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1 - 10 of 21 results for: SYMSYS ; Currently searching winter courses. You can expand your search to include all quarters

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SYMSYS 1: Minds and Machines (CS 24, LINGUIST 35, PHIL 99, PSYCH 35, 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. Note that this is a hybrid course. Students should plan to enroll by the first day of the quarter and check their Stanford email account for instructions on how to access the course material. If you have any questions about the course, please email symsys1staff@gmail.com.
Terms: Aut, Win, Spr, Sum | Units: 4 | UG Reqs: WAY-FR, GER:DB-SocSci

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

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)

SYMSYS 151D: Ethical STEM: Race, Justice, and Embodied Practice (AFRICAAM 151, ARTSINST 151C, CSRE 151C, ETHICSOC 151C, STS 51D, TAPS 151D)

What role do science and technology play in the creation of a just society? How do we confront and redress the impact of racism and bias within the history, theory, and practice of these disciplines? This course invites students to grapple with the complex intersections between race, inequality, justice, and the STEM fields. We orient to these questions from an artistically-informed position, asking how we can rally the embodied practices of artists to address how we think, make, and respond to each other. Combining readings from the history of science, technology, and medicine, ethics and pedagogy, as well as the fine and performing arts, we will embark together on understanding how our STEM practices have emerged, how we participate today, and what we can imagine for them in the future. The course will involve workshops, field trips (as possible), and invited guests. All students, from any discipline, field, interest, and background, are welcome! This course does build upon the STS 51 series from 2020-21, though it is not a prerequisite for this course. Please contact the professor if you have any questions!
Terms: Win | Units: 4-5
Instructors: Robinson, A. (PI)

SYMSYS 167D: Philosophy of Neuroscience (PHIL 167D, PHIL 267D)

How can we explain the mind? With approaches ranging from computational models to cellular-level characterizations of neural responses to the characterization of behavior, neuroscience aims to explain how we see, think, decide, and even feel. While these approaches have been highly successful in answering some kinds of questions, they have resulted in surprisingly little progress in others. We'll look at the relationships between the neuroscientific enterprise, philosophical investigations of the nature of the mind, and our everyday experiences as creatures with minds. Prerequisite: PHIL 80. (Not open to freshmen.) By application. This course is not offered in 24-25.
Terms: Win | Units: 4

SYMSYS 190: Senior Honors Tutorial

Under the supervision of their faculty honors adviser, students work on their senior honors project. May be repeated for credit.
Terms: Aut, Win, Spr, Sum | Units: 1-5 | Repeatable for credit

SYMSYS 195B: Design for Behavior Change (CS 247B)

Over the last decade, tech companies have invested in shaping user behavior, sometimes for altruistic reasons like helping people change bad habits into good ones, and sometimes for financial reasons such as increasing engagement. In this project-based hands-on course, students explore the design of systems, information and interface for human use. We will model the flow of interactions, data and context, and crafting a design that is useful, appropriate and robust. Students will design and prototype utility apps or games as a response to the challenges presented. We will also examine the ethical consequences of design decisions and explore current issues arising from unintended consequences. Prerequisite: CS147 or equivalent.
Terms: Win | Units: 3-4
Instructors: Wodtke, C. (PI)

SYMSYS 195N: Natural Language Processing with Deep Learning (CS 224N, LINGUIST 284)

Methods for processing human language information and the underlying computational properties of natural languages. Focus on deep learning approaches: understanding, implementing, training, debugging, visualizing, and extending neural network models for a variety of language understanding tasks. Exploration of natural language tasks ranging from simple word level and syntactic processing to coreference, question answering, and machine translation. Examination of representative papers and systems and completion of a final project applying a complex neural network model to a large-scale NLP problem. Prerequisites: calculus and linear algebra; CS124, CS221, or CS229.
Terms: Win | Units: 3-4
Instructors: Hashimoto, T. (PI) ; Yang, D. (PI)

SYMSYS 196: Independent Study

Independent work under the supervision of a faculty member. Can be repeated for credit.
Terms: Aut, Win, Spr, Sum | Units: 1-15 | Repeatable for credit

SYMSYS 197: Practicum in Teaching SymSys 1

The purpose of this practicum course is to prepare students to lead discussion sections of Minds and Machines ( SYMSYS 1 / CS 22 / LINGUIST 35 / PHIL 99 / PSYCH 35). The course will provide pedagogical training in the context of introductory cognitive science. Students will learn how to: implement strategies for effective discussion and engaging learning activities in section; effectively support students in 1:1 and small group learning; and consider a variety of strategies for student assessment.
Terms: Aut, Win, Spr | Units: 3-4 | Repeatable 3 times (up to 12 units total)

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

(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. Note that this is a hybrid course. Students should plan to enroll by the first day of the quarter and check their Stanford email account for instructions on how to access the course material. If you have any questions about the course, please email symsys1staff@gmail.com.
Terms: Aut, Win | Units: 4
Instructors: Krejci, B. (PI) ; Wu, J. (PI) ; Fergesen, C. (TA)
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