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81 - 88 of 88 results for: artificial intelligence

PUBLPOL 364: The Future of Finance (ECON 152, ECON 252)

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 fin more »
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
Terms: Win | Units: 2
Instructors: Beder, T. (PI)

STATS 315B: Modern Applied Statistics: Data Mining

Two-part sequence. New techniques for predictive and descriptive learning using ideas that bridge gaps among statistics, computer science, and artificial intelligence. Emphasis is on statistical aspects of their application and integration with more standard statistical methodology. Predictive learning refers to estimating models from data with the goal of predicting future outcomes, in particular, regression and classification models. Descriptive learning is used to discover general patterns and relationships in data without a predictive goal, viewed from a statistical perspective as computer automated exploratory analysis of large complex data sets.
Terms: Spr | Units: 3

SYMSYS 112: Challenges for Language Systems (SYMSYS 212)

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.
Last offered: Autumn 2017

SYMSYS 115: Critique of Technology

What is the character of technology? How does technology reveal aspects of human nature and social practices? How does it shape human experience and values? We will survey the history of philosophy of technology -- from ancient and enlightenment ideas, to positivist and phenomenological conceptions -- to develop a deeper understanding of diverse technological worldviews. This will prepare us to consider contemporary questions about the "ethos" of technology. Specific questions will vary depending upon the interests of participants, but may include: ethical and existential challenges posed by artificial intelligence; responsible product design in the "attention economy"; industry regulation and policy issues for information privacy; and the like. PREREQUISITES: PHIL80
Last offered: Spring 2017

SYMSYS 122: Artificial Intelligence: Philosophy, Ethics, & Impact

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.
Last offered: Autumn 2014 | UG Reqs: WAY-ER

SYMSYS 208: Computer Machines and Intelligence

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 wi more »
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.
Last offered: Winter 2019

SYMSYS 212: Challenges for Language Systems (SYMSYS 112)

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.
Last offered: Autumn 2017

SYMSYS 275: Collective Behavior and Distributed Intelligence (BIO 175)

This course will explore possibilities for student research projects based on presentations of faculty research. We will cover a broad range of topics within the general area of collective behavior, both natural and artificial. Students will build on faculty presentations to develop proposals for future projects.
Last offered: Spring 2018
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