-solving Lab for CS106B

Additional problem solving practice for the introductory CS course CS106B. Sections are designed to allow students to acquire a deeper understanding of CS and its applications, work collaboratively, and develop a mastery of the material. Limited enrollment, permission of instructor required. Concurrent enrollment in CS 106B required.
Terms: Aut, Win, Spr | Units: 1

CS 103: Mathematical Foundations of Computing

What are the theoretical limits of computing power? What problems can be solved with computers? Which ones cannot? And how can we reason about the answers to these questions with mathematical certainty? This course explores the answers to these questions and serves as an introduction to discrete mathematics, computability theory, and complexity theory. At the completion of the course, students will feel comfortable writing mathematical proofs, reasoning about discrete structures, reading and writing statements in first-order logic, and working with mathematical models of computing devices. Throughout the course, students will gain exposure to some of the most exciting mathematical and philosophical ideas of the late nineteenth and twentieth centuries. Specific topics covered include formal mathematical proofwriting, propositional and first-order logic, set theory, binary relations, functions (injections, surjections, and bijections), cardinality, basic graph theory, the pigeonhole prin more »
What are the theoretical limits of computing power? What problems can be solved with computers? Which ones cannot? And how can we reason about the answers to these questions with mathematical certainty? This course explores the answers to these questions and serves as an introduction to discrete mathematics, computability theory, and complexity theory. At the completion of the course, students will feel comfortable writing mathematical proofs, reasoning about discrete structures, reading and writing statements in first-order logic, and working with mathematical models of computing devices. Throughout the course, students will gain exposure to some of the most exciting mathematical and philosophical ideas of the late nineteenth and twentieth centuries. Specific topics covered include formal mathematical proofwriting, propositional and first-order logic, set theory, binary relations, functions (injections, surjections, and bijections), cardinality, basic graph theory, the pigeonhole principle, mathematical induction, finite automata, regular expressions, the Myhill-Nerode theorem, context-free grammars, Turing machines, decidable and recognizable languages, self-reference and undecidability, verifiers, and the P versus NP question. Students with significant proofwriting experience are encouraged to instead take CS154. Students interested in extra practice and support with the course are encouraged to concurrently enroll in CS103A. Prerequisite: CS106B or equivalent. CS106B may be taken concurrently with CS103.
Terms: Aut, Win, Spr, Sum | Units: 3-5 | UG Reqs: GER:DB-Math, WAY-FR

CS 103ACE: Mathematical Problem-solving Strategies

Problem solving strategies and techniques in discrete mathematics and computer science. Additional problem solving practice for CS103. In-class participation required. Prerequisite: consent of instructor. Co-requisite: CS103.
Terms: Aut, Win, Spr | Units: 1
Instructors: Guan, R. (PI)
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