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

A "AI & ML" Look Ahead for 2020

February 14, 2020 • The Cloudcast

0:00 | 42:35

Sam Charrington (@samcharrington, Host of TWIML & AI Podcast) talks about AI & ML trends in 2020, frameworks to understand usage patterns, hot new technology to explore, how long projects take to succeed, and the inherent bias built into every AI & ML model.

SHOW: 437

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SHOW NOTES:

TWIML Homepage (Podcasts, eBooks, etc.)
- eBook: The Definitive Guide to ML Platforms
- Study Groups & Education
TWIML Conference Homepage
Sam Charrington on The Cloudcast in 2019 (Eps.321)

Topic 1 - Welcome back to the show. Let’s start with the broad set of TWIML activities that you’re working on these days.

Topic 2 - You focus on AI & ML every week, across a lot of different domains and usages. It’s a broad scope. If you had to focus it on Enterprise/Business leaders, how do you structure a conversation around how to align business opportunity and technology choices?

Topic 3 - What are some of the most commonly used technologies being deployed around AI/ML systems? Any big shifts over the last couple of years?

Topic 4 - You’ve been around Cloud Computing and DevOps communities, which required companies to go through some people/process change to achieve success. What are the people/process changes that you typically see with AI/ML environments?

Topic 5 - If somebody asked you how they can put a timeline on when they’ll see value around their AI/ML, is that a realistic ask? What are the factors that go into achieving success in AI/ML projects?

Topic 6 - What are some of the interesting usages of AI/ML that you’ve seen in use recently?

Topic 7 - There has been quite a bit of discussion recently about bias in AI/ML algorithms. Can you explain what this means and how it could impact the system’s decision making?

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