[1] https://www.youtube.com/watch?v=t-09F87C6Ps

We're seeking an experienced software engineer to build simulations for the Satellite Communications Group. The job requires excellent geometric intuition and fluency in C++, but prior experience in satcomm is not necessary. We've had successful applicants with backgrounds in physics, graphics, FEM, control systems, and hardware. We get to work on really cool stuff and build systems that provide essential off-grid connectivity to users around the world!

For more information and to apply, go here: https://jobs.apple.com/en-us/details/200590817/simulation-sw...

[1] https://www.eia.gov/totalenergy/data/flow-graphs/electricity...

It really feels like a tools or language problem. Heck, we used to have to manually work out derivatives for continuous optimization problems, but nowadays programming languages with performant built-in autodiff often make this trivial. Removing the manual derivation hassle let loose a flood of cool ideas and applications, even though there was no technical hurdle preventing them in the first place.

Alternate problem specifications is a well-explored area (what is Prolog if not a way of describing problems for a constraint satisfier?), but I wonder how many other neat things are dammed up behind usability problems.

You pay for the device upfront, then a fee per drink. That’s it… all consumables are free, shipped and recycled by the company.

Comments URL: https://news.ycombinator.com/item?id=30543683

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That said, with FB/Google these days the bad weighs more than the good.

Just another resource that may help! You'll have no shortage of perspectives and approaches from the links here.

Agreed, a PhD is absolutely not useless in this industry, even if you don't end up in the research community. Understanding where the research frontiers of various fields are and being able to quickly find / digest relevant technical papers feels like a superpower. The gap between an undergrad education and a research frontier is enormous, and only working on a PhD really gives you the time and incentive to cross it. Having done it once, it gets easier to do it again.

For me, it has turned a huge volume of "unknown unknowns" into "known unknowns" and equipped me with the tools to then convert those into "knowns". Without it I'd be a fine coder, sure. With it I can work on a different tier of projects, and direct my career much better.

The costs are very real, though. Giving up ~6 years of early career earnings in a high-paying industry is utterly insane; you will never, ever make it up short of your startup lottery ticket number coming up. It's a meat grinder for mental health. Dozens of things outside of your control can go wrong and torpedo your aspirations. It is the right choice only for a vanishingly small minority.

Faking out machine learning systems is rapidly progressing from a few "fun proof-of-concept" examples to a serious area of study, and we've already seen it (gently) applied to autonomous vehicles [1] (ignore the overblown headline, it's just a piece of tape on a sign).

[1] https://www.technologyreview.com/s/615244/hackers-can-trick-...

Imagine a remote stretch of highway frequented by automated 18-wheelers. All that's required to bring the truck to a screeching halt is a bedsheet and some decent timing, at which point the vehicle has no way to prevent a robbery. The truck could put in a remote distress call, but it will still be some time before a human can get there. It's a new era for railroad heists!

We're no closer to solving this problem than in 2007; everybody is still trying to manage the long tail of merely safe driving. Handling humans in adversarial situations like the above is still completely off the map.

Comments URL: https://news.ycombinator.com/item?id=11893744

Points: 8

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Comments URL: https://news.ycombinator.com/item?id=10595958

Points: 1

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