View source for Assessing the Promise of Artificial Intelligence Agenda

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'''SFI ACtioN Virtual Topical Meeting<br />
June 24 - 26, 2020<br />
Co-sponsored by ACtioN member Goldman Sachs'''
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'''Wednesday, June 24, 2020'''<br/>
'''10:00 am US Mountain Time (12:00 pm EDT, 4:00 pm GMT)'''<br/><br/>
[http://www.santafe.edu/people/profile/melanie-mitchell Melanie Mitchell]<br/>
Artificial Intelligence: A Guide for Thinking Humans<br/>

In her book [https://us.macmillan.com/books/9780374715236 ''Artificial Intelligence: A Guide for Thinking Humans''], Mitchell turns to the most urgent questions concerning AI today: How intelligent—really—are the best AI programs? How do they work? What can they actually do, and when do they fail? How humanlike do we expect them to become, and how soon do we need to worry about them surpassing us? Along the way, she introduces the dominant models of modern AI and machine learning, describing cutting-edge AI programs, their human inventors, and the historical lines of thought underpinning recent achievements. She meets with fellow experts such as Douglas Hofstadter, the cognitive scientist and Pulitzer Prize–winning author of the modern classic Gödel, Escher, Bach, who explains why he is “terrified” about the future of AI. She explores the profound disconnect between the hype and the actual achievements in AI, providing a clear sense of what the field has accomplished and how much further it has to go.

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'''Thursday, June 25, 2020'''<br/>
'''10:00 am US Mountain Time (12:00 pm EDT, 4:00 pm GMT)'''<br/><br/>
[http://www.santafe.edu/people/profile/mike-price Mike Price]<br/>
Characterizing the Scaling of Human Organizations using AI<br/>

The typical machine learning task is either (1) regression or (2) classification with a single outcome label. Many important problems do not fall into this category, and I will discuss one such set of problems: estimating joint or conditional probability density functions. I first provide a brief overview of existing machine learning techniques for this, including generative models such as Boltzmann machines, then describe new work I am undertaking with colleagues on a conditional variational autoencoder. The application is the scaling of human organizations. Notably, our algorithm can accommodate missing data in input variables.

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'''Friday, June 26, 2020'''<br/>
'''10:00 am US Mountain Time (12:00 pm EDT, 4:00 pm GMT)'''<br/><br/>
[https://www.santafe.edu/people/profile/michael-kearns Michael Kearns]<br/>
The Ethical Algorithm<br/>

Understanding and improving the science behind the algorithms that run our lives is rapidly becoming one of the most pressing issues of this century. Traditional fixes, such as laws, regulations and watchdog groups, have proven woefully inadequate. Reporting from the cutting edge of scientific research, Kearns's book [https://global.oup.com/academic/product/the-ethical-algorithm-9780190948207?cc=us&lang=en& ''The Ethical Algorithm''] offers a new approach: a set of principled solutions based on the emerging and exciting science of socially aware algorithm design. Michael Kearns and Aaron Roth explain how we can better embed human principles into machine code - without halting the advance of data-driven scientific exploration. Weaving together innovative research with stories of citizens, scientists, and activists on the front lines, ''The Ethical Algorithm'' offers a compelling vision for a future, one in which we can better protect humans from the unintended impacts of algorithms while continuing to inspire wondrous advances in technology.