FCC moves ahead with Title II net neutrality rules in 3-2 party-line vote | Ars Technica

FCC moves ahead with Title II net neutrality rules in 3-2 party-line vote | Ars Technica:

The Federal Communications Commission today voted to move ahead with a plan that would restore net neutrality rules and common-carrier regulation of Internet service providers.

In a 3-2 party-line vote, the FCC approved Chairwoman Jessica Rosenworcel’s Notice of Proposed Rulemaking (NPRM), which seeks public comment on the broadband regulation plan. The comment period will officially open after the proposal is published in the Federal Register, but the docket is already active and can be found here.

The proposal would reclassify broadband as a telecommunications service, a designation that allows the FCC to regulate ISPs under the common-carrier provisions in Title II of the Communications Act. The plan is essentially the same as what the FCC did in 2015 when it used Title II to prohibit fixed and mobile Internet providers from blocking or throttling traffic or giving priority to Web services in exchange for payment.

[Yeah baby!]

What’s the Deal with Summed-Input Stereo Effects? – Quilter Laboratories

What’s the Deal with Summed-Input Stereo Effects? – Quilter Laboratories:

Recently, there’s been a lot of controversy about effects that use summed stereo inputs vs. independent inputs. Obviously, nobody wants their stereo signal unintentionally collapsed to mono. So, effects with independent stereo inputs are clearly better, right? How has this gone unnoticed for decades and is only now exposed as a cost-cutting measure to cheat unsuspecting consumers?

Except, that’s not what’s happening here — not even close.

In this blog post, I will explain how stereo effects are designed, why they are designed this way, and the source of confusion around channel summing. I will also explain where channel summing might actually be desirable and where it’s not.

[Great article…]

AI Risks

AI Risks:

Beneath this roiling discord is a true fight over the future of society. Should we focus on avoiding the dystopia of mass unemployment, a world where China is the dominant superpower or a society where the worst prejudices of humanity are embodied in opaque algorithms that control our lives? Should we listen to wealthy futurists who discount the importance of climate change because they’re already thinking ahead to colonies on Mars? It is critical that we begin to recognize the ideologies driving what we are being told. Resolving the fracas requires us to see through the specter of AI to stay true to the humanity of our values.

One way to decode the motives behind the various declarations is through their language. Because language itself is part of their battleground, the different AI camps tend not to use the same words to describe their positions. One faction describes the dangers posed by AI through the framework of safety, another through ethics or integrity, yet another through security, and others through economics. By decoding who is speaking and how AI is being described, we can explore where these groups differ and what drives their views.

[Context!]

Musk’s process

I find Musk very off-putting to say the least. But I still think this processis worth considering. (From Walter Isaacson’s book)

  1. Question every requirement. Each should come with the name of the person who made it. You should never accept that a requirement came from a department, such as from “the legal department” or “the safety department.” You need to know the name of the real person who made that requirement. Then you should question it, no matter how smart that person is. Requirements from smart people are the most dangerous, because people are less likely to question them. Always do so, even if the requirement came from me. Then make the requirements less dumb.
  2. Delete any part or process you can. You may have to add them back later. In fact, if you do not end up adding back at least 10% of them, then you didn’t delete enough.
  3. Simplify and optimize. This should come after step two. common mistake is to simplify and optimize a part or a process that should not exist.
  4. Accelerate cycle time. Every process can be speeded up. But only do this after you have followed the first three steps. In the Tesla factory, I mistakenly spent a lot of time accelerating processes that I later realized should have been deleted.
  5. Automate. That comes last. The big mistake in Nevada and at Fremont was that I began by trying to automate every step. We should have waited until all the requirements had been questioned, parts and processes deleted, and the bugs were shaken out.

Shop notebooks… with advice from Matt Kenney

I have no religion when it comes to shop notebooks. Not a type, nor paper or tech. But use “some thing” because not having anything at all will be a problem.

Your workflow will determine how the next step occurs. You may be prototyping something, and so there’s no notes to guide you. But at some point you will get to “nice” and you’ll want to build it “for real”. That would be the moment to take detailed notes. If you are working in CAD or a drawing app, it might be worthwhile to print out shop drawing and scribble some additional notes as you work. Etc. But these “sources of truth” are very helpful when questions and problems arise.

If I had one rule (snicker) it would be that your scribble paper, notebook, etc. assuming it is atoms not electrons should be graph paper. It was invented for just this sort of thing, take advantage.

Matt Kenny suggests the following…

  • Be as detailed as you possibly can. The more detail, the less work you’ll need to do to make sense of what’s written down.
  • Write in simple, clear statements and equations. A shop notebook is not a Faulkner novel or a Fields-Medal-worthy mathematical treatise.
  • Write down: dimensions, notes about materials, things you discovered that made construction easier, problems you encountered and how you solved them, and anything else that’s important to you.
  • Clearly identify which piece of furniture a note belongs to. A year from now you probably won’t remember.
  • Do not worry about how it reads or looks. If it makes sense to you and you can go back and make sense of it a month later, that’s all that matters. You will develop a style, organizing principles, etc. as you continue to work and fill up notebooks.

How Apple made the ultimate Snoopy watch: “You wouldn’t believe the minutiae”

How Apple made the ultimate Snoopy watch: “You wouldn’t believe the minutiae”:

That first meeting at the Charles M Schulz Museum in Santa Rosa, California, was the Watch team’s first in-person meet-up after the pandemic, and what started as a two-hour drive north from Mountain View ultimately ended with plans for 148 unique animations that would be contextual depending on the time of day, local weather and activities. When you go for a swim, Snoopy dons his scuba gear and floats through your watch screen. When night arrives he’ll howl at the moon, and when you’re not up to much at all you can find him draped over his iconic red doghouse in a series of panels that are a direct lift from the comics. It all amounts to over 12 minutes of animation work that stemmed from an unexpectedly chaotic tête-à-tête.

“I’m typically a very organised person,” says Gary Butcher, human interface designer at Apple. “So I felt, ‘We’ve got a limited amount of time together and there might be some uneasiness, so I’ll print out 148 pieces of blank paper and we need to leave the room having filled out every one of those pages.’ By the end of the day, we’d not touched the wall of A3 paper, but had tons of sketches littering the table.”

[This. Is. So. Good!!!]

Snoopy watch faces 1 jpg

The Technology Facebook and Google Didn’t Dare Release

The Technology Facebook and Google Didn’t Dare Release:

In the last few years, though, the gates have been trampled by smaller, more aggressive companies, such as Clearview AI and PimEyes. What allowed the shift was the open-source nature of neural network technology, which now underpins most artificial intelligence software.

Understanding the path of facial recognition technology will help us navigate what is to come with other advancements in A.I., such as image- and text-generation tools. The power to decide what they can and can’t do will increasingly be determined by anyone with a bit of tech savvy, who may not pay heed to what the general public considers acceptable.

[Oy.facial re]

A look at Apple’s new Transformer-powered predictive text model

A look at Apple’s new Transformer-powered predictive text model:

Apple wants a model that can run very quickly and very frequently, without draining much of your device’s battery. When I was testing the predictive text feature, suggestions appeared almost instantly as I typed, making for a great user experience. While the model’s limited size means it wouldn’t be very good at writing full sentences or paragraphs, when it exhibits very high confidence in the next word or two, they’re likely to be good enough to suggest to the user.

[Making progress one little step at time…]