Alis In Dataland

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 dev

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 environ

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

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