How to Get Started with AI - Best Practices from 4 Industry Experts

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Artificial Intelligence in Industry

What does it mean to tune an algorithm, how does it matter in a business context, and what are the approaches being developed today when it comes to tuning algorithms?&nbsp;This week's guest helps us answer these questions and more. CEO and Co-Founder Scott Clark&nbsp;of SigOpt takes time to explain the dynamics of tuning, goes into some of the cutting-edge&nbsp;methods for getting tuning done, and shares advice&nbsp;on how businesses using machine learning algorithms&nbsp;can continue to refine and adjust their parameters&nbsp;in order to glean greater results.

Tuning Machine Learning Algorithms with Scott Clark

Sales forecasting is big business. If you can better predict how much of a certain product or service you will sell in a given day, you can better stock inventory, better staff your facilities, and ultimately keep more margin in your business's accounts. This week on AI in Industry we interview Dr. John-Paul B Clarke, professor at Georgia Tech and co-founder / Chief Scientist at Pace (previously called "Prix"). Dr. Clarke shares details about how sales predictions are done today, and what AI advancements may allow for in helping businesses sell everything from groceries to hotel rooms. Read the full interview article online at:&nbsp; techemergence.com/ai-sales-forecasting-works-matters

AI for Sales Forecasting - How it Works and Where it Matters

As it turns out, often times terms like predictive analytics and data science are used incorrectly. By the end of this podcast, you'll have greater clarity on five potentially vague AI and data science terms that are sometimes overused in conversations about AI in the enterprise. This week, I introduce you to German Sanches, who focused his PhD on NLP and has done a lot of AI work in business. He also helps us with our research projects. This episode is all about addressing use-cases in reference to five terms that a lot of folks get wrong.

Five AI and Data Science Terms People Get Wrong

When we polled our audience about what they were interested in, the most selected response was "business intelligence." As a follow-up, we asked them what business intelligence meant to them, and their responses boiled down to anything about understanding the data businesses are already collecting. That kind of broad definition gets to the heart of the confusion surrounding the differences between business intelligence and artificial intelligence. The line is starting to get blurry.&nbsp; Our guest this week is Elif Tutuk, Senior Director at Qlik. Tutuk talks about how business intelligence is evolving and how we might define it now that a lot of BI is becoming AI. Tutuk discusses where AI is making its way into business intelligence and what that might enable for businesses. Read our comprehensive definition of machine learning for business leaders here:&nbsp;https://bit.ly/2Ya2NxK

What's the Difference Between Business Intelligence and Artificial Intelligence? - With Elif Tutuk, Senior Director at Qlik Research

It's the first episode of the new style of AI in Industry, in which we spend a month at a time on a specific theme. This month is AI adoption. This week we speak with Vlad Sejnoha at Glasswing Ventures, an AI-focused VC firm. Sejnoha spent many years as the CTO at Nuance Communications. He talks to us about the table stakes AI insights the C-suite have to know and the dangers of relying entirely on consulting firms and vendor companies for these insights. In addition, Sejnoha discusses the need for a "BS-o-meter" for when someone is making a claim about AI to determine if it's real or hype. Lastly, Sejnoha discusses how he would go about choosing a first AI project.

Table Stakes AI Insights for the Enterprise - with Vlad Sejnoha of Glasswing Ventures

This week we speak with David Carmona, General Manager of AI at Microsoft. Carmona discusses&nbsp;how redefining a business process is a very different kind of AI adoption project than working on something that is horizontal. He discusses how to attack both of these scenarios, which to handle first, and why.&nbsp; In addition, Carmona talks about proprietary data and things that are close to your own IP. How do you take advantage of the real strategic data value within your own organization? How should you be thinking about that differently? Carmona poses three different questions to determine where those valuable opportunities are for you.

Critical Questions to Ask Before Adopting Artificial Intelligence - With David Carmona of Microsoft

This week, we speak with Jan Neumann, Senior Director of Applied AI Research at Comcast. Comcast is an enormous company; it has lots of data, lots of application areas for AI, and a lot of opportunity for confusion about AI. As such, Neumann speaks with us about scaling AI expertise in the enterprise. Neumann talks about a very strong distinction between software and AI and how to think through problems to determine whether or not it's a software problem or an AI problem. He also talks about scaling the problem-solving abilities of business experts in the organization. Lastly, Neumann talks about his ideas for how to determine a first AI initiative.

Scaling AI Best-Practices in the Enterprise - with Jan Neumann of Comcast

This week we interview Jan Kautz, Vice President of Learning and Perception Research at NVIDIA. Kautz talks about what people underestimate when they start an AI initiative. In addition, he emphasizes the critical value of data storage. Kautz dives into&nbsp;the importance of getting started with an AI project when you already have a barometer of success. Essentially, he talks about why it's important to select a first AI project in an area where you already have a way of measuring success. Learn more about AI adoption in our full report,  Getting Started With AI: Proven Best Practices for AI Adoption.

Misconceptions About AI Adoption, and How to Overcome Them - with Jan Kautz of NVIDIA

This is the final episode in the month-long series on getting started with AI. In this episode, Emerj CEO Daniel Faggella breaks down the key insights from all four of this month's interviews, distilling them into core best-practices for getting started with artificial intelligence in business. In addition, Daniel discusses insights from our newest report: Getting Started with AI: Proven Best-Practices for AI Adoption

Getting Started with AI, Best-Practices - With Daniel Faggella of Emerj

This is the final episode of our series on the ROI of AI. This week is the monthly analyst call, in which Emerj CEO Daniel Faggella breaks down some of the key themes from this month's interviews. In particular, Daniel puts a large emphasis on connecting the dots between near-term and long-term ROI. A lot of these themes and core questions are discussed and answered for clients of our AI Product Development Roadmap services.&nbsp;

AI's Strategic Value is the Anchor for ROI - With Daniel Faggella of Emerj

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Getting Started with AI, Best-Practices - With Daniel Faggella of Emerj

The AI in Business Podcast