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. 

Show Sponsor Links:

• Liquid Technology - IT Value Recovery
• Try CloudLast Service, get a free t-shirt and chance at Amazon Gift Card
• Datadog Homepage - Modern Monitoring and Analytics
• Try Datadog yourself by starting a free, 14-day trial today. Listeners of this podcast will also receive a free Datadog T-shirt

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?

Feedback?

• Email: show at thecloudcast dot net
• Twitter: @thecloudcastnet and @ServerlessCast