It’s happening. The machines...are learning!
In this latest edition of our Handy Knowledge for Your Dark Future series, we’re pleased to bring you three heavily credentialed nerds to introduce us to the field of machine learning.
Maybe you’ll learn how to teach a machine to protect you when the singularity happens. Perhaps even...to love? Sorry, I was drunk when I wrote this.
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[read more] It’s happening. The machines...are learning!
In this latest edition of our Handy Knowledge for Your Dark Future series, we’re pleased to bring you three heavily credentialed nerds to introduce us to the field of machine learning.
Maybe you’ll learn how to teach a machine to protect you when the singularity happens. Perhaps even...to love? Sorry, I was drunk when I wrote this.
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Presentation #1
“Machine Learning and How it is Revolutionizing Computing,” by Dr. Raymond Mooney
The goal of Machine Learning (ML) is to allow a computer to automatically improve its ability to perform a task by learning from experience rather than being explicitly programmed. A half-century of research in ML has given us computers that can understand human speech, automated automobiles, soccer-playing robots and Jeopardy champions. Dr. Mooney will review the primary problem that lies behind these applications and discuss how ML algorithms are fundamentally changing the way we develop computers.
Raymond J. Mooney is a Professor of Computer Science at UT-Austin and the author of over 150 published research papers. He was the President of the International Machine Learning Society from 2008-2011, and is credited with contributing to Watson, the IBM Jeopardy champ.
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Presentation #2
“Teaching a Computer to Understand Human Language,” by Dr. David Chen
Building a computer that can converse naturally with people has been a long-standing goal in artificial intelligence. Dr. Chen will look at how computers can begin to understand the meaning of human language. Rather than manually encoding knowledge about language, we'll see how a computer can learn by observing how language is used in context.
David Chen is receiving his Ph.D. in Computer Science from UT-Austin this month (!). He will next join Google as part of their Search Group.
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Presentation #3
“Work Smarter, Train Faster: How to Train Your Robot Using Fewer Attempts,” by Chris Flesher
Do you find yourself frustrated, spending long hours programming your robot? Are you embarrassed to bring your robot out in public for fear that it might get stuck in a loop and smash itself repeatedly into a wall? How many times have you wanted to hold your computer above your head and cry profanities before smashing it to the ground after learning your code works well in simulation but completely fails in real life? If you've agreed with any of the previous questions don't get mad at your robot. Help your robot help itself -- with predictive models and policy optimization!
Chris is a mild mannered robot engineer by day and a mildly insane robot hobbyist by night. He has a Master's in Electrical Engineering from UT. At Stone Aerospace he served as the vehicle manager for the ENDURANCE, an underwater robot designed to explore an ice covered lake in Antarctica (Lake Bonney). Currently he is working on Bill Stone's next project, VALKYRIE, a rather large robot that will be used to explore glaciers in Alaska and Greenland by melting its way through ice.
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Doors at 7, talks at 7:30!
As it was then, is now, and ever shall be, Nerd Nite is FREE.
However, we will not turn down a beer.
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