tripletao
https://www.bbc.com/news/magazine-35766627
That was an antibody against human CD28, and I'd heard speculation at the time that the monkey trials failed to predict the human response due to differences between monkey and human CD28. That was apparently not correct, since "the sponsor had not only determined the relevant extracellular sequence (which is 100% identical to the human one) but also the binding affinities of the mAb, which were identical for human and monkey". That author goes on to propose a different explanation, though I don't know if any explanation is generally accepted.
https://pmc.ncbi.nlm.nih.gov/articles/PMC2990150/
It's funny to recall this in the context of Terence Tao's question of whether you'd take an AI-designed drug with no mechanistic insight. The sponsors of this trial had reason to believe they had strong mechanistic insight, but they were wrong. A pharmacologist from the nineteenth century would have probably run the trial more safely (lower starting dose, one participant at a time) without that false confidence.
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.
> 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 near-certainly 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 don't understand what you mean by this. Nobody is conducting phase 3 clinical trials of a new drug except as part of that regulatory drug development lifecycle, to gain legal permission to market the drug. The idea that it could be "unclear who conducted" a >$10M trial closely supervised by multiple government agencies does not make sense.
> No pharmaceutical is testing drugs today which have no scientific theory or reasoning or seminal papers behind it.
Even today, new drugs are routinely identified by screening with assays that give no mechanistic insight, as e.g. for ganaplacide. They found that drug by screening directly for whether the compound killed Plasmodium (the genus of parasites that cause malaria), by applying it to infected cultured liver cells[1]. That was probably a lot more expensive than something like a protein binding assay against a prior known target, but probably more fundamentally accurate. That also creates potential to discover an entirely new mechanism of action, without potential for cross-resistance with drugs developed against prior known targets, and that apparently happened here.
The first studies in humans were conducted around 2014[2], at which time I believe no mechanism had yet been proposed. Some progress towards a mechanism was proposed in 2020[3] and perhaps it's correct; but if it's later shown to be incorrect then nobody will care, and that in no way drove the discovery or initial human testing of the compound.
1. https://pmc.ncbi.nlm.nih.gov/articles/PMC3473092/
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.
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.
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.
> 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.
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 is literally how all drugs worked for almost all of human history--nature gave us the compound in the form of a plant or other natural product; a brave, stupid, or desperate human consumed it; and then we watched what happened and the survivors learned. It's obviously good to have more understanding (more survivors!), but even now we often don't.
https://en.wikipedia.org/wiki/Category:Drugs_with_unknown_me...
Most human endeavors are empirically grounded--we think an action will succeed not because we understand exactly why, but because it succeeded many times before. Math and CS are the weird exceptions, and the intuition that people with that background have for other fields is often bad.
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.
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. To some extent that's their problem and they need to get over it just like the chess players did; but the outside implications are broader here to the extent that 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 to the extent that AI-generated math contributes to the argument to allocate a large and increasing share of GDP to datacenter buildouts, that question of downstream value seems pressing.
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.
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".
> The reemergence was probably an accidental release from a laboratory source in the setting of waning population immunity to H1 and N1 antigens.19,31
https://www.nejm.org/doi/full/10.1056/NEJMra0904322
This explanation is now printed uncontroversially in major journals, though at the time the WHO rejected it:
> Laboratory contamination can be excluded because the laboratories concerned either had never kept H1N1 virus or had not worked with it for a long time.
https://pmc.ncbi.nlm.nih.gov/articles/PMC2395678/pdf/bullwho...
> An important feature in this distribution, consistent with earlier findings (4), is that the majority (76%) of infected cases shows no evidence of onward transmission amongst any of their contacts.
https://pmc.ncbi.nlm.nih.gov/articles/PMC7523148/
So there's no reason to expect that the first cluster that generates enough sickness and death to attract official attention will occur near the point of introduction. If we look at North America, then it in fact did not--the introduction must have been at an airport or seaport, but the first large clusters were in nursing homes, church choir practices, etc. This is different from SARS-1 or MERS, which were deadly enough to make that cryptic spread much less likely.
> We have done bat virus surveillance in Hubei Province for many years, but have not found that bats in Wuhan or even the wider Hubei Province carry any coronaviruses that are closely related to SARS-CoV-2. I don't think the spillover from bats to humans occurred in Wuhan or in Hubei Province.
https://web.archive.org/web/20210727042832/https://www.scien...
The virus doesn't seem to have been engineered from any previously known sarbecovirus, but the WIV had the largest collection of novel sarbecoviruses collected from nature in the world, and had proposed in DEFUSE to engineer them in collaboration with Dr. Baric at UNC. DARPA rejected that proposal, in part for safety reasons, but the WIV was technically capable of doing the work alone.
https://theintercept.com/2021/09/23/coronavirus-research-gra...
You linked an article about fiduciary responsibility, and that's also a legal obligation. If I'm an executive and I route contracts to a vendor that I own for personal gain, then the shareholders can sue me and I will lose. The state will likewise enforce that judgment, if necessary with physical force.
If I'm an executive and I choose to spend too much money (in someone's opinion) auditing a git hosting site for malware, then the possibility that I'll get sued for that and lose is zero. That's the "business judgment rule", which is a legal term of art that you can search. The shareholders might fire me, but only in the same way they could fire me for anything.
The financial incentives are obviously as you say, but the difference between "things I do because the state will physically punish me if I don't" and "things I do because I want a high-paying job" is valuable to me. I could probably make more money than I do now if I worked for a payday lender or an online casino, but I don't think that obligates me to do so.
If you look at the case law for fiduciary responsibility, then you'll find that executives have a strong obligation against self-dealing (decisions that clearly benefit them at the expense of the shareholder), but not much else. The "business judgment rule" makes it generally lawful for executives to make decisions that you, the shareholders, the judge, or anyone else might consider to be bad business judgment. It couldn't really be otherwise, since the difference between wasteful spending and a wise investment in the company's reputation might be unclear even decades later.
If shareholders disagree with an executive's business judgment, then their remedy is to fire that executive. That remedy has nothing specific to "making money"--the shareholders are just as free to fire a CEO for excessive attention to profit as insufficient.
> To quote the U.S. Supreme Court opinion in the recent Hobby Lobby case: “Modern corporate law does not require for-profit corporations to pursue profit at the expense of everything else, and many do not.”
https://www.nytimes.com/roomfordebate/2015/04/16/what-are-co...
Executives are free to pursue near-term profit at the expense of everything else if they choose, and the shareholders are free to replace them if they don't. That's a choice by those executives or shareholders though, not an obligation.
https://theintercept.com/2021/09/23/coronavirus-research-gra...
That said, Montagnier appears to have speculated that SARS-CoV-2 contains genetic information from HIV. That seems almost surely false, since the sequences he cited occur frequently in many organisms.
https://apnews.com/article/conspiracy-theories-iran-only-on-...
https://news.ycombinator.com/item?id=44805979
Your estimate of current AI spending is also low. Hyperscaler capex alone is around 2% of US GDP, not including other costs (neoclouds, employee comp, etc.).
https://news.ycombinator.com/item?id=44805979
The modern concept of GDP didn't exist back then, so all these numbers are calculated in retrospect with a lot of wiggle room. It feels like there's incentive now to report the highest possible number for the railroads, since that's the only thing that makes the datacenter investment look precedented by comparison.
What? Unless the thing you want to compute happens to be exactly that eml() function (no multiplication, no addition, no subtraction unless it's an exponential minus a log, etc.) or almost so, it is unquestionably less efficient. If you believe otherwise, then please provide the eml() implementation of a practically useful function of your choice (e.g. that Arrhenius rate). Then we can count the superfluous transcendental function evaluations vs. a conventional implementation, and try to understand what benefit could outweigh them.
> A 10-stage EML pipeline would be about the size of an avx-512 instruction block on a modern CPU
Can you explain where you got that conclusion? And what do you think a "10-stage EML pipeline" would be useful for? Remember that the multiply embedded in your Arrhenius rate is already 8 layers and 12 operations.
Also, can you confirm whether you're working with an LLM here? You're making a lot of unsupported and oddly specific claims that don't make sense to me, and I'm trying to understand where they're coming from.
I think you're missing the reason why the GPU kicks you out of the fast path when you need that special function. The special function evaluation is fundamentally more expensive in energy, whether that cost is paid in area or time. Evaluation of the special functions with throughput similar to the arithmetic throughput would require much more area for the special functions, which for most computation isn't a good tradeoff. That's why the GPU's designers chose to make your exp2 slow.
Replacing everything with dozens of cascaded special functions makes everything uniform, but it's uniformly much worse. I feel like you're assuming that by parallelizing your "EML tree" in dedicated hardware that problem goes away; but area isn't free in either dollars or power, so it doesn't.
The fact that addition, subtraction, and multiplication run quickly on typical processors isn't arbitrary--those operations map well onto hardware, for roughly the same reasons that elementary school students can easily hand-calculate them. General transcendental functions are fundamentally more expensive in time, die area, and/or power, for the same reasons that elementary school students can't easily hand-calculate them. A primitive where all arithmetic (including addition, subtraction, or negation) involves multiple transcendental function evaluations is not computationally faster, lower-power, lower-area, or otherwise better in any other practical way.
The comments here are filled with people who seem to be unaware of this, and it's pretty weird. Do CS programs not teach computer arithmetic anymore?
Of course the redundant primitives aren't free, since they add code size or die area. In choosing how many primitives to provide, the designer of a numerical library aims to make a reasonable tradeoff between that size cost and the speed benefit.
This paper takes that tradeoff to the least redundant extreme because that's an interesting theoretical question, at the cost of transforming commonly-used operations with simple hardware implementations (e.g. addition, multiplication) into computational nightmares. I don't think anyone has found a practical application for their result yet, but that's not the point of the work.
Efficient numerical libraries likewise contain lots of redundancy. For example, sqrt(x) is mathematically equivalent to pow(x, 0.5), but sqrt(x) is still typically provided separately and faster. Anyone who thinks that eml() function is supposed to lead directly to more efficient computation has missed the point of this (interesting) work.