AI 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: 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.
[This interview has been revised and updated.]
Episode Summary: How do neural networks affect your life? There’s the one that you walk around with in your head of course, but the one in your pocket is an almost constant presence as well. In this episode, we speak with Dr. Yoshua Bengio about how the neural nets in computer software have become more ubiquitous and powerful, with deep learning algorithms and neural nets permeating research and commercial applications over the past decade. He also discusses likely future opportunities for deep learning in areas such as natural language processing and individualized medicine. Bengio was a researcher at Bell Labs with Yann LeCun, now at Facebook, and was working on neural nets before they were the "cool" new AI technology as it's often perceived today.
Episode Summary: Statements about AI and risk, like those given by Elon Musk and Bill Gates, aren’t new, but they still resound with serious potential threats to the entirety of the human race. Some AI researchers have since come forward to challenge the substantive reality of these claims. In this episode, I interview a self-proclaimed “old timer” in the field of AI who tells us we might be too preemptive about our concerns of AI that will threaten our existence; instead, he suggests that our attention might be better honed in thinking about how humans and AI can work together in the present and near future.
Episode Summary: Emerj has had a number of past guests who have talked about neural networks and machine learning, but Dr. Pieter Mosterman speaks in-depth about the pendulum swing in this approach to AI from the 1960s to today. What we call neural networks as a general approach to developing AI has come in and out of favor two or three times in the last 50+ years. In this episode, Dr. Pieter Mosterman speaks about the shift in this approach and why neural networks have gone in and out of favor, as well as where the pendulum may take us in the not-too-distant future.
Episode Summary: Few astrophysicists are as decorated as Martin Rees, Baron Rees of Ludlow, who was a primary contributor to the big-bang theory and named to the honorary position of UK's astronomer royal in 1995. His work has explored the intersections of science and philosophy, as well as human beings’ contextual place in the universe. In his book "Our Final Century", published in 2003, Rees warned about the dangers of uncontrolled scientific advance, and argued that human beings have a 50 percent chance of surviving past the year 2100 as a direct result. In this episode, I asked him why he considers AI to be among one of the foremost existential risks that society should consider, as well as his thoughts around how we might best regulate AI and other emerging technologies in the nearer term.
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.