Episode Show Notes:
How Category Theory is Changing The Data Science Industry with Eric Daimler
Eric Daimler is the CEO & Co-Founder of Conexus.com. Daimler is an authority in Artificial Intelligence with over 20 years of experience in the field as an entrepreneur, executive, investor, technologist, and policy advisor. Daimler has co-founded six technology companies that have done pioneering work in fields ranging from software systems to statistical arbitrage.
Eric Daimler’s LinkedIn: https://www.linkedin.com/in/ericdaimler/
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Here’s the timestamps for the episode:
(00:00) – Introduction
(03:15) – The Obama administration made big efforts to bring in more technologists into government for innovation and digital modernization, and optimistically it will continue to trickle down into states’ governments for the benefit of all.
(09:00) – Human failure has come before machines got trained on human failures. Technologists can’t use massive amounts of data on every human problem and expect to come out with mind blowing results.
(09:28) – There’s limitations on technology. What can be done is to transform these whole domains of knowledge and map them onto others through a new type of math.
(11:31) – Categorical mathematics, or category theory, is above all those other mathematics that transform a problem from geometry into another problem called safe set theory, applying it to databases.
(12:41) – The math of category theory changes how we relate to data. This is “the math of the future”.
(14:09) – This is at a higher level of math, a level of abstraction to model the world in which companies operate their business, and make bigger decisions better and faster, reasoning large amounts of data to power a whole new change in our environment, as business people, as academics, as citizens.
(22:13) – Daimler’s three ways to solve data issues: matching data in a unified database, creating a silo and then selling a subscription to data silos and data interoperability math analysis through category theory.
(24:43) – AI definition has been misinterpreted over the years as algorithms that collect data and have machines do stuff, instead of a system that senses plans, acts and learns from the experience. And it senses plans and acts from inputs that are given to it.
(29:03) – Not everyone needs to be a programmer in a basement. There’s not just a choice between computer science or an English degree. What the current world of tech needs is policy considerations, places to get involved, and a way to focus educational efforts.
(33:46) – Automation doesn’t mean no human intervention. Societies benefit by that exchange of ideas and communication of values.