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This serves as a quick cheat-sheet for solving small Bayesian problems using a table.

Based in Sydney, Australia, Foundry is a blog by Rebecca Thao. Her posts explore modern architecture through photos and quotes by influential architects, engineers, and artists.

The Local Maximum Expand Your Perspective
Episode 238 - New York Times Finally Against Censorship
Aug 8

Aug 8 Episode 238 - New York Times Finally Against Censorship

Max Sklar
Cohost Discussion
Episode 237 - Thinking about Quality with Jonathon Wright
Aug 4

Aug 4 Episode 237 - Thinking about Quality with Jonathon Wright

Max Sklar
Guest Interview
Episode 236 - Alzheimer's Research Setback and Facebook News Demoted
Jul 25

Jul 25 Episode 236 - Alzheimer's Research Setback and Facebook News Demoted

Max Sklar
Cohost Discussion
Episode 235 - Seasteading with Tony Olsen
Jul 19

Jul 19 Episode 235 - Seasteading with Tony Olsen

Max Sklar
Guest Interview
Episode 234 - Simplexes and Distributions
Jul 11

Jul 11 Episode 234 - Simplexes and Distributions

Max Sklar
Episode 233 - Crypto Winter Raging
Jul 4

Jul 4 Episode 233 - Crypto Winter Raging

Max Sklar
Cohost Discussion
Episode 232 - Intro to Inflation
Jun 27

Jun 27 Episode 232 - Intro to Inflation

Max Sklar
Solo Show
Episode 231 - The Increasing Cost of Intelligence
Jun 20

Jun 20 Episode 231 - The Increasing Cost of Intelligence

Max Sklar
Solo Show
Episode 230 - Another Google AI Spat, Self Awareness, and the Eliza Effect
Jun 14

Jun 14 Episode 230 - Another Google AI Spat, Self Awareness, and the Eliza Effect

Max Sklar
Solo Show
Episode 229 - Fun with Bayesian Statistics with Will Kurt
Jun 6

Jun 6 Episode 229 - Fun with Bayesian Statistics with Will Kurt

Max Sklar
Guest Interview
Episode 228 - All Things Bayesian
Jun 6

Jun 6 Episode 228 - All Things Bayesian

Max Sklar
On Another Podcast
Episode 227 - Deepmind's Gato and Dreams of Human Level Artificial Intelligence
May 23

May 23 Episode 227 - Deepmind's Gato and Dreams of Human Level Artificial Intelligence

Max Sklar
Solo Show
Episode 226 - Podcasting 2.0 with Adam Curry
May 17

May 17 Episode 226 - Podcasting 2.0 with Adam Curry

Max Sklar
Guest Interview
Episode 225 - The Tsunami Arrives and Architecture Astronauts
May 9

May 9 Episode 225 - The Tsunami Arrives and Architecture Astronauts

Max Sklar
Solo Show
Episode 224 - Living on Crypto with Joel Valenzuela
May 2

May 2 Episode 224 - Living on Crypto with Joel Valenzuela

Max Sklar
Guest Interview
Episode 223 - Elon's Twitter Takeover
Apr 25

Apr 25 Episode 223 - Elon's Twitter Takeover

Max Sklar
Cohost Discussion
Episode 222 - Techno-Pessimism in the Stupid Ages
Apr 18

Apr 18 Episode 222 - Techno-Pessimism in the Stupid Ages

Max Sklar
Solo Show
Episode 221 - Emotion and AI with Alan Cowen
Apr 11

Apr 11 Episode 221 - Emotion and AI with Alan Cowen

Max Sklar
Guest Interview
Episode 220 - Twitter's Musk Factor, Chat Bot Intelligence, and Pizza
Apr 4

Apr 4 Episode 220 - Twitter's Musk Factor, Chat Bot Intelligence, and Pizza

Max Sklar
Solo Show
Episode 219 - Artificial Intelligence for Construction with René Morkos
Mar 28

Mar 28 Episode 219 - Artificial Intelligence for Construction with René Morkos

Max Sklar
Guest Interview
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Subscribe on iTunes | Soundcloud | Stitcher | Odysee | Locals

Get Max's Free Cheat-Sheet on Bayesian Inference

Just sign up for the email list to keep up with exciting developments, floating ideas, and new projects from Max and his guests.

Thank you for joining The Local Maximum email list!

Download your free Bayesian Inference Page

This serves as a quick cheat-sheet for solving small Bayesian problems using a table.

The “Local Maximum” is a mathematical term referring to a point at which one needs to step down in order to reach new heights. But people - not just points - can get caught in a local maximum. That's what happens when we've gone as far as we can through one strategy which has gone stale and need to search for new ideas. This podcast is about examining technology, engineering, and social trends by expanding perspectives and moving beyond the Local Maximum both for ourselves and our algorithms.

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