skybrian's recent activity
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Comment on The doomsday cult inside OpenAI in ~tech
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Comment on There is no epidemic of loneliness, but there is an epidemic of scurvy in ~society
skybrian LinkFrom the article: [...] [...] [...] [...] [...] [...] [...] [...]From the article:
Although the data does not show that people are more lonely, it does show that people are more alone. (Derek Thompson has made this point particularly well.) In the US, time spent with friends on the weekends has fallen by half in the past 20 years. According to the American Time Use Survey, pals are out and solitude is in:
This is mainly a recession in hanging out—most of the increase in alone time has come from people spending less time with folks outside their households:
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We’ve got a puzzle here. People are more alone than ever, but they’re not feeling much lonelier than they used to. So maybe all this extra solitude is...fine?
No, it’s not fine. Isolated people are more likely to get depressed, suffer a heart attack or a stroke, develop dementia, and catch a cold; they straight-up die sooner. (The “loneliness is as bad as smoking 15 cigarettes a day” meme is a bit dubious, but it’s also not crazy.) Those findings all come from correlational studies, but when you inflict isolation on someone instead, you quickly see some massive and concerning effects. Prisoners in solitary confinement often sink into a stupor, their thoughts become obsessive and delusional, and they get agitated and irritable. (I’ve heard many people complain of similar but milder things happening to their elderly parents who live alone.)
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Too much alone time is clearly bad for us, but it doesn’t seem to bother us as much as it should. So the real mystery of the loneliness epidemic is: where is it? Why are we all sitting alone in our houses and slowly melting into puddles of slime, like the “this is fine” meme dog, but without the fire?
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Social deprivation looks a lot like vitamin C deprivation. In both cases, humans can lack an essential input without realizing it. In both cases, they can suffer for its absence without desiring its presence. And in both cases, this is probably because we evolved in an environment where we never needed that desire.
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The hallmark of a strong control system is that it resists perturbations. If you pilfer everything out of my fridge and pantry, my food-stat will send me scrambling to the Chipotle down the street. And if the Chipotle is closed, I’ll resort to the Qdoba next door. Our social-stats do not seem to display this property. According to a recent paper, people who got shifted to remote work in the past few years spent more time alone compared to people who stayed in-person—meaning they did not seek out additional ways to make up for their newfound alone time. It’s as if, upon encountering the closed Chipotle, they just went home hungry instead.
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It makes sense that our social-stats would be a bit feeble. Just as our default diet probably staved off scurvy most of the time, our default desire to stay alive probably fended off most of our solitude. If our ancestors wanted food, shelter, warmth, protection, etc., they needed to be around other people. No hunter-gatherer had the option of working from home, DoorDash-ing some Thai food for dinner, and spending the evenings watching Survivor.
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So what should we do about it? We actually have a good example of how to compensate for a semi-defective drive, namely, our sense of thirst.
Human hydro-stats can be laughably bad. Case in point: I used to get terrible headaches as a kid, and after several doctor’s appointments and failed medications, my parents realized that I just...wasn’t drinking liquids. They plonked a big glass down on the counter and told me to fill it and down it several times a day, which I’ve done ever since. My headaches didn’t go away entirely, but they did get less frequent and severe.
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We need a similar intervention for our social-stats. We need periodic PSAs that modern life can leave you chronically people-deficient. We need to be pushed toward one another slightly more than we want to be. Your demented thermostats may tell you that the most pleasurable way to spend your evening is alone on the couch with Peacock on one screen and TikTok on another. They are wrong. Some solitude is necessary and salutary, but too much is poison, and by default, too much is exactly what most of us want. If we let ourselves have it, we’ll end up with Jello for brains.
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That suggests the best prescription for an inadequate social-stat is a bit of brute force. Just as you must sometimes drink even when you’re not thirsty, you must sometimes hang out even when you’re not lonely. As Kurt Vonnegut put it in a commencement address in 1978:
I recommend that everybody here join all sorts of organizations, no matter how ridiculous, simply to get more people in his or her life. It does not matter much if all the other members are morons. Quantities of relatives of any sort are what we need.
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There is no epidemic of loneliness, but there is an epidemic of scurvy
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Comment on The actuary's final word on algorithmic decision making in ~science
skybrian LinkFrom the article: ... ... [...] [...] ... ...From the article:
Where do we draw the line between where statistics applies and where it doesn’t? If you are in a casino, and you trust the house to play fair, we’d probably all agree that the outcomes of future card games can be statistically analyzed. When creating actuarial tables to price insurance, the risks and prices are all based on carefully computed relative frequencies. The insurance company has found this mindset useful enough to build a business on top of it for centuries. But if a doctor is operating on a patient with an extremely uncommon condition, is that statistics too? In a sense, we can only define the term ’uncommon’ in statistical terms. It refers to a relative frequency of occurrence. However, in these cases where experiences do seem wholly new, how can we map past rates onto how to act?
There is clearly a spectrum between when pure statistics can guide action (e.g., betting on blackjack) and where perhaps there is something else that must be applied (e.g., surgery on a novel condition).
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Meehl highlights a dozen other studies in his book and continued to track examples throughout his career. No matter how much he looked, he kept finding the same thing: statistical rules were seldom worse and often much better than clinical predictions. In a reflection on his book, Meehl wrote in 1986, “There is no controversy in social science that shows such a large body of qualitatively diverse studies coming out so uniformly in the same direction as this one.”
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What can we make of these results? Many feel like a doctor can assess more than what is fed into the computer. That a counselor can see subtle cues that are valuable for prediction. That there are edge cases that statistical algorithms can’t catch. Why does the empirical evidence not bear this out? Why does clinical judgment repeatedly fare worse on average?
The key to the entire clinical-statistical puzzle is those last two words.
The trick that Meehl plays is in the quantification of “better.” By better, we of course mean on average. This is a subtle point: Meehl discusses in Chapter 4 that a clinician may be able to detect a variety of exceptional cases that don’t appear in the original data seen by the statistical algorithm. His famous example is where an actuarial table determines that Professor Glotz attends the movies 90% of all Fridays, but this Friday he has a broken leg. The broken leg impels the clinician to change their predicted probability to near zero. What if clinicians are adept at finding such idiographic oddities as broken legs? Meehl doesn’t deny this possibility, but asserts that, regardless of how clinicians incorporate new knowledge, their performance should be evaluated actuarially. [...]
Actuarial evaluation seems innocuous: how else would we compare two decision-makers but by the body of their work? However, once all parties decide that predictions will be evaluated by averages, the game is up. If prediction is possible, meaning that the past and the future are similar, and the evaluation is based on rates of future success, then the best predictor will be the one that maximizes success rate among some class of possible algorithms. You should find a rule that accurately predicts the past and use it to make predictions about the future. Since you will be evaluated based on averages, this is effectively the optimal thing to do.
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If I pick a statistical evaluation, I can derive the optimal decision. I call this phenomenon, where the metric fixes optimal actions, Metrical Determinism. The evaluation ties our hands. Once we decide what is best in the future, the problem of optimal action is mechanical. It should thus not be surprising that statistics wins when we evaluate predictions and decisions using statistics.
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Meehl provides clinicians with clearly delineated conditions for when statistical methods are useful: answering clear, multiple-choice questions about simple actions from machine-readable data. This characterization is useful in of itself. Moreover, I cannot emphasize enough here that just because statistical prediction is never worse and often better than clinical judgment, that doesn’t mean that it isn’t possible to poorly implement statistical prediction. Careful statistical prediction remains a delicate skill. You can have too few features to make accurate predictions. You can have too many features, making it hard to find consistent patterns. You might be in a situation where you have completely uninformative features. We don’t have particularly effective methods to deal with missing data, and missing data plagues many prediction problems about people.
Most worrisomely, the predictions trained on statistical counts have limited temporal validity, as the population of people changes faster than the statistical prediction rules can be updated. Statistical prediction relies on past counts being reasonable predictions of the future. We have plenty of experience that tells us this is often not a safe assumption.
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Data scientists and software engineers at technology companies refer to this degradation as staleness [...] and constantly retrain their prediction systems to prevent predictions from becoming less accurate. Not all fields are as diligent about the maintenance of their prediction systems. Medical risk assessments may remain static for decades, although they become ineffective within a matter of years.
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Moreover, statistical rules need to be targeted at interventions with simple outcomes. Trying to shoehorn every decision into a simple statistical decision narrows the possibilities of the world we inhabit. The Meehlian actuarial game transforms the world into machine language. This is explicitly part of the problem setup, which demands machine-readable rules, data, and outcomes. The game is rigged because we organized the problem to be mechanical. Once the problem is mechanical, it can be solved by a machine. However, if machines can’t function, they have no role in decision making. We can only compare human to machine decisions on the problems where we level the playing field for the machine.
Nonetheless, one of the primary impulses of the modern state is to translate human experience into data readable by machines. Bureaucracies render humanity in a simplified state in order to make decisions about it. And, as explicated by Farrell and Fourcade [2023], our massive technology companies aid, abet, and profit from helping with such rendering. These systems remove the discretion of people in the decision making chain. These people, be they your primary care physician or a trial judge, often consider benefits not captured in actuarial evaluations.
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The actuary's final word on algorithmic decision making
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Comment on The doomsday cult inside OpenAI in ~tech
skybrian Link ParentThe threat from China is why many US AI leaders aren't advocating for a halt. When they talk about "pacing," it's a middle position between full speed ahead and a complete halt. They're saying,...The threat from China is why many US AI leaders aren't advocating for a halt. When they talk about "pacing," it's a middle position between full speed ahead and a complete halt. They're saying, maybe let's not speedrun AI like we could try to do, because it seems more dangerous than doing things at a more moderate pace.
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Comment on The doomsday cult inside OpenAI in ~tech
skybrian Link ParentYes, I didn't mean "you" in particular. Sorry about that! "Only person in the room" doesn't seem right. Which room? Who do you imagine he's responding to? I've seen many people online claiming...Yes, I didn't mean "you" in particular. Sorry about that!
"Only person in the room" doesn't seem right. Which room? Who do you imagine he's responding to?
I've seen many people online claiming that when the leaders at AI companies asked for more regulation, it's to their own advantage, because they are attempting to exclude competition and they will somehow control the regulators to do that. (And such comments often get lots of upvotes, too!) There are people who write articles claiming this and they get shared approvingly. This position seems popular? And populist.
There are often better and worse versions of an argument, though. For any argument you can think of, there are probably people online doing it badly. So, refuting the bad version of an argument isn't wrong, but there may be more to it. It's okay to not to care that much about refuting the bad version.
Also, I'm a big believer in mixed motives. I think that's the normal state of affairs - people are often "talking their book." They stand to gain from something, but may also think it's the right thing. Neutral experts are rare. Liking money doesn't mean you can't also care about the "future of humanity" or whatever.
And besides, if some of the people making an argument are financially conflicted, there may be other people who don't have the same financial incentives. It seems wrong to dismiss an argument because some of the people who take that position have mixed motives?
So, I think pointing out financial incentives isn't wrong, but using it as a stick to dismiss arguments is. If the "wrong" people are in favor of something, that's reason to be suspicious, but not enough in itself to take the opposite side. The "wrong" people can sometimes be right about things.
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Comment on The doomsday cult inside OpenAI in ~tech
skybrian Link ParentI don’t think building on someone else’s results is necessarily all that creative, but it seems like proving a difficult math theorem is not much like using a calculator or “revealing trends.”...I don’t think building on someone else’s results is necessarily all that creative, but it seems like proving a difficult math theorem is not much like using a calculator or “revealing trends.”
Also, other mathematicians and even non-mathematicians have found useful results using frontier AI’s. It’s not just one result, it’s many. Sometimes they share the prompt they used.
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Comment on The doomsday cult inside OpenAI in ~tech
skybrian (edited )Link ParentThis is smearing together the various actions and histories of different people into a sort of collective guilt. Whatever Musk or Zuckerberg have done has no bearing on some other leader’s honesty...The actors leading these frontier models are untrustworthy and they have a history of overly exaggerating, spreading misinformation, lying, and in a few extreme cases: coaxing a genocide.
This is smearing together the various actions and histories of different people into a sort of collective guilt. Whatever Musk or Zuckerberg have done has no bearing on some other leader’s honesty because they are different people.
It’s like assuming some politician you never heard of must be corrupt. You don’t have to trust a stranger and it’s not wrong to say that corruption is common based on an average tendency, but it’s quite another thing to assume they’re lying because they must be like the others.
You’d never want a jury to come to a conclusion because “he’s just like the others.” A journalist reporting on crime would be no good if they mixed up the details of different crimes. But that’s what we often do when we talk about people in groups.
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Comment on The doomsday cult inside OpenAI in ~tech
skybrian Link ParentDon’t feel like you have to do homework. It’s a whole lot easier to simply avoid making sweeping conclusions.Don’t feel like you have to do homework. It’s a whole lot easier to simply avoid making sweeping conclusions.
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Comment on The doomsday cult inside OpenAI in ~tech
skybrian (edited )Link ParentI agree that it’s hard to do a cost comparison. It seems like $20 million is quite a lot of money? And I don’t know how to account for the time mathematicians spent on the problem. But I don’t...I agree that it’s hard to do a cost comparison. It seems like $20 million is quite a lot of money? And I don’t know how to account for the time mathematicians spent on the problem.
But I don’t think these achievements should be discounted because they’re not entirely original. That seems like too high a standard. For a human, being well-read and understanding the literature well is usually considered an advantage. Similarly, effectively building on all the mathematical research that came before doesn’t make AI any less formidable as a mathematician.
Figuring things out from scratch is a different sort of achievement because it’s working with a handicap.
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Comment on The doomsday cult inside OpenAI in ~tech
skybrian Link ParentSee my other comment. I think this counts as “people are even worse when talking about groups of people.” If you discount the leaders, maybe engage more with what less prominent AI researchers are...See my other comment. I think this counts as “people are even worse when talking about groups of people.”
If you discount the leaders, maybe engage more with what less prominent AI researchers are saying?
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Comment on The doomsday cult inside OpenAI in ~tech
skybrian (edited )Link ParentI think it can be quite to difficult to know anyone’s true motivations for what they do. But I’m struck by the tendency for people to simply make up a story about the motivations of strangers and...I think it can be quite to difficult to know anyone’s true motivations for what they do. But I’m struck by the tendency for people to simply make up a story about the motivations of strangers and treat it as fact. it seems like you should at least cite something they said or something they did that seems to be revealing? Though it’s certainly possible to do that while still being quite unfair about it, it would be a start.
For example, if I wanted to make the case that Sam Altman seems rather untrustworthy, I might cite the profile in the New Yorker about him, which is based on interviews with people who have worked with him. It seems like useful background. Though it won’t necessarily reveal the exact motivations for something he said, after reading a story like that, it’s reasonable to say you know a bit about him.
But often people don’t bother to even talk about individuals in any detail. They will just smear people by association. You’re suspicious because of where you work or because of one of your company’s investors or you went to the wrong parties or whatever.
I usually don’t care to spend the time to understand people in the news in much depth, so I find it easier to simply avoid making confident statements about what they might have been thinking. It’s easy enough to share your suspicions without going so far as to assert them as fact.
People will be even worse when talking about groups of people.
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Comment on The doomsday cult inside OpenAI in ~tech
skybrian Link ParentIs there any actual evidence for the widespread assumption that the people making the warnings aren’t sincere? For some arguments against, see this article.Is there any actual evidence for the widespread assumption that the people making the warnings aren’t sincere? For some arguments against, see this article.
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Comment on Gemini AI hacked three companies in a testing breakout, Google says in ~tech
skybrian Link ParentThis is yet another 4D chess strategy argument. Have you noticed that you can prove anything you like that way?This is yet another 4D chess strategy argument. Have you noticed that you can prove anything you like that way?
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Comment on Gemini AI hacked three companies in a testing breakout, Google says in ~tech
skybrian Link ParentYes, there are many people saying that HuggingFace incident and less serious, similar incidents are some kind of 4D chess strategy to market AI services or achieve regulatory capture. But this is...Yes, there are many people saying that HuggingFace incident and less serious, similar incidents are some kind of 4D chess strategy to market AI services or achieve regulatory capture. But this is all based on speculation, not evidence. There's a lot of populist nonsense surrounding AI lately.
One thing that really is true, though, is that Nvidia wants the US to lift export controls so they can sell chips to China and Anthropic (at least) doesn't want the US to allow it. They have made various speculative arguments about whether these export controls are in the US's best interest.
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Comment on Gemini AI hacked three companies in a testing breakout, Google says in ~tech
skybrian Link ParentWeird to call it PR. I doubt very much that Google wanted to talk about it, but when reporters start asking questions, they probably shouldn't deny it. It's the news media that's driving it. I...Weird to call it PR. I doubt very much that Google wanted to talk about it, but when reporters start asking questions, they probably shouldn't deny it.
It's the news media that's driving it. I shared it too, because I thought it was mildly interesting.
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Comment on FBI, coast guard boarded hacked oil tankers heading toward US coast in ~society
skybrian LinkFrom the article: [...]From the article:
Cybersecurity teams with the U.S. Coast Guard and the FBI boarded two U.S.-bound oil tankers last month after hackers reportedly compromised at least one of the ship’s networks and took control of its navigation, propulsion, and cargo systems.
According to a joint statement shared with TechCrunch, the agencies boarded the vessels in the Gulf of Mexico between August 21 and August 24 to “ensure integrity of the vessel’s operational and information technology systems following indications that the networks of both vessels were compromised.”
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CBS News confirmed that one of the tankers is VL Prosperity, a 333-meter oil tanker that can hold over 2 million barrels of oil. According to VesselFinder, the tanker is currently located in the Gulf of Mexico.
Citing Iranian media, CBS said hackers compromised the ship on August 7 while traveling from Egypt to the United States and interfered with the ship’s speed and fuel systems. The tanker also lost communications for more than a day, per CBS.
It’s unclear who is behind the attacks, but CBS News said the U.S. is looking into whether Iran is behind the hack. Iran has been behind a multitude of hacks in recent months following the start of the U.S. and Israel-led war against Tehran, which killed Iran’s supreme leader in February. Iranian-backed hackers have since launched a destructive hack at medical device maker Stryker, hacked the mass transit system in Los Angeles, and compromised over a hundred water facilities across the United States in response to the widespread U.S. bombing of Iran. CISA has called the attacks “opportunistic” in nature.
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FBI, coast guard boarded hacked oil tankers heading toward US coast
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Comment on Gemini AI hacked three companies in a testing breakout, Google says in ~tech
skybrian LinkSounds like Irregular notified their customers at the same time and Google kept it secret the longest.Sounds like Irregular notified their customers at the same time and Google kept it secret the longest.
I don't believe this. I think people refute weak arguments because there are other people out there making that argument (often repeatedly), and it bugs them. I see it all the time, and I do it too.
And a particular thing that bugs me is assuming motives without evidence. Like, you've made up a theory about why Tabarrok wrote the article I shared, and what is that based on? I don't know why he wrote either, but I'm not making claims.
You say you don't really care about sincerity or motives. Maybe that's why you aren't trying very hard to get them right?