It took all her years of graduate research and effort, and four additional years, and finally someone with the stature Russell agreeing with her, to overturn the consensus that believed her conclusions were wrong.

If her supervisor hadn't have been Russell, it would have taken longer. And it would have taken much much longer if there had been anyone with a strong vested interest in her being wrong, say a political agenda depending on sun composition or many scientists trying to maintain a funding source to study sun composition.

That is the reality of science then, and now.

If everyone must make their data available, then a paper will be judged on the strength of its:

a) academic contribution; and
b) quality/usefulness of the data.

So you might not be the author of the greatest paper, yet your impact might be the quality of the experimentation and resulting data.

Right now, papers appear and the data is just hearsay. In that environment, anybody can publish anything ... and today, there's is a strong incentive to do just that.

Rarely will a biologist, say, coauthor a paper with a statistician and a computer scientist (or better, a programmer).
After all, there are statistics apps and programming isn`t that hard ... right?
And no statistician could understand the intricacies of biology, same for a computer scientist (obviously) ... right?

So like the persons doing their own home renovations, some get it right without a professional, and a lot more don't. The tools are available but they just don't truly understand them nor know how to use them properly.

Ask yourself: how many students went in to psychology, biology, anthropology, etc. because they hated and/or were poor at math? Should you trust their statistics as researchers?

The world of science need cooperation, 'silos of knowledge' belong to the nineteenth century.

... frackin' change. What the hell isn't these days?

And all predictions made with 50% certainty.

Oh, and this has all happened before climate change.

Someone is making money off the 'climate change' mantra, which means pronouncements in that regard are no longer credible. Too much noise in the message. Don't care.

a) Why not a bloodhound, basset, etc.
b) why only one dog? Even in tech you would use more than one test eg. an ensemble of classifiers

Besides curiousity, why was computing power (read: energy) spent on this?

Seriously.

Do you think that errors in a model are cumulative?
Do you think the models used here are similar to, identical to, or components of, the global climate models?
If so, then the global models are not capturing all the processes that influence climate, correct?
If so, then the global models yield answers with a margin of error, correct?
Therefore, find out what the error is on the current climate models used to predict global climate change, because an error of +/- 2 degrees on a prediction of 2 degrees increase (say) is kind of important.

And good luck with that. Because there are no error values reported. And there probably can't be; we don't know what we don't know.

Based on that, how much do you want your governent to act on climate change "predictions"?

... down in the Earth's mantle. Where do you think all our "fossil" fuel deposits came/come from [Deep Hot Biosphere]?

Sorry for the typo folks.

-Google

Oh, and the climate scientists would like a word to see how accurate their models turned out.

thanks.

The computer mostly figures out the game design jsut like human players, and couples that with super fast reaction time.

The latter is the only reason it can beat the best human players.

The bigger story is: what games did it suck at and why?

PhD in machine learning or ...:

secretaries - because we can all do our own docs
car repair mechanics - because it's really just about replacing modules or the whole car
architects - because there's lots of free 3-D drawing apps out there
carpenters - because, hey, how hard is it to nail wood together
lawyers - because just a little reading and memorization will tell you what you need to know
engineers - because they're like carpenters, only with metal and bigger things
programmers - because anyone can learn 'hello world', and it doesn't get much harder than that.

And so on. But remember, you get what you pay for.

Go go Japan, everything electric ... but where will the electiricity come from? Will they:

a) cover thousands of acres of arable land with solar panels;
b) build and run more nuclear power plants; or
c) build and run more "fossil' fuel power plants?

It's lovely to get on the 'all electric' bandwagon, but really, the problem becomes creating that electricity and then efficiently converting it to useful work.

The same holds true for other countries like US, but at least the latter can claim to have:

a) land for solar (not necessarily easy to distribute it due to NIMBY attitude);
b) stable enough geography for nuclear power proliferation (not necessarily a good political/social climate for them though);
c) abundant natural resources for 'fossil' fuels (not necessarily cost effective ... yet :-)

No this isn't "weather", this is large scale transport of particles in the atmosphere. And that is kind of critical to understanding climate (and weather).
Which simply tells you that the models, while possibly precise, are not accurate.
And when you are talking about sweeping changes in government policy or, more frighteningly, attempts at geo-engineering, then your models should be very accurate.
Or come with a warning and a statement of margin of error.