Let's be careful with our use of the word skew because it could be a linguistic synonym for "bias" or for a technical definition of data distributions. One comes with an intention that Google wanted to put down certain classes of people while the second one is factual statement about the types of comments that exist online. When you have a dataset with two classes (say, healthy/sick, valid/fraudulent, or "gay used in a positive light"/"gay used in a negative light") it may be the case that uniform random sampling will lead to very many examples of Type I and very few example of Type II. This is even with a representative sample of data from the wild. It is because the underlying phenomena is not necessarily balanced. For example, in medical diagnostic scenarios, very often, there are many examples of healthy people but sickness is relatively rare. We're glad this is the case as compassionate human beings, but extracting signal to find the disease pattern can be difficult. The problem is even worse when we have unlabelled anomaly detection. If we *want* to develop rules that are generated in a balanced way, we have to do random sampling that is *not* uniformly random over the whole population. We can either (1) weight our sampling by the inverse of the sub-population frequency or (2) perform stratified sampling where we guarantee some number (or some ratio) of samples from our sub-populations. Best, Mark

Your research budget numbers are a bit off (though it doesn't detract from your larger point). NIH gets the largest research budget (on the order of $30 billion dollars). https://www.nih.gov/about-nih/... In fairness, that money is basically all spent on applied bio-medical research (anything "basic science" in biology/medicine needs to go to NSF to get funding -- too applied, too basic is a common sticking point for research on the boundary). Also, some amount of the military budgets are used to fund the army, navy, and air force research labs (AFRL, NRL, ARL) and research under DARPA and (at least up to a certain time) the NSA. Again, it doesn't take away from your main point. Best, Mark

Actually, no. It's happened to commercial flights at least 3 times that are not in dispute, and 3 more that are disputed to varying degrees. Source: http://en.wikipedia.org/wiki/S...

That doesn't necessarily invalidate your point, but 6 times over 30ish years is somewhat disturbing. In general, I agree that off-work time should be off-limits to employers, but there are real cases where things that happen off-work have a real, clear, and direct impact on someone's ability to perform their job duties.

TFA has a bit of cherry-picked data. While full professors at R1 (Doctoral) institutions do indeed make ~135k/year, at other classes of institutions, the salary is significantly lower (Master's & Bachelor's full professors make ~92k): http://www.aaup.org/sites/default/files/files/2013%20Salary%20Survey%20Tables%20and%20Figures/Table%204.pdf

While the idea behind your statement has elements of truth to it, it is not entirely true (what ever is?). Scientists, as a community, rely just as much on the "commonly accepted wisdom (of their own scientific community)" as lay people do on the commonly accepted knowledge/wisdom of their (sociological) community. This is one interpretation of Kuhn's "normal" versus "revolutionary" science.

Of course one mark of a (good) scientist is skepticism with respect to "accepted" wisdom that allows for them to develop of new models/theories/experiments/measurements/etc and win fame and glory (heh!).

Best,
Mark

Wrong, but this is only an appeal to ridicule, a well documented logical fallacy, so I'll chose to ignore it.

I wish you would have said: "... fallacy, so this space is intentionally left blank." Either way, it made me smile.

Anyone have a guesstimate as to the size of the backend, frontend, and synchronization/distribution codebase sizes?

Much like in politics (paid by the people at uneven intervals -- more regularly by "interests" -- and definitely no QA) and the media.

Unfortunately, when IT departments insist on loading crapware, even substantial machines can grind to a halt.

And even fewer will realize that "statistical significance" and "real world significance" are orthogonal concepts. I can have a huge sample show a statistically significant tiny difference that doesn't matter.

Unfortunately, that is a false statement. Philosophers will go round and round, but I hope we can agree that science is a process of observation, experimentation, manipulation, and recording of outcomes. All of these are evident in computer science (particularly outside of "theory of computation").

For example, you develop a new algorithm. You perform some algorithmic complexity analysis on it and get some formal, mathematical results (and yes, math -is- a big part of computer science). But, you won't stop there. If you do, you are really falling into the trap of the medieval scholastic philosophers: arguing at the number of angels on the head of a pin. You are going to then actually run the program for different input sizes and record the amount of time the program took to run.

Actually running the program and recording the results is comparable to synthesizing a new chemical and checking its reactivity, verifying its molecular weight, etc. You are quantifying the properties of something new.

This -is- science. Furthermore, in many large systems, there are too many practical obstacles (size, interactions, etc.) to performing a formal complexity analysis and thus ... you HAVE to fall back on experimentation.

All that said, the end result of your claim is pretty spot on: if you are afraid of math, you might want to look elsewhere.

As a former and future CS professor, this issue is near and dear to my heart.

The conflation and confusion over what constitutes computer science is just as rampant at the college/university level as it is in high schools. Perhaps the "CS" moniker is even more abused in post-secondary institutions. At least those high school programs that were designed around the AP exam had *something* to focus them (I'm not wading into CS versus programming right now, just saying that the AP exam gave a concrete body of material that is at CSy enough).

Now certainly, CS is well- and correctly defined at R1 type schools and at the top 10 to 25% of liberal arts schools (the top 100 at Princeton Review or some such). It's not too bad at the top 5% of "master's" institutions (say top 50, but I haven't gone through the list carefully. I know there are VERY BAD examples further down the list -- say around 100. The "master's" category of institution is typically for schools that can't compete with R1 or quality liberal arts). [Note, those are my intuitive numbers from personal experience (I'm intimately familiar with about 15 programs in the broad northeast of the US at all three levels. I know the structure and reputation of another 50, but my comments are mostly based on those I have more personal knowledge of.)].

What makes the problem worse at the (weak) post-secondary level is that CS is turned into IT (or CIS) and the students wonder why they can't get jobs doing something other than MS system administration with a bit of "pluggy pluggy" networking and a side of "pointy clicky" databases. Of course, the same students shy away from anything 1. hard and 2. involving that evil, demonic subject: math. So, the schools take the path of least resistance and produce students who will peek their career in about 3 months (except for a few that have the natural political/business ability that will move up in management after 5 years).

*sigh*

I wish this were mere fancy. But I can name multiple schools, without stopping to think, that fall into this category. Sadly, almost any school that isn't good (as defined above) is going to be bad. Some of them are honest enough to name the programs CIS/IT and have a gutted/token CS department with two students; a few of the schools defined CS as CIS + two or four math classes; some schools just name it CS and let the dice fly. Fortunately, at some of the schools, there are folks working to improve things. But, it is an uphill battle with entrenched faculty who are tenured (can't get rid of them), don't have advanced CS degrees (aren't really qualified), are currently uncertain about the economy (have motive to keep earning money), don't have anything better to do (have motive to go to work), and may work for another 10-20 years (ugh).

I think the portion about cows is mostly clear -- there's different standard that the US applies to US cows (media) and that the US applies to non-US cows (media).

The puck *ahem* probably refers to the biological excrement of a cow used in a "sporting" fashion. So, foreign cow deposits US dirt and it gets a slap shot back in the US's face.

Best,
Mark

From Stroustrup, in the article:

"Know your fundamentals (algorithms, data structures, machine architecture, systems) and know several programming languages to the point where you can use them idiomatically.

Know some non-computer field of study well — math, biology, history, optics, whatever. Learn to communicate effectively in speech and in writing. Spend an unreasonable amount of time on some difficult topic to really master it. Try to do something that might make a difference in the world."

I guess he wouldn't do to well on Slashdot.

They did. They're called Republicans. You know, the same way that a clown and a clown carrying an umbrella are the same thing.