Nate Sauder – Millennials, Mountains, and Machine Learning– [ALIS in Dataland, Ep. 04] MOV37’s Founder and Chairman, Jeffrey Tarrant, takes time to speak with MOV37 Advisory Board Member, Nate Sauder, in Geneva, Switzerland. Nate’s background and involvement in machine learning development/deployment along with his unique perspective as a millennial within a scientist collective is discussed in tandem with the evolution and future of machine learning and artificial intelligence in general. Nate Sauder https://www.linkedin.com/in/natesauder/ MOV37 Website – http://www.mov37.com Twitter – @mov37ai Show Notes: 2:31 – Nate Sauder Background 4:43 – Tell us a little on what you were doing after [attending] Oxford; what was next? 6:01 - What broad conclusions do you have from that [work] experience? 8:06 - NS: …look back at Private equity being this sort of vehicle for bringing machine learning in to the real world. JT: And the private equity component that you’re talking about, it takes a long term to develop these things and develop a market, right? 9:17 – When we first met I remember asking you […] where do you live? […] you said I live in a scientists’ house, can you tell us about that? 12:28 - I saw the TOPOS [scientists’ house] logo […] so do you want to talk about that? 13:06 – Beyond Enlitic, then you went off and did something very entrepreneurial, you talked about your partner. Let’s talk a little bit about that and then go on and talk about the future of artificial intelligence and machine learning and where you see things going. 16:08 – What’s next? [For NS and world of machine learning] 18:04 – Any final comments?

ALIS in Dataland

Nate Sauder – Millennials, Mountains, and Machine Learning – [ALIS in Dataland, Ep. 04]

Raphael Douady – The Anti-Fragile Portfolio in Fragile Markets: A Stony Brook Mathematician's View – [ALIS in Dataland, Ep. 03] Raphael Douady, Robert Frey Endowed Chair for Quantitative Finance at Stony Brook, New York, and Adil Abdulali, President and Chief Science Officer at MOV37, delve in to the mathematical concept of fragility and anti-fragility and how it applies to quantitative finance and markets in general. Raphael Douady https://hal.archives-ouvertes.fr/hal-01151340/document MOV37 Website – http://www.mov37.com Twitter - @mov37ai Show Notes: 1:12 – Raphael Douady Background 2:40 - There is a concept of fragility, which has been introduced […] can you give us some thoughts on [fragility and anti-fragility] 4:34 – Isn’t this concept of fragility then linked to what we understand in markets as convexity? 5:41 - Are there examples of this in life […] away from the markets? 7:32 - Anti-fragility is very closely related to adaptability, in a sense, or the ability to adapt to different environments. Traditional quant was fragile […] the idea is to have something more adaptive, would that be anti-fragile in your mind? 12:20 – What are some of the inspiring characters of your past (intellectually or otherwise)? 17:10 – When you’re not doing mathematics/finance, what do you enjoy doing?

Raphael Douady – The Anti-Fragile Portfolio in Fragile Markets: A Stony Brook Mathematician’s View – [ALIS in Dataland, Ep. 03]

Zubin Siganporia –Homomorphic Encryption, Quantum Computing: An Oxford Fellow’s View– [ALIS in Dataland, Ep. 02] MOV37’s Chairman and Founder, Jeffrey Tarrant, interviews Zubin Siganporia, mathematician, founder of QED Analytics and Fellow at Oxford University, as well as a member of our MOV37 Advisory Board while visiting Oxford University. Zubin explains his work in the areas of cryptography, math, data, and blockchain and how and why the application of these areas are valuable to other industries. Zubin Siganporia Company website - http://www.qed-analytics.co.uk University website - https://www.maths.ox.ac.uk/people/zubin.siganporia MOV37 Website – http://www.mov37.com Twitter - @mov37ai Show Notes: 1:08 – Zubin Siganporia Background 2:04 – On the topics of data, data hacks, GDPR - how do we know, with the threatening environment out there, that we have security of data? In general, we’ve heard of homomorphic encryption and how it interrelates to these subjects, but what is homomorphic encryption and how can it be applied? 7:02 – Are there other forms of encryption that are anywhere near as sophisticated as homomorphic encryption, or is this the way the world is going, in respect to how you just described it, and what’s next? 8:37 – So far as blockchain is concerned, there is the threat quantum computing could have on making it less secure as in immutability of data. Can you talk about that in context of what we’ve been discussing or any form of cryptography that’s protecting the immutability of the blockchain? 13:10 – Methodology of digital signatures-has it arrived yet or does it need to be developed? 14:01 – What sort of applications are you seeing in other industries? 17:40 – You mentioned applications in sports and law, is there anything else? Health and life sciences? 22:05 – How do you get the domain knowledge to parachute in and find these solutions? 25:23 – Here at Oxford you’re the fellow instructing the students; how much of this is a dynamic back and forth of learning from your students as well? 29:53 – Is there anyone of a creative type that has influenced or inspired you in a particular area?

Zubin Siganporia – Homomorphic Encryption, Quantum Computing: An Oxford Fellow’s View – [ALIS in Dataland, Ep. 02]

Hein Hundal - Artificial Intelligence: The Nuclear Winter is Over – [ALIS in Dataland, Ep. 01] Hein Hundal of Random Order joins MOV37’s Chief Investment Officer Michael Weinberg to discuss the current “Machine Learning Revolution”, the history leading up to it, the challenges we are still facing regarding machine learning and AI, and its subsequent impacts on society. Hein Hundal Blog - http://162.243.213.31/ MOV37 Website – http://www.mov37.com Twitter - @mov37ai Show Notes: 1:21 – Hein Hundal Background 2:29 – “Machine Learning Revolution” and how did neural nets become the most commonly used machine learning method for pattern recognition? 6:09 – If we’re creating neural nets, why don’t we understand how they [neural nets with dropout techniques] really work? Shouldn’t we understand? 8:01 – Describe a generative adversarial network (GAN). How did we evolve from neural nets to GANs? 11:08 – Are there broader implications in society from GANs? 12:26 – How does a practitioner risk getting lost in the proverbial forest, or, having a neural net create a mirage? (Referring to overfitting). 13:44 – DeepMind’s Go computer, AlphaZero, is quite adept at playing chess, despite having been trained and built to learn Go. Does this imply that we are moving down the path of general intelligence? 17:00 - Is Machine Learning/are neural nets actually creative? 12:58 –What are your views on how disruptive AI will be to employment?

Hein Hundal – Artificial Intelligence: The Nuclear Winter is Over – [ALIS in Dataland, Ep. 01]

An exploration of the intersection of man, machine, and data science, and how its effects are creating a revolutionary new form of investment management: Autonomous Learning Investment Strategies (ALIS).

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ALIS in Dataland

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