Machine Learning & AI Articles

We’re curious people by nature. And we love to teach others what we’ve learned. So explore our blog to gain fresh insights from our expertise in areas ranging from culture to AI.

SEP Selected as a Nominee for TechPoint’s Innovation Mira Award

TechPoint has announced the nominees for its 25th annual Mira Awards, highlighting the most innovative companies, universities, and organizations in Indiana’s tech sector. The awards celebrate achievements across various categories, including the Innovation Partner of the Year. Nominees in this…
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Tiledriver – Making Wolfenstein 3D Levels with Machine Learning

This is the second post in my series on the value of personal projects. See the first one for more context. Marrying the Old with the New Tiledriver was an attempt to create levels for the classic 1992 game Wolfenstein 3D using machine learning. I thought this would be a fun challenge since I didn’t […]
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Getting Started with NLP and Python

Natural Language Processing (NLP) is a method of computer interaction at the intersection of human language, computer science, and machine learning. It can be thought of as a way of helping computers interpret text and speech as a human would, including understanding the subject, context, or both. This blog post will focus on getting started […]
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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 […]
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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. […]
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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 […]
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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 […]
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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 […]
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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 […]
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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 […]
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