Headshot of

Jordan Thayer

AI Practice Lead

Recent Articles

Accidental AI: 5 Everyday AI Problems

Introduction People often ask questions like “What is AI?”, or “Is AI worth of the hype?”.Both questions are non-trivial to answer, but let’s start with the first one:”What is AI?”. This is a perennial favorite at academic conferences on AI for a few reasons: Every AI researcher has to have an opinion, since it’s their […]
Read More

Planning for Randomizers

Introduction I’m a big fan of videogames. I like small, well defined boxes where I can get better at some task. I like measuring myself against my peers. As such, it’s probably no great surprise that I like speedrunning and randomizers. For the unititiated, speedrunning is trying to beat some game as quickly as possible. […]
Read More

Distributing Depth First Search to the Masses

Last Time Last time we talked about techniques for exchanging processor (and developer) time for reduced will clock time in heuristic search.  In other words, we talked about how to use multiple cores on a single machine to solve a problem faster.  That worked pretty well, but we noticed that it couldn’t scale beyond the […]
Read More

Parallel Problem Solving

Last Time Previously, we looked at a technique for reducing the memory footprint of a heuristic search. We talked about why it was important to reduce the memory consumed by a search.  Even if we move heaven and earth to reduce memory consumption, heuristic search is still prohibitively expensive in terms of time. Learning Goals This […]
Read More

Trying Deltas For A Change

Last Time Last time we took a look at how improved bounds computation and child ordering can improve the performance of heuristic search algorithms.  In particular, we saw how those techniques improved the performance of depth first search (or depth first branch & bound if you prefer) when applied to the TSP.  Even though we […]
Read More

The Importance of Consuming Search Results, Pancakes

Last Time Last time we looked at depth first search and how it could be applied to a simple optimization problem, the pancake problem.  We decomposed the pancake stacking problem into some components are very important if you want to apply heuristic search: A goal test States Actions to move between states We then looked […]
Read More

Flipping Flapjacks, Pruning Pancakes, and Depth First Steps

In the previous post in this series I spent some time trying to convince you that toy problems are worthy of your attention. In particular, I tried to sell you on the notion that the pancake problem was worthy of your attention.  It isn’t necessarily because flipping flapjacks is in of itself fascinating.  It’s because […]
Read More

Ridiculous Problems, Real Applications

Life is filled with compromises. Generally, we just don’t have the resources, be they time, money or and so on, to do the things we want to do as well as we’d like.  While I can’t solve that problem entirely or generally, I can help you intelligently trade time for improved solutions to problems that […]
Read More

Machine Learning for Classifying Turbine Engine Performance

Introduction Machine learning is at least as old as Arthur Samuel’s attempts to improve his checkers playing programs back in the 1950s (Some Studies in Machine Learning Using the Game of Checkers), if not older still.  Despite being around for decades, machine learning has received intense interest recently from the public, the scientific community, and […]
Read More