The early pre-United States had pamphlets and newsletters. Like, a lot of them. It was a big influence on the early post office. You could get rich running a printing press but you did it by printing for the masses.
The yellow journalism era, on the other hand, was a little closer to the rich owning the press.
The penny press, and a ton of those pamphlets amount to what we now call tabloids. They were funded by advertising too, and used fear & outrage to sell copies. Led to some pretty funny conspiracies like The Moon Hoax (https://en.wikipedia.org/wiki/Great_Moon_Hoax)
Times are not as different as we'd like to believe
I wonder if it partially because "write like a human" is kind of a vacuous request. Like, it's the objective everyone including me has been saying that we want, but there's no one way to write like a human and and humans don't even have a good definition past "I know it when I see it."
There's a lot of work in the humanities about different aspects of good writing, but that's not quite the same thing. And anyway they tend to assume a pre-existing level of writing ability. Students are supposed to learn good writing through practice; there are rules and exercises but they're incomplete.
I think as much it is that people write by grappling for the right phrase to represent some inner feeling or concept, writing in part for themselves, whereas LLMs write always and only for an audience.
It’s much easier to understand this once you think about other generative forms. MidJourney never just sits down and draws for fun, so fun never informs its art (only the outward appearance of others’ fun, separate from the fun itself). Suno doesn’t waste hours trying to find riffs on a guitar, so its output is never informed by the direct joy of getting it right. Its music is never optimised for playability on a particular guitar with a scratchy seventh fret and a too-high action. Neither Midjourney nor Suno have evolved their styles due to short-sightedness or carpal tunnel.
If you had a human writer who over a long career only ever wrote articles from an outline given to them by someone else, and you had all the outlines and all the resulting articles from those outlines, and you could train an LLM to generate an article from an outline, it still would not be kicking itself frustrated by an inelegant phrase in a prior article, it would not avoid certain phrases out of a passive aggressive reaction to some editor’s note, it would not ever just rush an article because everyone is gathering at the pub, and it would not choose an analogy just to rub the author of a bitchy critical letter to the editor the wrong way. An LLM could not “subtweet”. It could not write a series of articles hoping one important person will spot that they are auditioning for a job.
Creators have unseen, undocumented influences and motivations that inform their work over a long period. I don’t mean to say that these individual influences can be reliably detected in individual pieces of work. I do mean to say that I think their broad absence tends to be felt in LLM writing. As readers we develop an affinity for writers as much as for their writing, and we do this in part because we deduce things about them.
Very elegantly put! So much so in fact, that I finally put down my phone to grab my laptop in order to write this. I have had a similar much cruder version of this thought: LLMs are not human, they don't know what it's like to stub their toe, despite having read probably millions descriptions of it. Even amongst humans there are experiences which are impossible to share, despite us having evolved to communicate quite effectively. Writing is just a small part of it, and cannot replace the actual experience of being human.
Each person writes in a different personal way, so writing “like a human” would actually require a model being able to purposefully make the specific choices that an individual human writer does.
However, general purpose LLMs like Fable have been trained on huge amounts of all kinds of data, and therefore find it exceedingly hard to break out of the grooves carved by that data. They can’t avoid defaulting to centroids and averages, even when they are trying not to. This makes it possible for classifiers like Pangram to discriminate their writing.
A plausible way to work around this limitation would be to train a LLM on a limited and cohesive subset of writing materials, so it would absorb their specific writing style.
One example might be Talkie, a LLM trained on pre-1930’s English text. Talkie is a far smaller and less powerful model than Fable.
And yet, Talkie’s writing is so distinctive that it is often classified as human by Pangram.
The portable gas turbine is a somewhat separate issue, in that it's highly illegal but if you have more money than god you can just pay off the fines and the judges.
It's relatively easy to avoid being that egregious about it if you're a little bit patient on construction permits and power contracts.
NIMBY opposition to datacenter construction is orthogonal to that, except insofar as in both cases the construction will gravitate toward places that won't or can't object to the construction.
Yeah, unfortunately "LLMs are bad at counting" seems to apply to counting parentheses.
With modern tool calling I wonder if a better way to go about it is for the LLM to express the program changes as a function or otherwise use an editor that auto-balances parens. There's a lot of relatively simple tooling that makes it easier to write in a Lisp. The languages tend to lend themselves to being straightforward to check like that.
What's special about Lisp's repl is that it's perfectly possible to construct your entire program in the repl, testing each addition live as you write it. (Many Lisp-focused editors assume you'll want to do this, such as Emacs making it easy to run the interpreter on a single function in a file.) That tooling is lost if you just try to one-shot the file, and before 2026 the majority of LLMs originally just tried to one-shot every file.
But, just like a lot of early LLMs had huge problems with whitespace and numbers because the tokenization was taking efficiency shortcuts that made sense for text but absolutely wrecked code syntaxe, I wonder if the current optimizations are badly formatted for Lisp.
At the very least, using a varient like Clojure that also uses [] and {} in addition to () might help.
Except you can express the higher order structure in s-expressions too, and using an AST usually means expressing it as a different syntax than the other parts of your program.
Or maybe I've just been sleeping on the power of Javascript AST.
And that's more of a library ergonomics question how nice it is. There are plenty of other languages with nice ASTs and `eval`, and that's the only ingredient you need.
From the outside Meta's attempts to pivot from open source releases to fast follow closed models fell flat when they tried to prematurely monetize it. They could have owned the open weight model world but tried to pivot to closed weight chatbots before an actually viable revenue model appeared.
I did start a blog in that time period and this is the first time I've ever heard of this. (Admittedly, I wasn't trying to directly make a living off of it.)
Back when red LEDs were the new cheap lighting option everything was great: red doesn't affect noght vision as much and at least for me it doesn't seem to prevent sleeping on the same way that the bright white LEDs do.
The yellow journalism era, on the other hand, was a little closer to the rich owning the press.