It is not the student‘s money - academic education is basically free in Germany. But they are still defrauded of their valuable time and effort to follow classes they thought were worth it.
Rent, university taxes and all the other taxes are still due, i'm from the EU too and education is definitely not "free", freer than somewhere else for sure.
From my perspective as an employee of a German academic institution, administrations are still figuring out if and how to regulate the use of AI, while some professors rely pretty heavily on AI tools, so the story is completely believable. However the double naïveté demonstrated here is strange.
Generation of boilerplate prose for grant applications was the beginning around 2023, which is absolutely understandable. The DFG recently allowed the use of AI for reviewers, too, to read an summarise the AI generated applications.
Researchers using qualitative methods seem (in general) to be more sceptical.
I wish we had an open debate about the implications instead of half assed institutional rules that will and must be widely ignored by researchers.
Between the lines, it sounds like German academia doesn't bother to warn - formally or informally - its researchers that failing to have working backups can be a train wreck for their careers.
The problem is in parts, how confirmatory statistics work, and how journals work. Most journals wouldn’t publish „we really tried very hard to get significance that x causes y but found nothing. Probably, and contrary to our prior beliefs, y is completely independent of x.“
Even if nobody would cheat and massage data, we would still have studies that do not replicate on new data. 95 % confidence means that one in twenty surveys finds an effect that is only noise. The reporting of failed hypothesis testing would really help to find these cases.
So pre-registration helps, and it would also help to establish the standard that everything needed to replicate must be published, if not in the article itself, then in an accompanying repository.
But in the brutal fight for promotion and resources, of course labs won’t share all their tricks and process knowledge. Same problem if there is an interest in using the results commercially. E.g. in EE often the method is described in general but crucial parts of the code or circuit design are held back.
I was looking for an efficient algorithm calculating variance of a sliding window of the last n values an ongoing signal, and this came up.
Besides the content of the paper, the metadata on page 2 is interesting, as well as the barcode on the front page as well as the machine readable font accompanying the bar code. Did not know that was a thing in 1969 but maybe it was attached later in time.
Some resources are still scarce. And a lot of those 6E24 kg is iron and nickel we can never get to. Another big fraction is basically molten stone. And we really should stop putting more carbon into the atmosphere.
Also, if you go for measures like mass processed, the weight of microchips, pcbs, parts is only a tiny fraction of what has to be processed and build in the supply chain.
Agreed that it is smarter to use oil for plastics then to burn it directly.
RFID tags are powered wirelessly, one could imagine powering smaller particles when operating on higher frequencies (RFID is on 13.something MHz requiring relatively large coils). A directional antenna could send a pulsed beam to power a subset of the particles in the area and afterwards receive their signals.
It needs to be in the infrared spectrum at least to be useful for smart dust, otherwise the package size is still dominated by the size of the antenna. Even mm-wave radar is marginal here.
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