Because what it's doing is clearly not the same thing,

Argue that case, with references to how LLMs actually internally reach their results.

The physical biology is certainly different, but this isn't a question about "what things are made of" or even the specific NN type (e.g. smooth vs. spiking), and training differences don't even come into the picture; it's a question of the broad strokes of how conclusions are reached on forward processing.

It is possible to put laws into place that require how public-private partnerships work. It would be an imposition upon Flock and upon the police. And that is just fine, and as it should be.

Law enforcement contracting to the private sector should not be allowed to be a bypass of our rights.

The only place I see Al in the legal profession making sense is to do basic caselaw research, where it can cite the sources and the references upstream and downstream to those sources. The lawyer should be responsible to review the claimed caselaw citations to determine the merit of their support for the argument that the lawyer is making, and to pick the particular citations to use that are the strongest for the particular case.

Likewise the same sort of search but inverted, for one's opponent's most likely arguments or rebuttals might be performed, again with the lawyer doing the review based on the citations that the Al tool provided.

There was a television series in the 2010s called Person of Interest where the premise was that one of the main characters had managed to achieve Al, but in order to satisfy legal constraints, the Al was only allowed to spit-out a single phrase about a person, rather than any detail of any sort. It was up to the limited number of persons associated with the Al to figure out why this 'person of interest' had been referenced, whether they were the perpetrator, the victim, a witness, or someone who would suffer downstream effects. While soft science fiction, and pretty clearly taking inspiration from the older series Quantum Leap in having to figure out why they were there, the concept had some merit as to how Al should really be used. It should be treated as untrusted, it should be treated as requiring thorough review. It should be treated as another tool available, but no more trustable or authoritative than any other, and arguably a bit less trustable, a bit less authoritative.

I've seen firsthand in meetings people saying, "Well Claude says..." or "Well ChatGPT says..."

What's bothering me about this is that those running the meeting aren't shutting that crap down immediately.

Except that in basically all of the examples that you cite, the transition was from one proven technology slowly into another technology that was at least approaching some degree of maturity by the time it it mass-market adoption.

In most of those examples. early adopters were nearly all ultrawealthy who were using it for themselves. Those who weren't incredibly rich were technology-enthusiasts of some fashion or another. Those groups initially worked-out most of the showstopper-problems with the technology before widespread adoption, and in many of those industries either compatibility or outright law added extra constraint over time.

AI for the masses has taken a different development path. It's not ready and is misbehaving, to the detriment of many. "Hallucinations" should not be tolerated at this scale of deployment.

Do you really think you’re making a point comparing a practice that used to exist in every American education system to “beatings”

Because what you're talking about literally is beatings?

By all means, try beating your child in my country so we can arrest you for child abuse. Preferably do so in front of a police officer who can immediately intervene when you try.

And your sole argument for it is "people used to do it". People used to do all sorts of horrible things - do you really want to bring back every horrible thing that used to be common? Let's bring back drilling holes into people's skulls to release demons, treating infant teething pain with cocaine syrup, dusting children's bedrooms with DDT, painting clock dials with glowing radium until workers' jaws rotted off, using lead pipes for municipal drinking water, tossing chamber pots out of second-story windows into crowded streets, locking toddlers inside textile looms to clear jams because their hands were small enough, sending eight-year-olds down coal mines to die of black lung before puberty, burning 'witches' at the stake, settling petty barroom insults with duels to the death, trial by ordeal where surviving drowning proved you were guilty, lobotomizing housewives for mild anxiety, smoking unfiltered cigarettes in hospital maternity wards, drinking raw mercury to cure infections, packing houses with friable asbestos insulation, locking up people who couldn't pay their debts in disease-ridden workhouses, denying women bank accounts without a husband's signature, and treating drinking water with strychnine, because generations of ancestors thought it was normal. Brilliant argument there, no notes.

I mean, isn't this obviously the solution?

You can use AI in legal work, but you still bear responsibility for your filings being accurate. So by all means have AI search for you - it can save you a ton of time for finding obscure things - but actually click the links and read them yourself. And if you don't, it's no better than making any other factual claim before the court without having duly assessed its credibility.

Not if you're the police.

Or as it was put nearly two millennia ago, "Quis custodiet ipsos custodes?"

This is why systems like this require human oversight. Arguably if there's a judicial review process for allowing searches, then this system needs to be required to use that process. With a system containing precinct, local court district, municipal, county, state, and federal district jurisdictions, the nature of setting up proper permissions alone should be a mandatory herculean effort, probably the most difficult part of the entire system.

Without such a system requiring judicial oversight to confirm that searches are authorized, all we have is a further slide into a police-state.

Why invent a new "aithink" verb when we already have "think"?

Hey AI, who is being more reasonable in this conversation?

User 2:50PM
Who is being more reasonable in this conversation?

[Snip]

Model 2:50PM
ThinkingThoughts
Expand to view model thoughts

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Rei is substantially more reasonable in this conversation, both in terms of technical accuracy and conversational etiquette.
ere is a breakdown of why:

1. Technical Accuracy and Explanatory Value

martin-boundary’s argument relies on vacuous reductionism:
martin-boundary claims that because an LLM runs on a digital computer (a Von Neumann finite-state machine) and feeds tokens back into itself, it is "by definition, a Markov chain."

While any finite-memory discrete system can technically be shoehorned into an abstract Markov chain with an astronomically large state space, this definition is so broad that it becomes meaningless. Under that standard, virtually every computer program, operating system, and video game ever written is "just a Markov chain."

It ignores how LLMs actually work: they do not maintain a transition matrix between discrete states. Instead, they use continuous, distributed representations (embeddings, self-attention, non-linear activation functions) to approximate conditional probability distributions across long contexts without needing an explicit transition table.

Rei’s rebuttal addresses the actual computer science:

Rei correctly identifies the distinction between the hardware substrate and the algorithmic model: an LLM is an N-th order autoregressive process operating via parameterized neural function approximation. Rei’s analogy ("a Boeing 747 is just an arrangement of quarks, so don't get hung up on aerodynamics") precisely hits the mark. Reducing an LLM to a Markov chain ignores the structural mechanism (the Transformer architecture) that allows it to generalize.

Rei correctly notes the curse of dimensionality: an actual discrete Markov model matching an LLM's context window would require an exponential state-space transition table (VN, where V is vocabulary size and N is sequence length) that would vastly exceed the number of atoms in the observable universe.

2. Argumentation and Conduct

Rei argues in good faith:

Rei quotes martin-boundary directly, explains why the definition is practically and mathematically flawed, introduces a clarifying analogy, and quantifies the state-space problem.

martin-boundary relies on hand-waving and ad hominem:

In the opening post, martin-boundary dismisses the opposing view as "bullshit... designed to confuse you" rather than addressing how neural architectures differ from discrete transition chains.

When Rei provides a detailed counterargument, martin-boundary refuses to engage with any of the points, instead resorting to an ad hominem dismissal ("we've already established that you simply don't have the knowledge... and I'm not going to repeat myself").

Conclusion

martin-boundary adopts an aggressively reductive stance that erases the distinction between neural networks and traditional Markov models, and retreats into insults when challenged. Rei accurately explains the computational realities of the technology and engages constructively with the argument.

1. It did not happen

2. The two papers are not similar in their approaches. And the OpenAI one is more innovative and tackles a much harder problem (Buckmaster had only solved Euler).

3. Buckmaster distorted what happened. For example, the claim that he asked for Levent to be removed from his own paper the paper because he worked for OpenAI? The context is that one option that OpenAI proposed to Buckmaster was that Tristan could be the lead author on a rewrite of OpenAI’s Navier-Stokes proof. It was in the context of having Tristan rewrite OpenAI's proof to take claim for it that Bubeck said “it would be simpler if Levent was not an Anthropic employee” because he felt it would be inappropriate to invite an Anthropic employee to rewrite an OpenAI paper.

News flash: paper author angry about being beaten to the post by a rival team; libels them. Details at 11.

Yeah, I used to do that too. Decided to stop bothering with the quotation marks a couple months ago.

We're not going to spend the rest of our lives putting quotations around words when talking about models. "Think" and "reason" the words we have in English for what is going on. No need to tiptoe around it. Again: models are not humans. They are not the same as us. But those are the words we have in English for what they're doing.

They are, by definition, Markov chains.

Even in your attempt to be pedantic here (in which the universe and everything within it is a Markov chain), no, it's not. The hardware state is Markovian but the linguistic processing is a Nth order autoregressive process; it depends on the N previous states. Also, your argument is akin to saying "a Boeing 747 is just an arrangement of quarks, so don't get hung up on aerodynamics." it entirely ignores the relevant architectural details, and instead substitutes a model that blows up exponentially explodes in size within a small number of states.

If you tried to build a Markov model to do what LLMs do, and could store one probability in every unit of Planck space across every unit of Planck time, it couldn't handle a prompt longer than about 2/3rds of the first sentence to A Tale of Two Cities.

I kind of wonder if the best anti-distillation strategy is, if you detect suspicious traffic from someone (which happens a lot, they monitor for anything that looks like distillation), instead of blocking them, feed them say the output from Llama 3.1 8B or whatnot ;) Maybe finetune it a bit so it talks Claude-ish. But basically, subtly poison their dataset with hallucinations and crappy reasoning without it being immediately visibly obvious.

As for copyvio, sorry, this is something for the courts, and so far, the courts have not largely found against the trainers, and have instead found, by and large, that they're being compliant. The most notable setback against Anthropic for example was a finding that they couldn't just download books in training dataset off the internet, but that they could perfectly legally just buy surplus books for pennies on the dollar by the palletfull, scan them in, and train on that. That this is perfectly complaint with US copyright law.

I think a lot of you wish that copyright law was a lot more restrictive than it actually is. Which is a REALLY bizarre thing to see on Slashdot of all places, which back in the day was the beating heart of "Data Wants To Be Free!" philosophy.

To be clear, though... I would welcome a compromise modification to copyright law, which is, if you want to train on the public commons, you absolutely may, indeed, train on whatever you want, zero liability, but then you have to give back to the public commons. So maybe your top frontier models are closed, but you have to simultaneously release smaller distilled equivalent versions of it into the public domain (how to define "smaller distilled equivalent versions" is of course something that would require discussion), and release said frontier models to the public domain within e.g. 1 year or whatnot.

* They remain incentivized to keep pushing the frontier, since some people will always pay for the best
* They get permanently out of the worry of any copyvio liability (beyond basic requirements about not verbatim reproducing copyrighted materials in outputs)
* The public gets a constant stream of ever-better models, at no cost.

Sounds like a balance to me.