OK, I’ll bite. : )

The vast majority of coding data ingestion, mooel discovery, and training that we currently do will all go away.

The job will become much more interesting, because researchers will try and understand why certain architectures/training regimes are unable to perform certain tasks. Also, I think the architectures for some fundamental tasks like computer vision, and audio are going to become modular. This whole training models end to end is going to be verboten.

People have been working on the Author Identification problem for about 20 years.

https://dergipark.org.tr/en/download/article-file/2482752

https://en.wikipedia.org/wiki/Author_profiling?wprov=sfti1

There is no way to unmask all of Reddit though. Too many people and many text samples are way too short. Some Redditors only speak in emoji and gif.

Thank you. That’s pretty cool.

In practice, do libraries like CUDA and MKL do Matrix multiplication the standard way or do they have fancy decompositions?

I remember when I was young, the atlas library would look at your hardware and do a bunch of matmuls and figure out what the “optimal” configuration would be for your system.

I’m a little confused by the purpose of this paper too. If the point is to show that an RL algorithm found better bounds than Strassen, then that’s cool. But are they claiming that this is something that a compiler would use in practice? How does this work with fixed SIMD sizes.