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Skymind Machine Learning Applications for Enterprise

Daniel Faggella

Daniel Faggella is the founder and CEO at Emerj. Called upon by the United Nations, World Bank, INTERPOL, and many global enterprises, Daniel is a sought-after expert on the competitive strategy implications of AI for business and government leaders.

Skymind Machine Learning Applications for Enterprise 2

Episode Summary: CEO Chris Nicholson speaks on Skymind machine learning applications, which integrate with Hadoop and Spark. In this episode, Nicholson sheds light on current machine learning trends that he sees across industries and best practices for implementing AI solutions in order to gain consistent return on investment. For our readers who enjoyed out consensus on future trends in artificial intelligence consumer applications, it may be interesting to hear some of Chris’s specific use cases in industry.

Expertise: Deep learning, finance, economics

Brief Recognition: Chris Nicholson leads Skymind, the commercial support arm of the open-source framework Deeplearning4j. Skymind helps companies in telecommunications, finance, retail and tech build enterprise-level deep learning applications, notably fraud detection, using data such as text, time series, sound and images. Previously, he handled PR and recruiting for FutureAdvisor, an automated investment manager backed by Sequoia, Canvas and YCombinator. FutureAdvisor’s AUM and revenue grew by 45x during my 20 months there, while quadrupling the size of the team. Before that, I reported and edited for The New York Times, the International Herald Tribune and Bloomberg Businessweek.

Current Affiliations: Co-founder of Skymind.io

Interview Highlights:

(1:18) With Skymind, you folks are tangibly implementing machine learning (ML) into bigger industries…where are some of the trends of application that you’ve seen really have success?

(5:23) Because products can be so varied…it feels like in recommenders, you have to have enough data going through that customer’s own system – am I mistaken in how fraud detection works?

(11:54) What are some real-life examples (of churn); what are people picking up on to discern what it looks like when someone’s starting to leave?

(14:42) Have your found a lot of companies that are testing and quantifying, or do you see a lot of people finding a dark corner to experiment with AI and ML?

(17:35) What do you feel optimistic about in the coming 5 years for adoption of ML and AI…in terms of industry application?

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