This is a point that is radically misunderstood by almost all sides of the political debate around anthropogenic climate change. Think about what it implies: climate models do not predict observational reality. That, and only that, is why one cannot and should not expect a nice fit between the model and the reality.

This is OK, mind: non-predictive modelling is extremely useful, and there is very little doubt that human activity is adding about 1.6 W/m**2 to the Earth's heat budget (somewhat less than 0.5% of the total, equivalent to an orbital perturbation of about half the distance to the Moon). But climate models do not tell us in any meaningful or useful sense how the ocean/atmosphere system will respond to that additional heating.

There will be a response, but estimating its type, distribution and magnitude well enough to be considered predictive is well beyond current model capabilities. I haven't looked at AR4 or 5 code, but AR2 had approximations that made me cringe, up to and including fixing up energy conservation at the end of each time-step by adjusting cell temperatures.

Climate skeptics--the sane ones at least--are aware of this and take the strong claims of predictive power in the models with a large grain of salt. They also tend to assume that "you can't prove there will be a disaster" means "there won't be a disaster", which is utterly unwarranted.

Climate believers also ignore the poor predictivity of the models, which is unfortunate, because the logical response to that poor predictivity is to invest in robustness and flexibility rather than specific solutions, because we don't know what the specific future conditions will be.

Climate believers also undermine their case by an excessive focus on "abstinence only" policies, and are for some reason unwilling to contemplate any response to climate change that involves things like nuclear power and geo-engineering research. It's almost as if they think the climate-driven destruction of civilization is such a huge issue that we must be willing to do anything to stop it... except change anyone's mind on the relative value of nuclear energy.

I'm skeptical of testing (automated or otherwise), and I think point in TFS is well-taken: testing that would have caught this bug would have involved creating tests that virtually duplicated the system under test.

While some code is susceptible to test-driven development and thorough testing, and that should be done where-ever possible, the resources required to test some code effectively double the total effort required, and maintaining the tests becomes a huge headache. I've worked in heavily-tested environments and spent a significant fraction of my time "fixing" tests that weren't actually failing, but which due to changes in interfaces and design had become out-of-date or inappropriate.

That's not to say that testing can't be done better, but it's clearly a hard problem, and I've yet to see it done well for the kind of code I've worked on over the past 20 years (mostly algorithmic stuff, where the "right" answer is often only properly computable by the algorithm that is supposed to be under test, although there are constraints on correct solutions that can be applied.)

So I'm arguing that a culture of professionalism, that implements best-practices including coding standards and code reviews (possibly automated) that check for simple things like open if statements and unchecked memory access would be lower cost and at least as effective as heavier-weight testing.

This is a static-analysis vs dynamic-analysis argument, and while I certainly agree that dynamic analysis is necessary, both these bugs would have been caught with fairly simple-minded static analyzers checking against well-known coding standards from a decade ago.

One of the things that struck me about the goto fail bug was that it was specifically engineered out of coding best practices in the '90's.

Any reasonable coding standard from that time forbade if's without braces for precisely this reason. And yeah, that's a "no true Scotsman" kind of argument (if a coding standard didn't contain such a clause it was not by my definition "reasonable") but the point still holds: software developers at the time were aware of the risk of open if statements causing exactly this kind of failure, because we had observed them in the wild, and designed coding standards to reduce their occurrence.

So to be very specific about what kind of processes and culture would have prevented this bug: a reasonable coding standard and code reviews would have caught it (much of the code review process can be automated these days), and a culture of professionalism is required to implement and maintain such things.

The canonical attribute of professionals is that we worry at least as much about failure as success. We know that failures will happen, and work to reduce them to the bare minimum while still producing working systems under budget and on time (it follows from this that we also care about scheduling and estimation.)

Amateurs look at things like coding standards and reviews and say, "Well what are the odds of that happening! I'm so good it won't ever affect my code!"

Professionals say, "The history of my field shows that certain vulnerabilities are common, and I am human and fallible, so I will put in place simple, lightweight processes to avoid serious failures even when they have low probability, because in a world where millions of lines of code are written every day, a million-to-one bug is written by someone, somewhere with each turn of the Earth, and I'd rather that it wasn't written by me."

It's very difficult to convince amateurs of this, of course, so inculcating professional culture and values is vital.

The degree of molecular similarity in the DNA changes to achieve a particular result will depend strongly on the type of change one is looking at.

For the case of toxin-resistance, which is much closer to the molecular level, the odds of similar changes to the DNA are much higher than for complex morphological changes.

Molecular changes like toxin-resistance are more likely to involve a single gene that codes for a single enzyme, changing the enzyme so that the toxin is no longer metabolized in a harmful way. There are going to be a very limited number of ways to do this because it's pretty close to a one-gene/one-enzyme mapping in many cases.

Morphological changes, on the other hand, involve a whole network of genes that are turned on over the course of development, and the network can be altered in many different ways to get to the same result. Think about it like a road network where you're used to taking a particular route to get from A to B. If a bridge goes out on your your usual route, you may choose different alternatives depending on time of day, the kind of vehicle you drive, etc. Networks create choices.

Even then it will depend on the kind of morphological change we are talking about.

For example, there is a lizard in Mexico, which was studied in the '80's or '90s. There were several related species living inland, and a couple of isolated species on the coast near the Yucatan peninsula. Both the coastal species had an extra cervical (neck) vertebra, and it had been assumed on the basis of this similar morphology that their evolutionary history had been a general migration to the coast, an adaptation to coastal environments that involved having a longer neck, followed by a general die-back that resulted in the two existing but separate populations.

It turns out based on their genes the two coastal species hadn't had a common ancestor for millions or tens of millions of years, and the adaptation to coastal living had happened independently but fairly recently. In this case, because certain aspects of body plan are controlled by a highly conserved and relatively simple set of genes, the additional vertebra were the result of similar sets of genetic changes.

Things like body width, which is what TFA is talking about, are a lot more complicated in their regulation, so more likely to be achieved via different genetic changes that have the same morphological outcome.

I'm going to throw in a shameless plug here because it seems relevant to the topic at hand. I've just published a hard SF novel that's premised on a what-if about the role of mathematics and law-like descriptions in evolution. If you're interested in that sort of thing you should check it out: http://www.amazon.com/Darwins-...

Humans live insanely long lives for mammals: twice the average. The average mammal lives a billion heartbeats, humans live two billion. "Heartbeats" are a convenient normalization that accounts pretty well for differences in size, etc.

There are fairly plausible evolutionary reasons for this. Grandparents are the primary mechanism by which culture is transmitted, so if your grandparents (or the grandparents of your close kin) lived a long time you would have a better chance of reproducing yourself, assuming cultural knowledge is useful in your local environment. And people with long-lived grandparents tend to be long-lived themselves, so the trait gets selected for.

As such, animal models for human aging are extremely hard to come by, and ones as distant as worms are very unlikely to produce results that are generalizable to humans. This is why so many things cure cancer in rats but have no effect on humans: rats will get cancer from a dirty look, so their cancers tend to be relatively easy to knock over. Cancers that survive all the clever molecular tricks humans throw at them are much harder nuts to crack.

We don't even know if calorie restriction works in humans (not enough people have been starving themselves for long enough to tell) so this article is way, way out on a speculative limb. Good science, I'm sure, but the hook should be "Scientists learn something about metabolic control pathways" and not "You may live forever!"

I rarely write replies, but this comment is too funny not to make note of.

There are two possible interpretations of my comment.

The first is that electricity is generated (by burning coal, etc) and then transported long distances to charging stations.

The second is that I am a complete idiot.

While this is the 'Net and therefore the domain of idiots... really? You really, honestly thought the second interpretation was more natural and plausible than the first? Did the first even cross your mind? If not, why not?

Excellent suggestions all. I would add:

1) "Software Failure: Management Failure" by Stephen Flowers (somewhat dated, but an excellent collection of case studies of failed projects... technologies change but the lessons learned remain relevant.)

2) "Rapid Development" by Myers. The chapter on estimation alone is worth the price.

Let's add some facts to the discussion...

Electricity also involves losses in transmission and transformation.

Battery charge/discharge is only ~85% efficient under moderately realistic conditions: http://www.pluginhighway.ca/PH...

Electrolysis is reported to have efficiencies up to 80%: http://en.wikipedia.org/wiki/E...

Line losses for electricity are in the 10% or greater range (the figure for Canada is almost 40% due to the amount of power we get from relatively remote hydroelectric facilities). So electricity and hydrogen aren't too far off-base with respect to losses.

The weird thing about hydrogen vs electricity is that while hydrogen's energy density is great per unit mass, it's volumetric energy density is terrible given any reasonable storage technology. So while Li-Ion batteries have 10% of the specific energy density of hydrogen, they have almost equal volumetric energy densities: http://en.wikipedia.org/wiki/E...

And it is worth noting that hydrogen's volumetric energy density is 20% that of petrol, so to get the same range you'll need a fuel tank that's five times larger. Advanced storage technologies can help, but not all that much.

Hydrogen also suffers from handling issues (embrittlement) and is extremely explosive. Natural gas has a relatively narrow range of fuel-air mixtures (about +/-5% around the 50/50 mark) where it will go bang rather than just burn. Hydrogen goes bang from about 5% to 95% mixture.

So while batteries have their issues, hydrogen is so clearly not a competitor that it's curious that Toyota is going for it. On the other hand: prediction is hard, especially with regard to the future.

This is the great thing about capitalism: it encourages people to explore those avenues that look utterly wrong-headed to the rest of us, and sometimes... they are right, and we are wrong. No centrally planned economy of any kind has ever been able to figure out how to do that (nor yet to deal with the problem of corruption that is endemic in human societies of all kinds, including capitalist ones.)

While I largely agree with the sentiment, this story is not one of them.

There are peculiar solutions to the field equations of GR, including wormholes and black holes. Whether any of these solutions can be physically realized has been one of the most interesting questions in both observational and theoretical cosmology for decades. The possibility of detecting the difference between a supermassive black hole and a wormhole at the centre of the galaxy is definitely nerd-worthy, although I agree the hype is, uh, over-hyped.

Furthermore, these stories give lay-people a bit of insight into how science--which is the discipline of publicly testing ideas by systematic observation, controlled experiment and Bayesian inference--actually works.

Remember when the existence of black holes was still hotly debated, back in the '70's? Observations on an very small object with a mass of more than 1.4 solar masses (the theoretical upper limit for neutron stars) resulted in a general acceptance that it was a black hole, which likely therefore exist. But that conclusion was contingent on a lack of other plausible alternatives, and so is subject to modification as other alternatives become more plausible...

This is part of that ongoing story.

While argument from authority is invalid as a logical method, no serious Bayesian would treat "a team of profoundly experienced scientists who worked on the project for years and subject themselves to all kinds of internal reviews and published the work after independent scrutiny in a peer-reviewed journal" as less plausibly correct than "a physics blogger," ab initio.

Furthermore, as a purely practical matter, it is empirically possible for fools to raise objections, even ones that sound plausible to laypeople, far faster than it is for scientists to respond to them. This is why cranks are so often ignored, and why the process of peer-review, although grossly imperfect, is a useful filter for considering results. Peer review does not prove work is correct, but it does raise the odds that it is not obviously wrong.

So when this guy gets his re-analysis into a decent journal it will be time to discuss it. Until then, he's just another nut on the Internet.

Not that the requirement is unreasonable, given the business environment, but man I'd like to see Excel fall off that list. It's useful for non-programmers, but as a programming and visualization environment it is a hideous mass of unkempt hairballs.

I use matplotlib and python (including rpy, which lets me get most of the R goodness without having to remember much R syntax) as tools, but mostly what I see lacking in analysts is statistical knowledge and the ability to reason statistically, starting with an inability to pose issues in ways that are suitable to well-formed statistical analysis.

That's certainly in the right ballpark. The solar constant is about 1300 W/m**2 (at the top of the atmosphere) so call it 8000 W/m**2 at the surface, cut it by half for night-time and multiply by 0.1 for solar panel efficiency and other losses and you get about 40 W/m**2 or 40 MW/km**2. The GP's big numbers come out to about 400,000 GW, so this comes out to 400,000/40 = 10,000 km**2, which is a square just 100 km on a side (a 108 mile square is about 170 kilometres on a side.)

Now mind you, storing that power and distributing that power are non-trivial, and Greenpeace, the Bulletin of the Atomic Liars, and god knows who else would be mounting protests to "Save Our Desert!" if anyone actually dared build anything anywhere, but the raw numbers aren't at all insane given the scale and success of past human engineering projects.

Unfortunately, with the new "Can't Do!" attitude of the modern US it's very unlikely that this will happen.

Really? Why? They are an anti-nuclear, anti-science political lobby organization, and always have been. This kind of dishonest, misleading smear-job is their bread and butter. It's all they do.

Yeah, getting information on nuclear anything from an anti-science, anti-nuclear political lobby group with a grossly misleading name is not a good idea.

When I saw the organization promoting this I didn't bother to read it--life is too short to waste time debunking nonsense by political lobbyists who have zero credibility outside their little bubble of fanatical and fact-averse supporters. So thanks for taking the trouble to slog through the sewage and point out some of the howlers.

Since the primary purpose of owning a gun is to commit suicide (http://www.pewresearch.org/fact-tank/2013/05/24/suicides-account-for-most-gun-deaths/) that scenario will feature prominently in any rational discussion of smart guns.

It follows that any discussion of smart guns that does not focus on their primary use--killing their owner--is not a rational discussion, but rather an emotion-laden hysteria-fest.

Six in ten guns deaths in the US are suicides. Really smart gun technology would detect those cases and ask, "Are you really sure you want to do that?"

Simply locking out suicide attempts likely won't help--the firearm suicide rate in Canada is comparable to that in the US, even though our firearm murder rate is much lower. But a gun that calls social services and says, "Hey, my owner is trying to kill himself... please help!" might actually be useful.