The AI in Business Podcast

You might be aware that some of the articles online about sports or financial performance of companies are article written by machines; this machine learning-based technology is the burgeoning field of natural language generation (NLG), which aims to create written content as humans would—in context— but at greater speed and scale. Yseop is one such enterprise software company, whose product suite turns data into written insight, explanations, and narrative. In this episode we interview Yseop's Vice President Matthieu Rauscher, who talks about the fundamentals of natural language generation in business, and what conditions need to be in place in order to drive key objectives. Rauscher also addresses the difference between discover-oriented machine learning (ML) and production-level ML, and why different industries might be drawn to one over the other.

There is in fact a dark side to AI, although we're certainly not at the point where we need to fear terminators, but it's certainly been leveraged toward malicious aims in a business context. In data security, tremendous venture dollars are going into preventing fraud and theft, but this same brand of technology is also being use by the "bad guys" to try and steal that information and break into those systems. In this episode, I speak with Justin Fier, director of cyber intelligence at Dark Trace, who speaks about the malicious uses of AI and how companies like Dark Trace have been forced to fight these "AI assailants".

Most of our recent investor interviews have been Bay area investors, like Accenture and Canvas, and we don't usually get to speak with investors overseas, particularly in Asia. This week, however, we interviewed Tak Lo, a partner with Zeroth.ai, an accelerator program and cohort investing firm based in Hong Kong and focused on startup artificial intelligence (AI) and machine learning (ML) companies. Lo speaks about when he saw AI take off in China and the differences in that rise compared to the U.S. He also gives valuable insight on consumer differences in how the two populations interact with technology, and how these differences in the Asian market drive different business opportunities in China than in the U.S.

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.

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.

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.

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?)

There's been lot of hype around AI and ML in business over the past five years. Even among investors exist a lot of misconceptions about using ML in a business context, and how to get up to speed on and grasp and understand leveraging related technologies in industry. Recently, I talked with Benjamin Levy of BootstrapLabs in San Francisco, who I met through an investment banking friend in Boston. BootstrapLabs invests in Bay area companies, and Levy also travels around the world speaking about investing in AI companies and raising funds for new ventures. In this episode, Levy gives his perspective on what investors and executives get wrong about ML and and AI, and discusses how they can get up to speed on the applications for these technologies and leverage them and related expertise to really make a difference (i.e. increased ROI) in their businesses.

Uday Veeramachaneni is taking a new approach to machine learning in infosecurity, AKA infosec. Traditionally, infosec has approached predicting attacks in two ways: through a system of hand-designed rules, and through anomaly detection, a technique that detects statistical outliers in the data. The problem with these approaches, Veermachaneni says, is that the signal-to-noise ratio is too low. In this episode, Veermachaneni discusses how his company, PatternEx, is using machine learning to provide more accurate attack prediction. He also discusses the cooperative role of man and machine in building robust AI applications in data security and walks us through a common security attack scenario.

When it comes to finding an expert on interviewing and finding machine learning (ML) talent, Parshu Kulkarni may just be the guy to ask. Not only is Kulkarni one of a small subsegment of the global population with an advanced degree in data science who has also been hired to work in tech companies like eBay, but he's been on the unique side hiring of ML and AI talent. Today, Kulkarni works full-time as Head of Data Science at Hired, Inc., a giant platform for hiring top talent in tech and other areas. In this episode, he provide an interesting distinction between what individuals with experience in data science look for in potential hires versus those who do not have the tech background tend to look for, and also dives into the supply-and-demand landscape for data scientists now and in the future—an interesting interview for anyone looking to hire or be hired in the ML and AI space.