tashbarg
… would 10 fingers not be a better argument for base 11?
I hope (but do not know) that in the Schwachkopf-case, they just took the shortcut via insult instead of opening an investigation for the Nazi stuff.
But we don’t know that.
I believe, that we can express our opinions and discuss them without insulting people (in the legal sense). And I hope, that prosecutors do not lightly investigate each Schwachkopf they find on the internet.
And for all the other cases, the courts need to be involved.
And probably also tell it to some lawmakers. But start with me.
Just calling someone Schwachkopf doesn’t get prosecutors to investigate further.
But I can not see how the legal framework could be better. Insults are illegal. Prosecution needs to look into all reported cases.
The guy was sentenced for distributing forbidden Nazi materials.
The initial insult investigation was dropped, because of it being insignificant.
I really don’t see the problem.
And if you (or the first comment author) had read the article, then you would immediately have identified the problem with the comment.
Apparently, they did not use that control in the last 26 years.
Total trade with Ukraine in 2024: $2.9bn
https://ustr.gov/countries-regions/europe-middle-east/russia...
So, Ukraine will get an exemption, too, right? Because their trade is even a mooter point, right? Right?!
The proposed AI does not identify people and it will not report that it "found" the person in the training data. It does not know. And it won't tell you.
Assume twins, one is in the training data, one isn't. The one in the training data has a scar, the other one does not. We "invert" a picture of the twin without the scar and who is not in the training set. As you explained, the resulting image will have the twin from the data set including a highly detailed picture of the scar. And for some reason, that is a good thing.
You are attributing more to this AI than it conceivably can do. Even going as far as finding an excuse for putting false or unfounded data.
It is tremendously important to make clear: most (if not all) of current AI technology is not fit for forensic analysis beyond guiding humans in their own analysis.
https://innocenceproject.org/misapplication-of-forensic-scie...
It is dangerous to push the narrative that GenAI can "put information back" where it was once removed. Especially dangerous, because most GenAI is built to put something there that is extremely believable. And while an innocent comment on HN might not play the biggest role, the linked project claims exactly what it can - by definition - not do ("a PhotoDNA hash can be used to produce thumbnail-quality reproductions of the original image") and it looks scientific, too.
However, you can fill the gaps and generate photorealistic photos that fit to the extremely reduced information you get from the hash. You are generating believable (as defined by the training data) photos that fit the hash.
That’s a huge difference.
Statements like yours are extremely dangerous. Without proper understanding of what GenAI can and can not do, people start relying on things that are not there.
Imagine your photorealistic inversion AI putting a mole or a wrinkle in the face of somebody without any foundation in the actual hash. Just because it fits better to the trained data. Explain that to the judge, when the person with just the right facial features sits in front of them.
Putting second factor material in password managers is terrible advice. For reasons unknown to me, it might be the right solution for you. But in general, it defeats the two factor authentication purpose if you reduce the factors again to knowledge alone.
The whole point of tfa is, that the second factor is something you possess and not something you know (which is the first factor).
The default is an efficient stream of bytes that has some trade-off to latency. If you care about latency, then you can set a flag.
Dan Luu did cool experiments with input lag (https://danluu.com/input-lag/).
The clock frequency is not a good way of measuring performance. Never was. Even earlier designs as the 8086 did not do one thing (instruction) every cycle. They did far less.
Modern CPUs are extremely complex beasts that can take in a lot of instructions. They take a good look on those instructions, change them in a way that does not alter the result but makes some optimizations possible and then distribute those instruction to a bunch of internal workers that can work on those at the same time. More on this can be found in the wikipedia rabbit hole starting with instruction level parallelism.
One way to measure this is to look at how many of a selected set of instructions per cycle can be done. An 8086 could do 0.066. A 386DX did 0.134, a 486 could do 0.7. A Pentium 100 already could do 1.88, and so on. Modern CPUs get to 10, per core.
But wait, there's more. This comparison gives only a very rough idea of a CPUs capabilities since it focuses on a very specific thing that might have little to do with actual observed performance. Especially since modern CPUs have extremely specialized instructions that can do enormous amount of computations on enormous amounts of data in little time. And there we are in the wonderful world of benchmarks that may or may not reflect reality by measuring execution time of a defined workload.
Passmark does CPU benchmarks and their weakest CPU in the database seems to be a Pentium 4 @ 1.3GHz. Single Core, single thread. It comes in at 77 (passmarks?). An i7-13700 is rated with 34,431. Does that make it 500 times faster than the 1.3GHz P4? Hard to tell, but its a hell of a difference. And from the P4 to a Pentium or even a 486 running at 100Hz ... at least another hell of a difference.
We can also try Dhrystone MIPS, another benchmark. Wikipedia has - strangely enough - numbers for the Pentium and the 486 at 100MHz: 188 MIPS for the Pentium, 70 MIPS for the 486. The most modern (2019!) desktop cpu entry comes in around 750,000 MIPS. A Threadripper from 2020 over 2,300,000 MIPS.
So, how much more can a modern CPU do than an ancient one? A lot. And especially a lot more than you would expect from the faster frequency alone. Even with only one core, it can do several hundred times the workload. And we got a lot of cores.
And responsiveness back then was so good, because your program was very close to hardware with very little in between if not running completely free from OS abstractions.
Source: worked with algebra researchers using Magma.
Erlang has, however, excellent support for distributed computing using its own kind of processes.
Exposing the battery to high temperature and dwelling in a full state-of-charge for an extended time can be more stressful than cycling.
Most Li-ions charge to 4.20V/cell, and every reduction in peak charge voltage of 0.10V/cell is said to double the cycle life. For example, a lithium-ion cell charged to 4.20V/cell typically delivers 300–500 cycles. If charged to only 4.10V/cell, the life can be prolonged to 600–1,000 cycles; 4.0V/cell should deliver 1,200–2,000 and 3.90V/cell should provide 2,400–4,000 cycles.
On the negative side, a lower peak charge voltage reduces the capacity the battery stores. As a simple guideline, every 70mV reduction in charge voltage lowers the overall capacity by 10 percent. Applying the peak charge voltage on a subsequent charge will restore the full capacity.
Streaming should not have these problems.