[joshvm]

The comments here are very focused on LLMs, which makes sense - that's where the hype is. If you really don't mind ignoring the nuts and bolts, you can treat all the large language models as black boxes that are getting incrementally better over time. They're not difficult to interact with from a developer perspective - you send text or tokens and you get back a text response.

It's definitely worth trying them out as a user just to see what they're capable (and incapable of). There are also some pretty interesting use cases for them for tasks that would be ridiculously complicated to develop from scratch and "it just works" (ignoring prompt poisoning). Think parsing and summarizing. If you're an app developer, look into edge models and what they can do.

Otherwise dip your toes in other model types - image classification and object recognition are also still getting better. Mobile image processing is driven by ML models at this point. This is my research domain and ResNet and UNet are still ubiquitous architectures.

If you want to be sceptical, ignore AI and read ML instead, and understand these algorithms are just another tool you can reach for. They're not "intelligent".