Artificial Intelligence in Industry with Dan Faggella

If you're going to apply machine learning (ML) in a business context, you need a lot of data, and algorithms across the board perform better with more recent, rich, and relevant data. Today, there are companies whose entire business models are predicated on helping others make sense of and use of this type of information. In this episode, we speak with the CTO and Co-Founder of one such company—Palo Alto-based Cloudera. CTO Amr Awadallah, PhD, speaks with us this week about where he sees "data lakes" (or "data hubs", Cloudera's preferred term) and warehouses play an important role in ML applications in business. Based on his experiences helping a variety of companies in many countries set up data lakes, Amwadallah is able to distill and communicate these uses in three broad categories that apply across industries as companies look to solve tougher problems and ask more complex questions using unstructured data.

Direct download: TEP-Amr_Awadallah-Mixdown.mp3
Category:machine learning -- posted at: 7:55am PDT

One facet of business that nearly any industry has in common is the need to stay on top of news in their respective market, including competitor strategies or understanding changes in news related to the field. Media monitoring is a domain that machine learning (ML) is well suited for, with it's ability to coax out headlines, contextual information, and financial data from the seemingly endless stream of social, blog, and other information on the web today. Signal is a company that uses ML specifically for these purposes. In this episode, we speak with Signal Media's Chief Data Scientist and Co-founder Dr. Miguel Martinez, who dives into real business use cases illustrating the use of machine learning for media monitoring across industries.

 

Direct download: TEP-Miguel_Alvarez-Mixdown_v2.mp3
Category:machine learning -- posted at: 6:30pm PDT

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? This week's guest helps us answer these questions and more. CEO and Co-Founder Scott Clark of SigOpt takes time to explain the dynamics of tuning, goes into some of the cutting-edge methods for getting tuning done, and shares advice on how businesses using machine learning algorithms can continue to refine and adjust their parameters in order to glean greater results.

Direct download: TEP-Scott_Clark-Mixdown2.mp3
Category:machine learning -- posted at: 5:00pm PDT

In this episode, recorded live at Canvas Ventures in Portola Valley, I speak with Ben Narasin, a partner with Canvas and an avid venture investor in AI and ML companies, some of which we've interviewed (Crowdflower and Mulesoft), along with many others that we haven't (like Siri). Ben doesn't look for AI to invest in; instead, he looks for companies to invest in, a subtle but important difference in a business world increasingly caught up in the explosion of AI and ML technologies.

From investments in Nuance to more recent one such as Houzz, Narasin has solid ideas as to what makes an investment interesting when AI is involved, what might actually add value to a model with AI, and what's wholly irrelevant when it comes to overall business model. Besides making important distinctions on where investments can make a return and how to raise money for your AI startup, this interview is also chock full of great analogies (give me golden dragons all day long—anyone?)

Direct download: TEP-Ben_Narasin-Mixdown.mp3
Category:Startup Funding -- posted at: 5:00pm PDT

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