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Episode Summary: One of the most memorable moments from this interview is when our guest mentioned that Larry Page hired him to solve intelligence; very few people can say this, and this says something about today’s guest, Dr. Nando de Freitas - a senior researcher at Google and professor at Oxford - as well as the gravity of his present work. Today, I speak with Nando about a topic well known through his research at Google, deep learning. de Freitas gives his perspective on the basics of deep learning, the applications in conversational interfaces and recognizing images and videos, and what the future of this technology might look like in the nearer future.

Episode Summary: In this episode, we speak with Dr. Matteo Berlucchi, the founder of Your.MD, which uses artificial intelligence to create one of the first personal health assistant platforms in 70+ countries. Berlucchi talks about the challenges in making an AI do what you want, specifically helping people self diagnose and seek proper treatment. He discusses the multiple approaches to AI that are blended together in order to yield optimal results, and touches on the sometimes stark differences between what AI can do in the lab versus the functional application for tens of thousands of people. If you're interested in the diverse applications of AI and the challenges in running a startup, Dr. Berlucchi's makes for an interesting episode.

In the broad sweep of AI's current worldly ambitions, machine learning healthcare applications seem to top the list for funding and press in the last three years.

Episode Summary: CTO and Co-founder of Sumo Logic Christian Beedgen gives his take on how to glean return on investment from applying machine learning to companies. There are no easy answers, but Beedgen boils down simple concepts for thinking about humans thinking through causation, machines working out correlations, and how the combination of the two can glean us better ideas and get to answers faster than humans can do alone.

1 - People Favour Expressive, Communicative Robots Over Efficient and Effective Ones

Episode Summary: Artificial intelligence (AI) and virtual reality (VR) are often seen as different trends, but there is a lot of overlap in these areas, where you might not expect. Lucid VR's CTO Adam Rowell speaks today about how AI plays a role in making VR work, augmenting the accuracy of images and making a more immersive and convincing experience for users. Rowell also touches on non-gaming VR apps that he and his company are excited about launching in the future.

At the recent KDD2016 (knowledge discovery and data mining) conference in San Francisco, Managing Director at Amazon Development Center Germany GmbH and Director of Amazon Machine Learning Ralf Herbrich discussed three lessons that he’s learned while working with sparse machine learning models at scale.

Episode Summary: At Facebook headquarters, I learned there are 1 billion active users every month. In a more recent interview at eBay headquarters in San Jose, l learned that the well-known digital store has over 1 billion products for sale. eBay is, without a doubt, the world’s largest marketplace, and there’s enough incoming data to keep a large team of data scientists busy for years. I speak with Zoher Karu, eBay’s chief data officer, about how eBay leverages data and machine learning to create a better experience for its customers and also their sellers, shedding light on important lessons for anyone looking to sell a product online.

Episode Summary: In this week’s episode, I speak with Igor Baikalov, chief scientist at cybersecurity company Securonix, about the trends in data security and where security itself has had to take a step up in the last five years. Igor touches on major meta-trends that have forced data security to advance, as well as what has made AI and machine learning a ‘requirement’ of modern data security strategy, something that has changed significantly in the last decade. Igor sheds light on these issues and likely future trends in cybersecurity over the next five to 10 years.

Machine learning has had fruitful applications in finance well before the advent of mobile banking apps, proficient chatbots, or search engines. Given the high volume, accurate historical records, and quantitative nature of the finance world, few industries are better suited for artificial intelligence. There are more uses cases of machine learning in finance than ever before, a trend perpetuated by more accessible computing power and more accessible machine learning tools (such as Google's Tensorflow).