The Cloudcast - Weekly Cloud Computing Podcast Podcast Artwork Image
The Cloudcast - Weekly Cloud Computing Podcast
The Cloudcast #321 - This Week in ML and AI
November 16, 2017 Aaron Delp & Brian Gracely
Aaron & Brian talk with Sam Charrington (@samcharrington, Host of This Week in ML & AI Podcast) about the differences between AI and ML, how to manage data gravity, the maturity of the technology, press coverage, and the societal impacts of AI and ML.

Show Links:

Show Notes
  • Topic 1 - Welcome to the show. We’ve known you for a while as being heavily involved in Cloud since the early days, but you’ve been involved with AI and ML for quite a while now too. Tell our audience about your background and what you’re up to today.
  • Topic 2 - Is there a difference between AI and ML? What are some good examples of each?
  • Topic 3 - Let’s start with the basics. AI and ML always get talked about as a spectrum between basic things we all live with (like Google doing an auto fill on a search) to the scary SkyNet, Terminator stuff. Where are we on the maturity curve of AI and ML?
  • Topic 4 - What are some of the key technology elements that people should be aware of with AI and ML? Do we make a mistake by mentioning them today (e.g. AI and ML), and are they very different?
  • Topic 5 - Can we talk about data and data models/set and data gravity as it relates to AI and ML? Do you bring the data to the engine, or the engine to the data? How do companies deal with this today?
  • Topic 6 - AI and ML also have the ability to have major societal impacts on our work, from jobs to human privacy to better access to healthcare or studying global warming. Can you talk about how this interaction of technology and human interests is being covered in the press & media?
  • Topic 7 - Beyond listening to your show each week, what are some good resources for listeners to go learn about AI and ML?
Feedback?
Aaron & Brian talk with Sam Charrington (@samcharrington, Host of This Week in ML & AI Podcast) about the differences between AI and ML, how to manage data gravity, the maturity of the technology, press coverage, and the societal impacts of AI and ML.

Show Links:

Show Notes
  • Topic 1 - Welcome to the show. We’ve known you for a while as being heavily involved in Cloud since the early days, but you’ve been involved with AI and ML for quite a while now too. Tell our audience about your background and what you’re up to today.
  • Topic 2 - Is there a difference between AI and ML? What are some good examples of each?
  • Topic 3 - Let’s start with the basics. AI and ML always get talked about as a spectrum between basic things we all live with (like Google doing an auto fill on a search) to the scary SkyNet, Terminator stuff. Where are we on the maturity curve of AI and ML?
  • Topic 4 - What are some of the key technology elements that people should be aware of with AI and ML? Do we make a mistake by mentioning them today (e.g. AI and ML), and are they very different?
  • Topic 5 - Can we talk about data and data models/set and data gravity as it relates to AI and ML? Do you bring the data to the engine, or the engine to the data? How do companies deal with this today?
  • Topic 6 - AI and ML also have the ability to have major societal impacts on our work, from jobs to human privacy to better access to healthcare or studying global warming. Can you talk about how this interaction of technology and human interests is being covered in the press & media?
  • Topic 7 - Beyond listening to your show each week, what are some good resources for listeners to go learn about AI and ML?
Feedback?
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