https://www.theverge.com/2013/8/6/4594482/xerox-copiers-rand...

I still think the pendulum will swing back there again, to have even better battery/larger models on mobile.

AOO has already been used successfully in a few art projects. It's also used under hood by SonoBus. The Pd objects are already stable, I hope to finish the C/C++ API and add some code examples soon.

Any feedback from your side would of course be very appreciated.

In my country, public restroom facilities replaced all the buttons and levers on faucets, towel dispensers, etc. with sensors that detect your hand under the faucet. Black people tell me they aren't able to easily use these restrooms. I was surprised when I heard this, but if you google this, it's apparently a thing.

Why does this happen? After all, the companies that made these products aren't obviously biased against black people (outwardly, anyway). So this sort of mistake must be easy to fall into, even for smart teams in good companies.

The answer ultimately boils down to ignorance. When we make hand detector sensors for faucets, we typically calibrate them with white people in mind. Of course different skin tones have different albedo and different reflectance properties, so sensors are less likely to fire. Some black folks have a workaround where they hold a (white) napkin in their hand to get the faucet to work.

How do we prevent this particular case from happening in the products we build? One approach is to ensure that the development teams for skin sensors have a wide variety of skin types. If the product development team had a black guy for example, he could say "hey, this doesn't work with my skin, we need to tune the threshold." Another approach is to ensure that different skin types are reflected in the data used to fit the skin statistical models we use. Today's push for "ethics in ML" is borne out of this second path as a direct desire to avoid these sorts of problems.

I like this handwashing example because it's immediately apparent to everyone. You don't have to "prioritize DEI programs" to understand the importance of making sure your skin detector works for all skin types. But, teams that already prioritize accessibility, user diversity, etc. are less likely to fall into these traps when conducting their ordinary business.

For this audio codec, I could imagine that voices outside the "standard English dialect" (e.g. thick accents, different voices) might take more bytes to encode the same signal. That would raise bandwidth requirements, worsen latency, and increase data costs for these users. If the codec is designed for a standard American audience, that's less of an issue, but codecs work best when they fit reasonably well for all kinds of human physiology.

It might be an interesting test to compare what people mishear with and without this kind of compensation.

Also, on my system (Android phone + BTR5/BTR15 Bluetooth DAC + Sennheiser H600) all options sound realy crappy compared to plain old usb, everything else is the same. LDAC 990kbps is less crappy, by sheer brute force. I suspect it's not only codec but other co-factors as well (like mandatory DSP on phone side)

Folks set up pools all the time, but somehow they never offer indemnification for completeness of the pool - because they can't.

See https://en.kangxin.com/html/2/218/219/220/11565.html for a few examples how the patent pool extortion scheme already went wrong in the past.

The existence of a patent pool does not mean there are valid patent claims against it. But yes, you may be technically correct by saying "patent free" rather than "not infringing on any valid patents". That said historically Opus has had claims against it by patents that looked valid but upon closer investigation didn't actually cover what the codec does.

Just looks like FUD to me. Meanwhile, the patent pools of competing technologies definitely still don't offer indemnification they cover all patents, but have no problem paying a bunch of people to spew exactly this kind of FUD - they're the ones who tried to set up this "patent pool" to begin with!

Machine learning, statistical models, procedural generation, literally an usage of heuristics are all being called "AI" nowadays which obfuscates the "boring" nature in favor of "exciting buzzword"

Selecting the quality of a video based on your download speed? That's "AI" now.