kubeia

Generative linguistics has a longer prehistory than most linguists realize. The rewriting systems that Chomsky brought into linguistics as generative grammars were explicitly defined more than a century ago, as part of a project to formalize inference rules in logic, and were later applied to studying mathematical properties of certain kinds of infinite sets. Their developer was the mathematician and logician Emil Leon Post, whose work was inspired by Clarence Irving Lewis and Cassius Jackson Keyser. Post also proved the first two theorems about what linguists now call generative capacity. The idea of deploying Post’s systems within linguistics was first suggested in 1950 by the logician Paul Rosenbloom. I review the relevant pre-1950 work, and explore the reasons for its having remained so little known among linguists.

You don't. You teach math and writing — preferably with a pen.

For another take on reconstructing ancient greek music, you should look at the work of Annie Bélis (mostly in french but she has a wikipedia page https://en.wikipedia.org/wiki/Annie_B%C3%A9lis) and the musical ensemble - Kérylos - that she created https://www.youtube.com/channel/UCHzK2yVKDmpWEC-igDSeOYg

"Founded at the beginning of the 90s, the Ensemble Kérylos, directed by Annie Bélis, is dedicated to Ancient Greek and Roman Music. It plays only authentic scores as accurately as possible, using instruments that are faithfully reconstructed."

For a contrarian view on the benefits of drinking (up to one drink a day) see this interesting thread: https://x.com/chrismasterjohn/status/1731863379902349781

"In the study widely claimed to show that there is no safe level of alcohol for the brain, going from zero to one alcohol unit (half a drink per day) was not associated with any harm in females, and was associated with slightly better brain markers in males."

Crushing reminder, I will.

"Peter and I had been in the same 1982 incoming class at Carnegie Mellon. We knew each other, but were not really close. I bumped into Peter in the Computer Science lounge a few weeks before the Fredkin Masters Open, and mentioned what I was doing. Peter was asking a lot of questions, but I had no idea that he was thinking about getting IBM to hire us. Years later, Peter described how he got the upper management in IBM interested in continuing the project inside IBM. It was around the time of the Superbowl season. Peter happened to be in the men's room with Abe Peled, the IBM Research Vice President of Computer Science. They started talking about how expensive the Superbowl TV commercial spots were. Peter suggested that he knew a way to gain much greater publicity at far lower cost. He knew this group of graduate students working on computer chess at Carnegie Mellon, and he believed that this team would create the first chess machine to beat the World Champion. Given the historical significance of the quest, and the latent public interest, IBM could stand to gain huge advertising value from the endeav- or. Oh, yes, members of the team were world-class people that IBM would be interested in hiring anyway. Abe got interested and asked Peter to look into it."

"In the end, the name Deep Blue was chosen, submitted by Peter Brown, that same classmate who was partially responsible for our being hired by IBM. For winning the contest, Peter was treated to a nice dinner with us, and a chance to play Deep Thought. He declined the second part of his award."

Feng-hsiung Hsu, Behind Deep Blue

I've never made the link between that Peter Brown and Rentec... Also he confirms a lot of anecdotes from The Man Who Solved the Market by Gregory Zuckerman.

Listen to the audio: https://www.youtube.com/watch?v=hWX8V9KSZM8

From the Statistical Consequences of Fat Tails (N. Taleb):

"We will address ad nauseam the central limit theorem but here is the initial intuition. It states that n-summed independent random variables with finite second moment end up looking like a Gaussian distribution. Nice story, but how fast? Power laws on paper need an infinity of such summands, meaning they never really reach the Gaussian. Chapter 7 deals with the limiting distributions and answers the central question: "how fast?" both for CLT and LLN. How fast is a big deal because in the real world we have something different from n equals infinity."

https://arxiv.org/abs/2001.10488

For the discovery of those ‘too massive galaxies’ you can look at the following paper:

A population of red candidate massive galaxies ~600 Myr after the Big Bang

https://www.nature.com/articles/s41586-023-05786-2