Agreed. The concept of “don’t reuse your old work when you’re supposed to be creating new work” may be valid, especially in training environments, but it shouldn’t be called self-plagiarism or treated like plagiarism.
Perhaps an easier to intuit version of it is how full airliners are.
An airline might report that their flights are on average 60% full, and that might be completely absolutely 100% true. But that's not what passengers experience. If we assume (for convenience) that a plane holds 100 people, when the plane is 20% full then 20 passengers experience that, but when the plane is 100% full then 100 passengers experience that. On average, from a passenger's point of view, the flights are much more than 60% full--it might be 70 or 80%--because a full flight is experienced by more passengers than an empty flight.
For a concrete example imagine two flights, one 20% full and one 100% full: the average is 60% from the airline's point of view, but 100 passengers experienced a full flight and only 20 experienced the 20% full flight, so from the passenger's point of view the average is 86.7% full.
The same logic applies to outages. If you have an outage that lasts one minute then only a few users will encounter it. If you have an outage that lasts one hour then many more users will encounter that. The longer the outage is, the more likely any given user is to encounter it, so from the user's point of view the "average" outage is much longer than the "true" average where you weight every outage equally.
Again we can consider a concrete example: imagine you run a website that gets 100 visitors per minute. You have one outage that lasts 1 minute, then later a second outage that lasts 9 minutes. Your average outage time is 5 minutes. But 100 visitors experienced the 1 minute outage, while 900 visitors experienced the 9 minute outage, so from the point of view of a visitor the average outage is (900*9 + 100*1)/1000 = 8.2 minutes.
I have similar feelings about TV shows. There are shows that I wish hadn’t ended after a couple of seasons, but there are also a ton of shows that dragged on for 6, 8, 15 seasons when it clearly would have been better to end them years earlier.
Overall I lean toward appreciating things that end early more than things that end late.
> Remember, the only thing that stops a guy with an evil god-in-a-box is a guy with a benevolent god-in-a-box, and only Antrophic can lead us to the second one – but only if we act together as a nation and ban those subversive open weights models!
Eliezer Yudkowsky has made this argument explicitly, substituting himself for Anthropic.
A highly publicized recent example: the author (of a book about genAI!) who doesn’t understand why he should be held responsible for the fake quotes he copy and pasted into his book from ChatGPT [1].
> I do not understand why it's my job as an author to play whack-a-mole with a multibillion-dollar company who puts hallucinations into their feed as a business practice.
Fair. The concept predates Marx, but in contemporary thought is most closely associated with Marxism.
The quote about silver from Peru is particularly striking to my ears. That’s a long and dangerous journey, and obviously (to my modern sensibilities) the person making it should be compensated appropriately for the far greater risk taken on.
Labor theory of value is a Marxist idea, not an Adam Smith idea. Internet Marxists sometimes point to a passage in The Wealth of Nations to suggest that Smith also supported a labor theory of value, but this is—in the most generous interpretation—a misreading. Smith says that the value of a thing can be measured by how much labor it can be exchanged for: an exchange theory of value, not a labor theory of value (which says the value of a thing is based on how much labor it takes to create).
Doesn’t it? It seems in line with the matplotlib drama where the llm agent wrote a blog post attacking the maintainer for rejecting its pull request [1].
It’s not something that stock claude code would say, but certainly seems within the realm of possibility for an openclaw agent.
Giraffes neurons can be up to 15 feet long. Blue whales are speculated to have neurons up to 100 feet long, though they've never been directly observed (dissected).
> For an example, look at some of Julia Mossbridge's work.
Never heard of her but I just spent about 5 minutes looking.
Her PhD is in communication sciences and disorders [1], but apparently she’s a quantum physicist now:
> AMELIA is built on the Causally Ambiguous Duration-Sorting (CADS) effect — a breakthrough discovery by Dr. Julia Mossbridge showing that light, under classical boundary conditions, behaves differently based on future temporal boundaries.
[2]
Filed under crank, not going to bother investigating further.
> We expect our machines to behave in predictable ways.
I expect LLMs to produce randomly varying output. Maybe it's the thousands of hours I spent doing monte carlo simulations for my PhD.
> This is one of the best arguments against using LLMs I've seen.
> It reduces to the classic argument- at the point where you've described a problem and solution in sufficient detail to be confident in the results, you've invented a programming language.
I'm not an LLM true believer, but I use codex for various small tasks and it often (not always) does a thoroughly decent job. Yesterday I gave it a pretty vague request to set up a new Home Assistant dashboard and it handled it just fine--I told it what I wanted to see but it figured out itself which helper variables it would need to set up to realize that vision and wrote all the config for it.
I probably could have done it in 15 minutes if I was familiar with Home Assistant's yaml configuration schema and all, but I'm not so it probably would have taken me closer to an hour. Asking codex took me 30 seconds and it did just fine.
I am skeptical that LLM's are going to kill all white collar jobs or whatever anytime soon. Not being able to truly learn things is an issue. Reality has a surprising amount of detail[1], and while codex does well at things like writing Home Assistant configs and setting up a Minecraft server, where there are thousands of examples online of how to do it, when I've asked it to do some more esoteric things it has sometimes failed spectacularly. I don't think having the LLM keep notes and then read them back (filling up the context window) is a real solution here.
LLMs have a temperature parameter. At zero temperature they are deterministic: they always choose the most likely next token at each step based on what came before and the model weights, and they will always generate the same output given the same input.
As you raise the temperature they will start (pseudo)randomly choosing tokens other than the single most likely token (though that one will still be the most likely to be chosen). It turns out this is almost always better than zero temperature, which has a tendency to get caught in repetitive loops. I imagine all the frontier labs have spent thousands (millions?) of CPU hours tuning the temperature parameters on their models for optimal performance.
I didn’t claim that LLMs are people or that they reason.
If the behavior of the llm is the same as the behavior of reasonable people then the behavior of the llm is reasonable, regardless of how black of a box they generate tokens out of.
Reasonable people will generate divergent specs for the same prompt. Thus it is reasonable for an LLM to generate divergent specs out of the same prompt.
Edit: I use “reasonable” here in the legal sense of the “reasonable person” standard, not to imply any reasoning process.
If you ask 10 different humans to produce the spec with the same information (prompt and context) they will also produce 10 unique answers that will contradict each other and (depending on who you asked) may be just as confident.
There are real decisions to be made when going from a vague prompt to a spec. It's not surprising that an LLM would produce different specs for the same work on different runs. If the prompt already contained answers to all the decision points that come up when writing the spec then the prompt would already be the spec itself.
> Does an approximation to pi therefore slowly creep in as you increase the sides on the polygon?
Yes, under some assumptions. As the sibling comment points out, if there’s a single allowed angle theta then the expected number of intersections is cos(theta) * L/W (-pi/2 < theta < pi/2). You can get from this fact to the standard Buffon’s needle result by integrating wrt theta to find the average probability over thetas with a uniform distribution on (-pi/2 < theta < pi/2): \int 1/pi * cos(theta) * L/W d theta.
Now suppose you have two angles, theta_1 and theta_2. The expected number of intersections for each of them is as above, and if the needle falls at one or the other with equal probability then the overall expectation is 1/2 * cos(theta_1) * L/W + 1/2 * cos(theta_2) * L/W. Passing to the case with n distinct angles with equal probabilities we have \sum_i 1/n cos(theta_i) * L/W.
Now if we make the further assumption that the angles are evenly distributed over (-pi/2 < theta < pi/2), i.e. they are the angles of the sides of a regular n-gon, then we can interpret that sum as a Riemann sum. If we write it as
1/pi \sum_i pi/n cos(theta_i) * L/W
Then pi/n is the delta_i term in the riemann sum, and the limit is
We can pull the L/W out, leaving \int_-pi/2^pi/2 cos(theta) d theta = sin(pi/2) - sin(-pi/2) = 2, giving the final result of 2/pi * L/W.
Essentially, as we increase the number of allowable angles we are approximating an integral of the cosine function (times constants) from -pi to pi, which is where the pi creeps in. The angles don’t need to be strictly evenly spaced for this to work—if they are independent randomly selected from the uniform distribution then it will also work, as you’re then performing a monte carlo integration.