If this were serious, they'd present it at an APA or CogSci conference. Instead, they present it at DefCon. And while narcism and psychopathy are, "Machiavellian" is not a personality disorder.
Anyway, this is never going to be practical. Even if this method would have a false positive rate of 0.1%, it would flag over 500,000 people. That's way more than there are seriously dangerous psychopaths out there. And in reality, the error rates are a lot higher for this kind of techniques.
Don't underestimate these people. They've got a huge amount of experience between them. One of them worked on wind electricity, another studied astronomy and worked at an observatory, a third one is a graphical designer, and the fourth studied business and communication. That's way more engineering knowledge than NASA had for the Apollo missions. Well, perhaps not, but at least this mission will be on facebook with gorgeous illustrations. Suck that, NASA! You didn't manage to put Neil Armstrong on facebook!
And as someone pointed out on a Dutch forum: for 6G$ you're not likely to get more than the weight of a small van in orbit around the Earth.
Perhaps the ore paranoid here are right, and it's a scam. But what idiot would fall for it?
From my personal experience (I've taught programming to science students, and a lot of other topics to psychology majors), I can tell you that the interest and knowledge differs between the two groups. Generally speaking, psy majors are much less interested in maths per se, and have considerably less knowledge. At the uni where I taught, a certain level of math was prerequisite for psy students, but most of them somehow didn't have that level, despite the deficiency courses. In one of my classes, it turned out no-one knew what a vector was, and in my profs class a 3rd year student asked "what's a square root?". That never happens with sci students.
On the other hand, quite a few sci students take statistics for just another way of manipulating symbols. They can derive the formula for stddev from E(X^2) - (E X)^2, and do GLM in a few matrix operations. However, they're less knowledgeable about ways of applying statistics.
So I would say: stick to the application. Start by explaining the use of statistics, the different kinds (descriptive, H0 testing, Bayesian inference if possible), show concrete examples. Start simple, very simple, and gradually work your way up. Don't emphasize memorizing formulas. However, they should know thoroughly what a standard deviation actually describes. They should know about different distributions.
And they've got to be motivated. Math students can get motivated by just the math itself, but psy students don't. Good examples, and a lot of repetition is required. If you've got enough time, try doing simple experiments. One I liked is the "how to detect a false coin". By having everyone in the class flip a coin 10 times, you get a nice distribution, and that will show how difficult it is to detect a false coin with just 10 flips. Such examples can be discussed, and you can make them flip the suspicious coins 10 more times to see what happens. Then ask if that's a proper procedure, etc., and then transfer that knowledge to e.g. decision time statistics.
This gets label "insightful"? Here's another: the sun rises in the east. Jeez. From my personal experience, teaching both science and Psy students, I can tell you there's quite the gap. Like the 3rd year student that asked: What's a square root?
Calling yourself very intelligent isn't helpful, indeed. Citing Plato is way over the top. It doesn't prove a thing. Plato lived 2400 years ago in a rather different culture, invented Atlantis and thought that ideals existed, so what relevance his opinion could have on the discussion is a mystery to me.
And calling science gay doesn't help either. Science is absolutely neutral. Scientists, labs and departments are not, of course, but it just doesn't make sense.
I couldn't access the article, btw, but there is absolutely no reason to trust it. Social studies are flawed, face it. It happened to be my field of work for some 20 years, and the estimation that 50% (Ioannidis) is flawed is benign. It's rather 95% flawed, 50% bogus. An evolutionary psychologist sets out to prove some hypothesis and succeeds. That's enough to make it suspicious.
Ok, 2 to 3 times? That research is probably as flawed as it can be. I've worked long enough at uni and research, and the number of gay people there is certainly not as high. The number of gays isn't even 5%. It's always overrated.
The effect in such a study might come from environments where homosexuality is a taboo, and the only people answering such questions straight are the intelligent ones.
And your other sources: a movie and Plato? You've got some catching up to do on authoritative citing.
Your preconceptions are quite horrible. Why wouldn't a pretty girl be able to pass grade school math? Quite a few of the PhDs I've met were pretty women. Perhaps a bit less glamorous than the ad, but not by miles.
And the point of 'what society "appears" to want girls to be like' is lost on me. Girls WANT to be like that, at least a bit. It might be because of our society, although I doubt that explains it 100%, but that doesn't change the fact.
So, while you lead the revolution that makes society impose less demands on female looks, let these girls choose a science career.
O sure, but how are they even going to show the sentiment of the opening page on screen, and not p*ss off all those self-righteous "Americans", you know this bit:
There's only four things we [the US] do better than anyone else music movies microcode (software) high-speed pizza delivery
And microcode shouldn't even be in that list, of course.
Exactly. Who adopted internet and mobile phones and whatever when the tech was in its infancy (see how cleverly I used that word)? Men.
Curiously, I didn't see slashdot in the list of sites not dominated by women (and slashdot is a social site, isn't it?). Does that mean that we're all women?
So... sorting is machine learning? MS Word is machine learning? Don't think so.
Nowhere did I nor the GP claim that machines have to be involved. And the machine doesn't use humans in this case, it just uses their choices as its data. So your rebuttal is somewhat unfounded.
Machine learning is learning in the first place, through algorithm: a machine can learn to do a task on its own. Not: a machine assists in a task where someone else learns. In this case, the machine doesn't learn anything. It just acts as a biased dice. The outcome of the process might be called "learned", but the knowledge is in the head of the one that runs the experiment and overlooks the outcome, not in the machine. And the "learning" doesn't generalize, so it doesn't help in improving performance on any other task than selecting between these two designs.
Indeed, this has no relation to machine learning, whatsoever. The summary is once again... deceptive.
And I'm sure the proof, that the best one gets chosen, doesn't exist. I'm also sure that this [i]way of choosing[/i] an interface has a high probability of choosing the preferred one, but there is also a big difference with A/B testing: you'll never know how big the difference between the two is. In straight-forward testing with two groups (which is not really A/B, by the way: that is alternating between A and B and then ask the subject to chose the best one; it has its origins in perceptual testing, where ABX testing is preferred), you can find out the difference in scores. Here you can't.
True, true. Bunch of variables, bunch of analyses, and one or two are significant. That's no reason for joy, even apart from the rather low correlations they found (perhaps significant, but suggestive of very little predictability). This was a fishing expedition.
They were. Read the interpretations: "Proactively discovering depressive symptoms from passive and unobtrusive Internet usage monitoring". That only makes sense if depressive symptoms cause differences in internet usage. Otherwise you might just as well monitor hair length or shoe size.
Exactly. It's obvious that e.g. distances between stations can't be too short or too long. And obviously the structure is determined by the structure of the city, the distribution of its population and their destinations. And subway planners might also have taken a look at solutions in other cities. I think I'm going to do a study on mathematical properties of articles in the Journal of the Royal Society Interface. I will of course assume that such articles are self-organizing, and arrive at the surprising conclusion that they're all made up of words; I might even find that some words are much more frequent than others, despite there being so many opportunities in so many different pieces of text. I expect this conclusion to reach Slashdot in due time...
Indeed, brain size and IQ are not really related. Studies typically show r (not r^2) to be in the region of 0.3, which means brain size explains about 10% of the variation in IQ. However, the same can be said about genetic intelligence. There are correlations, but they explain almost nothing. Furthermore, these correlation studies are deeply flawed in most cases, like slapping a GLM on whatever data there is. Or transforming data just to get significance. Don't trust them.
The 0.4 you cite is, BTW, not in the article. They say: The best unbiased estimate of the population correlation between brain volume and intelligence is 0.33. And the numbers cited are r, not r^2. So, I'd say, it's just as I said above: brain size explains 10% of the variance in IQ.
Anyway, who cares about IQ in this discussion? I'm a meat eater, but I'd say there are more considerations than IQ. So what if the IQ drops a bit when we'd all stop eating meat. It will increase with 10 or 15 points in a generation anyway (the Flynn effect). If ethics are at stake, IQ, brain size and genetics should step back. We all know what happens if you use genetics for good (that's bordering on Godwin, indeed).
Wait, you're telling a fairy tale. It's called Red Riding Hood. And it's obvious that the wolf really wanted to dig in to the vegan cookies the girl had in her basket.
I have not been fond of all these 300+ new features that every new release of OSX brings, because most of them are of the type "you can now show the mail boxes on the other side of the window", but TimeMachine is one of those that really helps the user. I've been using it since it came out, made my father do it too, and it's been bliss. And yes, I have restored files, directories and used it for transfer to new computers.
My wife on the other hand has an Asus or Acer laptop (it starts with an A, is all I know) with Windows, and it has a backup daemon that just crashes. I mean: backup software that crashes?
I'm not sure you're talking to me, but: 1. Nobody has found logic gating in neurons at this level. There is a lot of spiking and thresholds, but logic gates at that level? You can build logic gates with Lego, but that doesn't mean that every Lego structure should be understood as such. 2. Having a logic gate isn't helping one bit to tell us how memory is organized. We have logic circuits. In computers. We cannot emulate our memory in a computer. Therefore identifying circuits that could make our brain work as a computer does not help us understand our memory.
Slashdot Mirror
If this were serious, they'd present it at an APA or CogSci conference. Instead, they present it at DefCon. And while narcism and psychopathy are, "Machiavellian" is not a personality disorder.
Anyway, this is never going to be practical. Even if this method would have a false positive rate of 0.1%, it would flag over 500,000 people. That's way more than there are seriously dangerous psychopaths out there. And in reality, the error rates are a lot higher for this kind of techniques.
It's the silly season on SlashDot...
-1? How undeserved. It's a great, ironic retort.
Don't underestimate these people. They've got a huge amount of experience between them. One of them worked on wind electricity, another studied astronomy and worked at an observatory, a third one is a graphical designer, and the fourth studied business and communication. That's way more engineering knowledge than NASA had for the Apollo missions. Well, perhaps not, but at least this mission will be on facebook with gorgeous illustrations. Suck that, NASA! You didn't manage to put Neil Armstrong on facebook!
And as someone pointed out on a Dutch forum: for 6G$ you're not likely to get more than the weight of a small van in orbit around the Earth.
Perhaps the ore paranoid here are right, and it's a scam. But what idiot would fall for it?
Well, I am in the market for a new router, and guess which brand just fell of my list. Yes, we can vote with our wallets.
From my personal experience (I've taught programming to science students, and a lot of other topics to psychology majors), I can tell you that the interest and knowledge differs between the two groups. Generally speaking, psy majors are much less interested in maths per se, and have considerably less knowledge. At the uni where I taught, a certain level of math was prerequisite for psy students, but most of them somehow didn't have that level, despite the deficiency courses. In one of my classes, it turned out no-one knew what a vector was, and in my profs class a 3rd year student asked "what's a square root?". That never happens with sci students.
On the other hand, quite a few sci students take statistics for just another way of manipulating symbols. They can derive the formula for stddev from E(X^2) - (E X)^2, and do GLM in a few matrix operations. However, they're less knowledgeable about ways of applying statistics.
So I would say: stick to the application. Start by explaining the use of statistics, the different kinds (descriptive, H0 testing, Bayesian inference if possible), show concrete examples. Start simple, very simple, and gradually work your way up. Don't emphasize memorizing formulas. However, they should know thoroughly what a standard deviation actually describes. They should know about different distributions.
And they've got to be motivated. Math students can get motivated by just the math itself, but psy students don't. Good examples, and a lot of repetition is required. If you've got enough time, try doing simple experiments. One I liked is the "how to detect a false coin". By having everyone in the class flip a coin 10 times, you get a nice distribution, and that will show how difficult it is to detect a false coin with just 10 flips. Such examples can be discussed, and you can make them flip the suspicious coins 10 more times to see what happens. Then ask if that's a proper procedure, etc., and then transfer that knowledge to e.g. decision time statistics.
This gets label "insightful"? Here's another: the sun rises in the east. Jeez.
From my personal experience, teaching both science and Psy students, I can tell you there's quite the gap. Like the 3rd year student that asked: What's a square root?
I'll try to expand later, in a separate thread.
Calling yourself very intelligent isn't helpful, indeed. Citing Plato is way over the top. It doesn't prove a thing. Plato lived 2400 years ago in a rather different culture, invented Atlantis and thought that ideals existed, so what relevance his opinion could have on the discussion is a mystery to me.
And calling science gay doesn't help either. Science is absolutely neutral. Scientists, labs and departments are not, of course, but it just doesn't make sense.
I couldn't access the article, btw, but there is absolutely no reason to trust it. Social studies are flawed, face it. It happened to be my field of work for some 20 years, and the estimation that 50% (Ioannidis) is flawed is benign. It's rather 95% flawed, 50% bogus. An evolutionary psychologist sets out to prove some hypothesis and succeeds. That's enough to make it suspicious.
And the author is not entirely free of writing bogus articles, so it seems: http://blogs.scientificamerican.com/guest-blog/2011/05/23/the-data-are-in-regarding-satoshi-kanazawa/
Ok, 2 to 3 times? That research is probably as flawed as it can be. I've worked long enough at uni and research, and the number of gay people there is certainly not as high. The number of gays isn't even 5%. It's always overrated.
The effect in such a study might come from environments where homosexuality is a taboo, and the only people answering such questions straight are the intelligent ones.
And your other sources: a movie and Plato? You've got some catching up to do on authoritative citing.
Your preconceptions are quite horrible. Why wouldn't a pretty girl be able to pass grade school math? Quite a few of the PhDs I've met were pretty women. Perhaps a bit less glamorous than the ad, but not by miles.
And the point of 'what society "appears" to want girls to be like' is lost on me. Girls WANT to be like that, at least a bit. It might be because of our society, although I doubt that explains it 100%, but that doesn't change the fact.
So, while you lead the revolution that makes society impose less demands on female looks, let these girls choose a science career.
O sure, but how are they even going to show the sentiment of the opening page on screen, and not p*ss off all those self-righteous "Americans", you know this bit:
There's only four things we [the US] do better than anyone else
music
movies
microcode (software)
high-speed pizza delivery
And microcode shouldn't even be in that list, of course.
Exactly. Who adopted internet and mobile phones and whatever when the tech was in its infancy (see how cleverly I used that word)? Men.
Curiously, I didn't see slashdot in the list of sites not dominated by women (and slashdot is a social site, isn't it?). Does that mean that we're all women?
How come all screen shots are iOS or OSX? I don't think I've seen anything else...
So ... sorting is machine learning? MS Word is machine learning? Don't think so.
Nowhere did I nor the GP claim that machines have to be involved. And the machine doesn't use humans in this case, it just uses their choices as its data. So your rebuttal is somewhat unfounded.
Machine learning is learning in the first place, through algorithm: a machine can learn to do a task on its own. Not: a machine assists in a task where someone else learns. In this case, the machine doesn't learn anything. It just acts as a biased dice. The outcome of the process might be called "learned", but the knowledge is in the head of the one that runs the experiment and overlooks the outcome, not in the machine. And the "learning" doesn't generalize, so it doesn't help in improving performance on any other task than selecting between these two designs.
That's why it's not machine learning.
Indeed, this has no relation to machine learning, whatsoever. The summary is once again ... deceptive.
And I'm sure the proof, that the best one gets chosen, doesn't exist. I'm also sure that this [i]way of choosing[/i] an interface has a high probability of choosing the preferred one, but there is also a big difference with A/B testing: you'll never know how big the difference between the two is. In straight-forward testing with two groups (which is not really A/B, by the way: that is alternating between A and B and then ask the subject to chose the best one; it has its origins in perceptual testing, where ABX testing is preferred), you can find out the difference in scores. Here you can't.
True, true. Bunch of variables, bunch of analyses, and one or two are significant. That's no reason for joy, even apart from the rather low correlations they found (perhaps significant, but suggestive of very little predictability). This was a fishing expedition.
They were. Read the interpretations: "Proactively discovering depressive symptoms from passive and unobtrusive Internet usage monitoring". That only makes sense if depressive symptoms cause differences in internet usage. Otherwise you might just as well monitor hair length or shoe size.
Exactly. It's obvious that e.g. distances between stations can't be too short or too long. And obviously the structure is determined by the structure of the city, the distribution of its population and their destinations. And subway planners might also have taken a look at solutions in other cities. I think I'm going to do a study on mathematical properties of articles in the Journal of the Royal Society Interface. I will of course assume that such articles are self-organizing, and arrive at the surprising conclusion that they're all made up of words; I might even find that some words are much more frequent than others, despite there being so many opportunities in so many different pieces of text. I expect this conclusion to reach Slashdot in due time...
Indeed, brain size and IQ are not really related. Studies typically show r (not r^2) to be in the region of 0.3, which means brain size explains about 10% of the variation in IQ. However, the same can be said about genetic intelligence. There are correlations, but they explain almost nothing. Furthermore, these correlation studies are deeply flawed in most cases, like slapping a GLM on whatever data there is. Or transforming data just to get significance. Don't trust them.
The 0.4 you cite is, BTW, not in the article. They say: The best unbiased estimate of the population correlation between brain volume and intelligence is 0.33. And the numbers cited are r, not r^2. So, I'd say, it's just as I said above: brain size explains 10% of the variance in IQ.
Anyway, who cares about IQ in this discussion? I'm a meat eater, but I'd say there are more considerations than IQ. So what if the IQ drops a bit when we'd all stop eating meat. It will increase with 10 or 15 points in a generation anyway (the Flynn effect). If ethics are at stake, IQ, brain size and genetics should step back. We all know what happens if you use genetics for good (that's bordering on Godwin, indeed).
Wait, you're telling a fairy tale. It's called Red Riding Hood. And it's obvious that the wolf really wanted to dig in to the vegan cookies the girl had in her basket.
Don't forget to replace Safari with "Google Chrome" or Firefox or Camino or Opera if you use one of these.
Anyway, the machines I checked were all clean. It seems installing MS Office 2008 or 2011 (and a bunch of other software) is enough to stop the thing from installing itself: http://www.f-secure.com/v-descs/trojan-downloader_osx_flashback_i.shtml
I'm fresh out of mod points, but I'd given you 5 double-plus-insightful!
I have not been fond of all these 300+ new features that every new release of OSX brings, because most of them are of the type "you can now show the mail boxes on the other side of the window", but TimeMachine is one of those that really helps the user. I've been using it since it came out, made my father do it too, and it's been bliss. And yes, I have restored files, directories and used it for transfer to new computers.
My wife on the other hand has an Asus or Acer laptop (it starts with an A, is all I know) with Windows, and it has a backup daemon that just crashes. I mean: backup software that crashes?
Time Machine: best personal backup system ever.
I'm not sure you're talking to me, but:
1. Nobody has found logic gating in neurons at this level. There is a lot of spiking and thresholds, but logic gates at that level? You can build logic gates with Lego, but that doesn't mean that every Lego structure should be understood as such.
2. Having a logic gate isn't helping one bit to tell us how memory is organized. We have logic circuits. In computers. We cannot emulate our memory in a computer. Therefore identifying circuits that could make our brain work as a computer does not help us understand our memory.
I, for one, welcome our new logic overlords.
O wait, that's us!
Linguistics knows quite a bit of mathematics. Just look at Chomsky's work.
Applying some random formulae to numeric observations on words doesn't make it linguistics.