Well said, AI in practice is just stats rebranded.

Neural networks are shiny and new, but they are just an implementation for solutions from stats that have been around for decades.

Regression? MSE loss. Now with a neural network trained on MSE loss.

Classification? Logistic regression with cross entropy loss.

Anomaly detection? Feature extraction? Plenty of people still use PCA, which is nothing new. Autoencoders may get you more mileage, but conceptually work very similarly to PCA for these use cases.

Image data? Use methods from signal processing, also decades old. Convolutions are nothing new, you're just now implementing them with neural networks, and adding a loss function based on what you're trying to predict.

Time series data? You could be better off just sticking to ARIMA. Depends on your use case, but using RNNs may not even work here.

Reinforcement learning is more exciting, and is solving new problems that weren't even being approached before. Same goes for GANs, and unsupervised learning in general stays exciting and fresh.

But most of the applications of AI are ho hum. Just use decades old methods, now implemented with neural networks. At least, sometimes. What has really changed is the amount of data available now, and the ability to process it. Not necessarily the approaches to analyzing it.

"AI" is inherently meaningless because it is a moving goalpost. At any point in time, AI essentially means "something normally done by humans that most people don't think computers could do proficiently". When, inevitably, someone programs a computer to do that thing, that thing quickly becomes no longer "AI" and the cycle continues.

I've been trying to explain to people why I think ML is Stats rebranded but this is the most succinct expression of that sentiment:

> Taking averages, grouped by something? That's AI now.

I think that is right. The algorithm that does the grouped averages is machine learning, and if you put error bars around it, it is stats.

To address your concern: I wouldn't worry about the relevance of applying math and logic to the world. It has always been growing.

There's certainly a lot of BS going on, especially if it helps sales or fundraising or whatnot... but there's also a lot actually happening in terms of ai/computerized-statistics, that regardless of what you call it, is permeating into life.