On moving from statistics to machine learning, the final stage of grief (2019) - <AUTHOR> https://ryxcommar.com/2019/07/14/on-moving-from-statistics-to-machine-learning-the-final-stage-of-grief/ <comment><USER2> There's this undertone of "I should be payed as much/more than Data Science people because I'm better than them at statistics and data science = machine learning = statistics".

My experience doing data science at small companies that can't afford to hire more than 1 person for the role is that it is so much more than just building models or doing statistics.

You have to:

1\. Build APIs and work with developers to get predictive models integrated into the rest of the software stack

2\. Know how to add logging, auditing, monitoring, containerizing, web scrapers, cleaning data(!!), SQL scripts, dashboards, BI tools, etc.

3\. Do some basic descriptive stats, some basic inferential stats, some predictive modeling, work on time-series data, sometimes apply survival analysis, etc. (Python/R/Excel who cares)

4\. Setting up data pipelines and CI/CD to automate all this crap

5\. Trying to unpack vague high level requirements along the lines of "Hey do you think we could use our data to build an 'AI' to do this instead of manually doing it" and then coming up with a combination of software / statistical models that perform as least as good or better than humans at the task.

6\. Work with non-technical business users and be able to translate this back to technical requirements.

Hey, if all you do all day is "build models" then that sounds like a very cushy DS job you have. It's definitely not been my experience. I would describe it more like a combination of software engineering and statistics and business analyst. That's why it pays higher than just statistics. But this is just my experience..

<comment><USER3> And in all honesty, no data scientist with an ounce of self-worth should work long-term for such companies, unless it just happens to be their own.

You're basically doing 3 jobs for the price of one: Software Engineer, Data Engineer, Data Scientist.

Sure, you'll be a jack of all trades, as far as data goes, but it'll be at the cost of some specialization.

I'm probably gonna get a lot of sh!t for this post - probably from data [x] people that are in that exact position themselves, but the above description is exactly why I'd aim for larger companies with somewhat established analytics / data / ML teams or offices. You get to focus on the important stuff, instead of juggling ten balls at the same time.

(And it's not only in the field of data science. Some of the traditional SE positions I see at startups or small companies look absolutely grotesque - basically the whole IT and Dev. department baked into one job)

<comment><USER4> > _You get to focus on the important stuff, instead of juggling ten balls at > the same time._

So on one hand, you can't build any models without the work of the engineers, but on the other the model building is "the important stuff"?

Maybe it's just me, but I _enjoy_ working on all aspects of the data pipeline.

<comment><USER5> Model building is often the trivial part, and often you can't build models without a solid understanding of things like the data pipeline.

</comment></comment></comment></comment><comment><USER6> > Machine learning is genuinely over-hyped. It's very often statistics > masquerading as something more grandiose. Its most ardent supporters are > incredibly annoying, easily hated techbros

This sort of fashionable disparagement of a group of people to signal that you're not part of the "bad group of tech bro's" is so trashy. Why are these random people you easily hate? Who are they? Why take glee in shared hatred?

I've worked as a sr DS at FAANG for 4 years. I've recently worked through Casella Berger, because I wasn't comfortable being one of those DS who didn't know math stats. But before I did work through it, I worked with people from PhD stat programs who were so ineffective. Despite knowing so much more stats than me, they would freeze up and fail everytime they had to deal with any sort of software system or IDE. It was so weird to me that my ability to use a regression, even before I knew the theory, was more valuable than their ability to use a regression to its full power, simply because I could fight the intense battle to take that idea and put it into reliable production code.

But generally I hate _hate_ this war between DS and stats. It's so stupid. Maybe not their first year, but eventually any DS who wants to be a master of their craft ought to learn math/theoretical stats. And some don't want to be a master of their craft, and instead want to go into management or whatever, and that's fine.

</comment><comment><USER7> > I'm sure you're asking: "why allow your parameters to be biased?" Good > question. The most straightforward answer is that there is a bias-variance > trade-off. The Wikipedia article does a good job both illustrating and > explaining it. For β-hat purposes, the notion of allowing any bias is crazy. > For y-hat purposes, adding a little bias in exchange for a huge reduction in > variance can improve the predictive power of your model.

I'm going to push back on this.

The author seems to understand the bias-variance tradeoff as applying primarily to y-hat, and allows that if you are primarily interested in y-hat then it can make sense to make that tradeoff (introduce bias in exchange for lower variance). But the bias-variance tradeoff is more general than that. There's also a bias-variance tradeoff in beta-hat, and you can make a similar decision there to introduce some bias in beta-hat in exchange for lower variance, lowering the overall mean square error.

There's nothing crazy about this. The entire field[1] of Bayesian statistics does this every day--Bayesian priors introduce bias in the parameters, with the benefit of decreasing variance. Bayesians use these biased parameter estimates without any problems.

Classical (non-Bayesian) statistics has tended to focus heavily on unbiased models. I suspect this is largely because restricting the class of models you're looking at to unbiased models allows you to prove a lot of interesting results. For example, if you restrict yourself to linear unbiased models, you can identify one single `best` (i.e. lowest variance) estimator. As soon as you allow bias you can't do that anymore.

[1] Except empirical Bayes, which is a dark art.

<comment><USER8> "Non-Bayesian" stats uses bias in the exact same way that Bayesian stats does, because Bayesian stats and frequentist stats are mathematically equivalent. When someone thinks they're not using priors, they're wrong, they're usually using a flat prior on the model parameters, but that adds bias just like any other prior! A flat prior on theta is different than a flat prior on log(theta) or some other parametrization, and flat priors are often times the wrong choice, so this notion that Bayesian stats is some "special" type of inference and that there is some way to do inference without bias is just a very large misconception.

<comment><USER7> > "Non-Bayesian" stats uses bias in the exact same way that Bayesian stats > does

This is incorrect.

Bias has a very specific specific mathematical meaning in statistics--the difference between the expected value of the estimate (under the sampling distribution) and the true value. There are many examples of parameter estimates in classical statistics that have zero bias under that definition.

> Bayesian stats and frequentist stats are mathematically equivalent.

Also incorrect. Bayesian and frequentist methods focus on different conditional probabilities and can give very divergent results even in simple cases. See e.g. Lindley's paradox [1].

[1] [https://en.wikipedia.org/wiki/Lindley%27s%20paradox](https://en.wikipedia.org/wiki/Lindley%27s%20paradox)

<comment><USER8> I appreciate your comments.

> Bias has a very specific specific mathematical meaning in statistics--the > difference between the expected value of the estimate (under the sampling > distribution) and the true value.

Right, I'm aware of what bias is, and how it's defined, and while the statement you make is true, it misses the point: regardless of the camp you're in (frequentist or Bayesian), inference involves a prior, and that prior will affect your inferred parameter estimates (it may bias them, it may not, but flat priors do not guarantee unbiased estimates). I agree, under various contrived scenarios, you can show your parameter estimate is unbiased when using a flat prior (yay!) but what happens if you're using the wrong parameterization of your model? A flat prior on \theta is not a flat prior on \log\theta or any other transformation of theta, but if you're a frequentist what do you do about that? If you're not conscious of the prior choice you are making, you can easily introduce bias you don't want _even with a flat prior_.

> There are many examples of parameter estimates in classical statistics that > have zero bias under that definition.

Elaborate. I assume by "parameter estimates" you mean MAP (or MLE = MAP with flat prior). E[\hat{\theta} - \theta] may be zero with a flat prior, but E[\hat{\log\theta} - \log\theta] wont be, so the bias in your parameter estimate depends on the parameter you really care about.

see e.g. [1] for an example where flat priors do actually bias inferences.

> Also incorrect. Bayesian and frequentist methods focus on different > conditional probabilities and can give very divergent results even in simple > cases. See e.g. Lindley's paradox [1].

OK so let me clarify because I agree that my wording is wrong: while I agree with you that there are differences between Bayesian and frequentist statistics, they are philosophical; they answer different questions: ironically the wikipedia article you linked to actually says it pretty well: "Although referred to as a paradox, the differing results from the Bayesian and frequentist approaches can be explained as using them to answer fundamentally different questions, rather than actual disagreement between the two methods."

[1]: [https://mc-stan.org/users/documentation/case- studies/weakly_...](https://mc-stan.org/users/documentation/case- studies/weakly_informative_shapes.html)

"Although flat priors are often motivated as being "non-informative", they are actually quite informative and pull the posterior towards extreme values that can bias our inferences."

<comment><USER7> > I agree, under various contrived scenarios, you can show your parameter > estimate is unbiased when using a flat prior (yay!) but what happens if > you're using the wrong parameterization of your model?

I wouldn't consider estimating, say, the mean length of a population of fish contrived (unbiased estimate: x-bar). Nor would I consider estimating the probability of an event based on observations of the event happening or not happening contrived (unbiased estimate: p-hat = #successes/#trials).

These kinds of simple estimation problems and the associated statistical tests account for probably 90% of statistical practice. Dismissing them as contrived is silly.

More generally, MLE estimates are always (under regularity conditions) asymptotically unbiased even if not unbiased for a finite sample. This means that the amount of bias decreases to zero as the sample size increases, no matter what the parameterization is.

Finally, there is very often a natural parameterization for any given problem. If you're interested in the arithmetic mean of a population, there's no reason to use a log-scale parameterization. Why worry about bias in other parameterizations when you can just use the natural parameterization, where the estimator is unbiased? Again, I don't think such scenarios are contrived: a very large proportion of statistical analyses deal with simple measurements in Euclidian (or very nearly Euclidian--we can typically ignore, for example, relativistic effects) spaces: real world dimensions, time, etc. If you're a Bayesian and very concerned about parameterization effects you can also use a Jeffrey's prior, which is parameterization-invariant. Notably, for the mean of a Normal distribution, the Jeffrey's prior is... the flat prior!

> OK so let me clarify because I agree that my wording is wrong: while I agree > with you that there are differences between Bayesian and frequentist > statistics, they are philosophical; they answer different questions:

Yes and no. The Bayesian and frequentist approaches answer different mathematical questions, but they are used by humans to answer the same human questions, such as "do these two populations have the same mean?"

> see e.g. [1] for an example where flat priors do actually bias inferences.

I don't consider that a good example of flat priors biasing the inference. The posterior with flat prior is diffuse because the data doesn't provide much information; in the lack of much prior or likelihood information, the posterior _should_ be diffuse, so that's a perfectly reasonable result. If you can't stand a diffuse posterior then either collect more data or (carefully!) introduce a more informative prior. The more informative the prior you introduce, the more biased your inference will be; that's fine as long as your prior is chosen carefully.

This is not to say that flat priors are never a problem. The setting I'm familiar with where diffuse priors are most dangerous is when doing model comparison--but that issue is specific to Bayesian methods, not frequentist. Indeed this is one of the primary reasons Lindley's paradox arises: the Bayesian model comparison (using marginal likelihoods or Bayes factors) gets tricked by the diffuse prior, while the frequentist model comparison (using null hypothesis testing) does not.

<comment><USER8> > I wouldn't consider estimating, say, the mean length of a population of fish > contrived (unbiased estimate: x-bar). Nor would I consider estimating the > probability of an event based on observations of the event happening or not > happening contrived (unbiased estimate: p-hat = #successes/#trials).

Sure, maybe not contrived; my point is that flat priors may work in many "typical" textbook stats problems, but they are one of many choices, and that choice is important to be explicit about and not sweep under the rug. Because if your entire life is measuring sample means, fine, you're never going to need to think about this very much and life will be nice. But when one fine day you decide to do something more complex, these are the land mines that you shouldn't really ignore.

> These kinds of simple estimation problems and the associated statistical > tests account for probably 90% of statistical practice. Dismissing them as > contrived is silly.

Whether it's 90% is totally dependent on the types of problems you do. I don't mean to dismiss them, you're right for many problems MLE is just fine. I meant to illustrate that "unbiased" comes with many caveats, and that in many real scenarios flat priors are not ok.

> More generally, MLE estimates are always (under regularity conditions) > asymptotically unbiased even if not unbiased for a finite sample. This means > that the amount of bias decreases to zero as the sample size increases, no > matter what the parameterization is.

Is this not true of the MAP for most priors? Gaussian/Laplace priors will have this property too, since priors become asymptotically less important the more data you have. If your prior is zero over some of the support, you're out of luck but this doesn't strike me as a good argument for MLE > MAP or for using flat priors everywhere. When we have infinite data, sure, priors are irrelevant, but we live in the real world where data is not infinite.

> Finally, there is very often a natural parameterization for any given > problem. If you're interested in the arithmetic mean of a population, > there's no reason to use a log-scale parameterization.

Sure, agree that parametrization isn't a problem a lot of the time, but it _is_ something important to be mindful of, and this points towards, again, not forgetting that you are always using a prior and that you should think about whether or not that prior makes sense.

> Why worry about bias in other parameterizations when you can just use the > natural parameterization, where the estimator is unbiased? Again, I don't > think such scenarios are contrived: a very large proportion of statistical > analyses deal with simple measurements in Euclidian (or very nearly > Euclidian--we can typically ignore, for example, relativistic effects) > spaces: real world dimensions, time, etc.

Yea, I mean sure: for easy problems, parametrization is obvious. That's kind of tautological. But sometimes it's not obvious, or sometimes for computational reasons you need to work with a log(theta) instead of theta, etc. If you're a frequentist and you're thinking life is great because you don't need to worry about priors, you're wrong and sooner or later you will get into trouble; be it a parametrization issue or something else, priors are not just something you can completely ignore. It's like saying "I always drive without looking in my rearview mirror" \-- ok, great, you will be fine a lot of the time, but eventually one day you will change lanes on the highway at the exact wrong time, and you will really regret your habit of not looking in your mirror.

> If you're a Bayesian and very concerned about parameterization effects you > can also use a Jeffrey's prior, which is parameterization-invariant. > Notably, for the mean of a Normal distribution, the Jeffrey's prior is... > the flat prior!

Yep, totally agree, I have no problem with Jeffrey's priors (when they make sense), and that's all well and good. Just to clarify: I am _not_ saying "don't use flat priors" \-- flat priors are extremely reasonable and a good idea in many cases, my point is flat priors are still priors, and you are still making a statement by using them: "lets assume all possible values of theta are equally likely a priori". Sometimes we don't really _believe_ that but it's useful to see the implications of making this assumption. But sometimes priors are extremely important (e.g. we want a time-dependent measurement of a poisson rate, like conversions per dollar of ad spend, and conversions are relatively rare: priors are your friend here, e.g. a GP prior = Cox process or something else, even if this prior is an operational assumption)

> Yes and no. The Bayesian and frequentist approaches answer different > mathematical questions, but they are used by humans to answer the same human > questions, such as "do these two populations have the same mean?"

Yes, agreed.

> Indeed this is one of the primary reasons Lindley's paradox arises: the > Bayesian model comparison (using marginal likelihoods or Bayes factors) gets > tricked by the diffuse prior, while the frequentist model comparison (using > null hypothesis testing) does not.

Ah lord, but this is a terrible justification for using null hypothesis rejection: we're almost always choosing a very simplistic distribution (e.g. Gaussian) to do this, and reducing the question to "we reject H0 because its very unlikely" is part of the reason why there's a replication crisis in e.g. social sciences, because they're taught this simplistic picture without any of the necessary nuance ('here are the assumptions we make, and under these assumptions + H0, it is a little bit unlikely that we would have observed x'). That's a recipe for disaster. Is it not much better to discuss the full posterior, "degrees of belief" and to be explicit about all of our uncomfortable prior assumptions? I prefer Bayesian model selection over null hypothesis rejection 100% of the time, especially because "Bayesian model selection" is the only logical way to do model selection, the only caveat is that it depends on reasonable prior assumptions and these are the hard part (but again, at least it is explicit!).

Also, the Lindley's "paradox" example certainly seems contrived: we believe there's a 50% chance that p = 0.5 _exactly_?? I just don't understand that type of analysis. Come up with a prior, derive your posterior, decide the answer to your question yourself (what is the chance that p=0.5 exactly? well, it's exactly 0%. How much more likely is it that p=0.5036 vs p=0.5? That's a better question...). By contrived, I mean that it appears designed to exploit the fact that Bayesian stats will automatically prefer simpler models, especially one with 0 degrees of freedom that is relatively close to the right answer, but that's a Good Thing (TM).

Both frequentist stats and Bayesian stats are easy to abuse: Bayesian stats gives a false sense of comfort because people don't worry enough about their choice of prior, _but at least Bayesian stats is explicit about the prior!_. I won't say that hypothesis testing is complete garbage, but it is quite dangerous and frankly dishonest to reduce things to a p value and pretend that's the end of the discussion.

<comment><USER7> > But when one fine day you decide to do something more complex, these are the > land mines that you shouldn't really ignore.

> in many real scenarios flat priors are not ok.

> eventually one day you will change lanes on the highway at the exact wrong > time, and you will really regret your habit of not looking in your mirror.

Can you give some examples where frequentists hit these alleged flat-prior landmines? I am admittedly a Bayesian by training, not a frequentist, so perhaps it's just my ignorance showing, but I'm not aware of any such situations.

Frequentist statistics generally relies on performance guarantees (bounds on the false positive error rate for tests, in particular, and coverage for confidence intervals) which are derived under the lack-of-explicit-prior, so as far as I can tell they should be doing fine. I'd be interested in seeing examples where frequentist analyses fail because of the (implicit) flat prior.

> we're almost always choosing a very simplistic distribution (e.g. Gaussian) > to do this

The Gaussian distribution is a marvelous thing. The central limit theorem is, in my humble opinion, one of the most beautiful and surprising results in mathematics.

> Is it not much better to discuss the full posterior, "degrees of belief" and > to be explicit about all of our uncomfortable prior assumptions?

Perhaps I'm just cynical, but I'd say probably not. A Bayesian decision process is still a decision process and still subject to all the problems that the frequentist decision process (null hypothesis significance testing) is subject to: inflated family-wise error rates, p-hacking (except with Bayes factors rather than p-values), publication bias, and so on. At best getting everyone to do Bayesian analyses might be roughly equivalent to getting everyone to use a lower default significance threshold, like 0.005 instead of 0.05 (which prominent statisticians have advocated for).

> I prefer Bayesian model selection over null hypothesis rejection 100% of the > time, especially because "Bayesian model selection" is the only logical way > to do model selection, the only caveat is that it depends on reasonable > prior assumptions and these are the hard part (but again, at least it is > explicit!).

Sadly there's a trap in Bayesian model selection (often called Bartlett's paradox, though it's essentially the same thing as Lindley's paradox) which can be difficult to spot. No names out of respect to the victim, but several years ago I saw a very experienced Bayesian statistician who has _published papers about Lindley 's paradox_ fall prey to this. Explicit priors didn't help him at all. He would not have fallen into it if he had used a frequentist model selection method, though there are other problems with that.

> Also, the Lindley's "paradox" example certainly seems contrived:

And here we are again calling a statistical test that thousands of people do every day "contrived." You already know how I feel about that.

Yes, it's a very simple example, because that helps illustrate what's happening. Lindley's paradox can happen in arbitrarily complex models, any time you're doing model selection.

> By contrived, I mean that it appears designed to exploit the fact that > Bayesian stats will automatically prefer simpler models, especially one with > 0 degrees of freedom that is relatively close to the right answer, but > that's a Good Thing (TM).

Preferring simpler models is not exactly what's going on in Lindley's paradox, at least not the way that most people talk about Bayes factors preferring simpler models (e.g. by reference to the k*ln(n) term in the Bayesian Information Criterion). The BIC is based on an asymptotic equivalence and drops a constant term. That constant term is actually what is primarily responsible for Lindley's paradox, and has only an indirect relationship to the complexity of the model.

<comment><USER8> Hi, sorry for the delayed response!

> Can you give some examples where frequentists hit these alleged flat-prior > landmines? I am admittedly a Bayesian by training, not a frequentist, so > perhaps it's just my ignorance showing, but I'm not aware of any such > situations.

You're probably right: I myself am also a Bayesian by training as you can probably guess but went through the usual statistics education from a frequentist standpoint, and once I learned Bayesian statistics it was almost an epiphany and much more intuitive and understandable than the frequentist interpretation (but that's just me). In all honesty, I think good frequentist statisticians and good Bayesian statisticians have nothing to worry about, since both should know exactly what they are doing and saying as well as the limitations of their analysis.

I wouldn't put myself in either the "good frequentist" or "good Bayesian" categories, by the way, I am just an imperfect practitioner, but I think that's the case for most people. My argument against frequentist statistics for the masses is a practical one: I found myself getting into much more trouble and having much less insight into what I was doing when I had a frequentist background than I did when doing things from a Bayesian standpoint, and I see many imperfect frequentist statisticians like myself running into the same problems I used to (mostly ignoring priors when they shouldn't or thinking a flat prior is always uninformative, etc.), but I admit that is a wholly subjective experience. I never once thought about priors before learning Bayesian stats, and I find many people I meet with a frequentist background also forget the significance of priors because they also are imperfect practitioners.

> Frequentist statistics generally relies on performance guarantees (bounds on > the false positive error rate for tests, in particular, and coverage for > confidence intervals) which are derived under the lack-of-explicit-prior, so > as far as I can tell they should be doing fine. I'd be interested in seeing > examples where frequentist analyses fail because of the (implicit) flat > prior.

Yea, I totally agree, I just find that statistics is important in many more contexts than just this. While you can do this sort of thing from a Bayesian perspective (using Jeffery's priors or whatever the situation calls for), in my experience frequentists have a tough time departing from this type of analysis once they start diving into areas where priors are important (unless they are also familiar with Bayesian stats!)

> The Gaussian distribution is a marvelous thing. The central limit theorem > is, in my humble opinion, one of the most beautiful and surprising results > in mathematics.

Agree with you, but CLM doesn't always help you. You may not always be interested in the statistics of averages in the limit of many samples. I agree when you _are_ doing this, CLM is a godsend.

> Perhaps I'm just cynical, but I'd say probably not. A Bayesian decision > process is still a decision process and still subject to all the problems > that the frequentist decision process (null hypothesis significance testing) > is subject to: inflated family-wise error rates, p-hacking (except with > Bayes factors rather than p-values), publication bias, and so on. At best > getting everyone to do Bayesian analyses might be roughly equivalent to > getting everyone to use a lower default significance threshold, like 0.005 > instead of 0.05 (which prominent statisticians have advocated for).

I disagree here. Discussing the full posterior forces you _not_ to reduce the analysis to a simple number like a significance threshold, and to acknowledge the fact that there are actually a wide range of possibilities for different parameter values, and it's important to do this when your posterior isn't nice and unimodal, etc. I don't disagree that sometimes (well, many times) the significance threshold is all you really care about (e.g. "is this treatment effective, yes or no"), but that's still a _subset_ of where statistics is used in the wild. E.g. try doing cosmology with just frequentist statistics (actually, do not do that, you may be physically attacked at conferences).

But again, I want to emphasize that doing Bayesian stats can _also_ give you a false sense of confidence in your results, I don't mean to say Bayesians are right and frequentists are wrong or anything, I just mean to say that sometimes priors are important and sometimes they aren't, and I personally find that have an easier time understanding when to use different priors in a Bayesian framework than a frequentist one.

> Sadly there's a trap in Bayesian model selection (often called Bartlett's > paradox, though it's essentially the same thing as Lindley's paradox) which > can be difficult to spot. No names out of respect to the victim, but several > years ago I saw a very experienced Bayesian statistician who has published > papers about Lindley's paradox fall prey to this. Explicit priors didn't > help him at all. He would not have fallen into it if he had used a > frequentist model selection method, though there are other problems with > that.

Like you say, there are problems with both approaches, but my point is that when the prior is explicit, we can all argue about its effects on the result or lack thereof. Explicit priors don't "help" you, but they force you to make your assumptions explicit and part of the discussion. If your only ever using flat priors, it's easy to forget that they're there

> And here we are again calling a statistical test that thousands of people do > every day "contrived." You already know how I feel about that.

I don't mean to be flippant about it or dismissive, I mean exactly what I said:

contrived: "deliberately created rather than arising naturally or spontaneously."

What test is it in lindley's paradox are you referring to when you say thousands of people use everyday? Just the null rejection? Or is there another part of it you're referring to?

> Yes, it's a very simple example, because that helps illustrate what's > happening. Lindley's paradox can happen in arbitrarily complex models, any > time you're doing model selection.

My point isn't that it's simple, my point is that it's incredibly awkward and unrealistic and not representative of how a Bayesian statistician would answer the question "is p=0.5" which is a very strange question to begin with. The "prior" here treats it as equally likely that p=0.5 exactly and p != 0.5, which if that's your true assumption, fine, but my point is that this is a very bizarre and unrealistic assumption. Maybe it seems realistic to a frequentist but not to me at all. If someone was doing this analysis, I would _expect_ to get a weird answer to a weird question.

> Preferring simpler models is not exactly what's going on in Lindley's > paradox,

Exactly! I'm not sure _what_ is going on in Lindley's paradox to be honest; I don't understand the controversy here: the question poses a very strange prior that seems designed to look perfectly reasonable to a frequentist but not to a Bayesian. But I suppose this is an important point about the way priors can fool you!

> at least not the way that most people talk about Bayes factors preferring > simpler models (e.g. by reference to the k _ln(n) term in the Bayesian > Information Criterion). The BIC is based on an asymptotic equivalence and > drops a constant term.

I'm with you so far, and BIC is a good asymptotic result, but I'm talking about the full solution here (which is rarely _practical*), that doesn't drop the constant term

> That constant term is actually what is primarily responsible for Lindley's > paradox, and has only an indirect relationship to the complexity of the > model.

I mean I think we're splitting hairs here? Maybe? My point was that Bayesian model selection won't make up for a strange prior, but given the right priors, Bayesian model selection just makes sense to me. But again, this is the important limitation of most Bayesian analyses: the prior can do strange things, especially the one used in the Lindley's paradox example in the Wikipedia page.

But honestly, if you think I'm missing some important part of Lindleys' paradox, please do elaborate, I have not heard of this before you mentioned it but I still am confused as to why this is considered something "deep" but I assume that just means I am missing something important.

</comment></comment></comment></comment></comment></comment></comment></comment><comment><USER9> Just a note that you can interpret regularization as placing a prior on weights. L2 regularization is a Gaussian prior, and L1 is a Laplacian prior. I.e. this is doing Bayesian statistics rather than an arbitrary hack to improve predictions.

Elements of Statistical Learning is firmly in the frequentist world from what I recall, so this might not be discussed in that book.

<comment><USER10> This is discussed in Chapter 1 (or maybe 2), I think, which suggests to me that the author should probably read a little bit more of it.

Mind you, it's a wonderful book, and I recommend that people should just read it in general (you may not be able to do very many of the exercises, but it's still worth it).

</comment></comment><comment><USER11> The author makes it sound like statistics is this grand beautiful mathematical edifice and ML is just a bunch of number crunching with computers. That contrast is just unfair; a huge portion of stats is just made up of hacks and cookbook recipes. Statistics has probably done more damage to the world than any other discipline, by giving a sheen of respectability to fake science in fields like nutrition, psychology, economics, and medicine.

I'm particularly annoyed by the implication that statisticians have better understanding of the issue of overfitting ("why having p >> n means you can't do linear regression"). Vast segments of the scientific literature falls victim to a mistake that's fundamentally equivalent to overfitting, and the statisticians either didn't understand the mistake, or liked their cushy jobs too much to yell loudly about the problem. This is why we have fields where half of the published research findings are wrong.

<comment><USER12> _> Statistics has probably done more damage to the world than any other discipline, by giving a sheen of respectability to fake science in fields like nutrition, psychology, economics, and medicine._

This seems really unfair. You can misuse statistics, but it's an extremely powerful tool when it's _properly used and understood._ Most powerful tools can be misused - you can write terrible code and (try to) publish bad mathematics too. But much of modern science would be intractable without statistics; including physics, chemistry, biology and applied math, because we'd be otherwise unable to draw reasonable conclusions from anything less than a totality of data.

As someone with a graduate education in probability snd statistics, I think it's fair to lay some of the blame for the reproducibility crisis at the feet of statisticians because of poor education. Statisticians should accept at least some responsibility if their students in non-math majors graduate without understanding the material, for sure.

But that being said, it should definitely be noted that actual statisticians have been talking about this crisis _for decades._ Statisticians have basically always known that there's nothing magical about the 95% significance threshold _p <= 0.05_, for example. And for the most part, it's not statisticians who are causing the bad science to occur. Rather it's a problem of non-statisticians using statistics without (qualified) peer review that they can't be expected to do correctly if it's not their core competency.

In my opinion it's something of a philosophical problem - many fields and journals are only realizing now that it's unreasonable to expect a e.g. professional psychologist to also be an expert statistician. Having a dedicated statistician - instead of another psychologist who hasn't reviewed the material since their upper undergrad course - is a giant leap forward in catching bad stats in new research.

</comment></comment><comment><USER13> If this topic interested you, it may also be worth reading Leo Breiman's "Statistical Modeling: The Two Cultures" from 2001. [https://projecteuclid.org/download/pdf_1/euclid.ss/100921372...](https://projecteuclid.org/download/pdf_1/euclid.ss/1009213726)

</comment><comment><USER14> The author appears to misunderstand the main difference between statistics and ML. Let me cite him:

> my gut reaction is to barf when someone says "teaching the model" instead of > "estimating the parameters."

Typical statistics work is to use a known good model and estimate its parameters. Typical machine learning work is to think back from what task you want it to learn and then design a model that has a suitable structure for learning it.

For statistics, the parameters are your bread and butter. For machine learning, they are the afterthought to be automated away with lots of GPU power.

A well-designed ML model can have competitive performance with randomly initialized parameters, because the structure is far more important than the parameters. In statistics, random parameters are usually worthless.

<comment><USER15> "Typical statistics work is to use a known good model and estimate its parameters. [...] For statistics, the parameters are your bread and butter" Ever heard of non-parametric statistics?

"For machine learning, they are the afterthought to be automated away with lots of GPU power." You seem to reduce statistics to undergraduate statistics and machine learning to Deep Learning.

"A well-designed ML model can have competitive performance with randomly initialized parameters, because the structure is far more important than the parameters. In statistics, random parameters are usually worthless." This is blatantly false see Frankle & Carbin, 2019 on the lottery ticket hypothesis.

<comment><USER14> Yes, I have reduced both statistics and ML to the subsets that are usually used when working in the field, because the blog post was about employment options.

I would wager that people doing non-parametric statistics are both very rare and most likely advertise themselves as machine learning experts, not as statisticians.

As for the random network, I was referring to [https://arxiv.org/abs/1911.13299](https://arxiv.org/abs/1911.13299) and I have seen similar effects in my own work where a new architecture was performing significantly better before training than the old one was after training.

If you want a generally agreed upon example, it'd be conv nets with a cost volume for optical flow. What the conv nets do is to implement a glorified hashing function for a block of pixels. That'll work almost equally well with random parameters. As the result, PWC-Net already has strong performance before you even start training it.

<comment><USER16> >As for the random network, I was referring to [https://arxiv.org/abs/1911.13299](https://arxiv.org/abs/1911.13299) and I have seen similar effects in my own work where a new architecture was performing significantly better before training than the old one was after training.

The fact that a dense neural network with 20M parameters performs equally well as a model with 20M random values and 20M _bits_ worth of parameters means nothing more than that the parameter space is ridiculously large.

The only models that perform well given random parameters are those that are sufficiently restrictive. Like weather forecasts, where perturbations of the initial conditions give a distribution of possible outcomes. Machine learning models are almost never restrictive.

<comment><USER14> Of course, I agree with you that the parameter space is ridiculously large. But sadly, that's what people do in practice. And with 20mio, their example is still small in comparison to GPT-3 with 175 billion parameters.

I disagree with you on the restrictive part. Those ML models that are inspired by biology tend to be restrictive, the same way that the development of mammal brains is assumed to be restricted by genetically determined structure. Pretty much all SOTA optical flow algorithms are restricted in what they can learn. And those restrictions are what makes convergence and unsupervised learning possible, because the problem by itself is very ill posed.

</comment></comment></comment></comment></comment><comment><USER17> It seems like the 'scientist' part of 'Data scientist' might cause this sort of misunderstanding.

There's a lot more 'engineering' and fiddling going on than any type of 'science-y' stuff it seems.

<comment><USER18> At one point, the word "scientist" in "data scientist" was used to distinguish between people who took the time to develop domain expertise from statistical consultants who applied standard methodologies without reference to what the data was or where it came from.

</comment></comment><comment><USER19> This is one of the clearest explanations I've read on the difference between traditional Statistics and Machine Learning.

<comment> yt-sdb A few years ago, Michael I. Jordan did an AMA on Reddit and discussed this distinction as well. Maybe you'll find it interesting as a counterpoint [1].

[1] [https://old.reddit.com/r/MachineLearning/comments/2fxi6v/ama...](https://old.reddit.com/r/MachineLearning/comments/2fxi6v/ama_michael_i_jordan/ckelmtt/)

<comment><USER20> Perfect link. He's the ideal commentator.

</comment></comment></comment><comment><USER21> > Traditionally, it's a cardinal sin in academia to use parameters like these > because you can't say anything interesting about the parameters, but the > trick in machine learning is that you don't need to say anything about the > parameters. In machine learning, your focus is on describing y-hat, not > β-hat.

This kind of philosophy will cause future generations to see machine learning as something worse than a fad, almost as something in between a fad and crank science. If this encapsulates how all (generally speaking) machine learning operates then we will enter big trouble, if we have not already.

> In machine learning, bad results are wrong if they catastrophically fail to > predict the future, and nobody cares much how your crystal ball works, they > only care that it works.

This has moved from Cargo Cult Science into numeromancy. It's leveraging the occult (=hidden, incomprehensible parameters) for predicting the future. Because there exist no first principles, nothing can be further interpreted. Only more of the occult can be leveraged in order to make more predictions not amenable to interpretation, which will in turn require MORE occult to make MORE inscrutable predictions, until the heat death of the universe....

And appealing to 80's AI (neural networks) as case precedence further harms the author's case. If ML operates like how AI neural network technology went, then this whole rigamole will go tits up by case precedence as well.

<comment><USER22> Have you considered that useful predictive models for reality are simply irreducible to being comprehensible by swollen savannah monkey brains? It's not magic. It simply cannot both be explained to a human and be worth anything.

<comment><USER23> Can a swollen savannah monkey brain drive a car safely on public roads, using nothing but its attached eyes and ears? Yes (most of the time). Can AI do it? Not yet...

<comment><USER24> Sure you can drive, but can you explain in details HOW you do it? Probably not and that's why a machine cannot do it yet (until it learns by itself, as we all did).

</comment></comment></comment></comment><comment><USER25> This is really nice write-up, much better than yet-another-skin-deep-sklearn- tutorial. Skimming some other posts of the author, his domain understanding looks quite impressive to me.

(Judging his writing as an ex-academic econometrician Data Scientist, about to be rebranded to Machine Learning Engineer by his megacorp employer, the author appears to have more insight in the field than many a PhD professional Data Scientist.)

<comment><USER26> It is basically the standard take of a statistician who tries to understand machine learning. "It's yhat, rather than betahat" is a common slogan.

</comment></comment><comment><USER27> Data science always seemed to me to be a profoundly boring job. Can anyone shed some light on what you find the most fascinating about it?

<comment><USER28> I can tell a story. I used to work for a HVAC installation company, pretty small in terms of staff but we subcontracted a lot. Initially brought on as a mechanical engineering intern, but moved on to sales engineering when I found an interesting statistical relationship.

A large factor in quotes to clients was the underlying cost of air conditioning equipment in our niche, and often a game of sales intel was played between suppliers and competing contractors (like us) for a given job site. Favorites were picked, and we could get royally screwed in a quote, losing the sale to the end-customer.

Fortunately, we had years of purchasing information. It turns out that as varied as air conditioners are across brands and technical dimensions, when you have years of accounts' line items and unused quotes, you don't get a dimensionality issue. Since we operated in a clear-cut niche, this was especially true. We could forecast, within a margin of error of two per cent, exactly what any of our suppliers would quote us (or our competitors!) for a job long before they could turn it around. Huge strategic advantage.

This was the watershed moment for me when I realized even basic multiple linear regression was a scarily powerful tool when used correctly.

<comment><USER27> That is cool when you put it like that. Uncovering hidden relationships that are useful sounds romantic. Thanks for posting

<comment><USER18> And incredibly boring. The usual estimate is that data science is 80% data wrangling: finding, collecting, and cleaning up data. The term "data scientist" replaced "data miner", because miners are looking for gold. Scientists are obsessed with finding out the nature of reality, gold or mud. They will do seriously boring stuff to set things up so that reality is revealed.

<comment><USER29> It is only boring if you do it the boring way.

If the data cleaning is follows standard patterns, you should already have scripts to offload that kind of work to. If not, then there some incredibly interesting decisions hidden underneath. Like in text: Should character casing be preserved ? What should be the unit of representation (word/character) ? How should data be filtered: Quality vs quantity trade-off ?

All of those are non-trivial questions which involve a lot of thought to reason through. You are correct that the modelling is only a small part of DS's day to day job.

But, the rest of it is boring in the same way that coding is boring. It is doesn't involve some grand epiphanies or discoveries, but there is joy similar to the daily grind of "code -> get bug/ violate constraints -> follow trace/problem -> figure a sensible solution" that a lot of software engineers love.

</comment></comment></comment></comment></comment><comment><USER30> Love the article. It inspired me to make a follow-up note on one of the memes: [https://win-vector.com/2020/07/03/data-science-is-not-statis...](https://win- vector.com/2020/07/03/data-science-is-not-statistics-done-wrong/)

</comment><comment> ur-whale From the article: " In statistics, bad results can be wrong, and being right for bad reasons isn't acceptable. In machine learning, bad results are wrong if they catastrophically fail to predict the future, and nobody cares much how your crystal ball works, they only care that it works."

<comment><USER18> Is that a typo? It makes more sense as "good results can be wrong, and being right for bad reasons isn't acceptable"

</comment></comment><comment><USER23> Off topic, but if someone uses "gut reaction" and "barf" in the same sentence, I'm tempted to think they really mean it literally...

</comment><comment><USER31> There is a big difference between ML practitioners and professional statistians. Former commonly are unaware[1] of a rich set of statistical biases and ways to tackle or mitigate them.

[1] [https://towardsdatascience.com/survey-d4f168791e57](https://towardsdatascience.com/survey-d4f168791e57)

</comment><comment><USER32> Can someone elaborate on what is meant by 'estimating a parameter with a natural experiment'? This seems to be the key difference but I don't quite get how this would work. What would be your input data and how would the process differ from an ML approach?

<comment><USER33> A natural experiment is an experiment (an AB-test if you will) that occurs by chance rather than conscious design. For example, two neighboring countries contemplate banning smoking in restaurants, but in one the bill fails with 49% of the vote, in the other the ban goes through with 51% of the vote. It's not perfect, but you could argue that these countries can now be used to estimate the effect of a smoking ban on mortality and health in a way that is almost as good (but not quite as good) as a randomized clinical trial, whereas you can't just compare two arbitrary countries with differential rates of smoking, because they might be different on so many other counts as well and there is no pre-intervention data to serve as a baseline.

More broadly, ML does not really answer questions like "was this death caused by smoking, or rather by a hundred other things associated with smoking like lower income and bad health insurance?", though it is excellent at predicting who is likely to die prematurely. So it's great for prediction, but not so great if you want to learn more about the underlying structure of a phenomenon.

Statisticians are sometimes surprised to see so much interest in machine learning given that its view of the world is not open to inspection (though there's [https://github.com/interpretml/interpret](https://github.com/interpretml/interpret) I guess) so _we_ as humans learn nothing, but it turns out that in many cases we really don't care all that much about the underlying mechanism, as long as we can make accurate predictions.

</comment></comment><comment><USER34> A pox in both there houses.

I kinda want to ban this stuff for economies like ours. Think about it, we have many entrenched inefficient separate actors all engaging in nonsense alchemy. Surely this ruins the convergence to economic equilibrium.

</comment><comment><USER35> Well, if you look at machine learning from the point of view of data science it's inevitable to be confused about its relation to statistics, but machine learning is a sub-field of AI and statistical techniques are only one tool in its toolobx. Statistical techniques have dominated the field in recent ish years but much work in machine learning has historically used e.g. Probabilistic Graphical Models or symbolic logic as the "model" language. e.g. one of the most famous and well-studied classes of machine learning algorithms, decision tree learners, comprises algorithms and systems that learn propositional logic models, rather than statistical models.

Tom Mitchell defined machine learning as "the study of computer algorithms that improve automatically through experience"[1]. This definition does not rely on any particular technique, other of course than the use of a computer. Even the nature of "experience" doesn't necessarily need to mean "data" in the way that data scientists mean "data"\- for example, "experience" could be collected by an agent interacting with its environment, etc.

Unfortunately in very recent years, since the big success of Convolutional Neural Networks in image classification tasks, in 2012, interest for machine learning has shifted from AI research to ... well, let me quote the article:

>> Or you can start reading TESL and try to get some of that sweet, sweet machine learning dough from impressionable venture capitalists who hand out money like it's candy to anyone who can type a few lines of code.

I suppose that's ironic. But the truth is that "machine learning" has very much lost its meaning as industry and academia is flooded by thousands of new entrants that do not know its history and do not undestand its goals. In that context, it makes sense to have questions along the lines of "what is the difference between statistics and machine learning", which otherwise have a very obvious answer.

___________

[1] [https://www.cs.cmu.edu/~tom/mlbook.html](https://www.cs.cmu.edu/~tom/mlbook.html)

The excerpt I quote is an informal definition. The wikipedia article on machine learning has a more formal definition:

[https://en.wikipedia.org/wiki/Machine_learning#History_and_r...](https://en.wikipedia.org/wiki/Machine_learning#History_and_relationships_to_other_fields)

<comment><USER36> Tom Mitchell book is still a great book to understand what Machine Learning is about

</comment></comment><comment><USER37> this reads like something 5 or 6 years old

</comment><comment><USER38> The author's pie chart showing data science to be 60% data manipulation is accurate. The biggest gap between good and bad data scientist is their comfort level with data wrangling. When interviewing candidates for data science positions, one of the simplest questions is to have them sort a 1 GB tab- delimited file.

1\. Poor candidates will try to open the file in Excel.

2a. Marginal candidates will use R or Stata.

2b. Okay candidates will use a scripting language like Python.

3\. Good candidates will use Unix sort.

To my knowledge, there are no university courses teaching the Unix toolchain and it remains very much a skill learned through practice.

<comment><USER39> Not sure why you think a candidate who uses R is inferior to one who uses Python?

Also, a really good candidate should use the right tool for the job, so if you expect them to use Unix sort you should somehow imply a situation where that is the best approach.

<comment><USER40> > if you expect them to use Unix sort you should somehow imply a situation > where that is the best approach.

I think the implied question is whether the interviewee is aware of the fact that trying to load a 1 GB text file could use up too much RAM space of the system. Unix sort is arguably the most memory efficient among the 4 choices there. It depends on the amount of available resources (which was not specified), and some companies might be willing to casually let people use 100 GB machines, though.

<comment><USER39> That is a fair point. A good script would be to move towards this scenario of more data than RAM and see what the candidate comes up with.</comment></comment></comment></comment>


Apple Card can be damaged by wallets and jeans - <AUTHOR> https://www.bbc.co.uk/news/technology-49435687 <comment><USER2> A few weeks before launch, Steve Jobs replaced screens on the iPhone because they would get scratched too easily. You don't see this level of care anymore.

> For weeks, he had been carrying a prototype of the device in his pocket.

Mr. Jobs angrily held up his iPhone, angling it so everyone could see the dozens of tiny scratches marring its plastic screen, according to someone who attended the meeting. He then pulled his keys from his jeans.

People will carry this phone in their pocket, he said. People also carry their keys in their pocket. "I won't sell a product that gets scratched," he said tensely. The only solution was using unscratchable glass instead. "I want a glass screen, and I want it perfect in six weeks."

[https://www.nytimes.com/2012/01/22/business/apple-america- an...](https://www.nytimes.com/2012/01/22/business/apple-america-and-a- squeezed-middle-class.html)

<comment><USER3> They showed Steve!

Now phones can't fit in pockets, especially with other items.

<comment><USER4> Just thought about this comment now. I kept all my iPhone in pocket together with keys never cared for scratches and never seen any. On iphone4, iphone5, iphone6. Now im typing this message on horribly scratched iphone8 screen done with same set of key that i carried my old iphone in the same pocket. That's sad. And few days ago i just discovered my old iphone4. Soeaker on it still plays smooth with strong bass. Speaker on my iphone8 sounds like cheap crap already after 6 months of ownership. Darn things really went south without Jobs around.

<comment><USER5> If you don't mind me asking, why did you change from that iPhone 4/5/6 which withstood those keys so well and still sound good? I'm still using a device from the iPhone 4 era - not an iPhone though - which still works fine and continues to be useable as an 'outdoor' phone due to its smaller size and the fact that it is waterproof. I was only forced to get a newer device about a year ago due to the fact that the Swedish electronic ID app - without which it is getting harder and harder to interact with many government services - stopped supporting Android 4.4, otherwise that phone would have remained my 'daily' device.

</comment></comment></comment></comment><comment><USER6> Apple Card may suddenly accelerate to dangerous speeds. If Apple Card begins to smoke, seek shelter and cover head. Do not taunt Apple Card.

<comment><USER7> Still legal in 13 states, just like happy fun ball!

<comment><USER8> I would rather have a Happy Fun Ball Card, than this ..

</comment></comment></comment><comment><USER9> Press release: Belkin releases CCARDFORCE InvisiGlass Ultra Screen Protection for apple card. $399.

THE NEXT GENERATION OF CARD PROTECTION InvisiGlass Ultra delivers screen protection like no other. Ultra-premium glass is chemically engineered to be even stronger than tempered glass. This added strength allows it to be precision-cut so thinly that it delivers a flawless purchasing experience. The top layer of the card protector is proven in testing to provide the highest level of scratch protection available, to keep your Apple Card looking and functioning like the day you brought it home.

</comment><comment><USER10> I'm pretty sure Apple is just fucking with us at this point.

\- They designed a $1000 phone that shatters when it's dropped.

\- A keyboard that fails after a single crumb.

\- A card that fails in jeans and leather.

I'm pretty sure they're aware of the real world, and intentionally building in the opposite direction as part of some great cultural experiment.

<comment><USER8> Honestly, I'm coming to the conclusion that a lot of the designers of these kinds of devices do not in fact live in the real world.

But then I wonder if the world I define as real, is really not just make- believe..

<comment><USER10> You mean you don't commute to work in a private air-conditioned bus to work in an all-white glass UFO?

</comment></comment></comment><comment><USER11> Are there cards that don't?

I don't remember specifics as to what was making this card special (think it had something to do with financing), but I'm pretty sure the physical cards durability wasn't one of them (would've probably eye-rolled pretty heavily if so).

<comment><USER12> No, but there are no other cards produced by Apple. Now that Apple has a card, people have to complain, because Apple.

<comment><USER13> Now that Apple has a card, people who complain about its inadequacies have to be ignored, because Apple.

</comment></comment></comment><comment><USER14> I think the issue here is the definition of "damage." Think about this from a design company's perspective. They've designed a Beautiful Looking Credit Card. A credit card typically takes scuffs, scratches, wear and even tear from everyday use - wallets, pockets, bags and purses. You put the Beautiful Looking Credit Card in those places, and it'll get scuffed, scratched, worn, etc, just like all the others. But now, because it's designed as a Beautiful Looking item, customers are more likely to complain.

<comment><USER15> I mean, they made it to be "resistant". Now it's not...

 _The Apple Card is a relatively plain matt white credit card made of titanium, which was designed to stand out against other credit cards._

</comment></comment><comment><USER16> I wish they had some photos of the damaged cards. It's not like they have to hide the cc#. I have one coming soon and would like it to stay nice and fresh. I remember getting my first debit card from a local bank back in the 90s and it came with a protective paper sleeve which most people promptly discarded. That sounds like a simiple solution to this problem.

amazon has a pretty big selection. [https://www.amazon.com/s?k=credit+card+sleeve](https://www.amazon.com/s?k=credit+card+sleeve)

<comment><USER17> Here's an example, apparently after two months of use.

[https://i.imgur.com/Bg8yOKT.jpg](https://i.imgur.com/Bg8yOKT.jpg)

<comment><USER18> That looks pretty much like any other card of mine after a couple of months.

In other words, not news?

<comment><USER19> Perhaps "white" is the last color you want to choose for the same reason.

</comment></comment></comment></comment><comment><USER20> I have a leather wallet. What material should my wallet be made of?

Apple's page says: Store your titanium Apple Card in a wallet, pocket, or bag made of soft materials.

What is soft wallet material other than leather?

<comment><USER9> The apple card dock. Its supposed to be amazing, but they are having technical issues with overheating so the release is delayed.

</comment></comment><comment><USER17> "Damage" is a bit of an overstatement.

The white will get discolored. I've got an all-white card from Simple, and yep, it's all scuffed up from my wallet. Who cares?

<comment><USER21> People who have an inverted sense of prestige from the use of credit cards. That's who.

It's really not a health or even all that smart community once you start thinking about it. It's almost like the Apple Card is a kind of peak travesty ... a card made of titanium (think about that), that has cleaning and care instructions, that has such sharp edges that it is cutting people; not to mention that it's a credit card, a signal that would tell people in a healthy society that "this person does not know how to manage money and therefore has to borrow money for basic daily things". The use of a credit card for anything but mid tier level purchases or where the secondary/ancillary benefits are useful, should make one not trust such a person for their inability to manage their finances. Think about it, credit to buy dinner and/or drinks. It should be a clear sign of untrustworthiness.

Yes, I realize that credit cards offer and obscure the reality of the matter through ancillary benefits that I also utilize on occasion. Those are not there though as the primary purpose of the card, they are there to obfuscate and obscure and mask through justifications and rationalizations, the use of the real reason that the financial institutions want you to use the cards, the credit feature that will rack up profits that are then also rolled into the cost of the goods you purchase. People don't realize that every credit card purchase drives up the cost of goods and services, especially due to the contractual agreements of payment processors that prevent different prices for card over cash use.

At the heart of it all, the whole credit card industry is a kind of con job monopoly by the very definition of the term: "an act or instance of duping, swindling, or persuading by deception." or a "trick of confidence", imbuing the target with confidence (in this case by tapping into the odd apple fan boy confidence through the shiny titanium apple card) in order to dupe them into using the credit card that will result in interest and fees and conversion arbitrage profits over n% of charges and balances; and all that through the forced monopoly of contractual limitation of competition of cash transactions.

I don't know about you, but if the free market/competition were not limited by this cartel of colluding payment processors (MC/Visa, mostly) I would use cash/debit if it meant I could save money (likely more than the cash back amount offered on credit cards). But that's precisely why they limit competition by cash

<comment><USER22> People use credit cards for almost everything because cashback is free money and so are 0% interest introductory period.

You act like credit cards are for the financially irresponsible, but IMO it's almost financially irresponsible to use cash. Why give up free money? Why ignore the time value of money when somebody offers you 18 months interest free?

Obviously there are significant repurcussions if you misuse them, but there are also significant benefits if you manage them well.

</comment></comment></comment><comment><USER23> From Apple's Support Document on these cards:

 _" Some fabrics, like leather and denim, might cause permanent discoloration that will not wash off."_

Good think I use a money clip. Oh, wait

 _" Store your titanium Apple Card in a wallet, pocket, or bag made of soft materials."_

I guess the solution is to buy a suede wallet to hold just the Apple card. Can't believe they haven't added that to their accessories...

[https://support.apple.com/en-us/HT210399](https://support.apple.com/en- us/HT210399)

</comment><comment><USER24> Or put in another way, credit cards can be damaged by wallets and jeans. Apple recommends best ways to care for your credit card. I don't think the Apple document means their card is more susceptible to damage, if anything I imagine its far less so that plastic, but unlike other card companies who don't have to tell customers the obvious like other objects can scratch an object such as a card, apple has to since unlike other companies, apple knows people will immediately try to put it through stress tests because apple x product has x issue gets lots of ad views compared to other companies.

</comment><comment><USER25> They had to make it white. You can adjust the thickness of the oxide coating on titanium to refract a wide range of colors - just not white.

<comment><USER17> The white (and the magstripe) is just a coat of something _on top of_ the titanium.

<comment><USER25> Exactly. They could have picked any color other than white and had a nearly indestructible titanium dioxide coating.

<comment><USER17> Not for the magstripe, they couldn't.

<comment><USER26> But I haven't seen any complaints about the magstripe, just about visual blemishes.

<comment><USER17> The magstripe seems particularly prone to damage. [https://i.imgur.com/Bg8yOKT.jpg](https://i.imgur.com/Bg8yOKT.jpg)

</comment></comment></comment></comment></comment></comment><comment><USER27> And blood. I cut my thumb trying to get mine out of my wallet - I would like it if they rounded the edges a little.

<comment><USER21> I guess it wasn't enough that they cut your wrists when using their MBP, now they want you to bleed every time you pull out their card. I see a disturbing trend here. Who knew that metal has sharp 90º edges?

On a side note: This makes the thumbnail for TechLead's video review of the Apple card that much funnier (it's the card slicing through a steak)

<comment><USER17> This winter will bring complaints that it scratches windshields when you use it as an ice scraper, I guarantee it. Quite a few folks use plastic CCs for this in a pinch.

</comment></comment></comment><comment><USER3> I personally don't associate prestige or beauty with MasterCard.

It would be like Apple partnering with Buick, instead of BMW.

<comment><USER28> The only card I've ever seen places not take is MasterCard, which is funny given their slogan of it being everywhere you want to be.

<comment> 52-6F-62 Oh so many places don't take Amex. Funny enough, I run into more places that don't take Amex in the US than I do in Canada.

</comment></comment></comment><comment><USER29> What a vapid article.

Their card can be damaged?

Yes, all cards can.

The main thing is: is this more easily damaged than other similar metallic credit cards.

The article doesn't mention this at all.

Next up on the news:

X Product can be Damaged, Will yours be?

Then just write generic text with no sources, comparisons or data of any kind.

</comment><comment><USER30> I'm reminded of modernist buildings, which looked nice and gleaming white the day they were built, and have looked old and shabby for decades since.

How long until folks realise that the real world is not a pristine white cube?

<comment><USER31> I agree, but there are some great exceptions. Le Corbusier's Villa Savoye still looks sharper and more modern than many new builds and it's approaching 90. [https://en.m.wikipedia.org/wiki/Villa_Savoye](https://en.m.wikipedia.org/wiki/Villa_Savoye)

</comment></comment><comment><USER32> It would be hilarious if they were to sell a case for the card too hahah!

</comment><comment><USER33> I'm sure you'll be able to get a case for your Apple Card to protect it or buy 3 years of Apple Care for $49 (there will be a $19 co-pay if Apple replaces your jeans or wallet, up to 2 accidents)

</comment><comment><USER34> Another thing: metallic cards feel unnaturally heavy, I don't understand why would you choose them over the plastic ones.

<comment><USER35> BECAUSE they feel unnaturally heavy. It's a status thing. When you hand your card over to the person at the till, you feel important, because your card is metallic, instead of plastic.

<comment><USER36> Kind of like business cards in American Psycho:

[https://www.youtube.com/watch?v=cISYzA36-ZY](https://www.youtube.com/watch?v=cISYzA36-ZY)

Some people still aspire to behave like that.

</comment></comment></comment><comment><USER37> I for one am all for Wabi-sabi when it comes to Credit Cards, Cameras or iPods/Phones. The more, the better.

</comment><comment><USER38> My wallet is leather but the credit card slots have a cloth material.

I better book a Genius Bar appointment to verify it is OK.

</comment><comment><USER39> It would have been perfect if they made the card out of frosted gorilla glass with maybe a anodized alum rim.

<comment><USER8> .. and if it maybe had a next-gen tiny little chip thingy that still does something super Apple'y, but over your money instead of .. pics and things ..

</comment></comment><comment><USER40> Can't be worse than my bank card, thing gets mangled and chewed up by both ATMs and my wallet.

</comment><comment><USER41> Do Apple make a sleeve for the card? I think there's a market here for a 29$ sleeve.

</comment><comment><USER42> Cant you order a free replacement card if you care so much about scuff and marks?</comment>


Version control of really huge files - <AUTHOR> http://alumnit.ca/~apenwarr/log/?m=200910#04

<comment><USER2> Here's how mine works:

[http://www.rockstarprogrammer.org/post/2006/aug/26/space_eff...](http://www.rockstarprogrammer.org/post/2006/aug/26/space_efficient_encrypted_backups/)

</comment><comment><USER3> How about just storing the file in Venti? A Git API could probably be written that uses it underneath: <http://en.wikipedia.org/wiki/Venti>

</comment><comment> brown9-2 Maybe I'm missing something but is this really a common use case? Wanting to store 100mb DB dumps in version control? I'm not sure VCS is the best for pure backup needs.

<comment><USER4> Well, the non-common use cases are mostly the ones that are harder to solve.

I don't have to do much with databases, but a lot with multimedia and large files for which you want some version control are rather common there. And it's more tricky because it's not text-based but binary and because you usually don't want every change to be a new version but only save very specific versions and otherwise changes should just replace the last version. Actually I haven't found yet a way to do that with mercurial which I use otherwise. So for now I just mix: Really large files are kept outside hg and saved and versioned by hand which means I have to distribute them additional to hg export when copying the project around. And for smaller files I just create new versions on each change even though I don't like it and would mostly prefer to just replace old versions. Unfortunately that is not the problem solved in this article.

What I would need would be a version control system that allows me to tag files so that those files are just replacing previous revisions on a commit (maybe still noting all the times they got updated) unless I specifically tag them as "new version".

<comment><USER5> I would really like this type of feature from a git or hg. One of my concerns about the state of version control is the sub par handling of binary files. Git works well for the intended use case of the strictly text projects such as the linux kernel etc. but not as good for complex multimedia applications with changes to binary files being common. I guess I could do a repack often but it would be nice for a version control system to handle these files better. I know it's a pipe dream right now but one can hope.

Edit: I guess I should say I was referring to open source DVCS and not the commercial stuff like perforce.

</comment></comment></comment><comment><USER6> anyone know how this compares to wuala's scheme?</comment>


50% of South Korean biology teachers do not accept evolution - E-for-Endetta http://www.nature.com/news/south-korea-surrenders-to-creationist-demands-1.10773 Quotes from the article:
A petition to remove references to evolution from high-school textbooks claimed victory last month after the Ministry of Education, Science and Technology (MEST) revealed that many of the publishers would produce revised editions that exclude examples of the evolution of the horse or of avian ancestor Archaeopteryx. ...
In a 2009 survey conducted for the South Korean documentary The Era of God and Darwin, almost one-third of the respondents didn't believe in evolution. Of those, 41% said that there was insufficient scientific evidence to support it; 39% said that it contradicted their religious beliefs; and 17% did not understand the theory. The numbers approach those in the United States, where a survey by the research firm Gallup has shown that around 40% of Americans do not believe that humans evolved from less advanced forms of life. ...
[A survey] also found that 40% of biology teachers agreed with the statement that "much of the scientific community doubts if evolution occurs"; and half disagreed that "modern humans are the product of evolutionary processes". ... [T]here are only 5–10 evolutionary scientists in the country who teach the theory of evolution in undergraduate and graduate schools. <comment><AUTHOR> We should exclude such people from educational institutes by nature of belief. They're not teaching science if they already have an agenda, bias or belief and allow it to interfere with objectivity and professionalism. In fact any religious school or education institute should be of independent of religion how the church and state supposedly are.

This shit is unfortunately rife.

I had to explain this to my daughter's teacher who refused to teach evolution under science, even though it is part of the curriculum in the UK at primary level. The most annoying thing is that I was called into the (non-religious) school because my 9 year old daughter said she didn't believe in god and the teacher found it offensive and didn't want to teach her!

<comment><USER2> It seems HN is just as prone to pulling out the "Save The Kids" rhetoric when it suits them.

I wonder why you aren't lauding the NSA surveillance program, as it gives you exactly the kind of ammunition to perform such thought police style discrimination.

You will never agree with everyone. The solution is never to "exclude" or "ban" a school of thought, but counteract it with logic and reason. I see no harm in teaching both sides of human origin, it gives children the chance to decide for themselves, and also offers parents a wonderful opportunity to explain their own beliefs while rebutting any they don't agree with.

If a teacher is not teaching evolution perhaps they aren't performing their job correctly, as opposed to being an insidious agent that must be removed due to their crimethink. I hope you can see the difference between job performance and belief systems.

<comment><AUTHOR> I think you have misinterpreted this entirely. This is not the "save the kids rhetoric". This is simply stating that rationality should be preserved above personal beliefs in education.

Your rhetoric is the typical miscalculated rebuttal against that attitude, which I do not possess.

An opportunity to explain beliefs should be presented in the correct context i.e. within the bounds of religious education, but that is it. The issue we have here is that discussion is prevented if beliefs are questioned and people are punished and threatened for questioning them.

That is not a belief system, nor is it something that should be promoted anywhere. It is simply forced indoctrination.

By your logic, a geography teacher should teach that the earth is flat if they are a member of the flat earth society.

<comment><USER2> I don't think they _should_ , but I think they should be allowed to. You're never going to stop crazy people from saying crazy things, and sometimes those people might just happen to be teachers. Besides, if crazy ideas were verboten in education we'd still be in the dark ages slinging arrows at each other. Tenure was actually explicitly created for respected teachers to be able to say crazy things without getting fired. What are you going to do when a tenured professor starts teaching your child something you disagree with? Are you going to go with the same heavy-handed approach?

If you have a problem with the way your child is taught, bring it up with the teacher. Bring it up with the principal. Bring it up with the school board. Ask your child to be moved to a different class/section. Ask your child to be re-zoned. Or, all else failing, teach your own child. Maybe you should be more concerned about hiring standards for our teachers, or maybe consistent accreditation.

<comment><USER3> (School) teacher aren't researchers. So, yes, them telling bullshit to children in class should get them fired pronto. They do not get academic freedom nor should they. It's not their job to push science ahead, it's not their job to do any research at all. If they think that's their job then tough luck. They picked the wrong career. And that's it.

If school teachers teach crazy and not generally accepted stuff they are doing it wrong and deserve to be fired.

Maybe I could have some sympathy if you were talking about university education – but school teachers? I mean, what the fuck?!

</comment></comment></comment></comment></comment><comment><USER4> The Catholic Church's current belief is there is no conflict between evolution and church teachings. They do believe evolution was divinely inspired. They also go with the scientific theories on age of earth and gradual appearance of life. Its interesting to see the difference between the actual Catholic doctrine and those that broke away.

If the non-evolution Christian truly needs everything spelled out, Genesis 1:28[1] would probably explain it all. Kinda hard to have dominion if you don't have a mechanism to change things. I would love the non-evolution version of how we get good hunting dogs and modern cows?

1) [http://www.kingjamesbibleonline.org/Genesis-1-28/](http://www.kingjamesbibleonline.org/Genesis-1-28/)

// ok, yes the Catholic Church did some bad things to scientists, I get that - look at the current doctrine

<comment><USER5> This. The Catholic Church supports both the Theory of Evolution and the Big Bang Theory (no jokes about the TV show please). So don't blame the Catholic Church for this one. Given that South Korea is split between Catholics and Buddhists, it doesn't seem like there is a particularly religious reason for this trend.

<comment><USER6> South Korean christians are mostly protestants, not catholics.

edit: it looks like Catholicism has been growing over the past 10 years, but there are still more Protestants.

In any case South Korea is not "split between Catholics and Buddhists," the largest denomination is Presbyterian and 46% identify as not religious at all.

<comment><USER7>
         Christian        31.6% (Protestant 24%, Roman Catholic 7.6%)
         Buddhist         24.2%,
         other or unknown  0.9%
         none             43.3%
        (2010 survey)

([https://www.cia.gov/library/publications/the-world- factbook/...](https://www.cia.gov/library/publications/the-world- factbook/geos/ks.html)) People and Society section.

</comment></comment></comment></comment><comment><USER8> Have you guys seen the comments there? Is it usual that Nature is read by people like that? What does God have to do with evolution anyway? Evolution is falsifiable theory on which some very useful models (which work) are based. They can believe in whatever they want, but they surely can't base any models on "God created everything".

Everybody can program a little evolutionary algorithm yourself and see that it can create something out of chaos... It works :)

<comment><USER9> Yes you are creating something out of chaos because you have an algorithm. No algorithm, no programming means you can't create.

<comment><USER10> Hi!

Have you ever played Conway's game of life? [http://en.wikipedia.org/wiki/Conway's_Game_of_Life](http://en.wikipedia.org/wiki/Conway's_Game_of_Life)

It hasno algorithm, only a few simple rules from which order arises from chaos.

Give it a shot, you may like it.

As a practicing Christian, I see no reason evolution can't be exactly what we scientists observe it to be: a process by which beneficial adaptations are passed down to succeeding generations. Much like in software, where design decisions pass down to future incarnations of concepts, like WASD for FPS game control.

<comment><USER11> A few simple rules is quite arguably an algorithm.

Of course, one could say the laws of physics are the same...

<comment><USER10> Yeah, that makes sense.

</comment></comment></comment></comment></comment><comment><USER12> Evolution can be further classified into "microevolution" and "macroevolution". Evidence for microevolution is directly observable (e.g. bacteria adapting to changes of nutrients in their environment, viruses adapting to vaccines). However macroevolution has never been directly observed. It is extrapolated from considering what occurs after a multitude of generations of microevolution. An interesting new documentary examines this in more detail: [http://evolutionvsgod.com/](http://evolutionvsgod.com/)

<comment><USER13> No, but we have found millions of fossils in the ground from different time periods, and it looks an awful lot like different species slowly changed over time (and occasionally branched off or went extinct). If this weren't the case, scientists wouldn't believe in macroevolution either.

<comment><USER14> The thing is - there ARE plenty of scientists who disagree; they are just shunned by the rest of the academic circle-jerk for not following the wagon. So you, as a potentially scientifically-literate individual, are the victim of journal bias. Evolution is a _theory_ that is heavily debated- the cambrian explosion is one such debate that specifically has evidence against what your comment is. Too lazy to read? Watch the docu "Darwin's Dilemma"

<comment><USER15> Science isn't democracy, things are not true or untrue depending on how many people beliieve them. You can't vote gravity away, for example.

<comment><USER14> Journal bias circle-jerking can 'vote' science away. Watch this TED talk about how journal bias is destroying the SCIENCE of medicine (Doctors don't know about the Drugs They Prescribe): [http://www.ted.com/talks/ben_goldacre_what_doctors_don_t_kno...](http://www.ted.com/talks/ben_goldacre_what_doctors_don_t_know_about_the_drugs_they_prescribe.html)

</comment></comment></comment></comment></comment><comment><USER16> I'm not really sure what to say, except wow. One interesting note: "However, a survey of trainee teachers in the country concluded that religious belief was not a strong determinant of their acceptance of evolution".

<comment> E-for-Endetta I wonder why they do not accept evolution then. Any guesses? To me the single most depressing line in the whole article must be this: "40% of biology teachers agreed with the statement that "much of the scientific community doubts if evolution occurs"; and half disagreed that "modern humans are the product of evolutionary processes"."

<comment><USER17> Sounds like the old "I'm not descended from a monkey!" thing to me.

</comment></comment></comment><comment><USER18> When your religious views conflict with reality it's time to re-evaluate your religious views.

<comment><USER19> The entire point of religious views is to provide another way of understanding reality.

You presumably believe that science alone can explain reality. And that's fine. But many people don't, and I'm not just talking about the traditionally religious.

Scientifically, the "love" we have for our children is simply a genetically- programmed strategy for best ensuring that our genes are passed to future generations. It has no meaning.

In fact, scientifically, _nothing_ has any meaning. Whether we live or die makes no difference at all, and when someone kills another, the only reason it's "wrong" is because society is trying to preserve itself so that individuals can pass on their genes to the next generation.

Again, if you're OK with this, that's fine. I'm not going to try to make you change your mind. But hopefully you can understand why others, many others, inform their view of reality with sources other than science.

</comment></comment><comment><USER9> I love science & technology therefore I'm alarmed because so many scientist and technologist don't see the inherit paradox in evolution. Natural selection makes it virtually impossible for animals to adapt via the proposed mutations. Natural selection happens immediately, valuable life preserving mutations are suppose occur (typically) over thousands of years.

To illustrate Arctic terns are dying because they are not able to adapt to climate change. Arctic terns eat herrings a cold water fish, as the water gets warmer, these fish seek colder waters, possible by going deeper in the waters. According to biologist other birds in the Arctic can dive deeper to grab the cold water fish Arctic terns cannot, so they and their chicks starve to death. This is natural selection, there is no time to adapt via mutations, adaptations must happening quickly.

<comment><USER20> Congratulations, you are front row center to a mass extinction event. There was not time for the dinosaurs to react to the meteor either.

<comment><USER5> The theory that dinosaurs died due to a meteor is already outdated.

<comment><USER21> Is it? Are you being pedantic about the term "meteor" or is consensus behind something other than a collision? Asking honestly and aware that Wikipedia isn't always going to be correct. From Wikipedia:

"It is generally believed that the K-Pg extinction was triggered by a massive comet/asteroid impact and its catastrophic effects on the global environment, including a lingering impact winter..."

Same page then suggests volcanic activity, etc is a less popular theory.

<comment><USER5> You probably won't read this, but:

[http://www.slate.com/articles/health_and_science/animal_fore...](http://www.slate.com/articles/health_and_science/animal_forecast/2013/02/dinosaur_extinction_was_an_asteroid_the_only_cause_of_the_cretaceous_mass.html)

It's not enough that an asteroid hit the Earth, but it's such a simple theory that everyone can remember, it has been popularized by everyone.

<comment><USER21> I thought it was a given (for anyone keen to read more than a headline) that the impact implied a long period of changes and involved more than just the strike itself?

I did assume however that volcanic activity could've been as a result of the impact.

</comment></comment></comment></comment></comment></comment><comment><USER22> That the South Korean school system would do this is discouraging, no question. But I am also discouraged by how, when reading academic scientists and academic journals (_Nature_ in this case), it seems considerably easier to find concern about unscientific nonsense beloved of the right than about unscientific nonsense beloved of the left.

E.g., individual genetic heritability is even more fundamental and even more loudly supported by obvious evidence than evolution by natural selection. Yet not only is this sometimes slighted in the high school curriculum, one can find academic work from prestigious institutions that pretends heritability does not exist (or is at least, for some unexplained reason, absolutely negligible). See, e.g., Chan and Boliver "The Grandparents Effect in Social Mobility", [http://users.ox.ac.uk/~sfos0006/papers/asronline.pdf](http://users.ox.ac.uk/~sfos0006/papers/asronline.pdf) (HT [http://www.arnoldkling.com/blog/a-grandparent- effect/](http://www.arnoldkling.com/blog/a-grandparent-effect/)). It is not easy (for this reader with a BS in Biology) to see why it's scientifically OK to assume that heritability is negligible at the level of detail considered by Chan and Boliver ("if mobility follows a first-order Markovian process"; "well-connected grandparents could also use their social contacts to help grandchildren with job searches"; citations into a more than a decade of research on multigenerational issues, e.g. the cite to Bengtson). It's easy to see how this taboo can help produce useful political soundbites, and why the BBC would take it and run with it [http://www.bbc.co.uk/news/education-23101446](http://www.bbc.co.uk/news/education-23101446) . It's just hard to see why scientists wouldn't be concerned about this behavior by the BBC and by the academic research community.

In general, it's sensible for a scientific journal to worry about unscientific claptrap. However, specifically skipping over the unscientific claptrap beloved of their faction in the BBC, in academic science, and in academic journals in order to zero in on the unscientific claptrap of rival factions is unbecoming in an organization that claims to be scientific. It doesn't resemble science as much as it resembles sleazy-think-tank-style selective invocation of research in order to advance a political agenda.

</comment><comment><USER23> When I see how Koreans are playing Starcraft I start to have doubts about evolution myself...

<comment><USER11> [http://en.wikipedia.org/wiki/Supernormal_stimulus](http://en.wikipedia.org/wiki/Supernormal_stimulus)

</comment></comment><comment><USER24> Evolutionists should know other theories as well, or do they fear them?

[http://www.evolution-is-degeneration.com/](http://www.evolution-is- degeneration.com/)

<comment><USER25> A book about evolution ... by a televangelist.

</comment></comment><comment><USER26> > do not believe in evolution

exactly... you need to believe in it because there can't be any proof as long as we can't travel in time.

get over it and recognize that everything that goes beyond the scientific method is mere speculation.

<comment><USER27> does recent historical record count, or do you need predictive results? Recent historical examples include the Industrial Moth. If you want to predict some evolution, pick an antibiotic and wait for it to become ineffective. Here's more:

[http://www.scribd.com/doc/46030115/142-Modern-Examples-of- Ev...](http://www.scribd.com/doc/46030115/142-Modern-Examples-of-Evolution-in- Action)

And while we're beating each other with the scientific method, I believe [!] the onus is on those who think evolution to be an incorrect theory to provide a result that falsifies the premise.

FWIW, if you haven't read The Origin of Species, I can't recommend it strongly enough. It is my opinion Darwin is a better writer than Dawkins, Gould or Jones. The man could put together a lovely sentence.</comment></comment>