Anyway, It was humorous seeing these concepts being treated as if they were scientific breakthroughs. It reminds me of the Dynamo paper where the authors believed that they had discovered, or at least revolutionized, the concept of "Tail Latency."

(1) and (2) show the disconnect between statistics and/or data science and computer science. It's very inefficient.

(3) In the "replacement" discussion, I felt a bit of elitism here. Some of the researchers that are enthusiastic about everyone being replaced with AI seem to the think that they are immune. It comes across differently to those that are not 100% in academia.

(2) There was a lot of focus on time series. The talks suggested that time series had just been re-discovered by computer scientists. It's been around for at least a hundred years.

With that said, statisticians and data scientists (or the companies that don't understand how to use them) tend to miss a big opportunity: helping improve systems, algorithms and AI through evaluation and experimental design. I don't get it.

(2) Most papers in AI overfit the data, this is why evaluation is important.
(3) System architects may be safe from automation for AI in the near future.
(4) Junior level roles will disappear (My opinion: this is a shift, not a deprecation)

I believe so. We are about to head for a cliff in the next couple of years when StackExchange shuts down and training data becomes old or limited. Sure there's Github, but there is less human annotation in Github.

Obligatory DuckDB plug