Biscoff is a registered trademark of Lotus Bakeries, while speculoos is a generic name. Hence why Lotus markets its under its owned name, and not the generic name; especially outside Belgium/Netherlands where people don't know about speculoos.
> Apple Reference Image is not an id system; it's primarily a way to attest that the pixels recorded by the camera sensor have not been altered in any way; the pixels, metadata and timestamp are all cryptographically signed.
> There's no way to link a reference image to a person; it's also not possible to determine if a pair of images came from the same device.
Some banking apps nowadays ask you to upload a photo of your ID and then use the webcam/selfie camera to confirm that's it's really you (sometimes asking you to move your head in a particular way). But how trustworthy is that process really? Apple Reference Image could be (part of) a solution, by certifying that both images were taken by the same device around the same time.
I consider, on the contrary, caveating and hedging annoying 'typical redditor'/internet behaviors: they care more about being "technically correct" than conveying the message. On the internet, if you make even the tiniest mistake or simplification, someone will criticize you, so you're trained to always hedge. In normal discussions with friends you can just make general statements and people get what you mean.
You aren't arguing a "contrary" to what I actually wrote above. You're arguing with a strawman Redditor, and a point adjacent to what I was posting about - experts responding to topics within their actual realm of expertise.
So I won't be addressing this, for those reasons and others.
There's a fundamental difference between the goal of code and math. The goal of code is to produce software that does something useful. As long as the code does what it's supposed to do, arguably, it's good to ship. (As you imply, we want the code to be good enough to also be reasonably certain there are not too many bugs, that it is maintainable and can be extended etc. This is what early models failed at but now seems broadly fine.)
But for math: what is the point of a proof if no one will read it and no one uses its result? To quote the article, "AI systems will [...] result in the production of an abundance of PDFs. The contents of some of those PDFs may even have important applications." But if there's no one reading the PDFs, what's the point - no matter how good your AI model.
The point of math is understanding. So mathematicians should feel free to use AI as much as you want, but in the end, they should've gained some understanding on what happened.
If the point of math is understanding then maybe our incentive structure is wrong. Maybe it should be teaching the concepts to as many people as possible rather than just continuing to write papers that 10 people in the world understand, which is the current state of a lot of math.
I won't understand it, but can benefit from it in better data structures with proven invariants, faster algorithms, convergence guarantees for some iterative computations, better practical linear algebra and matrix factorizations, deriving things in statistical hypothesis testing, tightening upper and lower bounds etc. I don't care if some mathematician somewhere who isn't me "understands" it or not. It has practical use. I know that practical is dirty peasant word for many ivory tower mathematicians but that's their problem and I don't interact with them much, except when they throw a hissy fit like this and try to ban restrict matchmaking to their guild and ban the plebs from getting math from anywhere but them on their terms.
The primary goal of all basic sciences is human understanding. "Truth" is no more a goal for mathematicians than the physical laws are a goal to physicists; they simply exist in nature. The goal is rather to develop useful language and conceptual frameworks for reasoning and communicating. That understanding underpins all practical applications.
Sciences don't have goals, people have goals and they differ. Some are fans of pure math as a kind of religious or almost erotic activity in elegance and beauty, others are application minded. Some are in it for the community and outreach and conferences, some are in it to just sit in an office alone and be left alone to do it in a zen like flow state all day and night. Some treat it as a 9-5 to pay the bills with a skill they happen to be fit for but aren't especially passionate about.
Research on 5G started ~2012 to be deployed since 2019 (7 years), research on 4G started maybe around 2000 for rollout in 2009 (9 years later). For 6G, research started around 2018 for an expected rollout around 2030 (12 years). These timelines are pretty typical in the telecom field.
According to the paper, 6G deployment is planned for 2030 and the "earliest plausible" deployment of 7G is listed as 2039-40. You'll have plenty of time to upgrade.
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