Some thoughts on security after ten years of qmail 1.0 Bernstein, 2007 I find security much more important than speed. We need invulnerable software systems, and we need them today, even if they are ten times slower than our current systems. Tomorrow we can start working on making them faster. That was written by Daniel … Continue reading Some thoughts on security after ten years of qmail 1.0
Month: January 2018
Spectre attacks: exploiting speculative execution
Spectre attacks: exploiting speculative execution Kocher et al., 2018 Yesterday we looked at Meltdown and some of the background on how modern CPUs speculatively execute instructions. Today it’s the turn of Spectre of course, which shares some of the same foundations but is a different attack, not mitigated by KAISER. On a technical front, Spectre … Continue reading Spectre attacks: exploiting speculative execution
Meltdown
Meltdown Lipp et al., 2018 I’m writing this approximately one week ahead of when you get to read it, so it’s entirely possible by this time that you’ve already heard more than you can stand about Meltdown and Spectre! Behind the news headlines though, there’s a lot of good information in the accompanying papers, and … Continue reading Meltdown
One model to learn them all
One model to learn them all Kaiser et al., arXiv 2017 You almost certainly have an abstract conception of a banana in your head. Suppose you ask me if I’d like anything to eat. I can say the word ‘banana’ (such that you hear it spoken), send you a text message whereby you see (and … Continue reading One model to learn them all
Emergent complexity via multi-agent competition
Emergent complexity via multi-agent competition Bansal et al., Open AI TR, 2017 (See also this Open AI blog post on ‘Competitive self-play’). Today’s action takes place in 3D worlds with simulated physics (using the MuJoCo framework). There are two types of agents, ants: And humanoids: These learn to play against each other (ant vs ant, … Continue reading Emergent complexity via multi-agent competition
Mastering chess and shogi by self-play with a general reinforcement learning algorithm
Mastering chess and shogi by self-play with a general reinforcement learning algorithm Silver et al., arXiv 2017 We looked at AlphaGo Zero last year (and the first generation of AlphaGo before that), but this December 2017 update is still fascinating in its own right. Recall that AlphaGo Zero learned to play Go with only knowledge … Continue reading Mastering chess and shogi by self-play with a general reinforcement learning algorithm
The case for learned index structures – Part II
The case for learned index structures Kraska et al., arXiv Dec. 2017 Yesterday we looked at the big idea of using learned models in place of hand-coded algorithms for select components of systems software, focusing on indexing within analytical databases. Today we’ll be taking a closer look at range, point, and existence indexes built using … Continue reading The case for learned index structures – Part II
The case for learned index structures – part I
The case for learned index structures Kraska et al., arXiv Dec. 2017 Welcome to another year of papers on The Morning Paper. With the rate of progress in our field at the moment, I can’t wait to see what 2018 has in store for us! Two years ago, I started 2016 with a series of … Continue reading The case for learned index structures – part I
the morning paper 