Appetite for Disruption: The Business and Regulation of FinTech

As a neuroscientist for decades, Dale has studied the human brain. Now he has directed what he knows about the human brain to artificial intelligence and machine learning – or, put differently, to the computer brain. Tackling questions about theoretical neuroscience (how the brain does what it does), consciousness (which he thinks is replicable in machines), and perception (what we see may not be reality), Dale offers fascinating insights into both human behavior and machine behavior and how they are linked through the adage of “practice makes perfect.”

Is it more difficult to create the tech or to get it adopted? We continue discussing with Amir his hopes for the internet of things and developing a sensing overlay on the world. We discuss what sparked him to want to create a web of connected devices in the first place and why building the technology is only part of the battle. It takes more than great tech to succeed – you also need people to accept and ultimately adopt your innovation. Acceptance and adoption begin with educating people about what the tech enables – how it can make people’s lives better – so that it can be unleashed in practice. Lee and Troy share some post-conversation thoughts.

Helium is about the convergence of two big ideas – blockchain and the internet of things – to help fashion the “data layer on the world.”  Helium’s innovation is to get “hotspots” (combining software and hardware) into people’s hands to effectively crowdsource a network of devices.  Using radio waves, blockchain, and the internet, the potential is to create an every-day-internet-of-things using every-day devices so that conceivably anything can be tracked anywhere (if the vision succeeds).  What’s one real-world possibility?  Connecting low-powered sensors that can detect a potential natural disaster, such as a fire, and alert people to the threat.  Starting in Austin, Texas, Helium is rolling out their hotspots to consumers this year.

Our interview with Roman Ginis continues in a more philosophical vein, as we talk about the perspective and spirit of engineers and the open source movement. We also cover what “optimization” (as compared to “maximization” or “minimization”) is all about. Optimization takes a lot of hard work, trial and error, a good sense of the problem you're trying to solve, and recognizing that decisions involve trade-offs. Troy and Lee have a post-interview discussion about where marketplaces and machines will fit in the world of tomorrow.

Roman Ginis is the CEO of Imperative Execution, a firm that is dedicated to using AI and machine learning to create a better trading experience. For example, can you buy or sell without your own buying or selling affecting the price at which you trade? Even though people have been trading securities (and other things) for a very long time, there is still room for technological innovation. We discuss with Roman how new technology allows new ways of addressing longstanding challenges.

Professor Hal Abelson of MIT continues to explain the world of machine learning, including its limitations. Have you ever considered that a computer can recognize some images better than people can, but the computer might not recognize that an image is a dog if the picture is upside down? Even the best computer scientists are still working to develop clear and fulsome explanations of why and how machine learning works. As we also discuss with Hal, even the best-trained machines lack what we, as humans, think of as judgment, and machines certainly lack human values. Perhaps new advances will move computers more towards possessing judgment and values – the ability to have compassion or empathy, for example. If so, that could have big implications for how key decisions that affect our lives get made.

Professor Hal Abelson of MIT joins Troy and Lee to explain key elements of the internet, machine learning, and the policy that governs them. He has broad experience from more than 40 years at MIT working with various types of computer systems. His insights into how things work and his efforts to educate policy makers is informative and timely given all technological breakthroughs that we’re living through. We take a deep dive into how machines learn, the limits of machine learning and artificial intelligence, and the sometimes inexplicable nature of what machines tell us. Our discussion got us thinking that machines and humans have some similar learning characteristics, even though machines do it with much less intuition and without any type of understanding... at least not yet.

In this second part of our conversation with Naval Ravikant, we talk about how financial services will be disrupted, from blockchain to payments and beyond. We then venture into the future of work, such as what will the gig economy mean for how we will work in the future? At the end, Troy and Lee chat a bit about their impressions from the conversation with Naval, giving some additional food for thought. We hope you like this new feature. Of course, nothing said in this episode is investment, legal, or any other advice of any kin

We welcome Naval Ravikant, a prodigious entrepreneur who has made a career helping other entrepreneurs. In this episode, we discuss how to be an effective entrepreneur, including the game theory of venture capital. Plus we cover complexity theory (or, Why can’t everything be simpler?), the “infrastructure of innovation,” and the first-principles thinking of science. Of course, nothing said in this episode is investment, legal, or any other advice of any kind.

More with Professor Johnson of Virginia Tech. This time, we learn about his “big humanities” response to “big technologies.” Lots to consider, from brain-to-computer interfaces, to robotic morality, to democracy, to trust. We liked his ideas about the inclusiveness that is needed for human beings to think these things through to ensure that voices and experiences are not left out. Some big philosophical stuff that left Troy and Lee in deep thought. We aren’t sure what the future holds, but we are glad people like Sylvester are helping the world grapple with these foundational questions.