The AI in Business Podcast

When it comes to data science and machine learning, what are the related skills that are getting people jobs and what are the industries that are supplying those in-demand jobs? These are two important questions that we discuss in this week's episode with CrowdFlower's CEO Lukas Biewald, whose company is providing a pragmatic perspective of the industry by focusing on assessing job listings and related information in the field of data science. If you're a company that is interested in finding someone with in-demand data science and related skills, or if you're in the market to find a position in this field, this episode will likely be very useful!

When you go to Harvard Business School and then to McKinsey company to work in private equity, there's really only one thing left to do - go to Silicon Valley and start an AI startup. At least, this is exactly what CEO Praful Krishna did when he moved to San Francisco to start Coseer, an AI company focused on understanding natural language and unstructured data. In this week's episode, we speak about where unstructured data lives in a business, and how a business can be changed if the right data is unlocked. Krishna also discusses his experience in how executives are making decisions around how or how not to leverage AI in their companies.

While we've featured quite a few companies that use and implement AI systems, we've more rarely gone behind the scenes with companies or consultants providing AI-related services to companies. In this week's episode, we talk with Machine Learning Consultant Charles Martin, a data scientist and machine learning expert who has done freelance consulting on machine learning systems at companies including eBay, GoDaddy, and Aardvark. In this interview, Charles talks about the areas in AI that he believes are ripe for implementation in a business context, and where he sees businesses getting AI 'wrong' before getting to the hard work of implementing systems that work for them.

Tad Slaff is the founder of Inovance, the creator of TRAIDE - a strategy creation platform that use machine learning algorithms to help traders uncover patterns in assets and indicators and build more reliable trading strategies. In this episode, Tad speaks about the state of machine learning in finance today, and touches on how future applications of machine learning and trends may alter what gives an edge to one hedge fund or institutional investor over another.

It's more common to ask what AI can to do to win at games, but it's less common to ask what games can do to help develop AI. This is a particularly fitting topic after Google's DeepMind's defeat of Go, and in this episode we talk with New York University's Julian Togelius about his research in how games can help us develop AI. We discuss how simple AI has been used in more common video games; the 'smoke and mirrors' effect that is more often used to mimic AI; and the more innovative ways that AI are being used in gaming at present, setting precedents for the future role of AI in gaming.

What is intelligence? For some researchers, it may be quite possible to create an intelligent machine 'in a box', something without physical embodiment but with a powerful mind. Others believe general intelligence requires interaction with the outside world, inferring information from gestures and other features of functioning in an environment. Dr. Vincent Müller is of the belief that intelligence may involve more than just mental algorithms and may need to include the capacity to sense rather than just run a program. Vincent focuses on cognitive systems as an approach to AI, and in this episode he talks about what this means and implies, how this approach is different from classical AI, and what this might permit in the future if the field is developed.

You're a business, and you've collected data - now how do you now make sense of it? Bring in a technology called 'sentiment analysis', a form of machine learning that determines whether text is positive or negative. Slater Victoroff's company Indico provides a sentiment analysis API product that specializes in this task. In this episode, we talk about about the common misconceptions that businesses have about where 'big data' may be applicable, and the lessons he's learned by gaining more tangible insights from smaller sets of data for companies. He explains why big data is not necessarily better, and discusses the steps that companies should take early on to make sure they're prepared when it's time to apply machine learning to their processes.

How does automation influence society today? This is an open-ended question with likely endless answers that can be observed in many different areas of society. As a Writer, Speaker, and Professor in Media Theory and Economics, Douglas Rushkoff has made it his livelihood to examine the impacts of automation in our evolving digital society. In this episode, we speak about his 'disappointment' in how automation has been used by many industries without regard for employees' long-term well being, and how a cultural shift in industry priorities may be what's needed to make automation beneficial for the majority.

This week's in-person interview is with Dr. Adam Coates, who spent 12 years at Stanford studying artificial intelligence before accepting his current position of Director of Baidu's Silicon-Valley based artificial intelligence lab. We speak about his ideas around consumer artificial intelligence applications and impact and what he's excited about, as well as what he thinks may be more 'hype' than reality. He gives a an idea about applications that Baidu is working, to potentially influence billions of mobile and computer users worldwide. If you're interested in the developments of speech recognition and natural language processing, this is an episode you won't want to miss.

People often mark progress by what they see, but there's often much more going on behind the scenes, the up and coming, that marks actual current progress in any particular field. The same can said to be true for natural language processing, and Dr. Dan Roth's research in this field makes him privy to the advancements that most of us are bound to miss. In this episode, Dr. Dan Roth explains what the last 10 years of progress in natural language processing (NLP) have brought us, what's happening with approaches in developing this technology today, and what the next steps might be in a computer capable of real conversational speech and understanding language in context.