He has also seemingly updated the paper after receiving some feedback. This is the link to a version dated today: http://www.hpl.hp.com/personal/Vinay_Deolalikar/Papers/pnp_updated.pdf

Whoa its from the future! (article is dated June 26th)

That's some seriously scary stuff from these guys. Has EFF or any of the other groups issued a response to either of these pieces yet?

soundexchange -> broadcast (internet radio)
ascap -> performance

typed in too much of a hurry sorry. This stuff just really pisses me off about these kind of groups.

They probably are relying on or hoping to attain the same standing RIAA has gotten through SoundExchange to collect broadcast royalties even from non-members.

So there is clearly precedent that suggests ownership and membership are not sufficient concerns to these types of organizations. Unless it is their material or members that is!

So in this case they are either seeking statute authority to collect song composing royalties for members AND non-members, or they intend to behave that way anyway and defend it on the premise that the copyright office already delineated similar powers to SoundExchange and that since ASCAP is a similar group to SoundExchange they are entitled to a similar wide scope of authority (performance royalties -> SoundExchange vs composing royalties -> ASCAP)

I'd really like to see this blow up in their face and get both groups rights to even try this sort of thing revoked, but there are too many MAFIAA members in DoJ (and probably other parts of gov't) now and they have the administration's support (much to my dismay as I do generally otherwise support the administration). So this could get ugly and have bad consequences quickly.

I really hope the copyleft groups start gathering funds and resources in a way to respond to this head on. I'd support it.

About RIAA lawyers at DoJ:
http://www.wired.com/threatlevel/2009/04/obama-taps-fift/

About RIAA/SoundExchange:
http://www.dailykos.com/story/2007/4/24/141326/870
http://slashdot.org/articles/07/04/29/0335224.shtml

Numerous researchers in synthetic biology have been trying to do exactly that.

Here is an example Open Wetware BioBricks

Synthetic biology has two paradigms the first is the top down approach which deals with gene knock outs to look for minimal sets necessary for life that can then be tailored to suit specific needs/tasks. The other approach is the bottom up which have inspiration from the Miller-Urey experiments. They are trying to spontaneously generate a new biological system from scratch. Some researchers in this camp trying to create synthetic cellular components in hopes of putting all the synthetic parts together to create a functioning cell such as synthetic Golgi bodies

There has been some promising results from both approaches. It is a pretty exciting time in Biology.

I've seen many many posts talking about "you need logic but not really mathematics"
I'm not really sure what the confusion is about why mathematics would be separate from logic in any respect.

Symbolic Logic is Mathematical Logic. Principia Mathematica clearly makes the case that math and logic are the same thing. As did many works around the same time and since. So how can you need logic for computer science or programming yet somehow not mathematics it just doesn't make any sense to me.

As Dvorkin put it, it isn't as simple as always use classical methods or always use Bayesian.

I also think sometimes whenever possible you need to use both to help give you more evidence. Particularly if the results are somehow unclear. If they both give similar or approximately the same results that might be pretty good evidence that the final result is reasonable.

If you get wildly different conclusions then you need to consider why you got different answers and maybe it is because one method better models the situation than the other (and there are some tools to help figure that out in some cases) or perhaps the irregularity is identifying a problem with the data collected that would have gone undetected if you only used one or the other (and there are tools for when this happens too in some cases).

Very careful model building, very careful experimental design, very careful analysis, and very careful model adjustment isn't a guarantee that the results will be correct with any methods, but it is the best that can be done and is critical to always follow.

Like Daniel Dvorkin has said Devore's book Probability and Statistics for Engineering and the Sciences is an excellent starting point.

Definitely learn to use R since its free you don't have to worry about paying licensing fees. It is also widely used (no matter what you here from SAS, Minitab, SPSS, etc).

Books I would recommend that I think fit his other suggestions are Bowerman/O'Connell Linear Statistical Models: An Applied Approach and Wackerly et al Mathematical Statistics with Applications

Devore talks about Bayes Rule as does Wackerly and Wackerly's last chapter talks about some Bayesian techniques, but these are merely primers for what is typical in a Bayesian course. So I recommend these two books as analogous with Devore's: Bolstad Introduction to Bayesian Statistics and to Wackerly's: Hoff A First Course in Bayesian Statistical Methods

Some things you need from mathematics are the ability to integrate, work with matrices and matrix operations, and algebraic manipulation. Familiarity with transformations and operators especially linear ones is useful since many procedures in statistics are linear operators. The highest levels of statistics will get even more math intense using mathematical results from areas like ODE/PDE, Galios Theory, or general Measure Theory.

The wikipedia's statistics articles are pretty good overall, but as Dvorkin noted some are more technical than what would be friendly to those that are new to statistics. When you feel that's the case try using the sources linked as citations in the article or google confusing parts and it is generally possible to find an explanation for almost any background level.

However if you can get through these texts you're background would be pretty strong.

Science is in the business of probably knowledge. So they really need to improve their probability and statistics knowledge.

It is not a shortcoming of statistics that other people, like various scientists who aren't statisticians, don't know how to use or properly interpret statistics. It is a shortcoming of their knowledge.

It is not a shortcoming of the Copenhagen interpretation of quantum mechanics or the Chicago school of economics if I don't understand or know how to correctly interpret their results. It is my shortcoming and fault for not knowing enough to connect the dots.

I do statistical research some of that is through interacting with researchers in the biosciences. Often when I go to talk to a researcher and ask them if they could use some statistical or mathematical or computational assistance with their research it has almost always been a fruitful starting point to long conversations and getting into the research. Now sometimes it was simply a matter of looking at their F-test results or ANOVA scores and telling them what it meant (like with a regression model relating proportions of certain characteristics between taxa), more useful interactions for me often mean working on new algorithms or estimators or working with fitting a model from their empirical data because there isn't a reliable standard model to work off of (like intergenic distance between genes in an operon) that kind of challenge makes less engaging work worth the hassle. Maybe I'm odd because I've worked hard to have a good background in both statistics and biology, but I shouldn't be.

Although here is an observation that perhaps supports some of the intent of the article from my own experience. I was speaking with a biology graduate student and it came up that they had a biostatistics course in the department. Of course as a statistician my mind goes towards survival function, failure rate, life tables, censored data, bioassy, epidemiology, microarrays, clincal trials, topics along those lines. It turned out their course focused z tests, t tests, f tests, confidence intervals, point predictions, least squares regression, multiple regression, ANOVA, and things along these lines just with simulated problems in a lab setting. That is not necessarily a bad thing, but much of the core math was under played or missing like model assumptions and alternate formulations or things like dummy variables. The worst part was that even though they were doing well with the class they had no confidence in actually using the statistics and didn't understand how to interpret the meaning of something like a confidence interval, they knew how to calculate one, but it wasn't clear what it actually meant to them.

The corollary to the notion in the summary I'd rant and claim is that scientists overall have less than desirable skills in mathematics, statistics, and computation than those who studied those disciplines principally and that's hurting science. However many in those three disciplines really know little beyond basic results in any of the sciences which hurts the applicability of these mathematical fields to the sciences and likely hurt our ability to develop certain types of discipline specific results that can be generalized from work in application problems.

In either case whether you're a typical scientist or a typical math/stat/comp person in order to become proficient enough in the other areas it requires going an awfully long out of the way compared to any counterpart who simply does not care and goes straight through as many before have. While in some areas of research on either side it is no problem to do as has been done and not further knowledge into those other areas. Increasingly results that have the highest levels of impact are coming more and more from truly interdisciplinary research. In order to further encourage that for those who are interested in such fields (aside from making more clear what areas in any of the fields fringe to such interdisciplinary work) we need more incentive to study more than one field and/or better ways of enabling fruitful cooperation between the camps.