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11 - 20 of 76 results for: artificial intelligence

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

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

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

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

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

Recent advances in computing may place us at the threshold of a unique turning point in human history. Soon we are likely to entrust management of our environment, economy, security, infrastructure, food production, healthcare, and to a large degree even our personal activities, to artificially intelligent computer systems. The prospect of "turning over the keys" to increasingly autonomous systems raises many complex and troubling questions. How will society respond as versatile robots and machine-learning systems displace an ever-expanding spectrum of blue- and white-collar workers? Will the benefits of this technological revolution be broadly distributed or accrue to a lucky few? How can we ensure that these systems are free of algorithmic bias and respect human ethical principles? What role will they play in our system of justice and the practice of law? How will they be used or abused in democratic societies and autocratic regimes? Will they alter the geopolitical balance of power, and change the nature of warfare? The goal of CS22a is to equip students with the intellectual tools, ethical foundation, and psychological framework to successfully navigate the coming age of intelligent machines.
Terms: Win | Units: 1 | Grading: Satisfactory/No Credit
Instructors: Kaplan, J. (PI)

CS 28: Artificial Intelligence, Entrepreneurship and Society in the 21st Century and Beyond

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

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

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

CS 202: Law for Computer Science Professionals

An overview of intellectual property law as it relates to computer science and other disciplines, including discussions of patents, trademarks, copyrights, trade secrets, computer fraud litigation and interesting historical tidbits. Emphasis on topics of current interest such as software and business method patents, copyright issues concerning software, music, art and artificial intelligence, and current disputes of note including the recently-settled Waymo v. Uber lawsuit and the ongoing Oracle v. Google, Apple v. Samsung and hiQ v. LinkedIn sagas. Guest lectures typically have covered open source and the free software movement, practical issues for business founders (including corporate formation issues and non-disclosure, non-compete, work-made-for-hire and license agreements), and other pertinent topics. Classes are presented in an open discussion format broadly directed to students with both technical and non-technical backgrounds.
Terms: Aut | Units: 1 | Grading: Satisfactory/No Credit
Instructors: Hansen, D. (PI)

CS 208E: Great Ideas in Computer Science

Great Ideas in Computer Science Covers the intellectual tradition of computer science emphasizing ideas that reflect the most important milestones in the history of the discipline. Topics include programming and problem solving; implementing computation in hardware; algorithmic efficiency; the theoretical limits of computation; cryptography and security; computer networks; machine learning; and the philosophy behind artificial intelligence. Readings will include classic papers along with additional explanatory material.
Terms: Aut | Units: 3 | Grading: Letter (ABCD/NP)
Instructors: Gregg, C. (PI)

CS 221: Artificial Intelligence: Principles and Techniques

Artificial intelligence (AI) has had a huge impact in many areas, including medical diagnosis, speech recognition, robotics, web search, advertising, and scheduling. This course focuses on the foundational concepts that drive these applications. In short, AI is the mathematics of making good decisions given incomplete information (hence the need for probability) and limited computation (hence the need for algorithms). Specific topics include search, constraint satisfaction, game playing, Markov decision processes, graphical models, machine learning, and logic. Prerequisites: CS 103 or CS 103B/X, CS 106B or CS 106X, CS 107, and CS 109 (algorithms, probability, and programming experience).
Terms: Aut, Spr | Units: 3-4 | Grading: Letter or Credit/No Credit

CS 227B: General Game Playing

A general game playing system accepts a formal description of a game to play it without human intervention or algorithms designed for specific games. Hands-on introduction to these systems and artificial intelligence techniques such as knowledge representation, reasoning, learning, and rational behavior. Students create GGP systems to compete with each other and in external competitions. Prerequisite: programming experience. Recommended: 103 or equivalent.
Terms: Spr | Units: 3 | Grading: Letter or Credit/No Credit

CS 231C: Computer Vision and Image Analysis of Art

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