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SCI 62 — The Frontiers of Artificial Intelligence Research and Applications

Quarter: Winter
Day(s): Wednesdays
Course Format: On-campus
Duration: 5 weeks
Date(s): Feb 19—Mar 18
Time: 7:00—8:50 pm
Drop Deadline: Mar 3
Unit: 1
Tuition: $320
Instructor(s): Catherine Lu
Status: Open
7:00—8:50 pm
Feb 19—Mar 18
5 weeks
Drop By
Mar 3
1 Unit
Catherine Lu
In recent years, many fields in artificial intelligence (AI) have developed at a pace far faster than even the world’s leading experts in AI predicted. From autonomous driving to game-playing, and construction to recruiting, many AI applications have rapidly progressed due to breakthroughs in their underlying approaches. At the same time, “AI” is becoming an overused word that has under-delivered to date in many areas. This course will discuss the real status of AI research and its applications. We will examine the frontiers of AI across many domains, including natural-language processing, speech recognition, computer vision, AI chips and hardware, and more. We will answer these and other questions: What is the current state of autonomous driving, and how far are we from general availability? When could a Siri-like digital personal assistant be able to plan my day? The goal of the course is for students to become up to date with what’s now possible with AI and what may be just around the corner. Each class session will consist of an interactive lecture, and many will include a guest speaker from industry or academia. Homework will consist of readings, self-directed research, and short essays; there will be optional additional readings and exploratory projects as well.

A technical background is not required.

Catherine Lu, Principal, Spike Ventures

Catherine Lu co-founded the retail AI company Fancy That, which was acquired by Palantir in 2015. She received an MS in computer science from Stanford, focusing on artificial intelligence. She is now a venture capitalist investing in Stanford-connected companies. Previously, Lu was director of product at NEA and DataVisor, which offered an unsupervised machine learning fraud solution.