About the Local Maximum Podcast

The phrase “Local Maximum” is a mathematical term, and it refers to a point at which you need to step down in order to reach new heights. But people - not just points - can get caught in a local maximum. That means they’ve gone as far as they can through one strategy which has gone stale and they need to search for new ideas.

In product design and machine learning, we sometimes ask if we’re in a local maximum, and whether starting from a fresh perspective can lead to better results.

So this podcast is about examining technology, engineering, and social trends through the lense of expanding perspectives and moving beyond the Local Maximum both for ourselves AND for our algorithms.

Sometimes I’ll interview engineers and entrepreneurs that I admire, that have actually built something valuable that most people wouldn’t have thought about, or that have ideas I want to explore further. I go over techniques to understanding the world of AI and Machine Learning that an average person can understand - and I show how to get our algorithms to be more flexible through the same process we use on people. I can also use my unique experience to examine current events.

The world is on the cusp of big changes - it’ll come from transportation, mainly automated cars and trucks. It’ll come from bitcoin and ethereum and all of these cryptonetworks. And we’re still seeing the spread of narrow AI vs general AI, but it’s a really really smart narrow AI we have now.

But I realized I didn’t want to do it alone; and I wanted to do with a community of interested and interesting people. And that’s where this podcast comes in, The Local Maximum.

The Local Maximum is hosted and produced by Max Sklar. Max is a software engineer and new product developer by trade, with a focus on machine learning, Bayesian inference, content discovery, and prototyping.

The bulk of his work as a machine learning engineer was at Foursquare, where he built Foursquare City Guide’s critically acclaimed 10-point venue rating system and the Marsbot app. More recently he led the development of a causality model for foursquare’s Ad Attribution product, and now works at Foursquare’s innovation lab.

Max has spoken at a variety of conferences and universities, including the ACM conference on Recommender Systems, the Cambridge Workshop on Urban Data Science, and Talkabot 2016. He also taught a course on Bayesian Thinking at the Lviv Data Science Summer School.

He holds a masters degree from NYU in Information Systems, and a Bachelor of Science in Computer Science from Yale. He is a former board member of the Yale Alumni Service Corps, and led a volunteer trip to the Fort Mojave Indian Reservation in October 2017.

Aaron Bell is an aerospace systems engineer at TTTech North America. He works with customers in the aviation (both defense and commercial) and space industries to implement deterministic networks for safety-critical systems. In addition to training customers on the use of this technology, he is also involved in the discussion of systems architectures and supporting integration efforts.

Previous roles have involved working at a number of major defense industry companies as a systems or electrical engineer, with a focus on radar systems as well as modeling and simulation.

He has a Bachelor of Science in Aerospace Engineering with a minor in Science, Technology, and Society from the Massachusetts Institute of Technology.

He used to work as a volunteer EMT and continues as a volunteer ski patrol. Other past adventures include sailboat racing, skydiving, and flying. Aaron is the father of two very energetic young children.