In a book that refers to Netflix as "a new upstart service that Blockbuster Video should be wary of."

Well, FWIW, I've never found a site *through* substack. They were always referrals from something else I was reading, which might have been *on* substack.

But that answer is false.

Yeah, I saw "...alternatives most people haven't heard of like... Patreon", and was thinking, "What year is this?"

Your post was quite reasonable, and probably true, until you wrote "AIs aren't capable of reasoning". There *are* definitions of reasoning for which that it true, but they aren't the ones in common use. Cicero would use that kind of definition in his "school of rhetoric", where he taught people how to win arguments". Socrates would not. He was trying to find truth.

Clearly AIs have limited reason. They can (at least in principle) do perfect logic, but the difference between that an reason is not well defined. (And logic can prove that you can't prove algebra to be self-consistent.) To me reason is evaluating a set of data and a goal, and using logic to plot a nearly-optimal path to achieve the goal. I think where AIs are generally most deficient is in their goals, though obviously they also have an imperfect understanding of the current state. (Well, so do people.)

That said, there are many areas where current AIs seem deficient when compared with people. This doesn't mean or imply that they don't have a modest amount of the features that they are deficient in, but merely that we expect them to have more. Think of capabilities as being gradients rather than boolean variables. This is commonly called "jagged capabilities". They're better at some things than most people are, and worse at other things than most people are.

How do you know?
I will grant that there are definitions of "feelings" that would make your statement true by definition, but I will guarantee that most people don't use those definitions.

If you want to claim "it's synthetic, therefore it can't be a feeling", you've deprived your mind of a tool for thinking in this space. Submarines don't swim, but airplanes fly. Perhaps it's not useful to think of submarines as swimming, and perhaps it's useful to think of airplanes as flying. And perhaps it's useful to think of LLMs as having feelings. (Also perhaps it isn't, but just asserting that isn't useful, you need to demonstate it. My wife found it useful to attribute feelings to her car. The model didn't work for me, but it worked for her.)

A quick search didn't provide an answer, but the indications were that the cosmic voids are much of the universe. The search of turned up things like https://link.springer.com/arti... , but didn't actually reveal the proportion of the volume of the universe that is contained within cosmic voids, but did *indicate* that it sure wasn't trivial.

Well, I'm no expert in the field, but that's what I've read.

That the universe is not uniform at the large scale is blatantly obvious. Just consider the cosmic voids. And that implies that "time" should be running faster within those voids (because general relativity).

This isn't generally dealt with by global theories because it's computationally intractable...but it's inherent in relativity. So unless you want to break relativity, areas with low mass have time running slower than areas with high mass. And the cosmic voids are HUGE.

OTOH, This is a different argument as to why the universe isn't globally uniform at the large scale. But it reaches the same conclusion (to that question).

My thoughts exactly. How exactly is this "local" and they clearly say it is using ChatPGT and other cloud services? It is just making queries to AI data centers. You can do that with any computer already. You can even do it semi-anonymously through something like Venice.

And "it is on 24/7"... so what? So is my Linux desktop computer at home. And interacting with it through Telegram??? Why? Wouldn't just a plain, direct web interface make more sense?

Clearly I am not the target market for such a machine, but I really don't understand who the target market is and why.

>"I swear that flying things have a radar set to seek and bite me, so I have to live with 1 acre bug-zappers indoors near all doors creating ozone because of this bullshit."

I, too, am one of those people who get ATTACKED by mosquitos, apparently way more than others. It really sucks. I am also apparently unusually allergic to the bites, so I can have a horrible welt that lasts for a week or longer. If I could snap my fingers and eliminate all mosquitos from the earth, I would do it in a heartbeat. I don't think it will upset the ecosystem much, but I kinda don't even care- they are responsible for more human (and animal) suffering than perhaps any other living thing.

In any case, I hate to break it to you, but biting mosquitos are NOT attracted to bug zapper lights. It will certainly attract flies, moths, and tons of OTHER flying insects, but not mosquitos. You might be able to get them to go near the zappers if you put a mosquito chemical lure inside them.

There are mosquito killers that work, but they typically burn propane to create heat, CO, and moisture, and typically have a vacuum fan that sucks them into a mesh bag where they are then trapped. They are expensive to buy/operate and require upkeep. But at least there is no annoying light or zapping sound :)

You are correct. The mitigation of banning of the modules for Dirty Frag also covers Fragnesia.

However, if you removed the mitigation after getting a patched kernel, the previous patches do NOT protect against Fragnesia, so you will have to mitigate again until the kernel is patched again.

I think it's probably safe to say that there is pressure to inflate grades, and that such pressure comes from people who think that way.

And I know you know all this, but for the rest of the folks reading, realistically, most of them got into Harvard for one of three reasons:

• They could afford to go to Harvard, and therefore applied.
• They thought they were the best of the best, and therefore applied.
• Their parents went to Harvard and convinced them to apply.

Note that all three of those include the word "apply" in one form or another. The ones who got in are presumably some of the best of the people who applied, with the caveat that there is a large pool of people who were equally good, but did not get in, because there is a limit to how many students they can take, and there is a much, much larger pool of people who were equally good, but did not apply, because they:

• didn't have the money to afford it,
• didn't perceive themselves to be good enough (impostor syndrome),
• didn't want to live in the Boston area (B is for Boston, B is for brr),
• didn't want to go to school with what they assumed would be a bunch of spoiled rich kids,
• wanted to save their money for a good grad school, preferred to stay closer to home, or
• were majoring in an area where Harvard is only middle-of-the-pack.

For example, in CS undergrad education, Harvard is tied with UC Santa Cruz down at #37. And UCSC is a short (though moderately painful) drive from Silicon Valley, which makes it more desirable for part-time employment. Harvard is a few minutes on the red line from MIT (#5), which at best makes it an easy trip to another school's recruiting fairs.

It's not a terrible thought. I'm not sure you'd see a meaningful difference in outcomes if you randomly picked from the top 20% of students nationwide and assigned them to Harvard versus carefully selecting with the level of rigor that they do. What would be really great would be if one of these schools randomly chose 2% of their incoming freshmen from the pool of all applicants, rather than going through the full process, and then compared outcomes.

Of course it's possible. It is exceedingly unlikely once the class size gets sufficiently large, but it is absolutely possible in small classes.

Consider an honors general psychology class where everyone is in the honors program and chooses to take that class rather than taking their A in the non-honors version of the course. If they do well enough to get an A in the non-honors course, there's no good reason to give them a B in the honors version of the course, because that just penalizes their GPA for taking a version of the course that covers the subject in more depth and breadth. Now assume that this class has ten students, all of whom would probably have gotten an A in the standard general psych course. Consider that the policy proposed would cap it at 6 As.

And even if you reject the idea that the honors classes should be graded similarly to the non-honors classes and want folks to wear an A in an honors class as some sort of badge of honor (why?), a small elective class still has a real risk of having a section some quarter/semester where everyone is really good or really bad. And just as you wouldn't want to assign As if nobody deserves one, you wouldn't want to deny As if everyone does.

Policies like this only make sense if you cancel any section that has a small number of students or exclude them from the policy. The smaller the sample size, the larger the standard deviation becomes. This is basic statistics (which I mostly picked up in Dr. Zachry's honors general psych). Any policy that doesn't take that into account is fundamentally flawed. Ideally, the grades for each class need to be evaluated with a t-test or similar against all of the previous sections of that class, taking into account the class size as though they were both samples of a larger population. And if that says there's too much difference between the mean/variance of one class and another, that *might* be a hint that the other class was graded unfairly, or it might mean that they're just smarter/better students. To find out which, you then need to compare the group of students' overall per-semester/quarter GPAs against that same metric for the other historical sections of the class.

Simplifying it to some fixed number makes it easy to write the policy, but it doesn't make it a *good* policy.

In a given class, though, there will be variation. If your grade depends not just on how well you did, but on how well the other people in your class did, it's a fundamentally useless metric, because you can have one person who just happens to get into a couple of classes where half the people were valedictorian, and ends up with a B, while another person in the same year who takes classes in a different semester or ends up in a different section of the same class with different cohorts, turns in exactly the same quality of work, and gets an A.

Any sort of stack ranking makes grading completely and totally worthless, even when evaluating people who were at the same school at the same time. It literally tells you nothing more than that a particular student was better than the people in that specific section of that specific class.

This sort of stack ranking also creates a strong disincentive for smart people to take classes with a smaller numbers of students, because the variability in quality of students will be higher.

I would say that any sort of limits like this should be applied over a five-year rolling window, and including all sections taught by a specific professor. That way, a professor who is approaching the threshold can adjust the grading slightly overall to stay within the limits without excessively penalizing students in a section that has all really smart people.

Alternatively, you could provide an escape hatch where a professor can justify exceeding the policy on a one-off basis, but where it has to be independently reviewed, and if it keeps happening, it becomes a problem for the professor.

If you don't do one of those two things, then what you're doing is causing artificial grade *deflation*, which results in an unfairly/randomly biased ranking signal. And I'm of the opinion that doing so makes grades even less useful than their current questionable level of utility.

Or we could just acknowledge that grades are a poor measurement of students' ability in the real world and abandon them entirely, replacing them with pass/fail signals, where each subject area within a course must provide a pass signal for the class as a whole to be passing. Better yet, make it tristate: P, NMP, F, where P means it should count across the board, NMP means it is good enough to pass if it isn't a course in your major area, and F means it isn't good enough to get credit.

Let companies actually spend the time and money to interview more candidates to find out whether they are worth hiring instead of relying on noisy numeric signals as a crude filter.