tripletao

> “The Court finds that because the ALPR systems Alaniz used to search Kyle’s historical location information intruded on her reasonable expectation of privacy in the whole of her physical movements, it was a search under the Fourth Amendment. Based on the information in the record, the only reason Alaniz conducted that search was because he saw her license plate was from California. That search was not supported by probable cause, and it was done without a warrant in violation of Kyle’s Fourth Amendment rights.”

If the cop had obtained a warrant before searching ALPR data, then the search would have been legal under this judge's standard. But the cop (a) didn't obtain that warrant, (b) probably couldn't have obtained it quickly enough to decide whether to follow her until she committed a traffic violation, and (c) probably couldn't have at all since he didn't have probable cause. The question of what level of ALPR search requires a warrant is evolving rapidly now, in response to both case law and new legislation (e.g. Connecticut SB397).

Kyle also disputes whether the ALPR search found anything that justified the detention. If the ALPR search was itself illegal then that's irrelevant though.

You are correct that the judge ruled the stop was "justified at its inception"; but the section immediately following your quote is titled

> Alaniz did not Have Reasonable Suspicion to Extend the Traffic Stop.

That's the newsworthy part. For example, Kyle stated that she had the marijuana only after Alaniz detained her so that he could attempt to search the car without her consent. If that detention was illegal then her statement isn't admissible, and that's what the judge ruled here:

> Because the government does not identify any adequate exception to the exclusionary rule, all evidence obtained after Alaniz initiated the ALPR search must be suppressed as fruit of the poisonous tree.

ETA: And while the initial stop was lawful, the grandparent comment is probably correct that it wouldn't have happened without Flock. Alaniz performed a Flock search that included "traffic violation" as the reason, but he hadn't yet observed a traffic violation at that time. Based on that ALPR history he correctly suspected drug trafficking, and followed the car until he observed a traffic violation that justified the stop (but not the subsequent fishing expedition).

> I'm sure they know how real engineering works

Tao apparently didn't though, if he thought the absence of a known mechanism would discourage anyone from using an AI-discovered drug. I feel like a pure mathematician's worldview might be so focused on human understanding that he simply couldn't imagine that billions of people would already be taking drugs nobody understands. To fall from that to puzzling over LLM-generated proofs must be brutal.

> At the research level, they have simulations based on real physics (not curve fits) that will determine the transistor properties quite accurately.

That's true, but that's why I restricted my statement to a "modern SPICE model". I said "modern" because the Level 1-2 FET models contain only parameters with known physical meaning. Subsequent levels improve accuracy, but some of the parameters they introduce do not have known physical meaning.

> A SPICE model is essentially (mostly) simple classical equations for a MOSFET, coupled with curve fits to fix Nth order effects. A lot of the curves are still based on physics knowledge, but parametrized to make it easy to fit the data.

I agree with that description, and that's the blend of theoretical understanding and empirical curve fitting that I was trying to capture with the phrase "semi-empirical". My point is that even for something as well-understood as a FET, we found that semi-empirical model to be more convenient for most practical purposes. As engineers we think that's fine (all models are wrong but some are useful, etc.), but pure mathematicians seem to find it unthinkable.

How do you think an LLM would get the nudge to do that? Per above the existing human-directed trial process works fine regardless of how the candidate was discovered; so there's no reason why any LLM would ever need to receive context mentioning that the drug was a candidate for regulatory approval.

Of course a human could deliberately prompt it to, but they could do that for any other malicious purpose too. The major disincentive there is that the human will face legal punishment if caught, not anything technical.

Most speculatively, one could imagine a world where so many rogue AI agents were running that one just happened to notice the drug candidate from news coverage and took the initiative. I think that would be technically possible, but so generally destructive that political pressure would put a stop to it on a timescale shorter than a clinical trial.

I certainly don't mean to suggest we know nothing, and our current imperfect level of mechanistic understanding gives a much better hit rate than eating random plants. I just mean that when the understanding fails, we're happy to use the drug anyways if the clinical trials pass. The confidence comes from those human trials, and the mechanistic insight is more about probabilistically improving our screening before that.

> Just... well... if you go down the "we don't understand anything anyways" path too much what makes your work different from alternative medicine?

"Alternative medicine" in the pejorative sense is medicine that doesn't work, i.e. that would fail the clinical trial. That seems like a different question from whether we understand why it works.

The idea that we could know that something works but not why is almost inconceivable in computer science ex ML. That's the default state of most applied science though, consistent with your observation that effects are often discovered before causes.

> All that to say, they might approach Phase 3 trials by swapping the placebo, falsifying results, enrolling patients that are false positives for being sick, falsifying labs, etc.

This problem extends to humans, and that's why a complex regulatory system including but not limited to the FDA was created. That can still be manipulated (the Sackler Family, Purdue Pharma), but not in any way that machines are good at now--I don't think making thousands of malicious requests against Hugging Face until something succeeds is particularly transferable to anything that a human regulator wouldn't immediately notice.

There's also no reason why the trial would need to involve AI at all. The benefits of that would be more evolutionary (improved transcription of patient conversations with the doctors or whatever), so an AI candidate could simply be tested through the usual human-directed trial process. As the grandparent notes drug discovery already works through optimization, just not purely in silico. So that problem of gaming your optimization target already exists, and the trials are already designed to mitigate that.

That's an unrealistically idealized view of drug development. The human use of drugs long predates anything resembling a modern concept of a biochemical mechanism. Nobody knew how morphine relieved pain, but they knew that it worked and they used it.

Today we'll generally have a proposed mechanism, but it's not necessarily correct. Acetaminophen is among the oldest and most commonly used synthetic drugs, and its mechanism is still debated. This isn't usually cause for any special concern, since our confidence in the drug's efficacy and safety comes more from animal or human trials than from mechanistic understanding. Serendipitous discoveries during human trials are still common; the first inkling that Viagra might treat ED came not from any "first seminal paper" but from the volunteers who were testing it for angina.

Clinical trials are regulatory matters. Your suggestion that it could be "unclear who conducted the phase 3" is very strange--the FDA knows who filed the application. Of course that filer could have committed fraud, but I see nothing in the slides to suggest that was the concern here. If it was, then the solution would be a non-fraudulent phase 3, not anything related to mechanistic insight.

The bad cancer example is interesting to the extent it reveals that one of the world's top mathematicians is apparently unaware that applied science runs almost entirely on half-understood semi-empirical methods. That's most true for medicine, given the extreme complexity of human life; but if you look at a modern SPICE model for a transistor, a device that we claim to understand at the level of subatomic particles, then you'll find it's full of curve fits. As an engineer, I find that perfectly normal. My job is to use all the tools at my disposal to meet some human desire (to not die of exposure, for a slightly thinner phone, etc.), and whatever fundamental understanding I might have is only one tool to that end.

My impression is that since pure mathematics doesn't attempt to meet those human desires they need some other objective, and that objective is human understanding. The loss of that is thus felt more heavily than in other fields. We could say it's their problem, and they need to get over it just like the chess players did; but the outside implications are broader here, since mathematicians working in fields they themselves considered useless have so frequently been wrong--in Hardy's Mathematician's Apology, he gave number theory as an example of such a field, unaware of what the cryptographers would achieve just decades later.

It's possible that AI-generated pure math will continue this trend of delivering extraordinary unexpected societal value. It's also possible that the humans won't ever sufficiently understand that math, and the machines won't ever sufficiently understand human desires, and that connection won't be made. I've never met a pure mathematician who considered those downstream applications to be an important contributor to their motivations; but as AI-generated math contributes to the argument to allocate a large and increasing share of GDP to datacenter buildouts, that question of whether downstream value requires human understanding seems pressing.

Can you clarify your point? Crowded, poorly-ventilated wet markets like the Huanan Seafood Wholesale Market are quite different from American (or Chinese) supermarkets, and uncommon in the USA. American-style nursing homes are likewise uncommon in China, where it's more common for elderly parents to live with their children.

The conditions for a super-spreader event can arise in many different ways--before the contact tracers gave up, they found clusters in that wet market, those nursing homes, cruise ships, meat processors, migrant worker dormitories, and many other types of venues. I don't see why we'd ever expect the first cluster in the same type of venue in both countries, except by chance.

> Never said that, labs are close to places with high viral reservoirs because it is easier to get animal samples to study, obviously,

You are continuing to imply that Dr. Shi's lab was "close to places with high viral reservoirs", but nobody--lab leak advocate, zoonosis advocate, or otherwise--thinks that's true. She collected "animal samples to study" from Yunnan and surrounding areas, >1000 km from Wuhan. For example, RaTG13 came from Tongguan (thus TG), in Mojiang County. The location of her lab did not make those samples "easier to get".

There are probably lots of other labs that are deliberately placed near natural reservoirs of the organisms that they study, but Dr. Shi's was not. If anything, she thought those sarbecoviruses were less abundant near Wuhan than in some random location in China, not more.

She could be wrong, but no evidence has yet emerged that she was. Even among advocates for theories of natural zoonosis, the pathway is usually proposed to be smuggling of an unknown intermediate host rather than any natural reservoir in Wuhan, thus the attention on the wet market (since a similar pathway probably did explain SARS-1).

As to the rest, what evidence do you think could distinguish one natural novel virus from a laboratory chimera of two novel viruses? I think it would be more helpful to explain the basis for your belief than to smear mine as a "conspiracy theory".