The good news in all this, is that not only does O want America to go to Mars, BUT, he backs nuclear engines and power. Sadly, the dems fight it. But, the current CONgress is likely to be pro-nukes, which I doubt that O will stop.

They could certainly send 50 times as many messages, but they'll improve their return on investment if they target all of them at people who are more susceptible to their message in the first place. Given the cost of the Big Data systems they may only be able to afford to send 10 times as many instead of 50 times, but as long as their message is 5% effective instead of 0.1%, it's still a vast improvement on ROI.

That's a great question. Do you think 80% accuracy is good enough for medical use? If you're a doctor facing an unfamiliar situation, and your data says treatment X helped 40% of patients it was tried on, treatment Y helped 35% of them, and all other treatments (Z, W, etc.) helped no more than 30%, but you know the data might only be 80% accurate, what treatment do you choose? Are those ratios even meaningful in the presence of so many errors?

Consider the case where the patient's condition is critical, and you don't have time for additional evaluation. Is X always the best choice? What if your specialty makes you better than average at treatment Y? Maybe that 20% inaccuracy works in favor of the doctor who has the right experience.

It could it be used for ill, too. What if you know you'll get paid more by the insurance company for all the extra tests required to do treatment Y? You could justify part of your decision based on the uncertainty of the data.

In the end, historical data is just one factor out of many that goes into each of these decisions. Inaccurate data may lead to suboptimal decisions, so it can't be the only factor.

You seem to be belaboring this mistaken impression that analyzing Big Data somehow replaces thinking in the board room. It does not. Big Data is a tool that can help provide evidence of what people have done in the past, statistically correlated to potential causes. Big Data doesn't decide "hey, let's buy GM." People make those decisions, and they try to make them based on the information they have -- and Big Data can be a good source of that info. But people can be idiots, they can be talented, they can be anywhere on the spectrum. Do not blame the tool, or the accuracy of the tool, just because it's capable of being swung by an unqualified, incompetent idiot.

As a friend of mine is wont to say, "A fool with a tool is still a fool."

When you're dealing with statistics, you ought to recognize that 92% accuracy is a huge improvement over a random distribution. You do not use big data to select a target for a sniper rifle, you use it to point a shotgun.

And just like your faulty GM CEO analogy (I assume you felt the need to apply a car analogy for the benefit of the slashdot crowd) only an idiot would send someone off in the woods blindfolded and have him fire his shotgun in a random direction hoping to bring home some kind of food animal. You still have to know what you're hunting for, you still have to know how to hunt, you still have to make wise decisions. It's just a tool, not a sage.

The difference between "92% accurate" and "accurate enough for my task" are profound.

If you were using these kind of analytics to bill your customers, 92% would be hideously inaccurate. You'd face lawsuits on a daily basis, and you wouldn't survive a month in business. So the easy answer is, "this would be the wrong tool for billing."

But if you're advertising, you know the rates at which people bite on your message. Perhaps only 0.1% of random people are going to respond, but of people who are interested, 5.0% might bite. If you have the choice between sending the message to 10000 random people, or to 217 targeted people (only 92% of whom may be your target audience), both groups will deliver the same 10 hits. Let's say the cost per message is $10.00 per thousand views. The first wave of advertising cost you $100. The second costs you $2.17. Big Data, with all of its inaccuracies, still improves your results by a wide margin.

Way too often people like this point out that perfection is impossible. They presume that "because it's not perfect, it's useless." The answer is not always to focus on becoming more accurate, but to choose the right tool for the job, and to learn how to recognize when it's good enough to be usable. At that point you learn how to cope with the inaccuracy and derive the maximum benefits possible given what you have.

I don't think this is related to the difference between special purpose use, or general purpose use. I think the problem is like anything else in education - parental involvement will increase children's learning. If the parent works with them to learn how to use the device, I'd bet their scores would go up, similar to a parent who reads to their child or helps them with math homework.

If the parent says "I don't have to teach them anything, the school gave them a computer for that", or "I can't teach them how to use the computer because I don't know how to use it", that child's education is going to suffer. Being computer-illiterate might simply be the current excuse for parents to ignore their children.

and yet, while A does the right thing, it is possible for B to be encouraged to do the right thing.
For example, China's emissions are mostly caused by 2 things:
A) Smelting plants that use coal.
B) power plants that use coal. Back in the early 90's, China was at 65-75% coal plants. They are now at over 80% and still growing FAST.

The vast majority of that electricity goes into manufacturing, not into civilians.
As such, the smart move is for America (and hopefully the entire west) to tax ALL GOODS predicated on where they come from and the emissions / $GDP (real GDP, not PPP GDP). This will quickly encourage China to change their direction, while at the same time, getting the west to change quicker.

Theres plenty of demand for mathematicians but most of it is going to be applying existing mathemical theorems/techiques or minor developments of existing theorems/techiques to someone's problem.

Top mathematicians don't want to be doing that. They want to be developing new theorems/techiques which may or may not turn out to have practical applications and that generally means working in a university under government grants.

But what is happening is BS. Unless ALL nations are involved, then nothing will change.
As it is, the largest polluter, China with more than 1/3 of the CO2 emissions, is being allowed to continue GROWING their emissions while only the west are to cut back.
This will NEVER succeed if this continues.

the only possible solution is if all nations cut back, and if they are cut back predicated on CO2 / $ GDP. The reason is that emissions are NOT tied to ppl,but dirty manufacturing.

In general, somebody who has loads of money will make use of these bands. Now, if I wanted to take a rich person as hostage for a payoff, I might get somebody close to them to simply change out some code. That way when rich person is walking around and device gets a signal, it says, here I am. Basically, an easy tracking device.

Money. More specifically, revenue from advertisers. Once they had the motive, it's pretty easy to justify the means to the end.

From who? Thirteen enraged nerds on Slashdot? Their average customer doesn't understand the difference between their phone and their browser; they certainly won't get up in arms over a "super-cookie".

Verizon could easily afford to piss off every paranoiac on the planet, and they'd still have so much money they'll need to buy another dump truck to haul this month's profit to the bank. They have no real reason to change, so I'd recommend a strategy other than OMGPANIC!

Huh? Of course it is, we've been doing template based deployments of 2012 since May of 2013. In fact it's easier than 2003/2008 because you don't have to install the sysprep stuff on the vcenter server, it's built into the OS so it doesn't need to be injected.

Yeah, the more I dig into it, the more it looks like an investigative tool than an evidence analysis tool. That's pretty cool, but as you say, it looks a lot like Wireshark. Still, when you're facing an unknown attacker, it may not hurt to have a couple different views on the problem.