Dale Purves, Duke Neuroscientist, Part 2: What Human Brains Teach Us About Computer Brains

As Dale explains it, our brains have been practicing how to respond to stimuli for millennia. While humans have been around longer than computers, computers can “practice” harder and longer than humans can. And so, through practice, computers can get really good at what they do really quickly. Interestingly, according to Dale, different computers may learn differently, and so machine learning may yield different results by different computers – sort of like how different people learn differently and thus make different decisions. Where will it all lead? Troy and Lee’s wrap-up conversation explores some of the themes they discussed with Dale.

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Dale Purves, Duke Neuroscientist, Part 1: Practice, Practice, Practice – The Makings of Artificial Intelligence

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.”

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Amir Haleem, CEO of Helium, Part 2: Creating Tech, Then Driving Adoption

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.

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Amir Haleem, CEO of Helium, Part 1: Blockchain and the Internet of Things

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.

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Roman Ginis, Part 2: Optimizing Marketplaces (and Everything Else)

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.

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Roman Ginis, Part 1: Making Marketplaces More Intelligent

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.

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Prof. Hal Abelson, Part 2: Lessons from a Computer Scientist: Should a Computer or a Person Make that Decision?

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.

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Prof. Hal Abelson, Part 1: Lessons from a Computer Scientist: Machines, Humans, Regulation, and More

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.

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Naval Ravikant Part 2: Will Your Phone Do Everything?

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.

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Naval Ravikant Part 1: Business Plus Science Equals Technology

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.

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Prof. Sylvester Johnson, Part 2: What does it mean to be human? “Big Humanities” to Match “Big Technologies.”

With the help of Professor Johnson of Virginia Tech, we discuss what it means to be human in an age of accelerating technology, where computers and robots can think and maybe even feel. Professor Johnson explains several of the amazing technologies that exist today, both “outside” people and those being incorporated directly into people’s bodies, and how industry and government is pushing this forward. He really expanded our views on how to think about humanity and technology.

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Prof. Sylvester Johnson, Part 1: What does it mean to be human? From Aristotle to AI.

With the help of Professor Johnson of Virginia Tech, we discuss what it means to be human in an age of accelerating technology, where computers and robots can think and maybe even feel. Professor Johnson explains several of the amazing technologies that exist today, both “outside” people and those being incorporated directly into people’s bodies, and how industry and government is pushing this forward. He really expanded our views on how to think about humanity and technology.

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Clearpool: Securities Trading Is Technologically Intense

We chat with folks from Clearpool, a financial services firm that provides a wide variety of technology tools and routing infrastructure to sell-side broker-dealers to help them seek best execution for their institutional clients. We cover the technical aspects of how trading works, the speed at which it happens, and some of the technologies being deployed. These technologies are cutting edge in terms of speed and reaction time, which may pave the way for speed in other industries. The Clearpool team also gave us insight into being a startup in a competitive space.

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