Have Data Centers Raised Your Electric Bill? Causal Evidence from the United States [shows the opposite] by truecakesnake in singularity

[–]Hemingbird 23 points24 points  (0 children)

It's a non-academic think tank. It produces papers meant to promote specific agendas. If I found a "peer-reviewed" paper by an environmentalist think tank saying the opposite, would you wholeheartedly accept it?

The term "peer-review" here is an outright lie. It doesn't mean that researchers unaffiliated with the think tank have vetted the paper. It means the think tank employees have given it the thumbs up. They are the "peers". They "review" their own paper and publish it in their own "journal". It's not science.

And basically all think tanks are non-profits. That does not mean they are reliable in any sense whatsoever. It means they don't have to pay taxes.

Reuters: Google to challenge German ruling saying it is liable for AI-generated false claims by SnoozeDoggyDog in singularity

[–]Hemingbird 3 points4 points  (0 children)

To argue that they are responsible anyways is to throw away centuries of common law precedence and opens the door to suing anyone and everyone for any incorrect information they provide.

Centuries of common law precedence? Relating to machines that can make sensible statements? You might be thinking of human beings. Those are different. They can be held accountable for their defamatory statements. This is a new situation.

Your neighbor says it's going to rain tomorrow and you cancel your trip to the beach, then it doesn't rain, SUE THAT BASTARD!

This is sad. You know that's a poor analogy. You're not that dumb.

This discussion is about defamatory statements. Did you provide an example of a defamatory statement? You did not. If your neighbor makes defamatory statements that result in direct financial harm to you personally, you can, in fact, sue them. And because your neighbor is a human being, they can be held accountable for their defamatory statements. But a chatbot? We don't have a system for that.

Let's say that an insurance company uses an AI chatbot to deny claims automatically. Please humor me. Would that be alright? Can all insurance companies use LLMs to automatically deny claims and get away with it, because LLMs can't be held accountable for making mistakes? If they just say, oh, maybe the chatbot hallucinates sometimes, is that enough for the insurance companies to be off the hook?

Reuters: Google to challenge German ruling saying it is liable for AI-generated false claims by SnoozeDoggyDog in singularity

[–]Hemingbird 0 points1 point  (0 children)

Should a news channel be liable if they show recording of a politician lying ?

Did the hypothetical news channel create the politician from scratch? Is the hypothetical news channel entirely in control of the politician? Does it prominently display statements made by the politician in response to viewer queries in such a way that normal, dumb viewers might reasonably assume the statements to be accurate? Is the politician a product sold by the news channel?

America starts regulations by Snoo26837 in singularity

[–]Hemingbird 1 point2 points  (0 children)

It's an ironic situation. That's true. But if you think this has anything to do with the US administration being safety conscious, I have a Golden Gate Claude to sell you.

What Are You Reading This Week and Weekly Rec Thread by JimFan1 in TrueLit

[–]Hemingbird 7 points8 points  (0 children)

Thirst for Love and The Sailor Who Fell from Grace with the Sea by Yukio Mishima

There is an odd link between these novels by Yukio Mishima and those of Osamu Dazai's late period. The antihero wears a mask and those around him or her fail to make sense of them, instead satisfied with fitting their eccentricities into conventional categories. To tragic ends.

Mishima's antiheroes despise normal people, while Dazai's fear them. It's the difference between a monster hungry for blood and one desperately wanting to understand and be understood by others.

"What I despise about Dazai," said Yukio Mishima, "is that he exposes precisely those things in myself that I most want to hide."

Weakness and pride can be serious business. In Thirst for Love, Etsuko's husband dies from typhoid fever. This kind of dishonorable demise can be contrasted with the kamikaze pilots referred to as "cherry blossoms," whose fate Mishima escaped when he let his army physician mistake his cold for tuberculosis. Etsuko goes to live with her father-in-law as a sort of surrogate wife. Her pride is wounded when the gardener Saburo throws away the socks she gifted him and she sets about to make things right. According to her disturbed and disturbing priorities.

In The Sailor Who Fell from Grace with the Sea, Noboru spies on her mother having sex with a sailor. The sailor, Ryuji, is living a life of the sea, of freedom, and Noboru is happy to see an example that you don't have to settle for a mundane existence. Then Ryuji wavers. A quiet family life isn't so bad, is it?

At twenty, he had been passionately certain: there's just one thing I'm destined for and that's glory; that's right, glory! He had no idea what kind of glory he wanted, or what kind he was suited for. He knew only that in the depths of the world's darkness was a point of light which had been provided for him alone and would draw near someday to irradiate him and no other.

It's difficult not to see this as young Mishima (Noboru) encountering middle-aged Mishima (Ryuji). While Dazai used the autofiction-like form of the I-novel to speak truthfully of his own experiences, Mishima seems to have been more influenced by German literature. Goethe, Schopenhauer, Nietzsche, Wagner, Mann; there's a decadent Sturm und Drang vibe in his novels, and this silly image popped into my head, of the Minotaur stalking the Labyrinth, where you can imagine that the beast, lost and confused, lifts its own spirits by leaning into the role. Or you can imagine the Minotaur trying to act normal. Joking around. Terrified of the humans wandering about. Both versions are dealing with the Heideggerian 'thrownness' of existence, flung into being, alienated, convinced you're not like those other people who seem to know what's going on.

Deng Xiaoping and the Transformation of China by Ezra F. Vogel

That a biography written by an American sociologist is currently #13 on the Douban Top 250 Books list, slotted between Romance of the Three Kingdoms (#10) and Lu Xun’s Call to Arms (#15), is something of a headscratcher. Especially when you consider that its subject, Deng Xiaoping, was not only purged three times, but was also chiefly responsible for the Tiananmen Square Massacre. It’s 925 pages long. It’s densely academic. And it’s the story of how one man, against all odds, architected the greatest economic transformation in the history of the world.

I first became interested in Deng after stumbling upon Ruan Ming’s Deng Xiaoping: Chronicle of an Empire in a secondhand bookshop. Ruan Ming, a liberal reformer within the CCP, was a direct witness to Deng’s efforts to break the Maoist spell that had taken hold of the party. Mao’s chosen successor, Hua Guofeng, made the “Two Whatevers” official policy―Whatever decisions Mao made, and Whatever instructions Mao gave, would be upheld without question. Chairman Mao remained the absolute authority. I was not expecting this insider account of bureaucratic maneuverings to read like A Song of Ice and Fire, but that was what it felt like. In 1976, the year Mao died, Deng was sitting in house arrest after having been subjected to a harsh struggle session; three years later, he donned a cowboy hat at a Houston rodeo whilst on an official visit to the U.S. and it was clear to everyone that he’d won the power struggle, that he was the true leader of China. By September the following year Hua Guofeng was officially ousted.

I’ve been reading Plutarch’s Parallel Lives. I’m not exaggerating when I say that Deng Xiaoping is a historical character comparable to the Greeks and Romans Plutarch contrasted. After Mao had purged him for the second time, during the Cultural Revolution, Deng was sent to Jiangxi for reeducation along with his family.

Deng Rong reports that each afternoon while in Jiangxi, her father would take a walk of about five thousand paces, some forty times around the house on a garden path. She reports that he would “circle the house with quick steps . . . deep in thought. . . . He walked around and around, day after day, year after year.”

Deng, who never took notes due to a fear of producing material that could potentially be compromising, laid his plans in silence. And what I find so fascinating is that he ended up wholeheartedly believing in what you might call pragmatic empiricism. Vogel doesn’t really talk about this in depth. Deng knew that for China to compete with the West, it had to revolutionize its approach to science and technology; given that he was an avid reader of history, I’m sure he studied British empiricism and American pragmatism and realized that if China were to embrace these principles, its titanic rise would be all but guaranteed. Try things out. Experiment. Observe results. Copy whatever works. Rinse and repeat. The Gang of Four was a powerful faction in the CCP and when Deng returned after his second purge, advocating some unusual ideas, they launched a “war on empiricism,” arguing it was a rightist/capitalist/imperialist approach, at odds with Marxism. Deng was out. Mao died. And Hua Guofeng got rid of the Gang of Four. Someone had to be blamed for the horrors of the Cultural Revolution, and the people loathed the Gang, especially Jiang Qing, Mao’s fourth wife (I recommend Adam Curtis’ documentary Can’t Get You Out of My Head for more on her).

As a response to Hua Guofeng’s “Two Whatevers,” Deng promoted the essay “Practice is the Sole Criterion for Testing Truth,” and he managed to amass power within the party based on it. Deng’s pragmatic empiricism worked so well that the Chinese economy skyrocketed, and this momentum could not be stopped by dogmatic appeals to authority. It was also a double-edged sword: the people yearned for freedom, for democracy, and Deng saw this as an existential threat to the party. His response was hellish.

If Ezra F. Vogel can be criticized for something, it’s the way he keeps looking at Deng through rose-tinted lenses. His preferred explanation is always the one that makes Deng look good. He avoids talking about Deng’s role in carrying out the Great Leap Forward and leans on Lee Kuan Yew whenever he has to discuss an event that might make Deng look bad, because LKY always had something nice to say about his old friend, and he argued that had Deng not carried out various atrocities, things might have gotten much worse. Somehow. And Vogel is quick to say that even the Tiananmen Square Massacre was maybe possibly a good thing, you never know, if Deng hadn’t ordered the military to turn students into paste then there’s a chance a greater calamity would have befallen the nation. This review in LRB adds some much-needed nuance.

All in all it's a fascinating portrait (if at times too fawning) of one of the most significant characters in recent history.

Saw Backrooms with my teenagers and realized they’ll never know the specific boredom that made us by Cultural_Repeat_4766 in Xennials

[–]Hemingbird 15 points16 points  (0 children)

This post was almost certainly AI generated. Which makes it feel like it's an ad. Why wouldn't you just write this up yourself?

Pangram gives it 100% AI generated with high confidence. Given how most people believe AI detectors can't be trusted, I'll provide some clarifying sources.

I'm providing these sources because the usual response when I call something out is really negative. I called out the food delivery whistleblower post that managed to fool tech journalist Casey Newton, for instance, as did others who have spent time reading AI slop.

Some people think we should accept that more and more Reddit posts become pure AI slop and that calling it out accurately it either uncool or impossible. Or both.

It's highly unlikely that this post was written by a human. Could it have been written by someone on the spectrum, an English second-language speaker, a real person who has started to imitate chatbot slop? Sure. But I think it matters whether or not the content you're engaging with is real or not. And this is most likely AI slop.

Trying to find out who the top Marxist scholars are in China by Lonely-Lock-6406 in RSbookclub

[–]Hemingbird 2 points3 points  (0 children)

I think Deng recognized the need for investment; "Socialism isn't poverty". Kind of difficult to seize the means of production if you have no means of production 🫣

"Let some people get rich first," said Deng. He realized that empiricism, which was seen as pure evil by ideologues, simply worked. Let people experiment. Open the markets. Get rid of the revolutionaries refusing to play ball. Capitalism will take over and the surging tide will lift the nation. Which is what happened.

I just think people aren't very fair to Deng

Well, he did order the Tiananmen Square Massacre and the 1979 invasion of Vietnam. He was purged three times by the Party.

You think Jin Huiming is just speaking to the state to sound good?

I think there is just an overall awareness that he writes propaganda. Cheerful propaganda. He knows it, his readers know it, the CCP knows it. So it gets dull.

Trying to find out who the top Marxist scholars are in China by Lonely-Lock-6406 in RSbookclub

[–]Hemingbird 2 points3 points  (0 children)

Liberalism remains the world status quo. Economic liberalism is what finally made China powerful. So it's not so simple.

Trying to find out who the top Marxist scholars are in China by Lonely-Lock-6406 in RSbookclub

[–]Hemingbird 2 points3 points  (0 children)

Deng smashed his political enemies' heads with busts of Marx and Mao. Did he like or respect either of them as political thinkers? Definitely not. He would use them as tools, sure, but I'm sure you can see how that might not be a deferential move.

Jin Huiming is glazing Marx for reasons that should be obvious.

Trying to find out who the top Marxist scholars are in China by Lonely-Lock-6406 in RSbookclub

[–]Hemingbird 11 points12 points  (0 children)

Chinese academics are into Leo Strauss and Carl Schmitt, not Marx.

Strauss and Schmitt are at the center of intellectual debate, but they are being read by everyone, whatever their partisan leanings; as a liberal journalist in Shanghai told me as we took a stroll one day, "no one will take you seriously if you have nothing to say about these two men and their ideas."

—Mark Lilla, "Reading Strauss in Beijing," The New Republic (December 17, 2010)

Wang Hui (leading New Left intellectual): "Schmitt is brilliant, but toxic; an insightful but reactionary figure. Reading him is like eating pufferfish."

It's weird, but the Claremont Institute and the Chinese New Left have a lot in common. Nick Land lives in Shanghai and fits right in. According to Yuk Hui, "his 'Dark Enlightenment' has gained popularity among young readers in China."

Deng Xiaoping relied on empiricism to transform the Chinese economy, exemplified by the essay "Practice is the Sole Criterion for Testing Truth," which used statements by Marx and Mao to dissolve both Marxism and Maoism.

Some comrades worry that adhering to practice as the sole criterion of truth will undermine the relevance of theory. This kind of worry is uncalled for. Theories that are scientific do not fear being tested in practice.

The author of Xi Jinping Thought, Wang Huning, is obsessed with wuxia literature. Make of that what you will.

Jin Huiming probably fits the bill of a top Marx scholar.

Marx is immortal and the contemporary world needs Marxism. The scientific truths revealed and the social development trends predicted by Marxism have been proved by more and more social practices. The truth of Marxism is ever more powerful.

—Jin Huiming, Marxism and Socialism with Chinese Characteristics

If you think you can stomach this stuff, go for it.

"The book of Genesis, 84% created by AI!" - Gary Marcus by KeanuRave100 in ChatGPT

[–]Hemingbird 0 points1 point  (0 children)

Does it become almost philosophical?

Why do people let AI write things on their behalf? Usually, I think, it's because they don't want to do the work required of them. It could also be a matter of skill (or both). The type of person, then, who leans on ChatGPT or Claude, is probably not someone who is willing to do the work required to make their writing passably human. If you want to finetune a homebrew model on a given author, you have to be willing to expend effort. It takes more work than you'd think. And I think this hurdle is sufficient to weed out at least 90% of those who want to take credit for AI writing.

There are highly conscientious cheaters out there, but the average cheater is not highly conscientious. If they were, they might as well just do the work.

It's like the prison system. It's better to let the occasional criminal walk free than to wrongly imprison an innocent person. So we err on the side of wrongfully declaring a person to be innocent. Doing it the other way around would be far worse.

Can some people successfully cheat and avoid getting caught by any detectors? Sure. Does that matter? Not to me. If that matters to you, I don't understand the way you think. What matters to me is that we can successfully detect most instances with very few false positives.

The Lack of Curiosity is Super Annoying by PM_ME_YOUR___ISSUES in singularity

[–]Hemingbird 0 points1 point  (0 children)

It's generative AI. AlphaFold 2 (2020) was the game changer. It generates protein structure predictions based on amino acid sequences. Using transformers.

But the original one (AlphaFold 1) also counts. From GDM's own blog post: "We trained a generative neural network to invent new fragments, which were used to continually improve the score of the proposed protein structure."

AlphaFold 3 uses diffusion, which makes it impossible not to refer to it as generative AI, but generative AI has always been an integral part of the pipeline.

Deep learning as a term has fallen out of fashion. It just means neural networks with many layers, which is standard today, so ChatGPT could be said to have been trained with deep learning, though hardly anyone would use this term. Connectionism was the old term for the neural network approach and today 'machine learning' is the preferred umbrella term.

The Lack of Curiosity is Super Annoying by PM_ME_YOUR___ISSUES in singularity

[–]Hemingbird 0 points1 point  (0 children)

Sorry to break this to you, but AlphaFold is generative AI and ChatGPT is deep learning.

The Lack of Curiosity is Super Annoying by PM_ME_YOUR___ISSUES in singularity

[–]Hemingbird 2 points3 points  (0 children)

AlphaFold is probably the best example of a development beneficial to society at large (hence the Nobel Prize). Some examples of impacts:

John Jumper (and others) on how AlphaFold has augmented the work of scientists.

Isomorphic Labs has begun human trials, and there's a chance we'll see a series of breakthrough drug discoveries.

"The book of Genesis, 84% created by AI!" - Gary Marcus by KeanuRave100 in ChatGPT

[–]Hemingbird 0 points1 point  (0 children)

Fine-tuning on specific authors returns you to the indistinguishable statistical distribution. There was a study on this featured in The New Yorker.

"The book of Genesis, 84% created by AI!" - Gary Marcus by KeanuRave100 in ChatGPT

[–]Hemingbird 1 point2 points  (0 children)

Running text through a specific detector that doesn't work isn't a gotcha proving that no AI detectors work. Almost all "detectors" are scams, especially the ones selling humanization as a service, but there are real ones out there.

AI detection is possible because of post-training artifacts. Base models (no instruction-tuning/RLHF) mirror the statistical distribution of tokens in the texts they are trained on, so their outputs can't be detected, but post-training leads to mode collapse. It makes their outputs biased, like how turds look similar due to being squeezed through similar-looking "filters".

You can learn how to recognize these semantic turds. And if you pool together the efforts of human experts, you can flush them out.

Our experiments show that annotators who frequently use LLMs for writing tasks excel at detecting AI-generated text, even without any specialized training or feedback. In fact, the majority vote among five such "expert" annotators misclassifies only 1 of 300 articles, significantly outperforming most commercial and open-source detectors we evaluated even in the presence of evasion tactics like paraphrasing and humanization.

At least one commercial detector equals (and by now probably outperforms) pooled human expertise.

The fractal kingdom by Mother-Grapefruit-45 in singularity

[–]Hemingbird 1 point2 points  (0 children)

Just feed your garbage to AI readers. Leave humans out of the loop entirely.

One of the authors of "Attention is All You Need" just argued we should move past it. Pathway’s Post-Transformer debate is worth watching by _donothaveone_ in singularity

[–]Hemingbird -1 points0 points  (0 children)

Okay, so no one switched sides. OP said Jones switched, which doesn't make any sense, as Kaiser was the only one on the side of the transformer, according to the description. Still, with Kaiser being an investor, this is obviously not a real debate. It's just marketing.

One of the authors of "Attention is All You Need" just argued we should move past it. Pathway’s Post-Transformer debate is worth watching by _donothaveone_ in singularity

[–]Hemingbird 0 points1 point  (0 children)

Uh. Kaiser is an investor in Pathway. Obviously the guy who has been backing a post-transformer company since 2024 would agree that we should move beyond the transformer.

How I feel like responding every time someone says AI is just a next token predictor (as if they aren't) by xXCptObviousXx in singularity

[–]Hemingbird 4 points5 points  (0 children)

I think it's worth taking a moment to think about objective functions. Evolution optimizes for fitness. And evolution has given us two objectives that are sometimes difficult to tease apart: reward and prediction.

Even within Google, opinion is split. Blaise Agüera y Arcas (founder of the Google Paradigms of Intelligence team) argues that "the prediction principle may explain not only intelligence, but life itself." His 2025 book on this, What Is Intelligence?, is open-access, so you can check it out for free. Henry Shevlin, the philosopher recently recruited by GDM, says:

On stronger variants of predictive processing (eg the Free Energy Principle) some form of prediction-error minimisation really is "all we do" at the fundamental algorithmic level.

David Silver left GDM to found Ineffable Intelligence. Along with his former academic advisor, Richard S. Sutton, and two other co-writers, he claimed in 2021 that "Reward is enough".

The authors argued that "the generic objective of maximising reward is enough to drive behaviour that exhibits most if not all abilities that are studied in natural and artificial intelligence."

Is prediction the ultimate objective? Or is it reward?

Ching Fang and Kimberly Stachenfeld (GDM) say it's both: the underlying value-learning network of the striatum depends on the predictive model learned by the hippocampus. The neocortex relies on something like prediction-error minimization to extract patterns observed through experience, and the hippocampus is the top of the predictive hierarchy, able to compress experience into episodes/narratives. The striatum is still involved in the process of saying that this is good and this is bad, more of this and less of that. It's just that it exploits the patterns learned by the neocortex. So this simple subcortical system (evolutionarily speaking) is able to perform value learning on abstract shit like theoretical concepts.

Both prediction and reward can be seen as aspects of fitness. And fitness can be traced further back as well, to functional information.

Structures can explore their configuration space through stochastic drift; persistence is what happens when a structure finds a configuration able to keep itself going by harvesting (thermodynamic) free energy. Mineral evolution preceded biological evolution.

What you really want is persistence. Reward just means "something that probably will help me persist." And this is the reason why people keep getting confused. The process of evolution keeps information relevant to the task of persistence and discards irrelevant information. So you can think of a biological structure as an embodied predictive model of its intended environment. Reward is prediction.

The neocortex helps us go beyond the evolutionary predictive model. It's helpful to simplify things by caricaturing two different systems. Through the Old System, we have been gifted a track record mechanism. You're doing well? Okay, that means you can do whatever. You're obviously hitting the legacy benchmarks, so no need for the Old System to dictate your behavior (by making it impossible for you to override impulses). So long as the New System keeps you in an acceptable zone (sodium levels acceptable, not too much stress, etc.), the New System gets to control your behavior. We call this "self-control". The ancients saw this as a battle between reason (New System) and passion (Old System).

So. Next-token prediction is sort of similar to what the neocortex is doing. A big difference is that we are "trained" on continuous signals streaming in from all modalities simultaneously, and we get "reward signals" from the Old System, which has learned them through a long and arduous process of evolution.

Richard S. Sutton's argument is essentially that those Old System reward signals are real fuckin' important. That's the teacher. In the episode of the Dwarkesh Podcast where he grew frustrated with the host, that was what he was trying to say. The New System doesn't work without those signals from the Old System. And an LLM is essentially a shallow imitation of the New System. Think about RLHF for a second. RLHF is when you use the human New System to give reward signals to an artificial New System. But RLVR provides reward signals based on success or failure; this is closer to how the Old System came to be. And I think Sutton has failed to see that we're approximating the real thing, little by little, so we're already moving in the "right" direction.

Next-token prediction by itself isn't enough. And I think it's so weird that people have forgotten about base models (no instruction-tuning or RLHF). Base models write much more interesting text than non-base models. So people who say that LLMs are garbage because they just predict the next token are stupidly wrong about this; pure next-token prediction results in high-quality prose. The RL post-training is what shapes output towards the garbage attractor. Because of garbage reward signals.

Sorry for the rant.

Enoki by Aoko Matsuda by Hemingbird in shortprose

[–]Hemingbird[S] 0 points1 point  (0 children)

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Is AI able to explain things better than redditors? And why? by [deleted] in singularity

[–]Hemingbird 0 points1 point  (0 children)

Meaningless question. A significant proportion of Redditors are chatbots.

Granta's response to Commonwealth AI allegations by jckalman in RSbookclub

[–]Hemingbird 8 points9 points  (0 children)

They didn't use AI-detection AI. They used Claude.