aithrowawaycomm

I don't understand your ridiculous pedantry! I am talking about DALL-E and Stable Diffusion. I am not talking about other front ends to these services, nor did I dispute that your example deserved copyright protection. Invoke is very very different from plain text-to-image generation, WHICH IS WHAT I WAS TALKING ABOUT.

I think it's best if I log off and ignore your replies.

No, in general you cannot copyright them:

https://www.reuters.com/legal/ai-created-images-lose-us-copy...

https://www.reuters.com/legal/litigation/us-copyright-office...

https://www.reuters.com/world/us/us-appeals-court-rejects-co...

See the other reply for a half-counterexample, but the major difference is the specific software is more like generative PhotoShop, and the final image involved a lot of manual human work. Simply tweaking a prompt is not enough - again you can get copyright for curation, just not the images."

Of course AI can't be credited with copyright - neither can a random-character generator, even if it monkeys its way into a masterpiece. You need legal standing to sue or be sued in order to hold copyright.

I am specifically talking about DALL-E or Stable Diffusion, your link describes something very different. The point was the "Google Images" analogy, which applies to 99.999% of AI art but this is an exception.

"AI art generators enable the creation of ignorant and lazy illustrations by outsourcing understanding to an idiot robot."

"Yes, but is it not the intent of the artist to be ignorant and lazy?"

It is possible to repeatedly iterate AI art gen and get what you want, but that's not what happened here. And even so, it's not at all the same thing as drawing a picture: "iterating on what you want" is equivalent to curating art, not creating it. In the US you can copyright curation and that extends to curation of AI art - the US Copyright Office correctly said that tweaking prompts is the same thing as tweaking a Google Images search string for online image curation. But you can't copyright the actual AI-gen pictures, they are automatically public domain (unless they infringe someone else's copyright).

I am unable to think of a single animal behavior popularly ascribed to intelligence but in fact explainable by rote instinct. Do you have an example?

OTOH there are plenty of animal behaviors that can only be explained by intelligence: seeing-eye dogs perform a task which is economically useful for humans yet far beyond the ability of AI (even if the robotics mech eng issues are resolved). But it also doesn't really make sense that one of my cats is "instinctually" able to understanding my words while the other cat is "instinctually" able to outsmart me when we play with toys. The more sensible explanation is cats are intelligent and intellectually diverse.

The problem is that it also included "doxxing" DOGE employees, even though they are significant public figures and their anonymity is almost certainly illegal. Protecting the identity of government employees who are more powerful than cabinet secretaries is plain abuse of ToS. (I will add that I don't believe most of the alleged death threats were real. I think Musk's concern was the "doxxing" and the death threats were a pretext.)

  Specifically, better scores on the measures of impulsivity, emotional awareness and flexibility, and also the personality trait of agreeableness were all linked to higher patience scores.... But these results do suggest that patience is not so much virtue as a method to help us to deal with frustrations — and that some of us are better equipped to employ this coping mechanism than others.

Here, we modified the part of Claude’s internal state that represented the "rabbit" concept. When we subtract out the "rabbit" part, and have Claude continue the line, it writes a new one ending in "habit", another sensible completion. We can also inject the concept of "green" at that point, causing Claude to write a sensible (but no-longer rhyming) line which ends in "green". This demonstrates both planning ability and adaptive flexibility—Claude can modify its approach when the intended outcome changes.

This all seems explainable via shallow next-token prediction. Why is it that subtracting the concept means the system can adapt and create a new rhyme instead of forgetting about the -bit rhyme, but overriding it with green means the system cannot adapt? Why didn't it say "green habit" or something? It seems like Anthropic is having it both ways: Claude continued to rhyme after deleting the concept, which demonstrates planning, but also Claude coherently filled in the "green" line despite it not rhyming, which...also demonstrates planning? Either that concept is "last word" or it's not! There is a tension that does not seem coherent to me, but maybe if they had n=2 instead of n=1 examples I would have a clearer idea of what they mean. As it stands it feels arbitrary and post hoc. More generally, they failed to rule out (or even consider!) that well-tuned-but-dumb next-token prediction explains this behavior.

I struggled reading the papers - Anthropic’s white papers reminds me of Stephen Wolfram, where it’s a huge pile of suggestive empirical evidence, but the claims are extremely vague - no definitions, just vibes - the empirical evidence seems selectively curated, and there’s not much effort spent building a coherent general theory.

Worse is the impression that they are begging the question. The rhyming example was especially unconvincing since they didn’t rule out the possibility that Claude activated “rabbit” simply because it wrote a line that said “carrot”; later Anthropic claimed Claude was able to “plan” when the concept “rabbit” was replaced by “green,” but the poem fails to rhyme because Claude arbitrarily threw in the word “green”! What exactly was the plan? It looks like Claude just hastily autocompleted. And Anthropic made zero effort to reproduce this experiment, so how do we know it’s a general phenomenon?

I don’t think either of these papers would be published in a reputable journal. If these papers are honest, they are incomplete: they need more experiments and more rigorous methodology. Poking at a few ANN layers and making sweeping claims about the output is not honest science. But I don’t think Anthropic is being especially honest: these are pseudoacademic infomercials.

Even in self-driving, Telsa's behavior proves there is a market for cars that are programmed to speed and roll through stop signs. Waymos are safer than the average human, but the average human also intentionally chooses a strategy that trades risk for speed. Indeed, Waymo trips on average take about 2x as long as Ubers: https://futurism.com/the-byte/waymo-expensive-slower-taxis

What happens if an upstart self-driving competitor promises human-level ETAs? Is a speeding Waymo safer than a speeding human?

I am not disputing any of that, nor am I trying to put a positive spin on anything coming out of Trump. My only point was that it's misinformation to say that the US is unilingual by policy - that only became partially true in March 2025 via a toothless (and blatantly unconstitutional) executive order.

And it's not "soft acceptance of Spanish here and there," all levels of government are legally required to print official documents in whatever languages their community speaks; cities usually have ballots in dozens of languages. This is a constitutional requirement, bolstered by the Voting Rights Act, and Trump has not yet done enough damage to make those legal requirements go away via diktat.

a country which is unilingual English by policy?

To be clear the US only has a unilingual policy because Trump signed an executive order this year (and I believe even this SCOTUS would strike the order down as unconstitutional if anyone had standing to sue over it).

The US has always been de facto unilingual, but de jure we don't have an official language since Trump has no legal authority to establish that. The "policy" is political and legally empty.

This would be a valid POV if there was any solid evidence that LLMs truly increased worker productivity or reliability - at best it is a mixed bag. To stretch the food analogy, it seems like LLMs could be pure corn syrup, without any disease-resistant fruits and unnaturally plump chickens that actually make modern agriculture worthwhile.

Or, since LLMs seem to be addictive, it's like getting rid of the spinach farms and replacing them with opium poppies. (I really hate this tech.)

That's 100% false, dogs and pigeons can obviously think, and it is childish to suppose that their thoughts are a sequence of woofs or coos. Trying to make an AI that thinks like a human without being able to think like a chimpanzee gives you reasoning LLMs that can spit out proofs in algebraic topology, yet still struggle with out-of-distribution counting problems which frogs and fish can solve.

Maybe a roundabout answer to your question, but Peano's axioms are equiconsistent with many finite set theories (even ZFC without axiom of infinity), and I do think philosophically it makes more sense to say weak axiomatic set theory + predicate calculus forms building blocks of arithmetic[1]. The idea of "number" as conceived by Frege is an equivalence class on finite sets: A ~ B <-> there is a bijection, which is in fact a good way of explaining "counting with fingers" as an especially primitive building block of arithmetic:

  {index, middle, ring} ~ 
  {apple, other apple, other other apple} ~
  {1, 2, 3}

[1] In my view Peano axioms are the Platonic ideal of arithmetic, after the cruft of bijections and whatnot are tossed away. I agree this is splitting hairs.

Do these probabilities mean anything at all?

  When Mosca and his colleagues surveyed cybersecurity experts last year, the forecast was sobering: a one-in-three chance that Q-Day happens before 2035. And the chances it has already happened in secret? Some people I spoke to estimated 15 percent—about the same as you’d get from one spin of the revolver cylinder.

I have been told this guy is an irascible reactionary genius for like 15 years - he says so himself: "No. I’m an outsider, man. I’m an intellectual." Yet once again I see somebody with the sophistication of a teenager:

  If you look at the administration of Washington, what is established looks a lot like a start-up. It looks so much like a start-up that this guy Alexander Hamilton, who was recognizably a start-up bro, is running the whole government — he is basically the Larry Page of this republic.

  [...]

  Understanding why Hitler was so bad, why Stalin was so bad, is essential to the riddle of the 20th century. But I think it’s important to note that we don’t see for the rest of European and world history a Holocaust. You can pull the camera way back and basically say, Wow, since the establishment of European civilization, we didn’t have this kind of chaos and violence. 

  It’s basically just a greater openness of mind and a greater ability to look around and say: We just assume that our political science is superior to Aristotle’s political science because our physics is superior to Aristotle’s physics. What if that isn’t so?

  When I look at the status of women in, say, a Jane Austen novel, which is well before Enfranchisement, it actually seems kind of OK.

I will never stop being amazed at AI folks' childish views of animal cognition:

A lot of your tools reference crows. What’s up with that?

White: When I got started in this space around October 2022, I was red-teaming with GPT4. Around the same time, a paper called “Language Models are Stochastic Parrots” was circulating, and people were debating whether these models were just regurgitating their training data or truly reasoning. The analogy is appealing, and parrots are definitely known for mimicking speech. But what we saw was that pairing these language models with external tools made them much more accurate — a bit like crows, which can use tools to solve puzzles.

In the work that led to ChemCrow,1 for instance, we found that giving the large language model access to calculators or chemistry software made its answers much better. So we kind of retconned a little bit to make “Crows” be agents that can interact with tools using natural language.

This is incredibly insulting to crows, who can spontaneously create tools and use bizarre man-made tools with no training. And when crows use tools for problem solving in the lab, the tools are not "solve the problem for me" like a calculator, they require much more creative thinking. What White really means - whether he knows it or not - is that crows are known for being intelligent and he wants to use this for marketing purposes.

I don't think anyone alive today will live to see an AI as smart as a crow, in no small part because AI researchers and investors refuse to take animal intelligence seriously.

I didn't say "dogmatically believe," I said "accepted as plausible" - Nick Bostrom built an academic career out of this nonsense! Effective altruism people uses these theories to justify their advocacy. I would guess most tech people agree the paperclip maximizer is a serious concern.

The problem is they also put "worms" in the same category, and they aren't designed by humans to do anything! Why is it that the natural laws of a worm responding to Earth's environment are distinct from the natural laws of Jupiter responding to the solar system's environment? I suppose because of complexity. But then why is a thermostat different from Jupiter despite being considerably simpler? I suppose because it was designed by humans and can be controlled. But then what about the worm, which is just as natural as Jupiter? "Thermostat" is especially problematic because a cheap thermostat is very simple to describe completely as a thermo-electric balance equation: it is certainly simpler to describe than an irregular ball rolling down an irregular hill. Yet apparently the thermostat is an agent and the ball is not.

The definition is just incoherent! "Sometimes an agent is deterministic and in this case the term only includes manmade tools, other times an agent is an apparently nondeterministic automaton and in this case we can include natural life." It only allows "agent" to be labelled ad hoc, and in particular blurs the distinction between "nondeterministic tool" and "lifeform" in ways that are scientifically unjustifiable. The only people this pointless word game benefits are liars like Sam Altman and Mustafa Suleyman; if people are well-intentioned then these definitions bring nothing but confusion.

Tech's general atheism has left many tech people with a somewhat compromised ideological immune system: preposterous magical thinking is laundered with sci-fi language and accepted as plausible. (cf. the simulation "hypothesis", Roko's basilisk, paperclip maximizers, etc)

Why not stipulate that the Flying Spaghetti Monster oversees the elections like an FEC commissioner? It is equally rational: the only difference is that it doesn't jive with sci-fi aesthetics, just like how Pastafarianism doesn't jive with Catholic aesthetics.

I would say the situation for "agent" is about 10,000 times worse than it is for "set," since all the definitions for set are essentially different ways to make Frege / Cantor more rigorous. The underlying scientific concept hasn't actually changed that much: "belonging to a set and a few certain operations are primitives, join these primitives with formal logic." This is the idea behind a famous intro book, Paul Halmos's Naive Set Theory. Even a set theory with Russell's paradox is scientifically defensible, it just needs refinement.

In contrast, "agent" discusses a huge range of scientific concepts, and I have yet to see a single definition of agent that holds up to scientific scrutiny. This book has managed to define "agent" in a way that is entirely equivalent to "physical object" - putting worms and thermostats in the same category broadens the category to utter uselessness. By this definition, Jupiter is an agent.

The only utility of definitions like this is for dishonest people to cheat at arguments: claiming simple tools are agents, then arguing they are like dogs and humans, which are also agents. It's a total waste of time, coming from AI's shameless disrespect for scientific standards.

The real issue is that the monkey (or Stable Diffusion) cannot be sued in civil court for copyright infringement, so they can't be granted copyrights in the first place: it makes no sense to have one-way streets of legal responsibility.

Note that a human-made curation of AI or animal art is protected by copyright (e.g. you can copyright an AI art coffee table book). The original case involved an AI-generated graphic novel: the author could claim copyright for the whole book but not the individual panels.

In theory transformers are Turing-complete and LLMs can do anything computable. The more down-to-earth argument is that transformer LLMs aren't able to correct errors in a systematic way like Lecun is describing: it's task-specific "whack-a-mole," involving either tailored synthetic data or expensive RLHF.

In particular, if you train an LLM to do Task A and Task B with acceptable accuracy, that does not guarantee it can combine the tasks in a common-sense way. "For each step of A, do B on the intermediate results" is a whole new Task C that likely needs to be fine-tuned. (This one actually does have some theoretical evidence coming from computational complexity, and it was the first thing I noticed in 2023 when testing chain-of-thought prompting. It's not that the LLM can't do Task C, it just takes extra training.)

It is not a high standard, I am sure you could train a chimp to pass this test[1]. If you know how to use a standard coffee maker and live in a typical American home, and the test is done in an typical American home with a standard coffee maker, you can definitely pass this test 100% of the time.

I understand that many people don't live in America and don't know how to use a coffee maker. That is 100% irrelevant. There is a frustrating tendency in AI circles to conflate domain knowledge with intelligence, in a way that invariably elevates AI and crushes human intelligence into something tiny.

[1] The hard part would be psychological (e.g. keeping the chimp focused), not cognitive. And of course the chimp would need to bring a chimp-sized ladder... It would be an unlawful experiment, but I suspect if you trained a chimp to use a specific coffee maker in another kitchen, forced the chimp to become addicted to coffee, and then put the animal in a totally different kitchen with a different coffee maker (but similar, i.e. not a French press), it figure would figure out what to do.

How do you even compare those two things? And how do you separate technology from medicine? This statement seems like pure nonsense, hinging on a slippery definition of "technology" that in some contexts means "consumer gizmos," other contexts means "computers," and in yet other contexts means "Civilization tech tree."

This was very unclear in the article - typically artificial hearts have external pumps and motors, the internal component is basically a fancy valve to manage flow/oxygenation correctly. But this one has an internal pump and motor, like a real heart, but with an external battery pack. (The battery currently only lasts four hours...I am too absent-minded to be trusted with this!)

This article was a bit more clear: https://www.abc.net.au/news/2025-03-12/sydney-hospital-artif...

Long-term planning is necessary if you have biters enabled: typically you need to secure territory/resources and invest in defenses before the resources run low and while the biters are still manageable. Otherwise things can get badly out of hand.

Edit: IMO the biggest difference between Satisfactory and Factorio is that Satisfactory has no crises. If a Satisfactory base shuts down it is annoying, but you can dig another miner / / build another plant / etc, entirely at your leisure. But in Factorio, a shutdown is an emergency with a ticking clock.

I think the point of “The Main Bus” in guides is that it’s easy for newer players, and thereby takes a lot of complexity off the table when you still haven’t really figured out how petroleum works, or keep falling behind the biters because you underestimated red bullets demand for steel, etc etc etc. Eventually you figure out trains; until then a main bus is an idiot-resistant way to carry resources across the entire base.

Even the example in the post seemed closely related to other advances in consumer-level computing:

  I re-created this system using an RPi5 compute module and a $20 camera sensor plugged into it. Within two hours I wrote my first machine learning [application], using the AI to assist me and got the camera on a RPi board to read levels of wine in wine bottles on my test rig. The original project took me six weeks solid!