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.

Benchmarks are saturated!!

When playing around with ChatGPT in the non-obvious space of prompts, you can find some interesting behaviours.

I’m definitely late to the party, but I think I just grok’ed how awesome GPUs are.

In his 2024 interview with Lex Fridman, Yann LeCun claims that LLMs “are doomed” due to their autoregressive nature. He states that if we assume that the prediction errors from token to token are independent then it will eventually lead to a sequence being ‘incorrect’. He does state that this is a strong assumption, but how strong is it?