No, fascism has a pretty clear definition, just read Ur-Fascism. DHH fits that to a T. You just don't know what fascism is, or you're struggling with some cognitive dissonance.
> No, fascism has a pretty clear definition, just read Ur-Fascism. DHH fits that to a T. You just don't know what fascism is, or you're struggling with some cognitive dissonance.
DHH repeatedly presents electoral democracy as the legitimate mechanism for resolving political disagreement. He has also repeatedly emphasized free speech, including criticizing European restrictions that he regards as excessive, and has argued for keeping political disagreements from destroying ordinary social relationships.
Historians generally use fascism for something considerably more specific than nationalism, anti-immigration politics, or right-wing populism: typically an authoritarian ultranationalist movement hostile to liberal democracy and political pluralism, historically associated with a powerful centralized state, suppression of opposition, and political violence.
I have never heard of this work, but thank you for sharing it. It's terrifying how nearly every single point is something that AI companies are embracing and marketing under.
> Every time the labs try this we see model collapse
The latest studies demonstrate model collapse is not a given and synthetic data can be used just fine. The latest models are proof of that, they're all trained on large swathes of synthetic data. It can't be used as the -only- data source of course, but that's not how it is being used. This is an obvious conclusion, too, because there's no difference between synthetic data and the data people can create, the difference is whether that data is revealing new information about the thing the model is trying to learn. If the synthetic data is just teaching the model the same thing over and over again it results in overfitting, so it needs to be done intelligently.
For example, if I have an example of a puzzle, I can generalize that example and create thousands of synthetic data examples, with different rotations/perspectives, rather than having to find the data naturally. It's not that the models are just generating data out of thin air, they're generating the synthetic data on top of real world data. The smarter the models get, the better they are at generating quality synthetic variations and finding valid synthetic variations.
> And I have seen zero evidence that AI is accelerating materials science in any meaningful way, let alone photonic computing.
It is accelerating how quickly researchers and engineers can do their jobs.
That's pretty clearly a hype article, the headline even says "The CrysVCD tool developed at MIT COULD cut the huge amounts of time and money spent". I'm asking for empirical measurements of timelines, not hypotheticals.
> This is only the beginning, too... Look ahead a year or two.
> Right, so human data creation would also have to scale up exponentially, and that's not gonna happen.
It doesn't need to. We're not even close to exhausting the useful synthetic data within the human data we have, let alone all of the new data that is being created.
> I mean, that's obviously false, otherwise model collapse wouldn't exist. The difference is statistical, but it's there.
It's not. It's just bytes of information. A machine and a human can write the same bytes (and often do). Like I already said, model collapse happens when you are overfitting on data without useful, fresh training signals. That's the key difference between the data. The data itself isn't in some way "special", some unique configuration of bytes that imbues special powers, it's that the useful information in it has already been exhausted by the model. You can get the same phenomena by having a poor distribution of human training samples as well. I think you're confusing LLM generated data with synthetic data. Synthetic data doesn't need to be created by an LLM, although an LLM can assist in the creation.
Wiki:
> In early model collapse, the model begins losing information about the tails of the distribution – mostly affecting minority data. Later work highlighted that early model collapse is hard to notice, since overall performance may appear to improve, while the model loses performance on minority data.[11]
In late model collapse, the model loses a significant proportion of its performance, confusing concepts and losing most of its variance.[10][12][13]
As models retrain on outputs sampled disproportionately from the higher-probability center of the distribution, rare words and uncommon syntactic constructions are among the first features to disappear.[25] Statistical analysis of recursive next-token prediction training has shown that, when language models are trained recursively on synthetic data, the learned conditional distributions concentrate probability mass on a small subset of highly predictable continuations (a phenomenon characterized as "total collapse")
> That's pretty clearly a hype article
It was just the first article I saw on a quick google search, there are thousands of these stories. It's easy to dismiss anything that doesn't align with your worldview as hype, but you're the one lacking evidence now.
> I'm asking for empirical measurements of timelines, not hypotheticals.
Go and find it then? You haven't bothered looking.
> Lol that excuse is getting really old
You're doing the same thing people have been doing for years, comparing this very second in time and failing to extrapolate. HackerNews was full of developers who said that AI would never be useful for programming, it can't do x, y, z. Now these same people don't write code by hand anymore and haven't looked at their codebases in months.
You had people in mathematics saying the same thing, now you have Terrence Tao posting articles about how AI is stealing their job.
You had artists, designers and photographers saying the same thing, now they can't tell the difference between something human created or AI created.
Eh. An intelligence with perfect knowledge of the entire universe, unlimited memory, and infinite processing speed, would have a tendency to go boom. But the real world has limits, and intelligence, even superintelligence, does not equate to godhood. Some tasks are still hard no matter how smart you are.
Plus, we have no real reason to think LLMs are anywhere close to AGI or ASI. So arguments like these are just distracting from the very real, very present danger that LLMs pose: information breakdown, societal collapse and environmental destruction. In other words, this is criti-hype.
The kind of person who thinks AI contributes to information breakdown rather than information diffusal has no theory of how information spreads. The other claims like societal collapse and environmental destruction are still FUD but slightly more possible
No, there was no a priori evidence when the big "fuss" around the ozone happened. I happen to agree with it. Why spend millions of choice your choice of first world currency to worry about 0.000000000001% of the pollution the earth, our earth is facing.
Perhaps you're not living in the same world as I, but I've both experienced the wettest and driest year on record.
Those are rocks, which are not made from the same stuff as satellites. (Also, the article says it's 44 tons, and other sources suggest around 50 at most.) In any case, I don't think anyone here is "freaking out", I just think this issue merits investigation.
No, in a much more direct sense. By the time a Starlink sat re-enters its fuel is gone so it’s mostly a pile of silicon, solar panels, lithium batteries, aluminum, etc.
The circuit boards are probably the goofiest chemical compositions but they represent a drop in the bucket mass-wise for the sats and pale in comparison to how many circuit boards get blown up every day in Iran/Ukraine/Russia.
Sometimes it's used to uncover crimes, but very often it's used to invent crimes that never actually happened, or used to deceive a courtroom when they don't actually have evidence.
It’s also a fairly traumatic thing to people through and I can imagine it does a lot of damage to people’s faith in law enforcement. Personal anecdote: when I was a teenager some cops gave me a list of fabricated evidence that I’d committed a crime, not intending to ever show a court; just trying to get me to confess. They said they’d go easy on me if I confessed but if I held out then they would petition the court to have me tried as an adult and sent to “big boy” prison. They also told me my parents were cooperating in the investigation and didn’t tell me. The whole ordeal basically made sure no one in my previously quite pro-cop family would ever trust an LEO again.
My first experience with the police was just as bad. A friend and I were waiting on the porch of another friend (all like 14 years old). We were waiting for him to come home. He knew we were there waiting for him.
His neighbors yelled something to us out of their window. We went to see what they wanted, but they didn't say anything when we got near.
We went back to the porch and waited for maybe 10-15 minutes, but decided to leave as we felt really weird about those neighbors. As we started walking, a police officer pulled up screaming at us, telling us we were under arrest for throwing rocks at the windows of those neighbors. The police officer took us to the police station, put handcuffs on us, as well as shackled our feet to the floor, where we remained for like 2 hours. He made us write a statement, and then told our parents to come get us.
It was truly the most confusing thing I ever experienced. Angers me a bit today thinking about it.
Fabricating evidence in an interrogation to try to get a confession out of someone versus "I'm not a cop" when infiltrating a drug ring, or lying to hide important information
Lying about evidence specifically is illegal in a lot of countries (not the US)
