wheresmypasswd

Upper-middle-class low-density areas are great for this. Your neighbors have too much to lose to steal anything, and it's too far to bother for out-of-towners.

The epitome of this is most of Switzerland. I went on a Tinder date with a girl recently whose apartment door did not even have a lock.

It's generally pretty obvious anyways. Perks start drying up and people start getting chastised disproportionately for mildly wasteful actions.

Build a robot that balances this broomstick was a sophomore-level engineering course at my university. xD

Presumably we don't spend $10k/trunk on better brakes because humans can deal without them. But if that was what's needed to make self-driving work, the economics could support it. Electric regenerative breaking would do nicely.

Exploding offers are fine. Sudden exploding offers are terrible. If a company recruiter tells you at the beginning of the process: "Hey, this interview process takes two weeks and we expect a response 3 days after your offer letter, which would be 3-5 days after your last interview", then I don't know if I'd be thrilled, but I wouldn't call it bullshit.

Avoid like the plague any company that only tells you about the explosion as the grenade already has the pin out.

The AlphaGo team tweeted that the single node version wins 25% of the time against the cluster version.

The bulk of the compute is in training the model... I would bet that a cell phone AlphaGo is <200 ELO (or whatever passes in the Go world) weaker than the massively distributed version--good enough to be competitive with Lee Sedol.

> What this underscores, if anything, is the accelerating pace of technological growth, for better or for worse.

While growth may be accelerating, this is simply the result of one big paradigm shift in deep learning/NNs. Once we've learned to milk it for all its worth, we'll have to wait for the next epiphany.

Generally, processes do not share memory, but threads may.

The nice thing about most of this deep learning stuff is that you can use a million machine hours to train your model, and almost no time to make an evaluation. So the single machine version has all of the pattern recognition given to it by the cluster, but a few ply less tree search depth.

So to me this underscores the relative importance of the deep learning model vs the tree search.