The resulting network is the algorithm. "Develop" usually means "propose a specific neural network architecture" in this context. So no, no meta-learning, nor novel optimization algorithm.
Although I'm completely with you on about everything in your post, it should nonetheless be noted that even plants are capable of pavlovian learning. Or so it seems: http://rdcu.be/yuI0
From https://aaai.org/ in the description of next year's conference:
AAAI-18 welcomes submissions reporting research that advances artificial intelligence, broadly conceived. The conference scope includes all subareas of AI and machine learning.
Now, if you think you are such an expert in the field to say that the Association for the Advancement of Artificial Intelligence, which was founded in 1979 as an academic association, is wrong about the definition of artificial intelligence, I'd like to hear what contributions to the field you made that can back up the idea. If you did none, then just let the scientists working in the field define what AI means and contains, and accept it.
We have since long already transitioned, in fact we've been in the "new" system for a very long time. If you thought that wealth comes from your labor capacity, think again : our system is called capitalism, and all wealth comes from capital.
Long time ago, capital was measured in the number of slaves you owned, until recently it was measured in the number of workers producing stuff for you, it is now changing to the number of mineral and digital slaves you own. The principles stay the same, if you own nothing, you get nothing and you lose.
UBI does not solve the problem (I've been long time advocating UBI on/.) because it does not reallocate capital, it's only there to prevent the riots you mentioned, like the chains were used to prevent slaves trying anything not wanted.
Also, remember that the goal of increasing wealth is stuff, not money. If you own (or co-own for what matters) a factory that can make you the stuff you want (clothes, cars, yachts, whatever), you don't really care if it makes money or not, as long as it can produce the stuff that you want when you want it.
We can easily imagine a society of wealthy owners, the 1%, that exchange stuff between them and it would be perfectly viable economically. In fact, it has always been the case, and it's just that slaves were replaced by workers who are now being replaced by robots. The economy that is heading to the abyss is the one of the 99%, not the wealthy one. The uncertainty lies for the 99%, as always.
I concede, you're right. I know almost nothing in this area. But you have to admit that usually, when people speak of a jet engine, that involves more than just a compressor.
To be honest, their website has a paragraph on "Electric Jet Engines" which is kind of non-sense since, as you mention, these are impellers. There is no engine (as in reaction engine) in this thing, nothing is ever burnt in normal operation.
Hire competent C developers and you should be good to go. Hire dumb-asses and sure the future doesn't look bright.
The usual PR stunt of "use my new language that is so good and so simple even a stupid high school teen with an IQ somewhere between stone and plankton in the phylogenetic tree could use it without wrecking anything" is not going to convince anybody here. It's boring at best, and no-one cares about your "advice".
The linux kernel is in its 3rd decade, as most gnu/bsd tools and all of that is written in plain C. They are good and safe. Oh, maybe it's because they are written by competent guys.
It's not because there is anti-white racism that all other racism is fine and should not be addressed.
Moreover, the article is not detailing which kind of racism: "Stephens-Davidowitz saw in the Google Trends data a racially polarized electorate, and one primed to respond to the ethno-nationalist rhetoric of Trump." I read that as all ${COLOR}-supremacists being more vocal than before because Trump is adding fuel to the fire.
If you are really concerned about anti-white racism, then you should understand that this is a global issue and that all racism, without any distinction, should be eradicated because that's the only way to make your concern disappear. As such, you should be concerned by TFA because it shows the goal is further away than expected.
The thing is that if that's a Java project, you can probably rewrite it in a better designed language and cut that down to 10 classes and 10 kloc. Java is just horribly wasteful of programmer time, creates tons of unnecessary complexity, and is wasteful of computer resources on top of that.
Sure, everything can be rewritten with a sed oneliner. However, none of this is going to be maintained very long, which is the true value of some critical programs.
People who complain that you can't build large scale systems without a compiler likely over-rely on the latter and are slaves to IDEs. If you write good unit tests and enforce Test Driven Development, the compiler becomes un-necessary and gets in the way.
Says the guy that never worked on a project with 100+ classes and 100k+ lines of code...
Don't get me wrong, I love python and it's become my main programming language. But hey, I'm a researcher and everything I code doesn't have to do more than producing a few results to put in a paper and then be thrown away and never looked at again.
You know why Java is the top language now? It's not only because it's so easy that many professional applications have been written 15 years ago by dumb interns, but also because it's so robust that these applications can still be maintained by even dumber interns today. You're never going to get that with python (or js, or ruby, or whatever new hype thing you believe is the new messiah).
Dude, I never claimed to be a feminist nor did I wish you to get assaulted.
Again, you are rephrasing something to give it a different meaning, and by different I mean false. This is going on a ridiculous level of distortion, and I'm not even sure you're aware you're doing that.
It seems to be common these days that people replace facts by their feelings and think to get away with it. Beware, you cannot change reality by ignoring it nor claiming it's something different, it will hit you hard back at some point.
It's not because you put yellow goggles on that the world has suddenly turned yellow. Just saying...
Except that your phrasing doesn't have the same meaning at all.
"When female employees didn't want to participate, they would be ostracized by the male employees and excluded from important meetings and lunches" is not the same as "They laughed at some workers who did not participate, and some of them were women".
It's not even remotely close, check your logic.
You're so biased against anything that is gender related that you refuse to see when there is a problem. Maybe that will change when you'll get harassed for something many will refer to as non-existent.
I'm fed up with those recurrent comments that statistical learning is not AI. Please base your opinion on evidences and not on your feelings.
For instance, go to the page of the major conference on AI: http://www.aaai.org/Conference... Look at the topics, they include machine learning. There you have it, the professionals in AI consider ML to be AI.
We can now safely have that useless comment die and never appear again.
The title should have read "Carefully crafted decoy using massive computation resources can fool not up-to-date AI".
Here's how it works: 1. Get access to the AI model you want to fool (and only this one). Not necessarily the source code, but at least you need to be able to use the model as long as you want. 2. Solve a rather complex optimization problem to generate the decoy 3. use your decoy in very controlled conditions (like stated in the linked paper)
While the method for fooling the model is fine (and similar work has been buzzing lately), the conclusion are much weaker than you expect. First, because if you don't have the actual model, you cannot do that. You need to run the actual model you are trying to fool. So that takes out all remote systems with rate limiting accesses. Second, your rely on tiny variation which can be more sensitive than real world variation. Take for example the sticker on road sign, if you took the picture on the sunny day, the decoy will very likely not work on rainy day or at night. Third, if the model evolves, you have to update the decoy. Here's the problem with statistical learning systems: they learn. It's very likely that the model got updated by the time you finished the computation and printing the sticker. Many people believe that future industrial systems will perform online learning which renders those static methods useless.
So yeah, actual research model can be fooled in very specific cases. However, It's not as bad as some article try to make it sound. I'm not saying it won't happen, I'm saying it's not as bad as you think it is. Hey, if you want to impersonate somebody, put some make up and if you want people to crash their car, cover the roadsigns with paint. There you have it, humans are easily fooled by some paint.
Joanna Bryson, a computer scientist at the University of Bath and a co-author, warned that AI has the potential to reinforce existing biases because, unlike humans, algorithms may be unequipped to consciously counteract learned biases.
"unlike some humans"
There, fixed that for you. Or even better: "like most humans".
Statistical learning does inferences based on what humans produced. If humans are crap, do not expect something better than crap. .
What if I could purchase a robot that could go out and earn a living for me?
You can. You just have to buy shares of a company and vote for a board that will fire employees and replace them with machines and algorithms in order to increase dividends.
The caveat is that you need to have so much money that you already don't need to work. If you don't, then you'd better vote for universal basic income, because those who have will do anything to increase their dividends, including replacing you with machines and algorithms.
Climatologists are not mechanical engineers, they are PhD's. I agree, engineers are very careful about the details. However, PhD's don't have life-risk to consider. I fact, there is overt manipulation of the data upon which most (if not all) of the climate "conclusions" are based.
Oh yeah, like the engineers at Volkswagen that cared about the details of their vehicles emission.
Engineers are in the first row when it's about cooking the data to fit the specifications. Dishonesty is everywhere the same, as long as it involves a gain. Most people don't care about being right or wrong, they just care more about themselves than about the facts. It's putting feelings over reality. Which might explain why you elected Trump.
Slashdot Mirror
On the same note: I used to have 20 top sites to choose from when opening a new tab, instead of just 8 now. How do I get that back?
The resulting network is the algorithm. "Develop" usually means "propose a specific neural network architecture" in this context. So no, no meta-learning, nor novel optimization algorithm.
If you think this is not new, you should wait for the dup coming tomorrow!
Although I'm completely with you on about everything in your post, it should nonetheless be noted that even plants are capable of pavlovian learning. Or so it seems: http://rdcu.be/yuI0
This one was funny too: http://random.irb.hr/signup.ph...
From https://aaai.org/ in the description of next year's conference:
AAAI-18 welcomes submissions reporting research that advances artificial intelligence, broadly conceived. The conference scope includes all subareas of AI and machine learning.
Now, if you think you are such an expert in the field to say that the Association for the Advancement of Artificial Intelligence, which was founded in 1979 as an academic association, is wrong about the definition of artificial intelligence, I'd like to hear what contributions to the field you made that can back up the idea. If you did none, then just let the scientists working in the field define what AI means and contains, and accept it.
We have since long already transitioned, in fact we've been in the "new" system for a very long time. If you thought that wealth comes from your labor capacity, think again : our system is called capitalism, and all wealth comes from capital.
Long time ago, capital was measured in the number of slaves you owned, until recently it was measured in the number of workers producing stuff for you, it is now changing to the number of mineral and digital slaves you own. The principles stay the same, if you own nothing, you get nothing and you lose.
UBI does not solve the problem (I've been long time advocating UBI on /.) because it does not reallocate capital, it's only there to prevent the riots you mentioned, like the chains were used to prevent slaves trying anything not wanted.
Also, remember that the goal of increasing wealth is stuff, not money. If you own (or co-own for what matters) a factory that can make you the stuff you want (clothes, cars, yachts, whatever), you don't really care if it makes money or not, as long as it can produce the stuff that you want when you want it.
We can easily imagine a society of wealthy owners, the 1%, that exchange stuff between them and it would be perfectly viable economically. In fact, it has always been the case, and it's just that slaves were replaced by workers who are now being replaced by robots. The economy that is heading to the abyss is the one of the 99%, not the wealthy one. The uncertainty lies for the 99%, as always.
I concede, you're right. I know almost nothing in this area. But you have to admit that usually, when people speak of a jet engine, that involves more than just a compressor.
To be honest, their website has a paragraph on "Electric Jet Engines" which is kind of non-sense since, as you mention, these are impellers. There is no engine (as in reaction engine) in this thing, nothing is ever burnt in normal operation.
but until people see robots going down the street killing people, they don't know how to react, because it seems so ethereal.
Says the guy that lives in a country where every day dudes carrying big guns shoot at each others for no reasons...
Seriously dude, I'll be worried when I see swarm of robots building killer robots factories, replicator style. Until then, humans worry me the most.
Hire competent C developers and you should be good to go. Hire dumb-asses and sure the future doesn't look bright.
The usual PR stunt of "use my new language that is so good and so simple even a stupid high school teen with an IQ somewhere between stone and plankton in the phylogenetic tree could use it without wrecking anything" is not going to convince anybody here. It's boring at best, and no-one cares about your "advice".
The linux kernel is in its 3rd decade, as most gnu/bsd tools and all of that is written in plain C. They are good and safe. Oh, maybe it's because they are written by competent guys.
Straw-man argument detected!
It's not because there is anti-white racism that all other racism is fine and should not be addressed.
Moreover, the article is not detailing which kind of racism: "Stephens-Davidowitz saw in the Google Trends data a racially polarized electorate, and one primed to respond to the ethno-nationalist rhetoric of Trump." I read that as all ${COLOR}-supremacists being more vocal than before because Trump is adding fuel to the fire.
If you are really concerned about anti-white racism, then you should understand that this is a global issue and that all racism, without any distinction, should be eradicated because that's the only way to make your concern disappear. As such, you should be concerned by TFA because it shows the goal is further away than expected.
The thing is that if that's a Java project, you can probably rewrite it in a better designed language and cut that down to 10 classes and 10 kloc. Java is just horribly wasteful of programmer time, creates tons of unnecessary complexity, and is wasteful of computer resources on top of that.
Sure, everything can be rewritten with a sed oneliner. However, none of this is going to be maintained very long, which is the true value of some critical programs.
People who complain that you can't build large scale systems without a compiler likely over-rely on the latter and are slaves to IDEs. If you write good unit tests and enforce Test Driven Development, the compiler becomes un-necessary and gets in the way.
Says the guy that never worked on a project with 100+ classes and 100k+ lines of code...
Don't get me wrong, I love python and it's become my main programming language. But hey, I'm a researcher and everything I code doesn't have to do more than producing a few results to put in a paper and then be thrown away and never looked at again.
You know why Java is the top language now? It's not only because it's so easy that many professional applications have been written 15 years ago by dumb interns, but also because it's so robust that these applications can still be maintained by even dumber interns today. You're never going to get that with python (or js, or ruby, or whatever new hype thing you believe is the new messiah).
what about this one? https://yro.slashdot.org/story...
This one is a backup in case the first one gets encrypted!
More to point: the old method worked only for WinXP, this one also for Win7.
Dude, I never claimed to be a feminist nor did I wish you to get assaulted.
Again, you are rephrasing something to give it a different meaning, and by different I mean false. This is going on a ridiculous level of distortion, and I'm not even sure you're aware you're doing that.
It seems to be common these days that people replace facts by their feelings and think to get away with it. Beware, you cannot change reality by ignoring it nor claiming it's something different, it will hit you hard back at some point.
It's not because you put yellow goggles on that the world has suddenly turned yellow. Just saying...
Except that your phrasing doesn't have the same meaning at all.
"When female employees didn't want to participate, they would be ostracized by the male employees and excluded from important meetings and lunches" is not the same as "They laughed at some workers who did not participate, and some of them were women".
It's not even remotely close, check your logic.
You're so biased against anything that is gender related that you refuse to see when there is a problem. Maybe that will change when you'll get harassed for something many will refer to as non-existent.
Are you mad because for the first time in history, France has a president that is better at English grammar than the US president?
I'm fed up with those recurrent comments that statistical learning is not AI. Please base your opinion on evidences and not on your feelings.
For instance, go to the page of the major conference on AI: http://www.aaai.org/Conference...
Look at the topics, they include machine learning. There you have it, the professionals in AI consider ML to be AI.
We can now safely have that useless comment die and never appear again.
Yeah, me too. Good memories of something that in retrospect was not that great.
The title should have read "Carefully crafted decoy using massive computation resources can fool not up-to-date AI".
Here's how it works:
1. Get access to the AI model you want to fool (and only this one). Not necessarily the source code, but at least you need to be able to use the model as long as you want.
2. Solve a rather complex optimization problem to generate the decoy
3. use your decoy in very controlled conditions (like stated in the linked paper)
While the method for fooling the model is fine (and similar work has been buzzing lately), the conclusion are much weaker than you expect. First, because if you don't have the actual model, you cannot do that. You need to run the actual model you are trying to fool. So that takes out all remote systems with rate limiting accesses. Second, your rely on tiny variation which can be more sensitive than real world variation. Take for example the sticker on road sign, if you took the picture on the sunny day, the decoy will very likely not work on rainy day or at night. Third, if the model evolves, you have to update the decoy. Here's the problem with statistical learning systems: they learn. It's very likely that the model got updated by the time you finished the computation and printing the sticker. Many people believe that future industrial systems will perform online learning which renders those static methods useless.
So yeah, actual research model can be fooled in very specific cases. However, It's not as bad as some article try to make it sound. I'm not saying it won't happen, I'm saying it's not as bad as you think it is. Hey, if you want to impersonate somebody, put some make up and if you want people to crash their car, cover the roadsigns with paint. There you have it, humans are easily fooled by some paint.
Just like for regular humans. People almost never question the religion there were born with, or views on races and culture for that matter.
Joanna Bryson, a computer scientist at the University of Bath and a co-author, warned that AI has the potential to reinforce existing biases because, unlike humans, algorithms may be unequipped to consciously counteract learned biases.
"unlike some humans"
There, fixed that for you. Or even better: "like most humans".
Statistical learning does inferences based on what humans produced. If humans are crap, do not expect something better than crap. .
What if I could purchase a robot that could go out and earn a living for me?
You can. You just have to buy shares of a company and vote for a board that will fire employees and replace them with machines and algorithms in order to increase dividends.
The caveat is that you need to have so much money that you already don't need to work. If you don't, then you'd better vote for universal basic income, because those who have will do anything to increase their dividends, including replacing you with machines and algorithms.
Climatologists are not mechanical engineers, they are PhD's. I agree, engineers are very careful about the details. However, PhD's don't have life-risk to consider. I fact, there is overt manipulation of the data upon which most (if not all) of the climate "conclusions" are based.
Oh yeah, like the engineers at Volkswagen that cared about the details of their vehicles emission.
Engineers are in the first row when it's about cooking the data to fit the specifications. Dishonesty is everywhere the same, as long as it involves a gain. Most people don't care about being right or wrong, they just care more about themselves than about the facts. It's putting feelings over reality. Which might explain why you elected Trump.