Stay out of camps, people!

So rewritten in his own voice. Maybe the m-dashes are from GLM, maybe from the author.

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)

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.

And the author discussed the use of AI pretty exhaustively in point 0 of the post.

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".

In the end, will be build a GAN loop?

Why am I now reminded of corewars?

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 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.

Any ideas are much appreciated.

A linear dynamic, say x_n+1=lambda x_n, or x_n+1=(1-x_n), is never chaotic. But if you multiply them, x_n+1=lambda x_n (1-x_n), it depends on lambda if the system is chaotic.

None of the components are chaotic. But for specific combinations, chaos emerges as an property.

In physics, the mass of mesons and the nucleon is emergent. It's completely different from the constituents' mass. Different from an atom, where its mass is very close to the mass of its nucleus and its electrons.

a) The brain might have an entropy source (then it can't be modeled as a function). Trivially to fix, and in some sense, with diffusion models starting from random numbers, AI has done so.

b) The hidden function might be not computable. I would have no idea how that would work, but I think this is what it boils down to if people say "the human brain is more than a machine".