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Show Notes:

[00:02:32] – How he came up with Numerai and how it related to his background

[00:04:08] – How he works with and models the data for his system

[00:05:24] – Describing machine learning as it relates to his work, and specifically linear regression

[00:07:11] – The important stages in his sequence

[00:08:46] – How the scale in the number of data scientists they use is different from other areas

[00:11:30] – Which is the most important aspect of creating alpha; their data, algorithm work, proprietary ensembling of those algorithms.

[00:14:30] – The idea of staking in blockchain

[00:17:30] – Does the magnitude of the stake matter in blockchain

[00:19:10] – Understanding the full incentive structure for both staked and unstaked work

[00:21:07] – How is the prize pool determined

[00:22:29] – Philosophy on how to source interesting data

[00:26:11] – His thoughts on the crowd model and the wisdom of crowds

[00:27:12] – The size of stakers for Numerai

[00:27:51] – Interpreting the models and knowing when something is broken

[00:30:03] – How they think about people not submitting their models

[00:31:48] – Their model building

[00:32:39] – Most interesting set of things they are working on to improve the overall process

[00:37:11] – How people can come along with their own data

[00:39:00] – His thoughts on the quantitative investment community

[00:40:44] – What else is interesting him in the hedge fund world

[00:44:03] – Building a marketplace and staving off competition

[00:46:16] – Kindest thing anyone has done for him