"So it’s 99.5% that it occurred due to warming over the industrial era"
There are at least a couple of statistical fallacies in this conclusion. And there isn't a lack of agreement about that.
One problem with p-value tests is precisely that people misunderstand what the p-value means, which is where basic education comes in. It could save people from believing a lot of things they shouldn't. (Like many health and fitness crazes over the last generation, for instance) Or at the very least, we could train science journalists.
I played a game of Monopoly where I started with a roll of 12. There was a 97% chance I correctly rejected the hypothesis that the dice were fair. Maybe this is true in some sense? But it's still somewhere between misleading and nonsensical to say.
The 0.5% quote is fine. It's turning it into a 99.5% that is not fine.
It's essentially like saying that a pair of dice that rolled a 12 has a 97% chance of being loaded, because there's only a 1 in 36 chance of rolling that high.
If you look at 200 rivers, it would not be surprising to find something that naturally occurs 0.5% of the time. It is not correct to say that there is a 99.5% chance that this is due to non-natural causes.
"The calculations put chance of the piracy having occured due to natural variability at 0.5%. “So it’s 99.5% that it occurred due to warming over the industrial era,” said Best."
We need to train children in school to understand what statistical confidence means, so that we stop saying wrong things like this.
Presumably "Machine Language" which is what we called Assembly, back when the translation between what you wrote and what the CPU actually did was pretty transparent.
Lots of people visit links on the clear web showing illegal and horrific acts. If the full extent of a crime is filling out an http form with a fake email to see some pictures and video, it's still not clear that this is so far beyond the pale that years of prison for hundreds of thousands of people is the best solution.
The problem is fundamental and serious, what to do when a growing number of people have literally nothing valuable to contribute to an economy. But horses aren't a good analogy to shed light on it.
Horses are more analogous to steam engines than people, in their historical function on the economy.
> you cannot pick this up in any meaningful way in a "few months" of after hours/weekend study
You can't pick up coding like this either. See Peter Norvig's famous "Teach yourself programming in 10 years" article. The delta in the wisdom you obtain, between a few side projects over months and battle hardened experience with real products and code bases over years, is immense.
Yet the glycemic index of whole wheat bread is higher than Coca Cola. (Look it up!) I.e. the same amount of calories of bread will spike your blood sugar more quickly than Coke.
Metabolically, starchy foods including grains are a lot more like sugar than people who think they are making rational health decisions want to believe.
Now consider that in human history, about 40% of men were able to breed. So 30% of the population goes extinct every generation without passing on genes, assuming all women reproduce. So with a few hundred iterations of this kind of 70:30 split, you could see how evolution could happen quite quickly even under normal circumstances.
We know that AlphaGo uses a Monte Carlo tree search, and presumably contains innovations and refinements to techniques applicable to turn based board games with perfect information and a clear binary win condition.
We also know it uses a Deep Learning algorithm to imitate play from the best humans. And presumably, being stronger than any human now, it could also recursively refine and train on its own games.
Do we know how important the Deep Learning component is? Would AlphaGo be just as strong or nearly so without that part?
I know that over the last few years chess engines have become dramatically more powerful, even on the same hardware, to the tune of hundreds of Elo points. They still stick to the classical techniques though (Alpha-Beta pruning, minimax is still the core). This is not to understate the refinements, but the general ideas are (I think?) only applicable to turn based games with finite moves and a binary mathematical criterion for victory.
Is it possible that the improvements AlphaGo has made are mostly of this type, and the Deep Learning parts are not that important to its actual play strength? Or can we totally rule this out? Is there an in depth discussion of this question somewhere?
There's one huge incentive I can relate to: a product for the n percent of lonely men each generation who don't have the social skills, status, or otherwise cannot attract a mate. And afterwards, the men who would find a perfect and sentient companion to be superior to dating and relationships. I'd bet that logs of Siri's queries support this idea quite well.
> Evolution had billions of years, working in a massively parallel way to work this out after all.
And it also got to work on it with a hundred billion stars with earth-like planets, in each of a hundred billion galaxies, and (maybe) in a basically infinite space of universes. All in parallel with infinite time to play with.
Is it so far-fetched to postulate that humans will hit our limits before getting this far?
That's what she said.