Comprehensive course analysis
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Who should attend
- Senior-level executives, policymakers, and nonprofit leaders who recognize the importance of AI and want to responsibly harness it
- Decision makers — from any size organization, any industry, and any country — who work with internal or external technology teams
- Ideal for executives with little or no expertise in artificial intelligence
- Due to the goals of this program, it is not appropriate for consultants or for those who work for consulting agencies
About the course
Experience an innovative, interdisciplinary approach to artificial intelligence and learn how your organization can apply it for strategic advantage.
Artificial Intelligence is rapidly shaping the future of industry, government, and society. Harnessing AI for Breakthrough Innovation and Strategic Impact will help you explore the power and potential of this transformative technology, what it means for your organization, and how to leverage it to gain a competitive edge.
This interdisciplinary and experiential AI program from Stanford Graduate School of Business and Stanford University Human-Centered Artificial Intelligence Institute (HAI) will demystify AI technologies and provide strategies and frameworks to help your organization innovate and take the lead.
For the first time ever, faculty members from Stanford GSB, Engineering, Law School, Medical School, and School of Humanities and Sciences will come together to share their research and strategic insights on artificial intelligence and its impact. This holistic approach will help you examine how computers and people can collectively solve business problems.
Every morning you’ll get an overview of AI technologies from machine learning to fully autonomous systems. In the afternoon you’ll discover how companies are currently using AI, practice design thinking to imagine how AI can be applied in the workplace, and weigh the ethical and societal implications. At the end of each day, you’ll brainstorm and share ideas for implementing AI in your own organization.
There’s no better place to learn about innovative approaches to artificial intelligence than on the Stanford GSB campus, in the heart of Silicon Valley. This AI program delivers a powerful combination of technological expertise and business innovation you simply won’t find anywhere else.
Learn about the real-world applications, innovations, and implications of artificial intelligence and how to leverage it for a competitive advantage.
- Gain a better understanding of AI technologies, current uses, and strategic applications for your own organization.
- Discover how AI can support your organization to improve efficiencies, cut costs, provide customer insights, and generate new product ideas.
- Strategize on how to thoughtfully harness AI tools, weighing the ethical, legal, workforce, and social implications.
- Learn how to communicate and align your organization’s strategic vision with AI technological goals.
- Collaborate with peers to think through potential AI applications for your organization.
Explore AI technologies, discover strategies and frameworks to gain a competitive edge, and evaluate ethical and social implications.
Intelligence is taking the world by storm, impacting everything from driving and diagnosing diseases to shopping, recruiting, and manufacturing. Harnessing AI for Breakthrough Innovation and Strategic Impact provides a truly interdisciplinary curriculum, exploring how this transformative technology is influencing business and society . . . and how you can leverage it most effectively to compete and succeed.
The rigorous and relevant curriculum, taught by faculty members from Stanford GSB, Engineering, Law School, Medical School, and School of Humanities and Sciences, examines how technological implications intersect with business objectives and opportunities.
Over the course of this one-week AI program, you will learn from faculty, guest speakers, and each other.
- Gain an understanding of key AI technologies — machine learning, perception, natural language processing, and autonomous systems.
- Discover who and how organizations are using these technologies.
- Explore and weigh the ethical, legal, economic, and social implications of AI.
- Use design thinking methodology to develop potential AI applications for your organization.
- Capture your learnings at the end of each day to review key takeaways and apply them to your organization.
Below are just a few of the sessions you’ll attend as part of the program.
Training Artificial Intelligence to Understand Humans
A growing proportion of human activities such as social interactions, entertainment, shopping, and gathering information are now mediated by digital devices and services. Such digitally mediated activities can be easily recorded, offering an unprecedented opportunity to study and measure intimate psycho-demographic traits using actual — rather than self-reported — behavior. Our research shows that digital records of behavior, such as samples of text, Tweets, Facebook Likes, web-browsing logs, or even facial images can be used to accurately measure a wide range of traits including personality, intelligence, and political views. Such Big Data assessment has a number of advantages: it does not require participants’ active involvement; it can be easily and inexpensively applied to large populations; and it is relatively immune to cheating or misrepresentation. If used ethically, it could revolutionize psychological assessment, marketing, recruitment, insurance, and many other industries. In the wrong hands, however, such methods pose significant privacy risks. In this session, we will discuss how to reap the benefits of Big Data assessment while avoiding the pitfalls.
Verification and Validation of Autonomous Systems
Autonomous systems, such as those for driving cars and flying aircraft, must be prepared to make appropriate decisions in a wide variety of circumstances. Building robust systems is challenging due to imperfect sensor information, uncertainty in the behavior of other human operators, and the delicate balance between safety and operational suitability. The space of possible edge cases that designers must reason about is vast. We will discuss an alternative approach to building robust autonomous systems and outline how this methodology has been applied to develop the next generation collision avoidance system that was recently standardized for use on aircraft around the world. We will discuss the process of verification and validation of autonomous systems to establish trust in their correct operation.
The Future of the Workplace: Demographics, Robots, and Other Disruptions
The only thing predictable about the future of work is that there will be lots of change. One day you read that there is a looming labor shortage as the population ages, the next you read that mass unemployment is right around the corner due to the advent of robots and other artificial intelligence. In this session, we will look at trends in the labor market and the future of work. We will consider how demographic changes present business and labor market opportunities, as well as challenges. Will the robots really come and, if so, what are the managerial implications for today's employers? We will discuss how the answers to these and related questions varies dramatically by labor market and with what level of skill your company needs.
Research Statement Susan Athey’s research is in the areas of industrial organization, microeconomic theory, and applied econometrics. Her current research focuses on the design of auction-based marketplaces and the economics of the internet, primarily on online advertising and the economics of th...
Research Statement Paul Oyer studies the economics of organizations and human resource practices. His work has looked at the use of broad-based stock option plans, how firms use non-cash benefits, how firms respond to limits on their ability to displace workers, and how labor market conditions af...
Research Statement Michal Kosinski’s research focuses on individual differences in behavior, preferences, and performance. Specifically, he is interested in the mechanisms linking psychological traits (such as personality) with a broad range of organizational and social outcomes, including job pe...
Mykel Kochenderfer is Assistant Professor of Aeronautics and Astronautics and Assistant Professor, by courtesy, of Computer Science at Stanford University. He is the director of the Stanford Intelligent Systems Laboratory (SISL), conducting research on advanced algorithms and analytical methods f...
Selected Awards Best paper Uncertainty in AI (UAI) 2017 Best paper nominee Educational Data Mining (2017) Selected for Early Career Talk, IJCAI (2017) Best paper award RLDM (2015) Office of Naval Research Young Investigator Award (YIP) (2015) (Press release) NSF CAREER award (2014) Best paper nom...
David Freeman Engstrom is a far-ranging scholar of the design and implementation of litigation and regulatory regimes whose expertise runs to civil procedure, administrative law, federal courts, constitutional law, legal history, and empirical legal studies. Professor Engstrom’s award-winning sc...
John Etchemendy received his bachelor's and master's degrees at the University of Nevada, Reno before earning his PhD in philosophy at Stanford in 1982. He has been a faculty member in Stanford's Department of Philosophy since 1983, prior to which he was a faculty member in the Philosophy Depart...
Fields: machine learning, natural language processing. Topics: unsupervised learning, structured prediction, statistical learning theory, grounded language acquisition, compositional semantics, program induction. Learning semantics: Natural language allows us to express complex ideas using a fe...
Christopher Manning is a professor of computer science and linguistics at Stanford University and Director of the Stanford Artificial Intelligence Laboratory. He works on software that can intelligently process, understand, and generate human language material. He is a leader in applying Deep Lea...
James Zou received his PhD from Harvard in 2013, working on applied mathematics and computational biology and supported by an NSF Graduate Fellowship. He collaborated with Brad Bernstein, Michael Brenner and David Parkes. Before Harvard, he read for Part III in Mathematics at the University of Ca...
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