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: In this episode we talk to Adam Spector, the Co-Founder & Chief Business Officer at LiftIgniter, a company which provide a service which modulates website experience per users, for an array of different businesses. Adam and I discuss what the tech giants are doing to customize their business experiences, what data they’re using to continually alter user experience and what industries and sectors might be impacted by this aggregate trend as it moves forward.
Episode summary: Imagine you work in a large organization with tens of thousands of employees across multiple countries, a business that’s been around for over a hundred years, and all of a sudden you have people in one department who are interested in applying chatbots, colleagues in another department who wish to implement sentiment analysis and still another department that wants to begin using AI for fraud and risk analysis. How do you manage to put all these pieces together?
Episode summary: In this episode we speak with Sangeeta Chakraborty Chief Customer Officer at Ayasdi and discuss the applications of data science and AI in healthcare, what hospitals and healthcare systems are doing to adopt this kind of technology, and how this approach can be extended to other forms of enterprise. This interview was conducted in-person at BoostrapLabs annual AI conference in SF.
Episode summary: Marshall Brain discusses how wetware (the human brain) is increasingly becoming a part of a bigger system which may in itself be managed by software systems. The roles and relationships of humans and machines are rapidly changing. With the increasing advances in technology, there are fewer and fewer skills or activities that an enterprise needs from human beings, and they only need those until they can be replaced by software or hardware.
Episode summary: Machine learning currently faces a number of obstacles which prevent it from advancing as quickly as it might. How might these obstacles be overcome and what impact would this have on the machine learning across different industries in the coming decade? In this episode we talk to Dr. Hanie Sedghi, Research Scientist at the Allen Institute for Artificial Intelligence, about the developments in core machine learning technology that need to be made, and that researchers and scientists are working, on to further the application of machine learning in autonomous vehicles.
Episode Summary1: Fraud attacks have become much more sophisticated. Account takeovers are happening more often. Many security attacks involve multiple methods and unexpected attacks can devastate businesses in just a few days, as we saw with Neiman Marcus and Target. False promotion and abuse is seen not only on social media sites but is also targeted at business. To combat these risks, fraud solutions need to be smarter to keep pace with fraudsters to prevent attacks and react quickly when they do happen. This requires a fast-learning solution with the ability to continually evolve - which calls for the application machine learning for fraud detection. In this episode we talk to Kevin Lee from Sift Science and examine the shifts in the info security landscape over the past ten or fifteen year. Lee also highlights what new kinds of fraud are now possible and what machine learning solutions are available.
Episode summary: Unlike the field of self-driving cars, the fields of construction, mining, agriculture, and other classes of “heavy industry” involve a huge variety of equipment and use-cases that go beyond traveling from A to B. The heavy industry leaders of today are no farther behind automakers in their understanding that AI and automation will be essential for the future of their companies. In this episode, guest Dr. Sam Kherat discusses the applications of AI in heavy industry, including: What type of capabilities and functions are automate-able, and at what level. He also shines a light on how AI might affect the future of the industry within the next 2-3 years, and in what ways we can expect large equipment to become more autonomous.
Episode summary: Although machine learning in finance is far from new, it is merely at the cusp of a much wider set of applications (in all segments of finance, from insurance to bookkeeping and beyond). Already machine learning has overhauled so many aspects of the financial landscape, from accounting to trading, and it is destined to have more and more impact as it develops further. Guest Alexander Fleiss and his team at Rebellion Research are developing and using AI which uses quantitative analysis to pick investments. Fleiss discusses the current status of machine learning in the world of finance as well as lesser-known niche applications that don’t make headlines - but do make a big impact on how businesses are run. He then goes on to explore the effects of future innovative applications of AI in the financial domain.