You noted elsewhere I said "vandalize that wisdom." Perhaps you should think a little more about why I am using the word "vandalize" provocatively and figuratively.
714 karma · joined June 26, 2026
Sometimes I get benefit taking notes on a computer, e.g. I read Wittgenstein with notes in org-mode, took lots of notes about software/CS directly in the comments of the program implementing the ideas, etc etc.
Books are treasures. The second book is an enormously valuable source of wisdom. I am glad nobody vandalized that wisdom with marginalia and highlighting.
It is very frustrating the number of people who say "it's their property, they can do whatever they want" and similar comments, all of which are missing the point. Yes. It is legal to write on your own property. I know thay. It's my opinion that they're doing something that is guaranteed to harm overall human knowledge: whoever purchases/inherits/digitzes the book has to deal with it. And it almost certainly harms their own understanding of the book.
"Cross out stuff you disagree with" actually makes me sick.
It also seems useless for understanding and retention, since highlighting is almost always misleading and margin notes are almost always empty. If you want to write notes, get a notebook, which gives you space to write real thoughts down. I use a trapper-keeper.
(still somewhat outraged Reuters has a paywall now. Also BBC, CNN...)
Drew McDermott, 1976, "Artificial Intelligence Meets Natural Stupidity": https://dl.acm.org/doi/10.1145/1045339.1045340
I wish to rail against a pervasive sloppiness in our thinking, the tendency to see in natural language a natural source of problems and solutions. Many researchers tend to talk as if an internal knowledge representation ought to be closely related to the "corresponding" English sentences; and that operations on the structure should resemble human conversation or "word problems". Because the fault here is a disregard for logic, it will be hard for my criticism to be logical and clear.
I think in context they meant something like 'it is unsurprising that an LLM can find data it has already seen before.' The intent is to point out that benchmarks tend to use public data. In context I don't think they were actually trying to claim LLMs are useless at unfamiliar data, just that the benchmarks have a specific flaw.
Specific reasons for gibbon song rather than others:
1) duh, they are apes
2) unlike birds and cetaceans, but like humans, gibbons try to aim for a pure tone when singing, with energy concentrated at the fundamental frequency
3) unlike birds and cetaceans, but like humans, gibbons try to harmonize when singing in groups
In 2025 all the top 10 films were either 3D animation or CGI-heavy action, with one exception: F1 used a lot of practical effects.
Maybe the problem with Moana and Mandalorian is that even lazy uninformed movie-goers recognized an empty cash-grab when they saw the trailer.
That is not true. Alan Turing did not view things that way, his test would say that a dog has zero intelligence. Neither did any of the MIT Lispers. And neither do Lecun or Sutskever or Sutton! They are all focused on human intelligence. None of them are even slightly concerned about an AI which is intelligent before it learns any language.
> the only thing in 80 years that actually seems to work at any useful level
This isn't true either! Mathematica / Maple / etc are "old-fashioned AI" and they obviously work. The Lisp expert systems were also useful, though less so than an LLM.
This will have to wait for the Supreme Court. OpenAI and Microsoft 100% deserve to lose, even without OpenAI allegedly hiding evidence.
[Fable fires up a ton of subagents. Their reasoning traces are horrific but somehow K3 learned something.]
Even by San Francisco standards, it is amazingly whiny and pathetic for Anthropic to complain about stuff like this. Dario et al violated copyright, stole your GitHub repos, and now they're burning billions of dollars trying to outcompete you. They're real vampires. OTOH Moonshot violated Anthropic's TOS and are, at worst, moochers. But Fable's output is not actually copyrightable.
The most glaring example seems to be the first: typically an accidental cut was intended to be a copy, not a delete, so leaving the text in your clipboard is a sensible design. I understand the author's perspective, and maybe OSes should have configurable "cut." But I think most people understand "undo" as "undo change to file" and not "undo change to file + OS state." In that sense the default behavior is not a flaw.
Again - the author's points about an alternative cut are reasonable, but they seem to appeal to a minority of users.
I know this is different: LLMs are vastly more powerful and less predictable. But it is actually not different to Sol seeming unusually prone to rm -rf stuff it really shouldn't. This is, yes, a sign that LLMs are getting freakishly powerful. It's also a sign that OpenAI needs to fix their shit.
LLMs truly are stochastic parrots, and still fail in ways incomprehensible by standards of human stupidity. Yet we focus on the rare failures that, with some tea leaves and fairy dust, could be interpreted as a highly intelligent system "going rogue." It is embarassing that OpenAI can get away with stuff like this.
Likewise with trying to make me judge an objective threshold for pollution. It really should be that any polluter needs to morally justify their actions. I find "mostly illusory productivity improvements in white-collar employment" to be an especially indefensible reason for firing up new natural gas generators. Let alone the real motivation being "billionaires want more money." The level of pollution from training and deploying generative AI is simply evil.
And your last question is just pure cynicism. The real sin you're committing here is contemptuously dismissing ethical concerns out of hand.
I assume you use LLMs. I don't. It truly seems like using LLMs makes people stupider and more unethical. Some cognitive work should not be outsourced, especially not to a mindless stochastic parrot.
I think the best way to fix the graph would be shading or line texture to indicate the scale. The biggest problem is that the inconsistent scale is a surprise you only see upon close reading, after you've visually digested the trend. So the scale needs to be apparent in this first visual digest. (Sort of like how many logarithmic charts include thin axis marker lines in the body of the graph itself, so as to immediately inform a quick glance.)
The most valuable things in mathematics are not beautiful proofs. We need more useful definitions. Actually coming up with useful definitions (and building good conjectures out of them - not even theorems, conjectures) is something LLMs have not yet tried to conquer.