This paper is great. It is like a good slice of what is going on in formal methods and machine learning right now.
- Methods of space convergence from AI, can, and do, outperform the brightest algorithm writers, especialy when the time complexity is high. And innacuracy is easy to control for.
- It is all just programming. It is research level, but still. ML algs can be used easily in code logic, if there is a layer to handle uncertainty.
- Many of the referenced papers are about software that generates code without programmers. Obviously computer vision is going to beat any human, but the best tech for other domains are referenced here. Much of it is about indirect code reasoning.
- Real data is not needed for ML (supervised) research to progress. This data, CLEVR, is synthetic. Sometimes richer synthetic datasets make a better playground.
"Android phones collect more data by volume, but iPhones collect more types of data, a study finds"
Now this is a proper use of tenure.
This whole lab is a big middle finger to USA government, and large company, censorship and spying.
That is more like it. Make the computer figure out security.
Math people either love or hate statistics.
What I have found that helps me is to think of the various theorems from the perspective of functions on spaces. Instead of the messy pseudo-math that intro courses present it as, there are underlying measure spaces. Past all the word salad, spaces are what is tossed around and modified to reason about reality.
I am pretty curious about how to use automated reasoning systems to help discover new things, use and verify old ideas, and generally make my life easier.
Current events I try to keep up on
- Math Logic community (The Journal of Symbolic Logic)
- Statistics community (JASML, AoS)
- Algebra community (JoA, JoAG, JoPaAA, SIGSAM)
- Formal Methods community (CAV/TACAS)
Passing the learning curve up to current events
- Abstract Algebra (Dummit, Foote)
- Commutative Algebra (Eisenbud)
- Algebraic Geometry (Hartshorne)
- Mathematical Logic (Mendelson)
- Model Theory (Marker)