There are some posts that see AI as the thing that will replace everything: programmers, writers, artists, workers, … people in general. I do not think that to be the case. I see the advent of modern machine learning similar to the advent of GUIs. GUIs had mainly two effects: it enabled new things: Desktop publishing, photo editing, consumption and creation of movies, spreadsheets, it increased the number of people that were able to use the computer to perform those tasks exponentially it changed the way professionals worked, but did not eliminate them I think AI will be similar: it will enable new applications it will open up the field to new players and increase the levels of beginners.

This is is something that is obvious to those who work with Large Language Models, but it was not immediately evident to me when someone asked me about it. Why do the tokenizer require training? Indeed coming from computer languages Tokenizers this is a bit surprising. For them the tokenizer is fixed and does not change the more data about a language is available. Tokenizers are often a bit ugly, the interface to the language grammar not totally clean, but are simple and fixed.

Social challenges of Artificial Intelligence Artificial intelligence and machine learning pose have several interesting technical challenges. Normally, working in the field I am mostly interested in those. There are also challenges that affect and should be discussed with the broader society. Social challenges are not less interesting than the technical ones. I thought about them for a long time, indeed, I started thinking about some things when, immediately after my diploma, I worked at Uptime, a firm that archived documents, including few GB of receipts of Migros per week.