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If we are going to consider using prior runs of the program having the file loaded in RAM by the kernel fair, why stop there?

Let's say I create a "cache" where I store the min/mean/max output for each city, mmap it, and read it at least once to make sure it is in RAM. If the cache is available I simply write it to standard out. I use whatever method to compute the first run, and I persist it to disk and then mmap it. The first run could take 20 hours and gets discarded.

By technicality it might fit the rules of the original request but it isn't an interesting solution. Feel free to submit it :)

This is even slightly more direct: access to WSJ data requires paying LDC for the download, and the pricing varies depending on what institution / license you're from. The cost may be a drop in the bucket compared to compute, but I don't know that these licenses are transferable to the end product. We might be a couple court cases away from finding out but I wouldn't want to be inviting one of those cases :)

Be wary of using this model - the licensing of this model seems sketchy. Several of the datasets used for training like WSJ and TED-LIUM have clear non-commercial clauses. I'm not a lawyer but releasing a model as "MIT" seems dubious, and hopefully OpenAI has paid for the appropriate licenses during training as they are no longer a research-only non profit.

I used to work for a drone company. I made sure to go watch the mechanical testing for prop safety. One of the test objects was a chicken leg, and the prop cut through more than 2mm of bone. The props can do some serious damage!

I don't know what, if any, approaches are implemented but one solution is that you can design a safety feature in the motor control to turn off the motor if it detects blockage.

Check out sse2neon as a way to substitute your x86 SIMD instrinsics for ARM neon. Perf was good enough to ship in one of the past projects I worked with.

This article is quoting employees conversations from 2016. It wasn't until late 2016 that Google published research on wide and deep recommender systems. I'd be interested to see if people still think behavioral data is poor and ML has not provided advances.

That said some demographics are broadly correct from behavioral tracking (e.g. a child who watches baby shark on repeat), but good luck telling the difference between a rich child and a less rich child.

You can get all this functionality and more working beautifully with Android with Garmin watches. Mine lasts for many days on a charge (perhaps a week without activity tracking), has better sport tracking features, but the screen isn't quite as good. There are currently a few good deals on the Vivoactive 3, but also many higher end models available too.

Don't forget Ron Graham who cowrote magical mathematics!

This is easy to resolve by staggering doses which I know at least other hospitals are doing.

Elon's was quoted saying "you have a forest of redwoods and the little trees cant grow." While pointing his finger at California, I can't help but think this really reflects back on him.

Elon Musk's success and several others began as the result of PayPal.

To my knowledge none of Elon's companies since have produced a group like the PayPal mafia. The PayPal folks are smart people, but the bay area is full to the gills of smart visionaries lacking capital to take on ambitious visions. Many have worked at Elon's companies. To really see little trees grow people like Elon would need to change their equity structures to let the next group of innovators thrive.

It feels like we are seeing the result of diminishing returns with technology advancement.

To make a product that Apple believes is significantly better than the competition they had to design a very intricate solution that includes:

* High end look and feel not similar to the bulk of their products with lots of textile webbing and memory foam ear cups. These get way more wear and tear than normal electronics being exposed to sweat, sunscreen, etc. On top of that these must be safe for long skin exposure and comfortable across many head shapes and sizes. * High quality magnets with custom speaker design for low THD and large frequency range. * 2 custom ASICs built for sound processing and low power bluetooth and 10 audio cores each. * 10 microphone array for ANC and wind noise cancelation. * Multiple accelerometers + gyros head tracking with spatial audio.

It you remove one of those components, I'd be surprised if Apple still shipped this. At least 40 people worked on designing and engineering this new headphone. There are not many companies in the world with the right kind of talent for this and Apple happens to be one of them.