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 we think about AI, we often think about optimizing some particular task. In most circumstances through computation there is an optimal chess move, or an optimal way to determine pattern in data, or solve a math problem, or route info through servers. Most of us are aware of these uses, but what about creative tasks? Can these also be optimized? If we want to give a computer information and tell it to create powerpoint slides, is there an optimal way to create such slides? Dr. Philippe Pasquier’s computational research is focused on artificial creativity. In this episode, we talk about how to define a very new field, train machines in this area, and also discuss trends and developments that might permit such technology to thrive in the next 10 years.
Episode Summary: There’s a small lab in Pennsylvania that may know your gender, age, and understands facets about your personality, whether you’re introverted or extroverted, for example…and it's using machine learning to help make conclusions from social media information. For those who are raising an eyebrow, know that they’re not tapping into people’s accounts without permission. The described study is happening at University of Pennsylvania and is led in part by Dr. Lyle Ungar. In this episode, we talk about the focus of his work - on finding patterns between users and their language on social media content, and building an understanding for how this information might help individuals and communities in the future.
Episode Summary: We’ve featured a number of artificial intelligence researchers on the show, but today we switch gears and dive into the business side of the industry. In this episode, Dr. Mazin Gilbert (who earned his PhD in Engineering) breaks down AT&T’s efforts to make more intelligent systems large-scale. How do they train their network to route traffic through the right nodes on holidays, when certain areas of traffic are overloaded? How can a system know, based on signals from hardware, which pieces might be going bad and need replacing and send out a message to alert the company? Making a network ‘aware’ is a large challenge, but Mazin gives an insider’s perspective as to how AT&T uses machine learning technologies in order to remain profitable.
Episode Summary: Are we losing something with technology? [hint text="There are two sides to every argument, including this one. Dr. Sherry Turkle is of the belief that there’s enough mounting scientific evidence that points toward loss of empathy and self knowledge due to increasing interaction with machines"] There are two sides to every argument, including this one. Dr. Sherry Turkle is of the belief that there’s enough mounting scientific evidence that points toward loss of empathy and self knowledge due to increasing interaction with machines. In this episode, we discuss Dr. Turkle’s research and her subtle fears for the future, particularly of those about machines that replicate emotions or conversation but that don’t actually feel anything - is the ability to form real connections between two beings at risk of being lost?
Episode Summary: A lot of AI applications are not really “smart”, at least not in the sense of the word as most humans might envision a true artificial intelligence. If you know how Deep Blue beat Gary Kasparov, for example, then you may not believe that Watson is a legitimate thinking machine. Our guest this week, Dr. Pei Wang, is of the belief that building a Artificial “General” Intelligence (AGI), what researchers define as an entity with human-like cognition, is a separate question from figuring out AI applications in the more narrow sense. In this episode, Dr. Wang lays out three differentiating factors that separate AGI from AI in general, and also talks about three varied and active approaches being taken to try and accomplish AGI.
Episode Summary: Ever had the perfect book recommended to you by Amazon or gave a pleasantly-surprised thumbs up for a song selected for you by Pandora? Both services are powered by recommendation engines, which are gaining steam int he commercial space. In this episode, we speak with Entrepreneur Raefer Gabriel, who works for Delvv on the commercial applications of recommendation engines. We talk about how this technology works, and how it comes to learn from reviews, ratings, and consumer interactions. Gabriel also gives perspective on how these engines might be enhanced and applied in the future, a good topic for those of you in the startup world.