Deep Learning is overhyped ... is overhyped: QUT Guest Lecture

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Jeremy Howard

Jeremy Howard is a data scientist, researcher, developer, educator, and entrepreneur. Jeremy is a founding researcher at fast.ai, a research institute dedicated to making deep learning more accessible. He is also a Distinguished Research Scientist at the University of San Francisco, the chair of WAMRI, and is Chief Scientist at platform.ai.

Previously, Jeremy was the founding CEO Enlitic, which was the first company to apply deep learning to medicine, and was selected as one of the world’s top 50 smartest companies by MIT Tech Review two years running. He was the President and Chief Scientist of the data science platform Kaggle, where he was the top ranked participant in international machine learning competitions 2 years running. He was the founding CEO of two successful Australian startups (FastMail, and Optimal Decisions Group–purchased by Lexis-Nexis). Before that, he spent 8 years in management consulting, at McKinsey & Co, and AT Kearney. Jeremy has invested in, mentored, and advised many startups, and contributed to many open source projects.

He has many media appearances, including writing for the Guardian, USA Today, and the Washington Post, appearing on ABC (Good Morning America), MSNBC (Joy Reid), CNN, Fox News, BBC, and was a regular guest on Australia’s highest-rated breakfast news program. His talk on TED.com, “The wonderful and terrifying implications of computers that can learn”, has over 2.5 million views. He is a co-founder of the global Masks4All movement.

Rachel Thomas

Dr Rachel Thomas is a co-founder of fast.ai, which has been featured in The Economist, MIT Tech Review, and Forbes. She was the founding director of the USF Center for Applied Data Ethics, and was selected by Forbes as one of 20 Incredible Women in AI. Dr Thomas earned her math PhD at Duke, and was an early engineer at Uber. Rachel is a popular writer and keynote speaker. In her TEDx talk, she shares what scares her about AI and why we need people from all backgrounds involved with AI.

Rachel’s writing has been read by nearly a million people; has been translated into Chinese, Spanish, Korean, & Portuguese; and has made the front page of Hacker News 9x. Some of her most popular articles include:

• The problem with metrics is a big problem for AI
• If you think women in tech is just a pipeline issue, you haven’t been paying attention
• How to change careers and become a data scientist- one quant’s experience
• The real reason women quit tech, and how to address it
• Google’s AutoML: Cutting Through the Hype
• An Introduction to Deep Learning for Tabular Data

Rachel’s talks include:

• AI, Medicine, and Bias: Diversifying Your Dataset is Not Enough
• Getting Specific About Algorithmic Bias
• The Barriers to AI are Lower than You Think
• The New Era in NLP
• Some Healthy Principles About Ethics & Bias In AI