The AI in Business Podcast

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. For example, computer vision systems are often still not as effective as the human eye, so we still need human vision systems to recognize text or to recognize object placement, and take action accordingly (in a store, warehouse, or other setting). A human can fill that role as a piece of wetware until the software or the hardware catches up. How will man and machine collaborate in the future? We explore these dynamics in depth in this week's interview. For more interviews and insights from leading thinkers in AI and automation, visit: www.TechEmergence.com

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. We also touch on some of the impact that might be made if machine learning is able to overcome its own boundaries in terms of computational research, in terms of certain algorithms, and what kind of impact that might have in the arena of autonomous driving and in the realm of natural language processing (NLP). See more episodes online at: www.TechEmergence.com

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. 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. See more episodes at: www.TechEmergence.com

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 areas in heavy industry where AI is currently playing a role in heavy industry, what type of capabilities and functions are automatable, 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.

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.

Guests Will Jack and Nikhil Buduma co-founders of Remedy Health Inc discuss the challenges involved in collecting, setting up and structuring data in order to implement AI in healthcare. By the end of this episode, listeners will have gained insight into the challenges of healthcare data systems, and the potential solutions to cleaning and organizing this data for healthcare AI applications.

If there's any industry ripe for disruption by AI and ML applications, it's healthcare. This week, we speak with ElevenTwo Capital's Founder and Managing Partner Shelley Zhuang, whose investment focus (among other spaces) is on innovative healthcare services. In addition to discussion how AI is helping propel genomics, diagnostics, therapeutic treatment, and other innovations, she touches on what the healthcare space might look like in the next 10 years. For healthcare startups looking to break into the healthcare market, Zhuang doesn't pretend to have simple answers; however, she identifies commonalities among companies that have been successful in smart preparation for meeting regulatory and other industry considerations. This interview was recorded live in San Francisco at Re-Work's Machine Intelligence in Autonomous Vehicles Summit in March 2017.

In the last few months, we've had a string of fantastic interviews with investors and have gained a cross-industry picture of what's important for start-ups and emerging trends in the AI and ML space. This week's interview is no exception. Ann Miura-Ko, co-founder and partner at Floodgate, starts with an explanation of the "self-driving enterprise" concept, her functioning idea about AI investing and the future of software in general. Her high-level insights embody an interesting emphasis on the dynamic of human-machine interactions and relationships cross industries, including the constant workflows and interactions of people using software and bolstering the predictive and prescriptive analytics capabilities of that software. While forward-thinking, Miura-Ko also paints a picture of how these synergistic relationships between humans and machines are happening with companies today.

Getting an investor's perspective in AI is always a good idea for companies looking to raise money, in terms of understanding of excites VC's, but even more broadly an investor's perspective can point to emerging factors in how AI is going to impact a particular industry, shining a light on industry developments, including the commonalities that matter for any company, in any industry, leveraging these tools that are increasingly embedded with AI. In this episode we interview Polaris Partners' Gary Swart, who speaks about elements of companies that are laying the right foundations for using AI optimally and making a more defensible, durable company in an increasingly competitive landscape.

The upsurge of malware and sophisticated attacks continue to keep cybersecurity in the spotlight, but new developments in AI and deep learning offer more advanced solutions to combat security threats. This week, we catch up with Eli David, CTO of Deep Instinct—a company founded in Israel with US headquarters in San Francisco—that applies deep learning to information security. David spoke with us about why and how the deep-learning approach to AI is relevant to the future of cybersecurity. Companies that are actively building their own security infrastructure, or are in growth mode and know they will eventually need to, should find this interview particularly relevant. David shares his perspective on how and where potential cyberthreats focus their attacks and the resulting ramifications for industries as they look for best ways to respond and prevent attacks.