A population is either spread across a wide area or there are semi-isolated pockets of it. The species evolves, and there's enough contact among the geographically distant bits of the population that the useful traits get passed around. The species doesn't evolve in multiple places at once completely independently, but it also doesn't evolve in a single place.
Morocco and Ethiopia are close enough to each other that it's conceivable populations in both places were in semi-frequent contact, so that kind of thing could happen. As opposed to an isolated population evolving in the rift valley and then deciding one day they were going to leave and go settle the planet.
Most of those studies are probably not blinded. I wouldn't be surprised if all of the flagged ones are not. There doesn't have to be any fraud at all. If you know what the groups are, your brain will introduce its own bias, without you even knowing about it.
That's not fraud. Most of those studies are primary biology or animal studies, non-blinded. They tend to have sample sizes of around 10, and use sketchy stats. It's not particularly surprising they can't be replicated.
The stats should be improved, and they need to be more cautious in their conclusions (as does anyone reading them), but the scientific literature is supposed to be more about "hey guys, look at this, what do you think?" and less about "this is the truth!".
"It only seems like a good deal for existing shareholders hoping to drive the price up a bit before they sell."
That sounds like it's close to the right idea. The current owners don't see a future for Twitter and want to dump it. If they dump it, the company will probably go down and no more Twitter. This proposal is basically saying to the users, hey, if this thing is of value to you then buy a share or two. If enough of you do that, then Twitter won't die (this year).
It's a way of getting money from users without saying the dirty subscription word.
I don't see that as being a flaw at all. Most software should be written like that.
The problem is with the people who use the software assuming that random special purpose projects like Hadoop have planned for security or are competent to do so. Just assume it's all insecure unless there's good reason to think otherwise, and access it via vpn or ssh.
Rockets don't work that way. The fuel use is exponential, not linear: shaving off the first 3% reduces fuel requirements by more than 3%. You also get benefits from saved gravity losses from being at higher altitude already and less drag and ability to use a more efficient engine design from having lower starting air pressure.
This is a bigger problem with non-machine learning algorithms. People have a great deal of difficulty figuring out how they know what they know. Sometimes they're honest and say they don't know, sometimes they're happy to make something up.
There have been studies on physicians specifically. The students tend to follow diagnostic criteria. Once you get good at it, you don't anymore.
With the machine learning algorithms you can crack them open and poke around to your heart's content. And the "it's a black box" is more of a sound bite: it takes a little more work to figure out what it's doing when you didn't explicitly program it. People get very pissed off when you try and do the same thing with their heads.
Yes they do. And occasionally English adopts a word or phrase from another language because it expresses a meaning more precisely or succinctly than the closest English equivalent does.
But verboten, particularly as it was used in the summary, is just a literal translation to German of "forbidden," a perfectly good English word. Verboten is not an obscure English word that has a subtly different meaning.
A lot of people questioned that statistic. Vigorously. It held up.
It's also supported by a lot of other research, ranging from what happens if you force a surgeon to use something as simple as a checklist (it prevents at least one potentially serious error in nearly every single surgery) to what happens if a community loses access to a hospital for some reason (death rates in that community decrease).
Medicine is the only profession where life-critical decisions are made based on personal expertise and opinion, rather than carefully specified standard operating procedure. It's a weird historical holdover.
If people knew the real statistics they wouldn't go to hospitals as much as they do, and they'd be much more skeptical about what doctors told them to do. The general public doesn't appreciate those statistics because they have a weird hero worship for physicians, and because the guild of physicians actively suppresses such information. Medical errors happen constantly, but are not routinely monitored or reported, unlike in virtually every other similar profession.
I expect in 10 years, 20 max, it will be illegal in civilized places to diagnose and prescribe treatment without consulting a computer, just like it is now without consulting a medically trained person.
This is going to change fast, as soon as it becomes widely apparent how bad the medical profession is at doing this stuff.
It's a stupid question that illustrates a misunderstanding about what diagnosis is.
If a fortune teller fails to predict you're going to get hit by a bus tomorrow, who's to blame, her, her crystal ball or it's manufacturer?
Physicians misdiagnose patients all the time because diagnosis depends on a variety of imperfect information and very often cannot be done accurately. That is nobody's fault.
Physicians also misdiagnose patients all the time because they aren't very good at keeping up with new developments in medicine or are otherwise negligent. That's their fault.
An AI could be wrong for the first reason. If so, nobody is at fault. If the AI is wrong because of a manufacturing defect, the manufacturer is at fault, or the supervising physician is, if they insist on being in that position.
The phrase "cock holster" itself very strongly denotes a dominant-submissive relationship, whether it refers to two men or a man and a woman, a mouth, a vagina, a whole person, whatever.
Colbert wasn't in any way calling Trump a bad person for being homosexual. He was calling Trump a bad person for being Russia's cock holster.
It depends how exactly it was trained. Most likely it was trained on a fixed-size board, so the architecture of the neural net wouldn't match a bigger board without modification. That's not necessarily true though, there are neural net designs that can take flexible sized input. Alpha Go could certainly be trained to do it.
The question is interesting. Given a human and computer that had never played on a larger board, which would do better? Go is a game that is fairly local (unlike chess) so it doesn't change radically if you make the board bigger, which means a lot of your strategy can be transferred... for both the computer and the human.
It's worth keeping in mind that although Alpha Go has played more games (against itself) than any human has, it has played for less clock time than any human champion has.
Funny you should say... alpha go is a general purpose learning system. It was originally taught to play Nintendo games. It isn't programmed to play Go, it learned to.
Go is a game that abstractly simulates some aspects of military strategy, as does chess, and have long been considered means to practice strategic thinking.
Machine learning is AI. It's a standard term, with a real definition, probably agreed upon before you were born, given slashdot's demographics and your UID.
You might be thinking of what people call hard AI or human level AI, where a computer can basically do anything a person can, at least as well.
Alpha go is trained by reinforcement learning, like a person would be. They let it watch some historical games until it gets the basics, then it plays itself to refine its game.
It's debatable whether chess is a simpler game or not, but chess can be effectively played with standard look ahead and tree pruning techniques. Those work poorly in go. The reinforcement learning used for alpha go could be used to teach it to be an unbeatable chess player too. And originated with deep mind for teaching the computer to play Nintendo games. Any Nintendo game.
One of the neat things about reinforcement learning is that you define the outcome you want (highest score, winning the most games) and what inputs are allowed (placing a stone, pressing buttons on a controller) and that's it.
The replacement time of vehicles is quite a bit less than 20 to 30 years. Some people hang onto them a lot longer than that, but a 20 year old car is verging on an antique.
The average age right now in the US is around 11 years. The article speculates that the combination of the switch to EVs and self driving, which will probably come with massive insurance savings, will encourage people to switch faster.
You didn't really follow through with those numbers. Sure, the fuel for an electric car is cheaper currently, although you've neglected losses and inefficiencies charging and holding charge. Also, electricity is more expensive some places, and gas would get cheaper if there was a mass switch to electric vehicles. But suppose we get really good at making cheap electricity, which looks probable given advances in solar and gas doesn't completely tank.
The article makes a lot of really grandiose claims, including that fuel costs for an EV are essentially nil (they're not, as you demonstrate) and that maintenance costs for an EV are nil and EVs will last much longer than a gasoline car. My car has never had any engine-related maintenance except for oil changes, but it has had lots of suspension work, brakes, etc. EVs need those too. I also have never had a car die due to engine problems: they were all due to rust and increasing non-engine maintenance costs.
People are certainly going to start buying EVs more and more, and that might get greatly accelerated when self driving cars are the norm because insurance costs are likely to be much lower, and most of the operating cost of a vehicle is insurance. I still don't see the massive and sudden shift the article predicts though, because it is predicated on EVs being basically free to run and lasting nearly forever.
I guess you can look at it that way. Really what happens is that a NAT router drops any packet that it can't figure out a destination for. It's kind of like the post office... they don't deliver mail for which they can't figure out the destination address.
It seems like a pedantic point, but it becomes important when you talk about IPv6. Computers behind NAT are protected because they don't actually exist on the Internet. They can only be reached via special tricks, and those tricks have to be implemented for the thing to work.
On the other hand, a device with an IPv6 address DOES exist on the internet. Unfortunately, protecting the IPv6 devices isn't as simple as just building routers that use the same filter as NAT routers do, because NAT routers don't use a filter. You could build an IPv6 router that imitates what a NAT router does, but you'd have to specifically include a stateful packet inspection system to do so (and you'd break everybody's fancy IoT devices). Want to bet most manufacturers don't bother?
NAT routers don't filter.* Any incoming traffic is addressed to the router. If you happen to have instructed the router to pass particular types of traffic to a specific machine, it does this. Otherwise it responds, or doesn't, to traffic addressed to it, just like any other machine would.
* some also filter, but that's not really part of NAT
Problem is, there are lots of idiots who have actual pull in education who think that video lectures and flipped classrooms and such are amazing ideas that nobody has ever thought of and are sure to revolutionize education.
One of the insidious things about the recent online learning craze is that people actually like watching the educational videos. People like them, and report that they're learning a lot, so they do very well on the self-assessments. But in objective measurements they're terrible.
Slashdot Mirror
A population is either spread across a wide area or there are semi-isolated pockets of it. The species evolves, and there's enough contact among the geographically distant bits of the population that the useful traits get passed around. The species doesn't evolve in multiple places at once completely independently, but it also doesn't evolve in a single place.
Morocco and Ethiopia are close enough to each other that it's conceivable populations in both places were in semi-frequent contact, so that kind of thing could happen. As opposed to an isolated population evolving in the rift valley and then deciding one day they were going to leave and go settle the planet.
Most of those studies are probably not blinded. I wouldn't be surprised if all of the flagged ones are not. There doesn't have to be any fraud at all. If you know what the groups are, your brain will introduce its own bias, without you even knowing about it.
That's not fraud. Most of those studies are primary biology or animal studies, non-blinded. They tend to have sample sizes of around 10, and use sketchy stats. It's not particularly surprising they can't be replicated.
The stats should be improved, and they need to be more cautious in their conclusions (as does anyone reading them), but the scientific literature is supposed to be more about "hey guys, look at this, what do you think?" and less about "this is the truth!".
"It only seems like a good deal for existing shareholders hoping to drive the price up a bit before they sell."
That sounds like it's close to the right idea. The current owners don't see a future for Twitter and want to dump it. If they dump it, the company will probably go down and no more Twitter. This proposal is basically saying to the users, hey, if this thing is of value to you then buy a share or two. If enough of you do that, then Twitter won't die (this year).
It's a way of getting money from users without saying the dirty subscription word.
I don't see that as being a flaw at all. Most software should be written like that.
The problem is with the people who use the software assuming that random special purpose projects like Hadoop have planned for security or are competent to do so. Just assume it's all insecure unless there's good reason to think otherwise, and access it via vpn or ssh.
Nice. That's how I feel about C and Perl.
People watch baseball too. I have trouble thinking of a video game that's more boring to watch than baseball.
Rockets don't work that way. The fuel use is exponential, not linear: shaving off the first 3% reduces fuel requirements by more than 3%. You also get benefits from saved gravity losses from being at higher altitude already and less drag and ability to use a more efficient engine design from having lower starting air pressure.
Yes. Nursing has a not-so-dim future, for the rich people who can afford it.
This is a bigger problem with non-machine learning algorithms. People have a great deal of difficulty figuring out how they know what they know. Sometimes they're honest and say they don't know, sometimes they're happy to make something up.
There have been studies on physicians specifically. The students tend to follow diagnostic criteria. Once you get good at it, you don't anymore.
With the machine learning algorithms you can crack them open and poke around to your heart's content. And the "it's a black box" is more of a sound bite: it takes a little more work to figure out what it's doing when you didn't explicitly program it. People get very pissed off when you try and do the same thing with their heads.
Yes they do. And occasionally English adopts a word or phrase from another language because it expresses a meaning more precisely or succinctly than the closest English equivalent does.
But verboten, particularly as it was used in the summary, is just a literal translation to German of "forbidden," a perfectly good English word. Verboten is not an obscure English word that has a subtly different meaning.
Plus he didn't italicize it.
A lot of people questioned that statistic. Vigorously. It held up.
It's also supported by a lot of other research, ranging from what happens if you force a surgeon to use something as simple as a checklist (it prevents at least one potentially serious error in nearly every single surgery) to what happens if a community loses access to a hospital for some reason (death rates in that community decrease).
Medicine is the only profession where life-critical decisions are made based on personal expertise and opinion, rather than carefully specified standard operating procedure. It's a weird historical holdover.
If people knew the real statistics they wouldn't go to hospitals as much as they do, and they'd be much more skeptical about what doctors told them to do. The general public doesn't appreciate those statistics because they have a weird hero worship for physicians, and because the guild of physicians actively suppresses such information. Medical errors happen constantly, but are not routinely monitored or reported, unlike in virtually every other similar profession.
I expect in 10 years, 20 max, it will be illegal in civilized places to diagnose and prescribe treatment without consulting a computer, just like it is now without consulting a medically trained person.
This is going to change fast, as soon as it becomes widely apparent how bad the medical profession is at doing this stuff.
It's a stupid question that illustrates a misunderstanding about what diagnosis is.
If a fortune teller fails to predict you're going to get hit by a bus tomorrow, who's to blame, her, her crystal ball or it's manufacturer?
Physicians misdiagnose patients all the time because diagnosis depends on a variety of imperfect information and very often cannot be done accurately. That is nobody's fault.
Physicians also misdiagnose patients all the time because they aren't very good at keeping up with new developments in medicine or are otherwise negligent. That's their fault.
An AI could be wrong for the first reason. If so, nobody is at fault. If the AI is wrong because of a manufacturing defect, the manufacturer is at fault, or the supervising physician is, if they insist on being in that position.
The phrase "cock holster" itself very strongly denotes a dominant-submissive relationship, whether it refers to two men or a man and a woman, a mouth, a vagina, a whole person, whatever.
Colbert wasn't in any way calling Trump a bad person for being homosexual. He was calling Trump a bad person for being Russia's cock holster.
It depends how exactly it was trained. Most likely it was trained on a fixed-size board, so the architecture of the neural net wouldn't match a bigger board without modification. That's not necessarily true though, there are neural net designs that can take flexible sized input. Alpha Go could certainly be trained to do it.
The question is interesting. Given a human and computer that had never played on a larger board, which would do better? Go is a game that is fairly local (unlike chess) so it doesn't change radically if you make the board bigger, which means a lot of your strategy can be transferred... for both the computer and the human.
It's worth keeping in mind that although Alpha Go has played more games (against itself) than any human has, it has played for less clock time than any human champion has.
Funny you should say... alpha go is a general purpose learning system. It was originally taught to play Nintendo games. It isn't programmed to play Go, it learned to.
Go is a game that abstractly simulates some aspects of military strategy, as does chess, and have long been considered means to practice strategic thinking.
Machine learning is AI. It's a standard term, with a real definition, probably agreed upon before you were born, given slashdot's demographics and your UID.
You might be thinking of what people call hard AI or human level AI, where a computer can basically do anything a person can, at least as well.
Alpha go is trained by reinforcement learning, like a person would be. They let it watch some historical games until it gets the basics, then it plays itself to refine its game.
It's debatable whether chess is a simpler game or not, but chess can be effectively played with standard look ahead and tree pruning techniques. Those work poorly in go. The reinforcement learning used for alpha go could be used to teach it to be an unbeatable chess player too. And originated with deep mind for teaching the computer to play Nintendo games. Any Nintendo game.
One of the neat things about reinforcement learning is that you define the outcome you want (highest score, winning the most games) and what inputs are allowed (placing a stone, pressing buttons on a controller) and that's it.
The replacement time of vehicles is quite a bit less than 20 to 30 years. Some people hang onto them a lot longer than that, but a 20 year old car is verging on an antique.
The average age right now in the US is around 11 years. The article speculates that the combination of the switch to EVs and self driving, which will probably come with massive insurance savings, will encourage people to switch faster.
You didn't really follow through with those numbers. Sure, the fuel for an electric car is cheaper currently, although you've neglected losses and inefficiencies charging and holding charge. Also, electricity is more expensive some places, and gas would get cheaper if there was a mass switch to electric vehicles. But suppose we get really good at making cheap electricity, which looks probable given advances in solar and gas doesn't completely tank.
The article makes a lot of really grandiose claims, including that fuel costs for an EV are essentially nil (they're not, as you demonstrate) and that maintenance costs for an EV are nil and EVs will last much longer than a gasoline car. My car has never had any engine-related maintenance except for oil changes, but it has had lots of suspension work, brakes, etc. EVs need those too. I also have never had a car die due to engine problems: they were all due to rust and increasing non-engine maintenance costs.
People are certainly going to start buying EVs more and more, and that might get greatly accelerated when self driving cars are the norm because insurance costs are likely to be much lower, and most of the operating cost of a vehicle is insurance. I still don't see the massive and sudden shift the article predicts though, because it is predicated on EVs being basically free to run and lasting nearly forever.
If a significant proportion of cars were electric somebody would have to figure out how to tax them. The roads to drive the things on cost money.
I guess you can look at it that way. Really what happens is that a NAT router drops any packet that it can't figure out a destination for. It's kind of like the post office... they don't deliver mail for which they can't figure out the destination address.
It seems like a pedantic point, but it becomes important when you talk about IPv6. Computers behind NAT are protected because they don't actually exist on the Internet. They can only be reached via special tricks, and those tricks have to be implemented for the thing to work.
On the other hand, a device with an IPv6 address DOES exist on the internet. Unfortunately, protecting the IPv6 devices isn't as simple as just building routers that use the same filter as NAT routers do, because NAT routers don't use a filter. You could build an IPv6 router that imitates what a NAT router does, but you'd have to specifically include a stateful packet inspection system to do so (and you'd break everybody's fancy IoT devices). Want to bet most manufacturers don't bother?
NAT routers don't filter.* Any incoming traffic
is addressed to the router. If you happen to have instructed the router to pass particular types of traffic to a specific machine, it does this. Otherwise it responds, or doesn't, to traffic addressed to it, just like any other machine would.
* some also filter, but that's not really part of NAT
Problem is, there are lots of idiots who have actual pull in education who think that video lectures and flipped classrooms and such are amazing ideas that nobody has ever thought of and are sure to revolutionize education.
One of the insidious things about the recent online learning craze is that people actually like watching the educational videos. People like them, and report that they're learning a lot, so they do very well on the self-assessments. But in objective measurements they're terrible.