The deep learning boom caught almost everyone by surprise

The article mentions Support Vector Machines being the hot topic in 2008. Is anyone still using/researching these?

I often wonder how many useful technologies could exist if trends went a different way. Where would we be if neural nets hadn't caught on and SVMs and expert systems had.

in insurance we use older statistical methods that are easily interpretable, because we are required to have rates approved by departments of insurance

I've been looking at SVMs for use with a binary classification experiment. Training and operating these models is quite cheap. The tooling is ubiquitous and will run on a toaster. A binary decision made well can be extremely powerful. Multiple binary decisions underly... gestures broadly.

Obvious contextual/attention caveats aside, a few thousand binary classifiers operating bitwise over the training & inference sets would get you enough bandwidth for a half-ass language model. 2^N can be a very big place very quickly.

Expert systems did catch on and do see widespread use - they're just not called AI anymore. It's 'business logic' or 'rules engine' now.

The issue with SVMs is that they get intractably expensive for large datasets. The cost of training a neural network scales linearly with dataset size, while the kernel trick for SVMs scales with dataset size squared. You could never create an LLM-sized SVM.