The most common underlying basis of humor is subverted expectations. We expect people to behave according to the norms of society, we expect people to act to the best of their intelligence, we expect misfortune to be avoided, and we expect that words will be used according to their common meanings.

Subvert any of those expectations, and you have various kinds of humor. How funny a particular joke is perceived to be is related to how strongly the viewer is attached to their expectations. Since a computer is only an expert in the things they've been explicitly exposed to, it's very difficult to subvert their expectations. Watson would be familiar with all of the meanings of each word in a script, for example, so it would have a difficult time identifying the usual meaning that a human would expect from a situation, and would therefore likely fail to notice that when a different meaning was used, it was an attempt at humor.

As another example, consider a military comedy, like Good Morning, Vietnam. Much of the humor is derived from Robin Williams' fast-paced ad-lib radio show contrasting the rigid military structure, and the inversion where a superior at the radio station is practically inferior in every way. A computer, properly educated in the norms of military behavior, might recognize that the characters' behaviors are contrary to expectations, but then to really understand the jokes, the computer must also have an encyclopedic knowledge of pop culture from the period to understand why Williams' antics were more than just absurd drivel.

Finally, a computer must also understand that humor is also based largely on the history of humor. Age-old jokes can become funny again simply because they aren't funny in their original context any more, so their use in a new context is a subverted expectation in itself. Common joke patterns have also become fixed in human culture, such that merely following a pattern (like the Russian Reversal) is a joke in itself.

Algorithms simply haven't combined all of the relevant factors yet to recognize humor. Here on Slashdot, for instance, a computer would need to recognize the intellectual context, the pacing of a comment, the pattern of speech, and even how much class a commenter maintains, in order to realize when someone is trying to be funny.

Poop.

Calculon is an actor, not a businessrobot.

It's cheaper to include extra screws than to pay customer service to deal with the the complaints of missing parts, or to cover the extra cost of a more thorough inspection process. The easiest way to tell if a unit was packed properly is to weigh it precisely, and only ship the units that weigh the correct amount. A missing screw may fall within the error margin of the scale, so by throwing in an extra screw or three, the risk of actually being short a part is greatly reduced.

Of course, they won't promise those extra screws in the instructions, so it's entirely possible to be human/robot error, unless you counted the parts prior to assembly... you did count your parts, right?

I recently returned from a vacation, and drove home from the airport to return to my house filled with small robots, vision-enabled game consoles, and mechanized automatons of all kinds. I guess I need some pills.

No, I don't think I will live on Mars, but I think that some human will, someday. The ultimate distinguishing feature of a human is the extent to which it modifies itself and its environment, so I find it perfectly reasonable to expect that the hostilities of another planet can be overcome with the right technology. There will need to be advances in several fields (rocketry, communications, biotech, medicine, and logistics, to name a few offhand), but we're close.

To make an analogy, if we were walking from New York to Los Angeles, we've probably hit the California state line by now. The road ahead is still going to take a lot of effort, and it's still going to take a long time. We're not done yet, and everybody knows it. There is some uncertainty as to exactly how long it will take to make those last few steps, but perhaps it's time to start thinking about what we'll do when we finally arrive at our destination.

Interesting. Are you actually an expert in what it takes to have a "leisure society with basic income and healthcare for all", and do you understand the sheer amount of resources required to make that happen? And you want that to happen for all people... Let's do some math*.

If we all split everything equally, then every human gets 71,538 square meters. That's it. That's your whole life. From that area's resources, you must derive your "basic income and healthcare" using today's technology.

Of course, much of that is ocean, which really means you only get around 24,000 square meters of land If you want to use the ocean's resources, you'll have to build suitable boats from the resources on the land. About a third of of that area, though, is practically devoid of easily-accessible resources since it's desert. That leaves only about 16,000 square meters of usable land with resources.

Do realize that's a square patch of land about 415 feet on each side. It's roughly double the area of a FIFA-sanctioned international match soccer field, and that is your whole fair share of non-desert land.

Looking toward your "healthcare" need, you only have about 2000 square meters of arable land, most of which overlaps your 5000 square meters of grassland.

For illustration, that's a square patch 146 feet on each side. 1.6 times the size of an Olympic swimming pool, and that's going to feed you (fairly) for your whole life. If you need to grow raw materials for your medicinal needs, that will come out of your food supply. If your "leisure society" includes grilling a steak in the summer, you're going to have to devote quite a lot of your farmland to raising your bovine recreation.

Fortunately, we have an economy to make life easier. Your yearly fair share in that economy is $10,610 worth of production, to meet the current average world lifestyle. If we consider all labor to be equally valuable, you need to produce enough from your 71,538 square meters to sell for $10,610. That sale can take many forms, like selling off your ocean wholesale to a fisherman in exchange for a share of his income. You can turn around and use that income to buy tools from the guy who built a metalwork shop, and now you can farm more efficiently. Producing $10,000 isn't that much effort.

However, you're probably a member of the rich Western society, making over $30,000 per year, so it's not unreasonable to consider that the lifestyle you're used to is about three times as rich as the world average. If we merely distribute everything evenly to "all", then everyone's living in the same wretched state, at $10,610 gross income per year.

Let's consider, then, what it would take to bring everyone's standard of living up to a rich American level... perhaps at the equivalent of a $60,000 salary. That means that every person's average production must increase by 500%, without using any additional resources. That means farmland efficiency must rise dramatically, resource recycling must become the norm, and absolutely everything must be done as quickly as possible to meet the demands of such a lavish lifestyle.

We don't have the technology for that. We still need to make major advances in agriculture, genetic engineering, materials, and automation to hit that improvement goal. Some aspects are within reach, but at the same time we're depleting resources faster than we can replenish or recover them, and the world population is always increasing. In many ways, having the resources to give everyone a leisure society is as much of a dream as giving everyone a magical castle.

Both plans seem to me to be equally absurd, as they both require advances in several fields that we simply can't approach yet. Ultimately, the hope is that Mars will provide more resources on its own, and the effort to go to Mars will inspire new technologies to help achieve the efficiency goals we want here on Earth. You and I share the same Utopian dream, but with different routes to get there.

* Some disclaimers apply to my math: I'm not particularly concerned with precision, since every figure used is an estimate. My methodology is to find the most reliable sources for figures that I can, and apply them in interesting ways. Your mileage may vary, void where prohibited, and so on and so forth, et cetera.

So you have a woman's name and phone number. Good for you. I have a large book full of them. It still gets dropped on my porch now and then.

So some guy has your address. Good for him. He could have just as easily followed you home, and there's a very good chance that you never would have noticed.

Now, both of you have a choice to make. What will you do with that information? Will you get on with your life peacefully, as a law-abiding member of society, or will you jump the line over to being a criminal stalker or arsonist? That's the question that really matters, not whether some third party recognized you and announced that recognition to the world.

To that, I'll say again:

As you said, "the parent teaching didn't force the issue", and I see that as the parent's key failure. When the three brothers started having trouble, the parents should have recognized their shortcomings and moved the kids to a more capable environment.

It's not easy to admit that you can't meet a need, but for the sake of the student, it must be done.

I was homeschooled until high school, and had to deal with exactly that problem.

My father was at work (as an programmer/engineer/manager), and my mother was not particularly strong in the sciences at which I excelled. By 7th grade, I had surpassed their knowledge of basic science, and especially anything computer-related. My daily lesson plan devolved into a cycle of reading material I mostly already knew, asking questions to which I wouldn't get answers, and eventually doing a half-assed job in other subjects to meet the required level of completion that would let me escape to more entertaining things, like teaching myself another programming language.

In retrospect, the single thing my parents did right, above all else, was to teach me how to learn. By the time I got to the public high school, I was able to appreciate my classes as a source of knowledge, rather than a daily prison forcing doctrine into my head. That survives to this day, and is one of the main reasons why I continue to find new fascinating things to explore and learn about the world.

My advice, as someone who survived, is to see homeschooling as a chance to influence the core values your child uses through the rest of his life. Emphasize sportsmanship, creativity, logic... whatever you hope for your child, you can instill at an early age, but you should also be aware of your limits. As soon as your child needs something more than you can supply, you must put their needs first, and send them somewhere with more resources.

That's pretty much it.

Firefighters know that being a firefighter is risky. There are the obvious dangers during the emergency, and some less obvious ones like lung damage, extended fatigue, traumatic horror... ...and now a bit of chemical risk as well. To someone who expects to risk their lives for the sake of others, this is just another item in the long list of hazards.

Sure, we should do our best to investigate the hazards and minimize them, but overall I think this might just be one of the least-dangerous parts of the job.

That's almost exactly what one aspect of my project was.

My project was, in brief, allowing medical researchers to search through patient records for patients matching particular criteria. The system could recommend related criteria, as well, based on the correlation to the already-shown results.

Early tests were particularly useless, as the system noticed a strong correlation between being pregnant and being female. It also suggested that if you included people who had smoked within the last six months, there was a strong correlation with those who had smoked within the last year.

Once the obvious correlations were flagged as being obvious, though, the system started making some valid observations, noting that particular variations in drug treatments had particular variations in outcome. That was enough to get a few researchers' attention, but I left the project before seeing any published papers.

As someone with a bit of experience in Big Data and medical technology...

A test that falsely indicated HIV 9 times out of 10 is absolutely wonderful, if it actually catches that one correct positive reliably. A false negative is far more dangerous, and it's the job of the doctor to try multiple tests to confirm a diagnosis. If the initial screening comes back positive, the patient can be warned off intercourse for a while or start some initial therapy while another test is tried, without significant risk to the patient's health.

Similarly, Big Data is just a term for a particular approach to model design. Having huge amounts of data doesn't magically improve your analysis algorithm, and you still need to have a properly-skilled expert testing your algorithm to make sure that it's correct before it's used for business decision.

NASA has a 68% approval rating, according to TFA.

TFA is actually covering opinion polls relating to several government agencies, but in typical Slashdot form, TFS only focuses on the NSA section, because that will be more inflammatory.

Ah, yes, of course a well-respected research center doesn't know how to run a simple opinion poll...

Don't worry. I'm sure the majority of Slashdot will ever-so-helpfully explain exactly how to run the survey to get the results that Slashdot expects, validity be damned.

Are your findings the results of a properly-controlled survey, or are they the results of talking among your technologist peers and subject to confirmation bias?

It's poorly worded above, but perhaps a better way to say it is that the time-dependent churn in a particular model is negligible (to a statistical irrelevance) if you can get enough data quickly enough. Effectively, once your data stream outpaces the time-dependent effects, those effects may no longer be relevant variables in your calculations.

For example, I'd expect that Google can collect enough data in an hour to determine if a UI improvement is helpful, or if a particular change to PageRank results in more accurate results. Because Google has such a high volume of data collection all of the time, a very short sampling duration all but eliminates the variation due to the time of day, day of the week, or season of the year.

I'm not suggesting that a Big Data solution is somehow magically independent of time. Rather, what I'm saying is that the "store first, ask questions later" approach that is central to Big Data lends itself readily to collecting useful samples quickly enough that delta-t is negligible.

This pretty much sums up the entirety of Big Data.

Data analysis can highlight the correlations that would otherwise go unnoticed, and the "big" data sets involved help to ensure that the noticed correlations are statistically significant. With a large enough sample size, the effects of time can be eliminated from the statistics, supporting analysis of even highly-dynamic models. To a statistician, this is all trivial, given a large enough data set.

Once correlations are discovered, interpreting them in the business context is a different matter for which computers are not well-suited. As the phrase goes, correlation is not causation. A business expert must analyse the observations and figure out what it all means. There may be a correlation indicating a causal relationship, or there may be a hidden cause not covered by the available data.

Even if a causal relationship can be identified, the management may not want to act on it. Sure, the company might make more money by changing their behavior in a particular market segment, but if that segment is dying, it may not be worth the expense to change now. That's also not a task for computers, yet.

Big Data techniques are effectively just a tool. It does one job particularly well, and does a few other jobs well enough to be useful. It is still up to humans to determine if Big Data is the best tool for a particular situation.

They don't produce any offspring that live long enough to breed, making them effectively sterile for generational purposes.

Eventually the first-generation males will die, as well, leaving just the greatly-reduced next-generation population.