For decades AI was an obscure field covering Natural Language Processing, Computer Vision, Bioinformatics and similar areas. People considered it a difficult research area with unclear value. When deep learning gained momentum, the field got even more complex - research papers filled with mathematical formulas, code that no one knows how to run, and constant CUDA errors when you try to train something.

he output, though, was fairly understandable - we can classify reviews on Amazon for your product into good or bad, divide your documents into invoices and contracts, find all the addresses and people’s names, translate documents. Those were obvious repetitive tasks, not hard to understand in terms of what they could do and very hard to understand in terms of how. But the capabilities were clear and they fitted neatly into existing workflows and established processes. And when the what is that clear, working out the how is a technicality.

And then something interesting happened - ChatGPT arrived.

I thought this made things worse - now it was hard to understand what it can do and hard to understand how.

Not everyone shared my opinion. A colleague, in a heated argument with me about the need for deep learning experts as such, said something like: “AI is very simple now, anyone can do it, you do not need AI experts anymore, anyone can do AI.”

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