True, but I think the US requirements are indeed different. Ukraine must repel invading forces in their own country -> lowish range, mass produced, not necessarily precision strike.
US want to project power far away from its shores -> long range, precision strike, long loitering time.
10 Gig ethernet is 10GBps usable rate (before packet overhead). The line rates are higher to accommodate this.
For 10GBase-R, it's typically 10.3125 GBps, with a 64/66 encoding. For 10GBase-T, it's 4 lanes with PAM-16 at 800 MBaud -> 12.8 Gbps raw.
The question is: If you have enough full body scans of many healthy people, and the statistical tools to model it (beyond "this range is OK"), whether this would reduce these false alarms to an acceptable level.
The real crux of it remains though: Let's say it finds something that increases your death risk by x=0.1%. Could you sleep? I'm not sure. Let's say the operation has 2x=0.2% risk. What do you do? What value of x makes this a problem for you?
Just for illustration: Gravitational wave detection is on the femtometer scale. The proton is about that size. We can measure these things, but the machines are, let's say, "big".
This is somewhat speculative, but as I see it, there are two ways to retain excellent people:
a) You pay them handsomely
b) You do shit they like, they way the like.
Sometimes it overlaps, of course. But this is essentially the reason why people stay in academia in the hard sciences. Most of us could earn considerably more in industry.
I'm not sure midjourney can compete with the bigwigs on a). But doing healthcare stuff is probably more fulfilling to the researchers, and with less "we stole from all the artists" vibes.
Of course, if this all works out, they might me able to do a) easily :)
Why it is probably and regrettably true that few people in camps will change their mind, data analysis can help people who haven't been captured yet to either stay away from the camps or at least fall into the "more correct" one.
But: "After posting this on Hacker News and recieving [sic] almost no substantive input, discussion, or response on the actual content of the article, I decided to rewrite all of the prose in my own voice. If anyone complains about my verbosity or sentence structure — as they usually do, which is the reason I originally let the AI write the prose, among other reasons obsoleted by templating — they can go fuck themselves."
So rewritten in his own voice. Maybe the m-dashes are from GLM, maybe from the author.
Haven't looked at the code, but the allocated memory could be larger than necessary to make "off-by-one" or "off-by-a-few" errors less deadly. Then zeroing it out makes it even less so. Defense in depth.
Or it's an allocation for an arena? The zeroing might help trigger 0 derefs earlier if the overrun happens for the object that are then allocated in the arena (and not by allocating more objects than the arena can provide)
Many statistics were presented. In the view of the author (and I think he is correct), none of them show evidence for an increased bug rate from Claude. That is absence of evidence (...for the increased bug rate).
The two examples you bring are not claims of absence of evidence, but claims of evidence of absence. The author takes the result as evidence that there is no effect. As I wrote, the author shouldn't do that, because indeed you cannot distinguish between "no effect exists" and "no effect observed". But again, these are (wrong) claims for evidence of absence.
The author can absolutely claim: I did these statistical tests, and none showed evidence that there is an effect. Absence of evidence. It's not a claim that there will never be evidence. Just that there is none from these tests.
Edit: To convert the absence of evidence into evidence for absence, indeed you need to understand the statistical power of your test, and how it is affected by alternate hypotheses. And for that, without having done the math, having only two data points seems very thin.
No. It's a description of the result of the maybe underpowered study. the underpowered study did not find evidence. Evidence is absent. Because it is underpowered, it's not evidence that the effect is absent.
The claim is not "two experimental conditions did not differ". The claim is "The data do not show evidence that the experimental conditions did differ".
The problem here is that it will work for now, but how do you make sure the LLM talks to the student, and not a different LLM? I guess vision models FTW?
Absolutely university has to change. But it's not a simple change. I say this as a professor for Physics:
My colleagues say "We must fully embrace AI as a tool". I agree. But how do you teach it? It's a moving target, and you can't even give homework like: "Research <this topic> with an LLM of your choice, and submit the transcript" because they can do that, or they can just copy the task into an LLM and have the LLM do it. It becomes meta quite quickly.
And independent what and how we teach, we have to change how we assess a students learning result:
The first thing we have to change is that homework needs to be completely ungraded. Reviewed and corrected, yes, but not part of the grade. That's the only way to make sure that people who don't want to cheat have to cheat anyway to compete with those that do.
Second, all exams have to be in person. Online, cheating is so trivial it's not even funny (many students are so stupid about it that we have a pretty clear idea what's going on). In person, we have maybe 2-3 years until we have to make sure its proctored and people's glasses are checked. I think in less than 10 years, local mobile AI will be good enough so even a Faraday cage will not help.
Maybe we have to go to oral tests only.
Of course, none of this scales. Some of our intro courses have a thousand students.