jmde

I think direct care models, in all but name, are probably coming. I might be wrong, but the way federal governance in the US is headed it seems increasingly rather than decreasingly likely.

In our region there are already hospital systems that are owned as subsidiaries by corporations that also own insurance companies. These corporations are gobbling up smaller hospitals as those merge and then subsequently get purchased by regional systems.

The monopoly in the provision is problematic enough, but it reaches new levels of problems when they are also merging with insurance companies.

So for some people, even if they aren't getting direct care technically, they are, because their choices are limited geographically for all intents and purposes to one provider, and their insurance is coming from the same provider.

The challenge these direct care models faced, as alluded to by other posters, is in competing with conglomerates that offer a complete spectrum of care. You either cater to people who can afford it, or have to lower costs enough to make it reasonable for people who might not otherwise. The first option limits your customer base; the second is not really currently feasible given government regulation and how it inhibits competition in delivery models.

I worry about the future of health care in the US, only because I don't see the GOP implementing anything other than the not-too distant status quo, which was broken. I sympathize with their stated ideal of reducing costs and increasing competition, but it seems like doublepeak for reducing costs and decreasing competition for established financial interests and increasing competition for everyone else. The net effect will just be squeezing more and more money out of the common individual.

Reasonable point, but I'd argue that the purchasers of dedicated cameras are already not the general market, who will just be using their phone camera anyway. For purchasers of these cameras, I think the cameramakers going down this route would be a clear signal that they care about the users' freedom of speech and security concerns and are taking their use seriously.

Also, my guess is compared to other features they work on this would be fairly trivial, or at least in the same ballpark. And I think it would immediately give the camera a certain cache that would signal "professional" or "serious" that would increase its value, regardless of whether or not the user really needs it.

Without meaning to be dismissive of the large numbers of people who do need these types of cameras (I have close loved ones who are professional photojournalists who rely on having the best cameras), I think a large proportion of people who who buy them don't need them relative to what's on decent phones today. They're just a status symbol for them anyway.

Bingo. The fact this is one of the two most popular TED talks says volumes about TED, and how to interpret TED vis-a-vis the broader scientific community.

I started reading the essay not knowing what to think, and it turned out to be more relevant to my work than I thought.

The issues being discussed in the essay have been a central issue in some area of psychology and behavioral sciences for some time--how to interpret components such as these.

One thought about your "coming into focus at a certain level of compression" comment: I've done some analyses of these vectors as applied to text samples, and one thing that struck me was how unreplicable some of them were across datasets that should be ostensibly similar (but are not the same). Others, in contrast, reappeared across multiple corpora. To the extent some of these components represent "real" features, they should reappear consistently across different datasets where you'd expect them to. That is, they should be robust to changes in idiosyncratic features of the database.

Whole fields have been devoted to defining science; I don't think experiment is necessary to the scientific endeavor. Experiments in many fields, for example, often suffer from problems related to generalizability or applicability to real-world scenarios. It's a major reason for observational science, although not the only one. Problems with replicability have also been shown to be just as endemic to fields dominated by experiments as those more influenced by observation, if not more so, across different scales of analysis from the molecular to the societal.

I think a better definition of what science is is broader, something like "logical argument based on empirical observations," although that too isn't right. I think the appeal of experiments is in line with that, to the extent that you accept that the experimental rationale involves a logical argument pertaining to randomization over potential confounds.

The underlying problem being discussed in the article, to me, is the denial by scientists that science is fundamentally a human endeavor, subject to all the problems of humans: things like greed, deceit, ideology, and so forth. Scientists like to pretend that they are somehow above all that, which means they pretend it doesn't exist, which makes the effects of human weaknesses in science all the more insidious. We see this play out with the problems in academics, financial conflicts of interest, and so forth.

What happens is that a certain cultural subgroup, already primed to be skeptical of science through their social context, recognizes the human weaknesses of science. They then make the mistake of rejecting science wholesale because of this. Scientists, in turn, often make the mistake of rejecting the notion of human influences on science, thereby placing undue confidence in their conclusions, leading to a lack of accountability or explanation when failures occur. Some degree of evolution in scientific theory is due to random variation, or lack of appropriate data, but some of it is due to human factors.

This was one of my first thoughts--these are extremely, extremely low base-rate events with not much measurement power. People mess with the surveys, or simply fail to understand the questions, or make mistakes in responding. The combination of them makes extrapolating very difficult. This was an internet sample too, and biased in that regard (which the authors acknowledge). It was interesting to me to read, though.

I think the bigger question for me, aside from this, are the benefits and costs associated with any response.

Even if you redid this and found that the likely percent of noncitizen voters was nonzero, you'd have to balance the benefits of any enforcement strategy against its costs.

Given that respondents were also saying that photo id didn't stop them, you'd have to also determine the number of citizens who were unable to vote because of voter identification. To me, the cost of denying a citizen a vote is much greater, and also more certain, than the cost of allowing a noncitizen to vote.

Overall, I'm concerned that the typical "innnocent before proven guilty" logic is thrown out the window when it comes to immigration, citizenship, and voting rights. The government should have to prove to some reasonable level of certainty that I do not have the right to vote, not the converse.

... but later the whole line of inquiry and interview of him was ruled in court to be immaterial to the case. So there's some disagreement with the perspective you're outlining.

So, I'll try to respond as someone who voted in support of Johnson, and can get the appeal of Trump as well as the Clinton, as well as visceral hatred of either.

I get that huge swaths of the electorate feel like this system hasn't been working, and that' it's all manipulated by career political insiders who have their own brand of benevolent authoritarianism. I get the distrust of a liberal culture that overvalues its own contributions to society, and has a false sense of how correct they are and why they are where they are, and overlooks their own serious problems and prejudices. I understand how the media plays into this, and believe that a lot of what Trump has said has been taken too seriously or distorted, that he was projecting a character rather than an argument, and in doing so, demonstrated empathy with a huge group of the population. I get that population has been so screwed by the current system they just want something different, whether that be Sanders or Trump.

However, I strongly believe that there's a false equivalence in these discussions, and that calls for "reasoned debate" on the American right are often intentionally or unintentionally meant as a negotiating tactic, to cover up arrogant intransigence on their part. They don't get what they want, so rather than contributing something constructive, they accuse the left of "not having a reasoned discussion" or not "compromising." When the GOP and Trump talk about the left "not compromising," they're really meaning "we're not getting what we want."

Relatedly, do you really believe that the things that Trump has said about minorities, women, and so forth are acceptable ways of leading the United States, or treating those who disagreed with you? What would the GOP do if the liberal candidate said the same things about Trump?

Which party shut down congress repeatedly rather than have a reasoned discussion? Which party has been obstructing and crippling the supreme court because they didn't get their way?

The reality is that the GOP lost this election in terms of votes, and yet are still entering the white house because of a broken electoral voting system. Like it or not, Clinton won more votes than Trump. So Trump and the GOP are going to undo everything the majority voted for to get their selfish agenda, because they don't care about the majority of the electorate that voted for someone else. Who is being unreasonable in this situation? It's not the liberal party.

I have plenty of reasons to be angry with both parties, but Trump is dangerous in a way that Clinton was not. Sure, I could find some bright sides to his ticket, but it's overshadowed by a lot to be terrified by.

Yeah, all these arguments against non-GMO products, painting non-GMO proponents as antiscientific, seem to me to be strawman arguments that ironically miss the point. It's a thinly veiled means of asserting intellectual superiority that backfires to me because it overlooks the real arguments against GMO products.

Great, you're parents and scientists. I'm a parent and a scientist too. But I understand that many of the arguments against GMOs have nothing to do with science (at least biological science) and everything to do with economics and human rights.

Just because something involves biology doesn't mean that arguments about it can be reduced to that.

A major downside of Stan is its lack of support for discrete priors. This isn't really advertised very well, but is more of a problem than it might sound initially. Its type handling also can get a little frustrating at times. Overall, I highly recommend it but it does have its downsides, and there's some room for alternatives or improvement.

... and/or deregulation (reregulation?) to open up competition. Often if not usually, the same task could be just as skillfully by done, or even more skillfully or appropriately done, by someone with a different educational route, with or without training beyond what is now typical (e.g., NPs, nurse anesthetists, PAs, optometrists, psychologists, pharmacists, RNs, you name it).

PAs provide an instructive example, but there are many others of different sorts. Often, PA schools have stricter prerequisite experience requirements, and PA students often take the bulk of their courses with the MD students, and have maybe a semester less of courses, before clinical training. So the end effect is that a PA's actual experience is often the comparable to an MD's after a couple of years of practice. Hospitals and health care systems know this, and have been gradually replacing MDs in many areas with PAs because they're cheaper but provide similar care.

We can't afford to maintain this fiction that the only way to have something skillfully done is by the MD training model.

As someone in this field somewhat close to the article in a certain sense, I wouldn't say Susan Fiske's attitudes are typical of psychology. Maybe not rare, but not typical either. Her comments have been very controversial to say the least, and many are disturbed by them. Moreover, if you level that statement against psychology, I'm afraid you have to level it against medicine as well, given that there have been similar sentiments expressed in major medical journals as well recently.

Also, without meaning to defend Fiske, her comments here are taken out of context somewhat. Her reference to "methodological terrorism" (from what I have heard through the grapevine) is more, or at least in part, about the trend toward having scientific debates outside of the peer review process, in social media. So my guess is that she might say that part of what she objects to about Statcheck is that it crawls through the papers, labels an error, and then we end up discussing it on HN rather than through peer review. What if Statcheck made an error, which it does sometimes? I don't agree with it, but I think the position I'm describing (which I think is her point in part) isn't unreasonable either. That is, it's not the checking of stats, it's the chaos and disintegration of the peer review system, and "extrascientific" discourse that's happening in science today, if you define "extrascientific" as "outside of peer reviewed journals," where your critics are attacking you on twitter, forums, and facebook, more so than in professional published outlets in a sort of mob.

Again, I do not share her perspective at all (I'm in favor of a shift away from journals) but I do think here her original point was twisted a bit.

Then you have to summarize those replications and decide whether or not the replication studies replicated findings... which means meta-analysis.

This could either be brilliant or a total nightmare:

"Support for arrays with indexing starting at values different from 1. The array types are expected to be defined in packages, but now Julia provides an API for writing generic algorithms for arbitrary indexing schemes (#16260)."

Originally I thought total nightmare but now I'm not sure.

I agree with your general sentiment, but think those error rates hide a lot as well. Human error might be at x% overall, but when you eliminate malfunctioning humans, broadly defined, it's probably much lower than x%.

The recent death of the Tesla owner, for example, as far as I know, was due to the vehicle accelerating into a semi. This is something that most people would not do even in their worst driving state unless they were intoxicated or seriously mentally impaired. I don't want AI driving errors to be compared to human benchmarks that include people who are seriously intoxicated.

A lot of speech frustration problems, similarly, are not only about poor recognition in general, or lack of appropriate prompting to increase classification certainty, but recognition failures in situations where a human would not have any trouble at all, such as in recognizing names of loved ones, or things that would be clear in context to a human. I.e., maybe humans listening to speech corpora would have x% error rate, but that's strangers listening to the corpora. The real question is, if I listen to a recording of my spouse or coworker having a conversation what's the error rate there?

So, although humans are far from perfect, which is something that's often forgotten, the true AI target is also probably not "humans broadly defined" but rather "functional humans" or something like that. AI research often sets the bar misleadingly low because it's so hard to reach as it is.

The article frames the question as a sort of strawman in some ways. Rather than ask "is running good or bad for you?" you could ask "is an alternative form of exercise, that has fewer damaging side effects, better?" For example, would you be just as well off walking, skiing, or biking? My understanding is the answer to that is even less clear.

The problem is that it is not actually a free market. The government grants monopolies to prescribers, restricting access that way, and to drug manufacturers, through the FDA, directly or indirectly (it's crazy to me that the government grants these monopolies and then complains when businesses take advantage of them financially). Then you have the insurers, who are not actually the beneficiaries of the product and who are therefore not in a position to fully judge the benefit:cost ratios of the products.

To me, the problem is lack of competition. The government should remove or sharply curtail licensing laws, or expand them to other professions substantially, and eliminate or loosen drug schedules (the new kratom fiasco is just one example of many spanning decades of how inept the government is at regulating substances). Basically, decriminalize and deregulate drugs completely. Let the FDA focus on purity and rigorous testing and education, but take away its regulatory authority beyond that. Let pharmacies produce more drugs, and get the government in the business of producing drugs. Nothing will drive down drug prices faster than a public entity producing drugs in competition with private corporations. Give tax incentives to businesses for producing or researching drugs in the public interest.

It's madness to think that somehow problems with overregulation and monopolies are going to be solved by increasing regulation and monopolies.

I don't know if I entirely agree with the "fall on your sword" argument. Sometimes it applies, but not always. There's definitely a grey area. At some point, the leverage becomes coercive, and to deny that allows force to hide in the guise of distributed responsibility. It allows the corrupt to shift blame onto middlemen who are sometimes just as much the victims of force as those down the line.

It does matter if you're aware of the cost:benefit ratio involved in adopting it relative to the existing tech, or even more commonly, the cost:benefit ratio involved in adopting it relative to adapting or modifying existing tech. Maybe as an employee, it might not be the right thing to be pushing with an employer enamored of the new tech, but as an objective onlooker, it very well may be.

There's plenty of amazing innovation happening all the time, so I don't want to come across as a luddite or whatever, but hype does happen, and as you get older, you witness more of it and become more skeptical of it. You see the fads come and go--the faster they come, the faster they go--and the survivor bias. The problem is that it's there, and it feels sometimes like you face the choice of correctly pointing that out, or being labeled a cynic.

I sort of agree with you, but the laws overstep what I consider acceptable.

I'm fine with an operation banning employees from filming, for example, and employees getting fired for doing so, or for arresting people for trespassing.

The laws referenced by the OP go further, though, to criminalize recordings that were taken by individuals who were allowed on the property by the owner, and who were documenting what happened. In my opinion, owners have the right to fire employees who violate their policies, and to sue former and current employees for libel, but not to sue former and current employees for distributing recordings of things that actually occurred, when there's no reasonable expectation of privacy (which almost by definition is impossible in a workplace, except for maybe things like medical settings).

I agree with you, though, that if there's a public health interest, the government should create laws regarding that, or at least give authority to DPHs to do that.

People also underestimate the market for baby name resources. It's true in the US as well, something I didn't realize until I had my own child. Add to that additional uncertainty about a different culture and language, and it probably amplifies the market appeal.

Depending on how you define it, you could say my research is in this general area. My sense of how to interpret these sorts of findings has changed greatly over time, and become much more moderate and humble. Discussions over these issues tend to be very heated and people oversimplify issues on both ends.

These tests are predictive, and as you point out, it's disingenuous to claim otherwise. They're measuring something valid. However, they're doing so relatively poorly, or rather, roughly. Even the quote you cite suggests that the total amount of variance accounted for was about 19%, which isn't too shabby by any means, but is also very crude when you think about implications for real-world consequences. Interpreting that 19% also becomes very murky, when you consider factors like teaching to tests, that tests like those are used for academic and career selection, leading to self-fulfilling prophecies. In rare circumstances where researchers have been able to remove some of these effects (for example, due to unusual legal decisions about acceptable use of test scores), the relationship between test scores and career performance has been shown to be very weak in the short-term, and almost non-existent in the long term.

Somewhere underlying the distribution of test scores is some distribution of ability that makes it easier for some people to thrive in certain settings. But ability is just one thing in a long list of things, along with practice, resources and other environmental factors that contribute to success, and sociological dynamics. Add to that test scores being very crude indices of ability to begin with, and you're in a very messy situation.

What's frustrating to me is that there's this sort of all-or-nothing attitude about testing, where tests are seen as useless, or as a perfect indicator of skill or ability. The truth is in-between.

Without meaning to sound antagonistic, you're framing the question in the wrong way.

Research suggests depression is more like blood pressure, varying continuously in the population. There really isn't a clear line between depression-as-illness and other varieties of depression, only an increasing risk of problems as depression increases.

So in one sense, the research does support what you're asking, in that most people who experience depression aren't extremely depressed. But that's sort of tautologically true, in that if you have a normal distribution, most people won't be at the extreme by definition.

It's difficult to approach these things at a deeper level that I think you're getting at, which is "are most people who are depressed not seriously impaired by their depression" or something along those lines. That's harder to answer because it depends on defining "seriously impaired" which depends on sociological trends and so forth and so on.

How do you know an intelligence service didn't create this system, and turn it loose only to be watching and listening the whole time?

This paper is interesting to read, but confusing in my opinion and sort of misled.

I'm not sure what point the author seems to be making about the DSM. They seem to be stating something along the lines of "The DSM isn't successful because it's atheoretical, it's successful because it adopted a biological theory, and because its emphasis on reliability facilitated sociopolitical control by the psychiatric profession." But later they recognize that the DSM isn't successful, seeming to contradict themselves.

The theory of the DSM goes far beyond biology, being explicitly neo-Kraepelinian and to some extent psychodynamic in nature, although that would probably not be acknowledged by any of the DSM's authors unless you pressed them on it. The success and failure of the DSM has everything to do with sociopolitical factors and the biological and nonbiological tenets of neo-Kraepelinian classification, and not on "atheoretical versus natural" or "scientific versus nonscientific" or "ontological versus nonontological" or "descriptive versus theoretical" or "ontological versus epistemological" or any of those sorts of distinctions.

The DSM adopted Kraepelinian categories and assumptions, shoehorning psychodynamic theories into this framework in some cases, and then further elaborating the system with very biological criteria. It essentially asserts that the major Kraepelinian categories are valid, that mental illness is discretely distinguished from normality or wellness, and that its categories reflect disruptions of biological systems that can be determined by certain patterns of biological observations. It further asserts that reliability is a central feature of classification and assessment. Some of these assumptions have been accepted, cementing its success, but some assumptions have been rejected by the community, leading to the current crisis that the author recognizes. Underlying all of this are sociopolitical factors, such as professional practice conventions, boundaries, and laws, and popular perception, that have both augmented the DSM, but also undermined it by highlighting the political processes underling its continued use.

The question is not whether a classification system is atheoretical or natural, or descriptive versus natural, or ontological versus epistemological, it's what criteria or theoretical principles are used to derive the system. In the case of psychiatric classification, the tension historically, even with the DSM itself, has not been about "atheoretical" versus "natural" systems, it has been about whether classification systems are at the level of behavior, which is proximate to the phenomena of interest and relatively tractable, or biology, which is proximate to the causes of those phenomena, but relatively intractable due to those causes being unknown. It's a bit like trying to classify species, genera, etc. without knowledge of genetics. You can proceed with a biology-based classification system under the assumption that your understanding is close enough, or you can acknowledge that you don't understand it and rigorously classify lifeforms based on what you do understand. Or you can try to do something intermediate. None of these are "natural" or "atheoretical," they're just at different levels of analysis. Another analogy might be found in computer science: do you derive document/file ontologies based on biology, or something like unsupervised AI classification? People like to assume that going down to a lower level of scientific analysis is always better, and it might be, but it might also not be, depending on your goals.

My position is that the FDA's role should be strictly limited to testing products for labeling fraud (i.e., that ingredients are as they appear on the labeling) and maybe doing tests and releasing those results to the public so consumers can make informed decisions. They should have no regulatory authority beyond those things.

I don't buy antibacterial soaps, but I'm concerned about this decision because it seems overreaching. I get the tragedy of the commons aspect, though, so am a bit unsure of that.

However, I agree with original position taken by one of the parent posters: there's a very politically dangerous position implicitly being supported in the US now, which is the default is that citizens are not competent to do anything, and that they have to show that they are, as opposed to the alternative, which is to assume that they are competent, and that the burden of proof should be to show that they aren't. What people are implicitly buying into, without meaning to seem hyperbolic, is a totalitarian state, where the default is lack of rights that you have to earn, rather than a default of rights that the state has to take away.

Sure, along time ago medicine was full of quack remedies, etc. But what do we have now? A corrupt system run by monopolies in all corners, and unaffordable by most citizens.

If people take nutritional supplements, that's their decision and their responsibility. If a supplement manufacturer adulterates their products, the problem is fraud, not the ability of the consumer to make the decision.

People do all sorts of stupid things. Preventing them from doing so is not the responsibility of the government.

I keep going back to the example of taxes, which is a good parallel. Imagine that the government, a year from now, mandates that no one can do their own taxes anymore, and that only individuals with a specific degree and license, requiring hundreds of thousands of dollars, and years of training, can do any taxes. What would people think of that? There would be an outcry, and legitimate concerns about corruption and collusion. But we are completely fine with the government telling us what to eat, and how to wash our hands, and what drugs we should be putting into our bodies, and what not.

The same government that classifies cannabis as a Schedule I substance, so dangerous that no one should possess it. Absolutely no problem with government regulation of health decisions. Totally trustworthy. (btw, I've never used cannabis either, so that's not my skin in the game)

That's an interesting question - I agree it's an interesting parallel and one I hadn't thought of before.

I have always been puzzled by the term "computer science" a bit also, because so much of it isn't really science per se (more math or theory along with engineering). When I've thought about it, I usually come to some peace with it because there is a scientific aspect to the field via the hardware side of things, which is really the foundation, at least historically, and there is a historical emphasis on demonstrating results empirically. It's sort of a crude awkward label but I accept it. But then again I went to a school where/when comp sci and EE were the same department.

"Data science" has bothered me more, though, because it's so vague, "data" and "science" are so inextricably defined relative to one another, and because it's arguably misleading - it's not really the science of data, whatever that means, and to the extent it's science, it's just science, but it's not, it's really just statistics.

More appropriate terms to me would be "computational statistics" or "statistical computing", "informatics", or "quantitative computation" or something. Anything but "data science." It's like some stereotypically ignorant but buzzword-compliant management committee, being unfamiliar with data or science, somewhere commanded HR to "find us some of those... you know... data science people!"

... and now venerable universities have whole departments with that title.

This seems like a nice compilation for introductory material in one place.

I still can't get over the term "data science", though. Not only is it ridiculously meaningless - what sort of science doesn't involve data, and how often would data be useful to something that isn't scientific at some level - its meaninglessness derives from the hyped buzzword trendiness that drove its upswing.

I say this as someone whose expertise is really sitting at the nexus of what would be considered data science. I feel as if I have been doing what might be considered data science for a long time, before there was a label for it, but watching its ascendance in demand and popularity has been troubling. I should be happy, but I feel like it's being driven by fashion rather than fundamentals, which makes me worried about the trajectory going forward, and disturbed by some communities being thrown under the bus.

I had the opposite reaction, which is that the problem is most definitely not rooted in the media. The problem is with science itself, which is fundamentally broken as a community.

I agree that the media are maybe perpetuating certain problems, or are providing dysfunctional incentives, but the root problem is with the scientific and academic community, not the media.

Scientists run the journals that have gone amok, are in charge of the tenure processes that reward outdated practices, and run the grant systems that are broken. Universities' public relations departments bait media to hype their scientists' findings. Etc. etc. etc.

I was an exchange just now where this played out with a project. I don't want to get into details, but the media were pushing hard for a misleading--and blatantly unethical--way of pushing something to the public. So you could blame the media. But anyone who knows anything about the situation knows that certain PIs groom things, arguably over years, to produce this very type of outcome, in part because they can sit back and reap the benefits while claiming to wash their hands of any responsibility when this happens. They manipulate the situation, and then push the blame onto others, including the media, when it suits their interests.

Obviously not everyone is like this, but it's naive to believe it's not a systemic problem.

I think this is a serious problem in various areas of society, and touches on the debates regarding the value of a degree or certification.

I have no doubt that pedigree, as you refer to it, does stochastically index quality. There's too much evidence from various studies that it does. But the strength of relationship between pedigree and quality, is much, much, much weaker than people typically assume. We end up relying on things like test scores in school, and degree in the workforce as a proxy for talent or ability or motivation or whatever, when it's more like a weak predictor. In the process, we end up granting monopolies to meritocracies and driving up things like income inequality, where income distributions don't parallel skillsets.

I agree that this paper probably reinforces false beliefs, but my guess is that it does so because it oversimplifies a kernel of truth, not because there's no truth to it to begin with.