“Mathematics - Application and Applicability”, by Mark Steiner. Included in The Nature of Nature: Examining the Role of Naturalism in Science (2011). Edited by Bruce L. Gordon and William A. Dembski. ISI Books: Wilmington, Deleware. Key ideas Canonical applications (theories developed to describe an application) vs. non-canonical (applying mathematics in situations other than those that created them) Distinguishing applications of mathematics from mathematics itself Exploration of individual thinkers and their attempts to reconcile mathematics and the empirical world (including Gottlob Frege, Hartry Field, Eugene Wigner), with the group-theoretic leading the charge

Choices, Risks, and Reward Reports: Charting Public Policy for RL Systems. Thomas Krendl Gilbert, Sarah Dean, Tom Zick, Nathan Lambert (CLTC, 2022). With new machine learning advances comes new risks. This white paper addresses the risks associcated with Reinforcement Learning with an aime to empower policy makers with some richer understanding to ensure safety. The unique challenges facing RL are summed up well in the intro - “In ML, the primary risks have to do with outputs that a model generates.

What comes after minds? by Marvin Minsky. From The New Humanists: Science at the Edge (2003). Edited by John Brockman. Barnes & Noble Books: New York. Key quotes “No uniform scheme will lead to machines as resourceful as the human brain. Instead, I’m convinced that this will require many different ‘ways to think’ - along with bodies of knowledge about how and when to use them”. “Computer science has helped us envision a far wider range of ways to represent different types and forms of knowledge,…” “I see each emotional state as a distinctly different way to think”.

“Topology and Data” by Gunnar Carlsson Introduction Today I asked ChatGBT how much data Google stores, and was given the non-specific response that the volume would be measured in exabytes (billions of gigabytes) or zettabytes (trillion gigabytes). In problem spaces where there is a proponderance of data motivates my interest in this article, where we are exploring how topology can aid us in findin signal in a world of noise.