Machine Learning

Introduction

The AI landscape continues to evolve rapidly. Here’s your monthly update on what’s changed, what’s emerging, and how to make the most of the tools available. I did my original post at https://sharpstone.blog/posts/ai-models-compared/ around 30 days ago and there has been a whirlwind of change since then. I decided that I would leave that post up and offer a monthly summary of new things features, updates and models to watch. The original post has good background - these will just focus on what’s new each month. I may then do a deep dive into new features or particularly compelling information as an extra post.

Introduction

As Simon Sinek says “Start with why”. When you know the why you are wanting to use the AI model the other questions and answers can follow relatively easy. Current AI as sophisticated as it may be - is still what is known as ’narrow’ this means they are built for specific tasks and contexts. These specific tasks and contexts may be expansive but the range of the expertise is relatively narrow. Due to the AI’s being narrow it’s necessary to be literate in the many models that are available and what they excel at vs what they are just ok at vs what they cannot do at all.

I recommend reading "Artificial Intelligence: A Guide for Thinking Humans"

I just finished up "Artificial Intelligence: A Guide for Thinking Humans" by Melanie Mitchell. It was a great, quick read. I recommend it to anyone that wants a great primer on AI with or without a background in tech. Mitchell does an excellent job making complex topics approachable and sharing metaphors and examples that make AI's nuances understandable for non-specialists. Dr. Mitchell manages to explore the topics to a certain depth without drowning you in tech speak.