Games are fun. Humans love watching a variety of them from NFL to League of Legends to Mahjong. The competitive nature of games is enticing and the infinite strategies help keep them fresh. These factors also make them great testing grounds for AI. For example, recent work from the same group behind the ARC-AGI challenge pits LLMs against each other in games of snake to find out which is the best. In this article, let’s discuss their findings along with other work towards using competitive games to evaluate AI.

We use functions every day to do useful things, but what is a function? A collection of lines of code? A graph? Let’s explore various representations that can be useful in different scenarios.

Reasoning is a trait that has long been lauded among humanity. Among many other capabilities, it allows us to take seemingly disparate ideas and combine them together in interesting ways to form new ideas. It’s something that is often sort out for in the workplace, ideal among friends, and now a trait that we wish to see in our programs. But how can you evaluate reasoning?

There’s a lot of talk about whether LLM’s can reason or not. With the release of OpenAI’s o1 and now the upcoming release of o3 which are touted to be strong reasoners, it seems we’re getting close to ‘reasoning capabilities’, but what does that mean? Let’s try to debug what reasoning is.

Learning has been the centre of the field of AI since its inception, however its meaning and focus has continued to evolve over time. An effective AGI must be able to learn effectively from its environment. In this post, let’s explore the different ways that systems can learn.

The following describes the framework that I use when thinking about general intelligence. There are already many out there, but none that resonated strongly with me, or were expressed succinctly enough. That led me to create my own for reasoning through the problems that we’re tackling. Following is the framework itself and a quick view on our current status.