The Reasoning Show

The Reasoning Show - Reasoning through the AI Revolution

The industry's leading AI podcast, explores how leaders think through AI, technology, and transformation. Each week founders, investors, and operators unpack the decisions behind the systems shaping modern business.

Hosts: Aaron Delp and Brian Gracely

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The Reasoning Show

An AI and ML Look Ahead for 2019

January 23, 2019 • Aaron Delp & Brian Gracely

0:00 | 41:06

Show: 382

Description: Brian talks with Sam Charrington (@samcharrington, Machine Learning & AI analyst, advisor & host of “This Week in Machine Learning & AI” podcast) about trends in the industry, the evolution of AI at the edge, new research areas in 2019, and a discussion about adding AI and ML to business applications.

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Show Interview Links:

This Week in Machine Learning & AI Homepage - http://twimlai.com
Kubernetes for Machine Learning, Deep Learning and AI (eBook) - https://twimlai.com/kubernetes/
Sam Charrington on Eps.321 of The Cloudcast - http://www.thecloudcast.net/2017/11/the-cloudcast-321-understanding-ai-and.html

Show Notes:

Topic 1 - Happy New Year and welcome back to the show, it’s been just over a year. For those that didn’t hear that show or might be new to TWIML & AI, tell us about your background and some of your AI/ML focus now.

Topic 2 - Let’s start with the things that are considered “mainstream” with AI & ML today. Fraud detection, recommendation engines, facial recognition, speech recognition, auto-completions. What’s missing from that list, and how “commodity” have those technologies, tools, datasets, cloud services become?

Topic 3 -On the flipside, what are some of the areas where research or just the massive cloud providers are focused today?

Topic 4 - A couple years ago it seemed like TWIML & AI was a mix of technology discussions and business/social impacts. This past year seemed to be a deeper focus on the underlying technologies. What’s the current state of the balance between AI & ML for computing improvement vs. concerns about personal privacy, etc.?

Topic 5 - What’s the “getting started” curve look like for companies that want/need to add or integrate AI & ML into their applications? What are some numbers you hear about cost of engineers, sizes of datasets, number of experiments and models needed to run, etc.?

Topic 6 - What are some of the things you’re really looking forward to in 2019, whether it’s technology or trends or something else?

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