Fortunately(?) everyone is affect by spam equally.
This includes judges.
They will hate the prosecution from the word go and have them held in contempt of court just for sneezing, including the laywers, and hand out capital sentences.
What needs to happen is that we give up these ridiculous notions that everyone is equal at everything.
We have to accept that yes, women are worse at some things than men.
And then go the next step, and find the things that women are particularly good at.
For example, I'm willing to bet that women are better in understanding dynamics that are difficult to study mathematically. Their brains will be able to make sense out of puzzles that defy mathematics using emotional reasoning mechanisms.
I'm going to take a memorial plaque with me and everything. It'll be sitting right beside me on the beach while I sip expensive cocktails and watch topless women cavort in the water.
If this software works as advertised and isn't a load of hot-air, it sounds fairly acceptable.
In theory, it checks who has what parts and if those parts are actually legit pirated data and stores that in a database. Isn't this exactly what one needs to do to catch pirates? Yes the DMCA is Bad and has chilling effects, but it doesn't seem to be abused in this case.
Where do you think the money comes from? I'll tell you: it's sucked out of grants that used to go to much more efficient methodologies, like EEG, psychophysics, modelling and even simple behavioral research.
The money you spend in just magnet fees (nevermind the cost of building it in the first place) from just 1 single experiment is enough to pay someone's salary for an *entire year* running 3-4 psychophysics experiments.
Just so people understand what I'm ranting about you're often talking about some $800 in operating fees *per subject*, and at 30 subjects, that's $24,000.
Other methodologies are insanely cheap in comparison. You can buy an entire EEG rig for just $40,000, and each subject costs about $10-$20.
The fact that you consider the atrocious amount of grant money you (I'm guessing you do imaging research from the text of your post) gobble down *insufficient* is frightening to those of us who scrape by on experimental methodologies that are two orders of magnitude cheaper.
Imagers are like army ants, consuming all available grants in their path and always hungry for more money.
The entire field of neuroscience is being slowly dragged into fMRI research because the money is there. And the money is there because brain pictures are pretty, so people who don't understand the underlying science are eager to throw money at the method. That's the really sad thing, an entire field of research is being corrupted because of aesthetics.
Every day valuable non-fMRI methodologies are thrown out the window in favor of crippled methods that are scannable because magnets are being built like Starbucks throughout the world. Inside a magnet, your experimental options are very limited compared to outside.
And for what? it's not as if knowing what part of the brain lights up tells you about how the brain is doing that thing. This article is an excellent example of the layperson naivete that feeds the fMRI cash-cow. Scientists have known about these failings of human decision making for many years. The idea that we are flawed at rational decision making is hardly news. But throw someone in a scanner, see part X light up and suddently we understand how the brain works?
Bollocks.
These imaging studies are useful yes, especially in the context of other things we know about what different parts of the brain do.
But they do not represent some bold (heh) new understanding of "neuroeconomics", which is just decision making theory and neuroscience given a fancy name.
To date, movement sensors in mobile phones have been limited to slope calculations and applied to some games and bio-related features. However, the SCH-S310 can recognize continuous movement in 3-dimensional space.
Two technical problems with this that I see.
Accelerometers have accumulation errors that always render them inaccurate. For true accuracy you need an external point of reference.
Consider, your phone senses that it accerates 5 m/s/s for 2 seconds, it can compute its current velocity no problem.
Now in stopping it, sensor error causes it to think it's accerlated -4.9999 m/s/s for 2 seconds. It's stopped, but it thinks it has a nonzero velocity. Not a big deal yet, but over time these errors accumulate, and after a day or two your phone thinks it's cruising along at 500mph. Perhaps a constant decay term on the stored velocity can force the system to tend to zero over the long term.
But a second and bigger issue is that of frame of reference. For many of the applications described here, I don't care how fast my phone is moving with respect to the earth, I care how fast it is moving with respect to me. So if I get in a car in stop and go traffic, how does the phone discriminate that motion from motion I do with my hands? Or what if I'm just walking along trying to edit my phone book with gesture motions and someone steps in front of me and I stop short? bye bye Cindy, guess we won't be going out tonight after all.
Maybe very clever software design can mitigate this problem of discriminating intended from unintended motion, but it's a difficult problem.
The argument is not so much about whether CO2 can potentially cause a greenhouse effect, but whether it's worth the money and economic damage to limit CO2 production, when we're not at all sure if we're putting out enough CO2 to make a difference.
In theory these robots should be able to kick footballs around at tremendous speed with precision people will never be able to match.
Vision systems will allow them to map the trajectory of the ball onto their own appendage with pixel accuracy, figuring out the precise power and angle of the return kick necessary to land that ball anywhere they want (within reason).
And where vision fails, touch sensors on the surface of the foot will be able to figure out what part of the ball they are touching, and what speed, and readjust the kick while it is in the process of being carried, to enable even greater precision. This is something people will never be able to do. By the time our brains get information from touch sensors (some sluggish 50+ msec after the impact), it way too late to change the motor program.
Once the physical technology gets into place, sports against robots will be as futile as fighting against railgun bots in quake.
These statements apply to all search algorithms for parameterized problem domains. No search algorithm is very effective if it is bottled off from significant portions of the domain.
Having the ability to "exhaustively search" imples not only access to the entire problem domain, but also the computational power to try them all (or at least a significant percentage).
GA's, IMHO, are designed for problems in which the amount of computational firepower is practically insignificant in comparison to the size of the domain.
I think you'd do better finding a different term than "exhaustive" for what you are trying to say.
Admittedly, I don't understand this problem space very well. My point was just that I am wary of GA's in any situation in which reliability is a concern. But it could be that in this case the worst a GA can do is come up with a solution that is 10% slower for some unexpected type of problem.
There's just this sense among lay people though that GA's are some kind of magical cure-all, and I think it's because this type of search is a bit of a black box. Put something in, turn the crank, and wait for a solution.
But this black-boxiness is also the Achilles heel, as our GA's can often outsmart us by finding a loophole in our fitness function that solves the letter of the problem, but violates the spirit of it.
Nothing about properly implemented GA's can be considered "exhaustive". They are, by definition, a means of effectively navigating a hopeless search space. For example, it is impossible for a GA to "explore all the really bad solutions", as there are far too many in any interesting problem space.
First thing: A GA is only truly effective if you let it exhaustively search the search space
If you have the resources to exhastively search the space... you don't need a GA.
A GA is generally used when the search space is hopelessly huge and you need to chart a course from A to(wards) B but you don't know the way.
And in finding this solution, which is "grown", not engineered, it's much easier for unintended wierdnesses to creep in. A GA might solve problem X by ruining performance on problem Y, something that you, as a software engineer, would never even consider viable, and hence you forgot to force the fitness function to check problem Y along with problem X.
If a parameter space is complex enough that you can use a genetic algorithm to tune it, the solutions it finds may have all sorts of unexpected potholes, bugs, etc.
In other words, non-competitive genetic algorithms are only as smart as the fitness function you give them. If your fitness criteria aren't smart enough to cover all the bases, your solutions will have unpredictable deficiencies.
This presumes we are unable to remove or nullify the RFID tags in our own items, and I would presume that these tags are included in the packaging, not the items itself. It would considerable headache to the manufacturing process in factories all over the world to include the tags in various bits of apparel.to
IOTW, This is about pre-purchase tagging. And if they can make my goods cheaper by reducing theft/inefficiency, then I'm all for it. It's about time inventory management got out of the "hope and pray" methodology that it currently employs.
And if they start tagging my underwear inside the elastic, I can always get rid of it one way or another (a hammer might work)
As for the credit cards, shielded wallet. You know you'd buy one if they came out anyway, just for the cool factor:)
Slashdot Mirror
Fortunately(?) everyone is affect by spam equally.
This includes judges.
They will hate the prosecution from the word go and have them held in contempt of court just for sneezing, including the laywers, and hand out capital sentences.
Someone was coming to visit me in the UK from the US. She had to give my address, didn't know it, and there was some trouble.
She got through it eventually, but they gave her quite a hard time.
Replacing your organs isn't really a problem.
Grow new ones, who cares.
All they are is a life support system for your brain.
That's the bit you have to worry about.
What needs to happen is that we give up these ridiculous notions that everyone is equal at everything.
We have to accept that yes, women are worse at some things than men.
And then go the next step, and find the things that women are particularly good at.
For example, I'm willing to bet that women are better in understanding dynamics that are difficult to study mathematically. Their brains will be able to make sense out of puzzles that defy mathematics using emotional reasoning mechanisms.
If someone got up on stage and claimed that men were innately bad at having babies.
It would be an ugly, ugly scene.
It's not ridiculous!
I'm going to take a memorial plaque with me and everything. It'll be sitting right beside me on the beach while I sip expensive cocktails and watch topless women cavort in the water.
It's what he would have wanted.
I'm going to take a vacation to Tahiti as a memorial to my recently deceased friend.
I'm taking up a collection via paypal, email me for information.
If this software works as advertised and isn't a load of hot-air, it sounds fairly acceptable.
In theory, it checks who has what parts and if those parts are actually legit pirated data and stores that in a database. Isn't this exactly what one needs to do to catch pirates? Yes the DMCA is Bad and has chilling effects, but it doesn't seem to be abused in this case.
Not enough?! Argh, it hurts to hear you say that.
Where do you think the money comes from? I'll tell you: it's sucked out of grants that used to go to much more efficient methodologies, like EEG, psychophysics, modelling and even simple behavioral research.
The money you spend in just magnet fees (nevermind the cost of building it in the first place) from just 1 single experiment is enough to pay someone's salary for an *entire year* running 3-4 psychophysics experiments.
Just so people understand what I'm ranting about you're often talking about some $800 in operating fees *per subject*, and at 30 subjects, that's $24,000.
Other methodologies are insanely cheap in comparison. You can buy an entire EEG rig for just $40,000, and each subject costs about $10-$20.
The fact that you consider the atrocious amount of grant money you (I'm guessing you do imaging research from the text of your post) gobble down *insufficient* is frightening to those of us who scrape by on experimental methodologies that are two orders of magnitude cheaper.
Imagers are like army ants, consuming all available grants in their path and always hungry for more money.
The pro-fmri bias is infuriating really.
The entire field of neuroscience is being slowly dragged into fMRI research because the money is there. And the money is there because brain pictures are pretty, so people who don't understand the underlying science are eager to throw money at the method. That's the really sad thing, an entire field of research is being corrupted because of aesthetics.
Every day valuable non-fMRI methodologies are thrown out the window in favor of crippled methods that are scannable because magnets are being built like Starbucks throughout the world. Inside a magnet, your experimental options are very limited compared to outside.
And for what? it's not as if knowing what part of the brain lights up tells you about how the brain is doing that thing. This article is an excellent example of the layperson naivete that feeds the fMRI cash-cow. Scientists have known about these failings of human decision making for many years. The idea that we are flawed at rational decision making is hardly news. But throw someone in a scanner, see part X light up and suddently we understand how the brain works?
Bollocks.
These imaging studies are useful yes, especially in the context of other things we know about what different parts of the brain do.
But they do not represent some bold (heh) new understanding of "neuroeconomics", which is just decision making theory and neuroscience given a fancy name.
100,000 armchair climatologists fiddling with their own simulations, proving their theories to themselves.
To date, movement sensors in mobile phones have been limited to slope calculations and applied to some games and bio-related features. However, the SCH-S310 can recognize continuous movement in 3-dimensional space.
Two technical problems with this that I see.
Accelerometers have accumulation errors that always render them inaccurate. For true accuracy you need an external point of reference.
Consider, your phone senses that it accerates 5 m/s/s for 2 seconds, it can compute its current velocity no problem.
Now in stopping it, sensor error causes it to think it's accerlated -4.9999 m/s/s for 2 seconds. It's stopped, but it thinks it has a nonzero velocity. Not a big deal yet, but over time these errors accumulate, and after a day or two your phone thinks it's cruising along at 500mph. Perhaps a constant decay term on the stored velocity can force the system to tend to zero over the long term.
But a second and bigger issue is that of frame of reference. For many of the applications described here, I don't care how fast my phone is moving with respect to the earth, I care how fast it is moving with respect to me. So if I get in a car in stop and go traffic, how does the phone discriminate that motion from motion I do with my hands? Or what if I'm just walking along trying to edit my phone book with gesture motions and someone steps in front of me and I stop short? bye bye Cindy, guess we won't be going out tonight after all.
Maybe very clever software design can mitigate this problem of discriminating intended from unintended motion, but it's a difficult problem.
That the Serenity trailer will be airing in this movie also.
The argument is not so much about whether CO2 can potentially cause a greenhouse effect, but whether it's worth the money and economic damage to limit CO2 production, when we're not at all sure if we're putting out enough CO2 to make a difference.
N-gagajAJAJAJAJA
In theory these robots should be able to kick footballs around at tremendous speed with precision people will never be able to match.
Vision systems will allow them to map the trajectory of the ball onto their own appendage with pixel accuracy, figuring out the precise power and angle of the return kick necessary to land that ball anywhere they want (within reason).
And where vision fails, touch sensors on the surface of the foot will be able to figure out what part of the ball they are touching, and what speed, and readjust the kick while it is in the process of being carried, to enable even greater precision. This is something people will never be able to do. By the time our brains get information from touch sensors (some sluggish 50+ msec after the impact), it way too late to change the motor program.
Once the physical technology gets into place, sports against robots will be as futile as fighting against railgun bots in quake.
These statements apply to all search algorithms for parameterized problem domains. No search algorithm is very effective if it is bottled off from significant portions of the domain.
No, it's just that the word exhaustive has a very particular meaning in the context of search algorithms.
Actually, some flavors of GA's are fundamentally different from other types of search, particularly those with an open ended problem space.
But in the case of simple parameter-fitting problems (ie this one) , you're right.
Having the ability to "exhaustively search" imples not only access to the entire problem domain, but also the computational power to try them all (or at least a significant percentage).
GA's, IMHO, are designed for problems in which the amount of computational firepower is practically insignificant in comparison to the size of the domain.
I think you'd do better finding a different term than "exhaustive" for what you are trying to say.
Admittedly, I don't understand this problem space very well. My point was just that I am wary of GA's in any situation in which reliability is a concern. But it could be that in this case the worst a GA can do is come up with a solution that is 10% slower for some unexpected type of problem.
There's just this sense among lay people though that GA's are some kind of magical cure-all, and I think it's because this type of search is a bit of a black box. Put something in, turn the crank, and wait for a solution.
But this black-boxiness is also the Achilles heel, as our GA's can often outsmart us by finding a loophole in our fitness function that solves the letter of the problem, but violates the spirit of it.
Nothing about properly implemented GA's can be considered "exhaustive". They are, by definition, a means of effectively navigating a hopeless search space. For example, it is impossible for a GA to "explore all the really bad solutions", as there are far too many in any interesting problem space.
First thing: A GA is only truly effective if you let it exhaustively search the search space
If you have the resources to exhastively search the space... you don't need a GA.
A GA is generally used when the search space is hopelessly huge and you need to chart a course from A to(wards) B but you don't know the way.
And in finding this solution, which is "grown", not engineered, it's much easier for unintended wierdnesses to creep in. A GA might solve problem X by ruining performance on problem Y, something that you, as a software engineer, would never even consider viable, and hence you forgot to force the fitness function to check problem Y along with problem X.
If a parameter space is complex enough that you can use a genetic algorithm to tune it, the solutions it finds may have all sorts of unexpected potholes, bugs, etc.
In other words, non-competitive genetic algorithms are only as smart as the fitness function you give them. If your fitness criteria aren't smart enough to cover all the bases, your solutions will have unpredictable deficiencies.
This presumes we are unable to remove or nullify the RFID tags in our own items, and I would presume that these tags are included in the packaging, not the items itself. It would considerable headache to the manufacturing process in factories all over the world to include the tags in various bits of apparel.to
:)
IOTW, This is about pre-purchase tagging. And if they can make my goods cheaper by reducing theft/inefficiency, then I'm all for it. It's about time inventory management got out of the "hope and pray" methodology that it currently employs.
And if they start tagging my underwear inside the elastic, I can always get rid of it one way or another (a hammer might work)
As for the credit cards, shielded wallet. You know you'd buy one if they came out anyway, just for the cool factor