kctess5

No worries :) the drone basically generates a big dataset of "crashing" and "not crashing" video clips from the camera. It then feeds all that into a convolutional neural net, which can (after training is complete) give control decisions based on the camera which avoid obstacles. This is very "black box" in the sense that it's hard to say exactly how the system is working.

The car, on the other hand, uses hand written algorithms to forward simulate various controls. Based on the forward simulations, it can pick controls which are predicted to give good results. Forward simulation relies on a model of how the car reacts to any possible control. However, this model is complicated because of the nonlinear dynamics going on (inertia, wheel slip, etc). Therefore, they use ml techniques to identify the model.

That's not exactly what the car in [1] does. They use stochastic optimal control methods, which are more domain specific. They perform forward simulation on a lot of trajectories and effectively pick a good one. They also use localization so control is based more on current position than sensor inputs. The machine learning component is the dynamics model identifications - determining how the car reacts to control inputs. The model is basically a complicated function with a few inputs and outputs, which tend to be smoothly varying, so ml techniques work very well. This is fairly standard in model predictive control since empirical motion models tend to outperform ones that are physics based.

Edit: looking at the paper, they apparently use many physics based models of the car as a basis, but then use ml to mix the models together.

I watched "The Last Kingdom" from BBC (which I recommend) and it made me want to know more about 9th century England. So far the book "Alfred the Great: the man who made England" by Justin Pollard has been fantastic if you're into that kind of thing. It's very well researched and historically accurate but not overly dry, Pollard makes a point of not including too many footnotes in the book as many of the more academically targeted books do.

Reminds me of when a family friend of mine (a professor of civil engineering) built his own barn on his property. He got permits and did everything the ordained way, but still got hassled over tons of details. The inspector almost couldn't grasp the idea that the tens of thousands of stainless steel screws he had bought and used significantly exceeded the specs of the nails required by code. He did manage to persevere eventually.

Yeah that was my reasoning. The salted hash would be an easy way to implement single-thread username consistency.

Edit: for better readability it could be further mapped into a table of human readable handles, similar to how Google does the "Anonymous Lemur" thing in gDocs.

Is that so bad? Granted maybe it's annoying looking at old threads, but as a user, the ability to go through and remove old and potentially regrettable posts is quite welcome.

Maybe a good compromise would be to remove the user information after a certain time period (~2 years). Hashing the username salted with the post title would be a decent way of systematically respecting user privacy while also keeping old threads readable. I wouldn't mind if HN did this.

I'm not sure, but that will never happen unfortunately. The support infrastructure always comes with cost.

It was GS

I agree. Interns also generally receive no extended benefits like 401(k), stock, medical, and so on, so the comparison isn't very fair in terms of total comp.

I've had several big name tech internships, and I got an internship offer from a bank to do tech work. The bank offered around 2/3rd the comp with significantly worse benefits, which surprised me considering that a) they were in NYC b) they were a big name and c) they approached me, not the other way around. Not sure if they pay the trader type interns more...

The expected value of both strategies is to have 1 success, but they have different variance. The first strategy has variance of 0.99, and the second strategy has variance 0. The chance of n out of 100 hits with the first strategy is: binomial(100,n) x 0.01^n x 0.99^(100-n)

edit: asterisks as multiplication signs => italics

I built a desktop with a quad core i5, 16gb ram, a GTX970, an Intel SSD, and around 2tb of spinning disk storage. The whole build cost around $700 and it's a very fast machine. I also have 2x 1440p 25" monitors, a mechanical keyboard, and recently added a (normal looking) gaming mouse. I use it primarily for programming/research and occasionally games/oculus rift. Also makes a great web server.

I run Ubunutu, with i3 window manager. I really like this combo after some personalization. It's very stable, and lightweight. I also have two Windows versions and a secondary Linux installation which come in handy.

I also have a 2013 15" rMBP of which I think very highly. I can mount my 2tb of desktop storage, and my SSD as a network drive on the MacBook for sharing files. I also use SSH to run intensive scripts (sometimes GPU stuff) on the desktop.

10/10 would recommend this setup

Assuming there is a law. I think we can already detect methane well enough to know that cows make a lot of it.

You don't want to make the law something they must "escape" from, but rather something they can profit from by not polluting. Like tax credits.