henrythe9th

After being a software founder for the last 8 years, I spent the holidays diving into AI research papers, lectures and trends to try to catch up to frontier of public AI research.

I tried putting together a guided list of some of the key papers and topics to help filter through the noise and crowded landscape: https://github.com/henrythe9th/ai-crash-course

Are there any other seminal papers or topics that you'd recommend or that I missed?

We're looking to implement a new data pipeline architecture at work. The primary goal is speed (data size is small enough to fit entirely in memory, sharded across multiple machines if needed). The primary bottleneck is feature extraction, transformation and iteration, which is both CPU and read/write intensive. Model building is not too slow, so no need to distribute training/testing as of yet.

I've heard good things about Spark/Shark and Storm. Does anyone have any experiences or recommendations? Maybe we don't even need a super sophisticated system and a Riak/Redis K-V store cluster would do?

Thanks in advance