| Matt: Hello and welcome to Elucidations, an unexpected philosophy podcast. I'm Matt Teichman, and with me today is Sam Enright, editor-in-chief of The Fitzwilliam, which is an online publication about Ireland policy, philosophy, literature. And he is also a non-resident fellow at the policy think tank Progress Ireland and an Emergent Ventures fellow. And he's here to discuss lifelong learning. Sam Enright, welcome. | |
| Sam: Thanks for having me. | |
| Matt: So, I would definitely put you at the top of my list of people who are r- really good at learning. Um, but before we sort of talk about some of the, uh, ways that you like to learn, maybe we could just address this question of, like, is it a good thing to learn? I mean, I think a lot of us assume the answer is yes. I certainly assumed that. I love learning. But, like, I don't know, is that wrong? Should we... Is it a waste of time to be learning? | |
| Sam: (laughs) I think the question of the returns to effort of learning about different fields is a really non-t |
ASAT
Frontier Safety
Um.
Hello and welcome back to video games. I'm Tim Rogers. You are watching the Action Button review of DOOM — a video game developed by id Software and published for the Microsoft Disk Operating System on December 3rd, 1993; the Sega 32X and Atari Jaguar in 1994; the Super Nintendo Entertainment System and the Sony PlayStation in 1995; the Panasonic 3DO in 1996; the Sega Saturn in 1997; the Acorn Archimedes in 1998; the Nintendo Game Boy Advance in 1999; the Microsoft Xbox 360 in 2006; Apple iOS in 2009; the Microsoft Xbox 360 again in 2012; and on the Nintendo Switch, the Sony PlayStation 4, and the Microsoft Xbox One in 2019.
Updated with a fourth episode, "Thy Flesh Consumed," in The Ultimate DOOM — released for Microsoft personal computers on April 30th, 1995 — and by John Romero with a fifth episode, SIGIL, released for personal computers on May 22nd, 2019. Created by John Carmack, John Romero, Adrian Carmack, Kevin Cloud, and Tom Hall, with
| library(matlib) | |
| gamma <- 2 | |
| rho11 <- 1.15 | |
| rho22 <- 0.05 | |
| mu1 <- 0.3 | |
| r <- 0.05 | |
| delta <- 0.05 | |
| x <- rnorm(1000000) |
| https://twitter.com/g_leech_/status/1728731355029393447?t=VN50RyPqwWFfcC63tMP_ng&s=19 | |
| https://twitter.com/g_leech_/status/1723630099507871780?t=YFYUi0eNkkLXOzzmbMtoGA&s=19 | |
| https://twitter.com/g_leech_/status/1623044128190697483 | |
| https://twitter.com/g_leech_/status/1639725932448456705 | |
| https://twitter.com/g_leech_/status/1630276985963556865 | |
| https://twitter.com/g_leech_/status/1622321776595189765 | |
| https://twitter.com/g_leech_/status/1668960035068694533 | |
| https://twitter.com/g_leech_/status/1715654603973312949 | |
| https://twitter.com/g_leech_/status/1713930232632135986 | |
| https://twitter.com/g_leech_/status/1711441986878644597 |
| import numpy as np | |
| import gjp | |
| # https://github.com/niplav/iqisa/blob/master/iqisa.py | |
| import iqisa as iqs | |
| import matplotlib.pyplot as plt | |
| df=gjp.load_markets() | |
| df["isCorrect"] = (df.outcome == df.answer_option).astype(float) | |
| fig = plt.figure(figsize=(9, 9)) |
| # You can get your own copy of the MCMC trace: https://github.com/g-leech/masks_v_mandates#run | |
| # but I've included the quantiles in this script for reproducibility. | |
| import numpy as np | |
| sns.set_style("whitegrid") | |
| def exp_reduction(a, x): | |
| reductions = 1 - np.exp((-1.0) * a * x) | |
| return reductions.mean() |
| import numpy as np | |
| # impossible 0 | |
| # v unlik 10 | |
| # unlik 25 | |
| # lik 75 | |
| # vv likely 99.5 | |
| preds = [ | |
| [0, 1], | |
| [0.75, 1], |