DiscoverArtificial Intelligence in Industry with Daniel Faggella
Artificial Intelligence in Industry with Daniel Faggella
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Artificial Intelligence in Industry with Daniel Faggella

Author: Daniel Faggella

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Learn what's possible - and what's working - with artificial intelligence in the enterprise.

Each week, Emerj founder Daniel Faggella interviews top AI and machine learning-focused executives and researchers in sectors like Pharma, Banking, Retail, Defense, and more.

Discover trends, learn about what's working now, and learn how to adapt and thrive in an era of AI disruption. Be sure to subscribe to "AI in Industry."
296 Episodes
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This week we speak with David Carmona, General Manager of AI at Microsoft. Carmona discusses 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.  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.
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.
This episode of the AI in industry podcast is all about where the rubber meets the road for AI in Insurance. We interview Jerry Overton, Head of AI and a Fellow at DXC Technology. He speaks to us about his experience implementing AI in insurance, about where there's real traction with AI in insurance, and where there's only hype. In particular, Overton discusses how anomaly detection technology is a natural fit for AI in the insurance sector. This is the last episode of its kind on AI in Industry. Starting next Tuesday, we'll be kicking off a new format for the show. Each month, we'll focus on a specific theme, and in August, we're focusing on AI adoption in the enterprise. We hope you'll join us.  
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.  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: https://bit.ly/2Ya2NxK
This week, we interview Jay Budzik, CTO at ZestFinance, about where AI applies to the world of auto-lending. We speak with Budzik about how underwriting and credit scoring is evolving as a result of advances in machine learning. In addition, we talk about how companies might solve the "black box" of machine learning in finance, particularly how ZestFinance is focusing on transparent models. The financial sector has to contend with complex regulations that prevent certain information from being leveraged in credit models. It can be near impossible to determine how machine learning comes to the conclusions it does, but ZestFinance claims their software in part solves this problem.
Some say that the competitive dynamics between the US and China in terms of AI are overblown, but there's a lot of truth to them. The US has access to more of the base research, but China can orchestrate various organizations (corporations, government bodies) and secure government funding. That said, very few people talk about K-12 education and what countries are doing to prepare their future workforce for AI. David Touretzky talks to us about just that. He is a research professor in the Computer Science Department and the Center for the Neural Basis of Cognition at Carnegie Mellon University. He's heading up an initiative for K-12 education, and he discusses what countries should be doing to secure their positions and technological leadership in the 21st century.
While AI is certainly finding its footing in finance, we still find most of our subscribers are in a phase where they're trying to catch up in terms of data and data infrastructure and figure out where there's real traction with AI in finance: in banking, investing, or insurance. In this episode, we explore AI use-cases in a number of these areas of the financial industry. We interview Carlos Pazos and Anwar Ghauche at Spark Cognition about how to maximize a smaller data science team at a financial institution, how AI and alternative data is being used for quantamental investing, and how AI is automating some financing and underwriting processes.
Building an AI strategy - there's hardly anything more vague and open-ended than that. Business leaders have probably gotten the idea that they should develop one, but where should they start? That's what we talk about this week with Charles Martin, PhD. Martin talks about how to go about starting an AI strategy, what to avoid, and the challenges and struggles of applying AI at existing businesses. Also, Martin discusses what business leaders should ignore and what business leaders should tune into and prioritize for an effective AI strategy that will propel them toward success in the coming years.
One of the best conversations I ever had on the topic of AI business strategy on the podcast was with the guest I've brought back this week: Madhu Shekar, Head of Digital Innovation for Amazon Internet Services in Bangalore. I wanted to do a deeper session with Madhu, who has seen a lot of companies go from no AI to beginning with AI, about where to start with AI adoption. How do companies build the expertise and experience with AI that lets them scale it to their organization? He also talks about how to prepare realistically for AI, including data requirements, integration times, and more. 
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.
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Comments (5)

Atul Nevase

Nikhil is awesome

Mar 4th
Reply

Zazzy Jan

good

Oct 22nd
Reply

Tracey Drake

Congratulations to Dan on his patience in this episode, with a completely uncooperative guest. Not worth the time.

Oct 2nd
Reply

Dom Hernandez

awesome podcast.

Sep 25th
Reply

Albert Reynaud

very good

Nov 18th
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