> Since the 2022 price change, according to the undoubtedly reliable numbers from the US government, consumer-price inflation has added up to almost exactly 20%.
Enjoyed the sarcasm, but does anyone actually believe these numbers? Is there a more reliable metric that isn't affected by Goodhart's law and can be trusted?
It was neither backwards nor goofy for a man in 18th century Europe to create a system only his 18th century European peers could use (the only peers whom he could even come into contact with in 18th century Europe).
What actually would be goofy and backwards would be for this man to consider all of the world's cultures with whom he had never come in contact with, and try to create a system that worked for them. That would be very goofy and backwards, it would make him a crackpot.
The mere mention of this conciliatory statement for those "[pesky] leftists" (of which the author is one), the author is asserting that "Linnaeus was backwards and goofy but yet he made a good system!". Why does this even need to be said?
Do we call Confucius backwards and goofy for only writing his virtues in Chinese and only considering his Chinese peers? Why didn't he, or the Chinese (they had an empire and the means to do this), send envoys to the west so they could divine and predict what cultures would exist in a thousand years, allowing him to write his virtues in all of the languages and to consider all of the different mores of the world when writing his virtues? It's too Chinese! Or what about the Mayans, why didn't they in their backwards and goofy manner realize that a thousand years later their script would be indecipherable (for a while) to the European-language-speaking researchers of the future? If they had written in Latin we would be able to understand what they were trying to say! Very goofy and backwards of them. \s
The author's statement is what is backwards and goofy, and it's a cultural shibboleth that worked -- I stopped reading the moment I saw that, because to me it is a goofy and backwards thing to say.
Same here, I stopped reading right when he said this idiotic "guy from 300 years ago - when science as we know it basically only existed within the realm of white european culture" didnt thought about the rest of the world BS
My strategy these days is to scan and look for the tells and click out when I see them. Mine was the same "honestly ranked, with no silver bullets on offer". I suspect in less than a year we won't be able to tell the difference.
I’ve thought this for a while, but why hasn’t it happened yet? At this point, OpenAI and Anthropic and friends could definitely remove the AI “smell” from writing output, or give users a first class way to specify a writing style.
So why haven’t they? My theory is they see this as a sort of fingerprint, useful to not train on later. Or something. Maybe they just don’t care. Certainly feels either intentional or a result of ambivalence.
It’s certainly true today that I probably wouldn’t know an AI written article if the author went out of their way to use one of the many prompts available to tone down the AI-isms.
You're overthinking it. LLM's are massive scale regressors. Rhetorical techniques, cliches and memes (in the original Dawkins definition) spike the dataset and skew the data such that certain phrasings or rhetorical techniques will appear in the output in a similar ratio to what exists in the training set; however, the reality is that humans are able to change writing techniques depending on the nature of the conversation and the medium. LLM's don't have that, all of the scholastic papers, buzzfeed articles, and reddit posts factor into the output regardless of what it's writing. Meanwhile your average human can switch tone, style, and register depending on the situation and what they're writing.
I asked claude to take the above and rewrite it in it's own words and it came up with:
"LLMs are essentially large-scale statistical regressors — they're built on huge volumes of text, so the rhetorical patterns, clichés, and memes (in the Dawkins sense of self-replicating cultural units) that saturate the training data show up in outputs at roughly the frequency they occur in that data.
The key limitation: a human writer naturally shifts register, tone, and style based on context — what they're writing, who it's for, the medium. An LLM doesn't really do that in the same way. Every academic paper, Buzzfeed listicle, and Reddit thread it was trained on bleeds into its output regardless of the actual writing task at hand, whereas a person adapts fluidly to the situation."
Even with the context, it still included an emdash, the rhetorical technique of threes, and "the key limitation". It's an inherent weakness of LLM's. There is no fixing it.
If the underlying prompt of the model stays the same, it seems to me that LLMs will always have common tells unless overridden with a thorough prompt from the end user. It's like if you had 1 person write half of the content on the internet. You'd probably get pretty good at noticing their writing style.
Maybe my understanding of LLMs is wrong, but it seems obvious to me that when you have a large corpus of LLM output you will eventually notice common tells when everyone is using the same models, weights, and base prompt.
Because it's doing what is does best: making predictions, which works great when you're not being judged on aesthetics, such as coding or math, but the human thought process is messy or erratic. It just falls apart when you do the next token process to it. If the goal is to "convey information in readable chunks," AI does great at this.
It’s natural to the fact it’s the same model. Everyone has tics, and when a given model is asked to write millions of texts, they become visible. But! Some portion of the audience and user base can’t see it, so there is no benefit to fixing it. Case in point, yet another hustler felt very clever posting slop, and the likely actual audience (Google’s ranking system) probably does like it.
I have started beating the hell out of Claude with stylometric analyses of writers I admire; usually technical writers like Terry Winograd, Leslie Lamport, Rodney Brooks. Then the “no or minimal rhetorical flourishes” rule; no British em-dashes, and minimize the negative phrase thesis-antithesis fun”. It helps.
I think you are right that in a year or two LLMs will be able to do a good impression of many technical styles. But not Nabokov, Kundera, or Kafka for subtlety.
If one manages to channel Edsger W. Dijkstra I will be impressed and rank it high on my leaderboard.
It has been for years. The actual audience for a great deal of text you see is Google’s ranking system. Look up any recipe and ask yourself who reads the ten paragraph story time about grandma’s cookies. It literally isn’t intended to be read.
I believe this also partly sprung up because recipes in isolation don't qualify for copyright. The flavor text gives you grounds to sue if a clone of your recipe site pops up somewhere else.
It's an interesting read. Curiously, it doesn't really debunk anything.
The fact that given X and Y random and independent, that Y-X is correlated with X doesn't disprove the Dunning Kruger. It in fact proves that Y = 1 X is a poor predictor, and the true model is Y = 0 X. In other words, perceived ability (of the human) cannot predict the actual test scores. Which is exactly what DK claims, but to a very extreme effect.
Note that, if there is actual signal (plus noise), e.g., if Y = X + eps; so the actual score is exactly the perceived score plus some added variation, the (Y-X)~X will be uncorrelated. In such case, there will be no DK effect, because the users are good at predicting their actual test scores, plus some constant variation.
it will get busier. the goal is for write to cause the small load every time action happens. this way there is never single heavy query that would cause big load at read time. the main issue with MongoDB I've seen is that writes tend to slow down once you go over 1000+ keys in the aggregated payload. this can easily happen if you have something like yearly bucket. on the other side Redis is not affected by it as increments are done individually, so writes stay flat.
about your update: dashboards are the easy side here. rendering one is a lookup for few points from pre-aggregated buckets. all you need is a key and a timeframe with granularity. unless you are trying to get something like 1 month worth of per-minute data, it will be quick.
if you want real isolation, you can give driver dedicated user with its own limits or point it to dedicated database. we started with Redis to keep it easy, then Postgres as I wanted to ensure persistance and ended up with MongoDB which handled the writes with ease.
remarkable / remarkable2 also work with KOReader. KOReader provides a very good ereader experience - can import dictionaries, export word lists (ie it tracks words you look up), and supports numerous ebook formats and lets you modify the padding/margins/layout. A very good piece of open source software
It is very good except the UI could really use some love IMO. I use it on all my kindles but it really took some doing to get there in terms of getting comfortable with using it.
the irony is that all of this money will go to rent-seeking publishers who won't pass it on to the artists; basically a dispute between the wealthy you're upset with
Default payout is 50/50 author/publisher. If the author and publisher have a contract that states otherwise, then their contract overrides the default.
Source: I’m an author and signed up to be part of the class action, and this was the class action documents said.
> If there is a current publisher(s) (which still possesses an exclusive license), the author(s) will split the $3000 with the publisher. Any co-authors will share the author portion and, if there are multiple publishers (e.g., different publishers have exclusive rights to different formats), they will share the publisher portion. Assume that the co-authors and co-publishers will share the portion equally unless their contracts provide otherwise. The standard default split between publishers and authors of noneducational texts is 50/50, as described below. Authors who are the sole rightsholder in a work—such as self-published authors and authors whose rights have reverted or where the contracts have otherwise terminated—will receive the full award amount.
It is split between the publisher and the author, also publishers will have a large catalog of books they will submit, an author typically will only have a few -- the payout will be going to the lawyers and mostly to publishers.
>It is split between the publisher and the author, also publishers will have a large catalog of books they will submit, an author typically will only have a few -- the payout will be going to the lawyers and mostly to publishers.
This is innumerate. If it's split 50% between authors and publishers, then it won't be "mostly to publishers". Mathematically it will be equal between "authors" and "publishers", and because lawyers are taking their cut, neither would be able to get "most" of it. Yes, the average publisher will get a bigger paycheck, but that's because there's less of them, not because "most going to publishers".
> That means that rightsholders can expect at least $3,000 per title (less costs and fees), which will be shared among the rightsholders for that title (if there is more than one rightsholder)
> if there is more than one rightsholder
Again, a publisher will have a whole catalog of books / titles, a non-negligible portion of that the publisher will own the copyright to (no one to split it with). There's all kinds of books outside of novels, there's media tie-ins, IP franchise books (ie Star Wars), childrens books, textbooks / reference materials, etc etc etc. Yes, with novels the author tends to own the copyright, but you're forgetting all of the other kinds of books out there.
Fighting over that patch is one of the older continuous activities of the species, and while anything is possible, I would never bet in favor of MENA peace.
> When conditions are adverse, i.e. fog, heavy rain, the system simply shuts off and reverts back to manual driving.
I also own a Tesla, and there is no indication shown to the user that FSD's vision is degraded. They need to add this in.
For example, numerous times I have been driving my Tesla with FSD activated with ostensibly a clean and clear windshield when suddenly the car will do the "clean the windshield in front of the camera routine" without any indication that the car's camera is degraded. If people haven't seen this "clean the windshield routine", the wiper fluid is dispensed and the wiper will vigorously wipe in front of the camera only -- the rest of the windshield only gets a cursory wipe.
This indicates to me that the camera has poor visibility and I am not informed or aware of this as a driver, which is concerning. I am often curious if there is a thin occluding film on the windshield in the camera box in front of the camera, or something that has degraded FSD's vision, but they do not give you the ability to view the camera feed, nor do they notify you that the vision is degraded. I think a "thin occluding film" may be in the camera box because my normal windshield outside of the camera box started to show a thin chemical film after a couple of months, which apparently (according to a Google search) happens when a new car off-gasses, adding a thin film of chemical byproduct to the windshield. This is my first new car so I've no idea if this is normal or not.
> yes it does, and it's annoying as all hell. Dirt, sun, etc all pop an alert about degraded performance
As with all things FSD, it does sometimes and not others. I've driving my parents' Tesla with FSD engaged and it did complain when the windshield got dirty but didn't say anything when it drove into fog. (I took over manually.)
Out of curiosity, was the camera view compromised? I would probably take over too, but like the poster above, I get the warning in all kinds of conditions.
Absolutely could be a clouded windshield on the inside (where it's really hard for normal people to clean). I brought this up when I got my last Model Y that it was foggy and they said it was "fine". Took it into service over a year ago and noticed they cleaned it. Clearly it's a problem but they're not being too transparent about it. I suspect they don't want to because it's not the easiest thing to remove the cover for normal people to clean.
Recent Tesla updates will detect dirty glass inside the camera enclosure and offer to schedule (one!) free glass cleaning. You can do it yourself if you have a trim tool. (A thin plastic prybar) https://www.notateslaapp.com/news/3327/tesla-now-offers-free...
Enjoyed the sarcasm, but does anyone actually believe these numbers? Is there a more reliable metric that isn't affected by Goodhart's law and can be trusted?
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