Data Myths

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Where do liberal arts and artificial intelligence intersect? How transparent is AI? Will discovery be dead by 2020? Brian and Malinda discuss the problems that AI solves and the problems AI creates.

Show Notes

Summary: In this episode, Brian and Malinda talk about the softer side of AI and how it spans more than just PhD math and physics majors. They also cover how AI bias and algorithms are changing to adapt to the initial AI challenges, and they cover how Google's machine learning bets will pay off.

What We Covered:
1:00 - Techstars pitch competition audio clip Part 1 - AI and the skill sets that make it useful in a company. (Referenced in All Things CES 2019)
4:00 - NeurIPS 2019 (Neural Information Processing Systems)
5:00 - What is applied AI?
7:00 - Audio clip Part 2 - Do you really need a PhD to utilize AI properly?
10:00 - How Google is creating infrastructure to help people and companies easily solve problems in AI/ML with tools like Dialogflow.
11:00 - What is machine learning and Tensorflow?
14:00 - Audio clip Part 3 - Are you an AI consumer or supplier?
17:00 - AI bias - What is it and how can we avoid it?
25:00 - Audio clip Part 4 - Companies can use AI/ML without PhDs
29:00 - Diane Greene’s departure from Google and how Google Cloud’s AI/ML infrastructure is shaped for the future.
32:00 - Will Google or Amazon prove to be the future of voice?
34:00 - Input & Output bias - How to get the results you’re looking for.
36:00 - Curation vs Discovery - If everything in your life is served up based on past behavior, will you ever learn anything new?

What is Data Myths?

Uniting dataphiles and dataphobes one podcast at a time. Listen as the Gagnons interview industry leaders and startup founders, review new tech trends and products, and examine how data and technology drive our professional and personal worlds.