Something on attention: How Lyft predicts a rider’s destination for better in-app experience
- We tackle the destination recommendation problem using the rider’s historical rides. The main idea is to limit candidate recommendations to addresses where the rider has previously taken a Lyft ride to or from. Within this candidate set, we use an attention mechanism (discussed in more detail below), to determine which locations are most relevant to the current session.
- Even Q-Anon was only one of several “anons” including FBIanon and CIAanon, etc, etc. Q rose to the top, so it got its own YouTube channels. That tested, so it moved to Reddit. The theories that didn’t work, disappeared while others got up-voted. It’s ingenious. It’s AI with a group-think engine. The group, lead by the puppet masters, decide what is the most entertaining and gripping explanation, and that is amplified. It’s a Slenderman board gone amok.
- Still working on the relationship between communication, hierarchy, aggression and cult behavior
- 10:00 and 1:30 Meeting with Vadim. Everything is almost working. There seems to be a sign problem, where the rotations are the opposite of what they should be. Need to clean up the ddict and then put in more useful stuff for figuring this out and plotting it
- Installed Python 3.8 on Dreamhost. Looking to serve up the GPT-2 model if possible