podcast interviews Articles and Reports
Our podcast interviews feature the best and brightest executives and researchers in artificial intelligence today, and each episode highlights current and near-term AI use-cases of value for business leaders. Explore our full list of AI podcast episodes below:
Episode Summary: 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!
Episode Summary: 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 launch 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.
Episode Summary: 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.
Episode Summary: Tad Slaff is the founder of Inovance Financial Technologies, the creator of TRAIDE - a strategy creation platform that uses 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.
Episode Summary: 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 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.
Episode Summary: 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.