Absolutely. Seems like a simple, easy solution. It's a win for the guys who otherwise have to queue up for years for H1B, and it's a win for the guys that dont want to compete for jobs with people paid three times less than them.
So, why not fork wikipedia? What's to stop that? Or is it like, it's not ideal, but it mostly works ok?
On the whole, my own experiences with Wikipedia have been more or less ok: - as a contributor: on high traffic pages, trying to edit something is not going to get very far. But... whatever - as a contributor: on low-traffic pages, as far as I know, edits mostly go through, unless you say something that conflicts too much with someone else's agenda. Adding equations: works ok. Adding some opinion on someone's personal bio page: might not get very far - as a reader/end-user: most stuff is approximately correct, most of the time, at least for the uses I make of it, ie looking up some scientific or mathematical concept
So, it's more like, ios somehow manages to run things efficiently, using less memory than its android counterparts. But it doesnt take much to push ios outside the envelope of reasonable performance, given the limited available memory of iphone devices.
I have a ZUK Z1, and after a full day, 6am to 6pm, it's somewhere around 60-80%, depending on how much I've been using it. Admittedly it weighs 180g or so, but seems not to be a downside to me.
Or more concretely: - our primary industry, ie mines and so on, are increasingly automated - second industry, ie factories, are heavily automated, already - tertiary, ie services, are well under way - military (drones and so on) is well under way
There is a quota of 80,000 H1Bs per year. This is currently filled randomly, according to a lottery. Why not simply rank the applicants by their salary, from highest first, and give an H1B to the top 80,000 salaries?
Solves two problems, win-win: - makes sure that only the most critical employees enter - means that those critical employees can enter easily, without having to do endless lotteries...
Have you seen the roads around Baidu headquarters, ie near Wudaokou? Jam packed with traffic, except at 4am. From around 7am in the morning, it takes 20-30 minutes to go a couple of kilometres...
Usually, you need a mixture of approaches to get things to work. Idealism in software engineering, or in engineering, works about as well as idealism in politics, ie it doesnt really, it misses key points. But, in both areas, it's much easier to create a platform on idealism, and so people who propose one single idealistic viewpoint often do quite well.
In practice, in software engineering, saying 'all tests must be automated, 100%', misses that some things are really hard to test automatically, but can be tested by hand quite simply. Similarly for creating test harnesses, mocking, which this article is the hardware-engineering equivalent for. Sometimes it's easier to mock, and do real 'unit-testing', and sometimes it isnt, and insisting that every project, and every part of every project, uses the exact uniform standard, might not always work as well as it looks like it will in the Powerpoint presentation:-P
Pale Moon is trademarked, https://github.com/MoonchildPr... If you fork it, you have to change the name, just like the original Firefox, and also Truecrypt.
They're using a standard technique. Convolutional networks started to become big with LeCun's 1998 paper on learning to recognize hand-written digits http://yann.lecun.com/exdb/pub... . His lenet-5 network could identify the digit accurately 99% of the time.
Convolutional networks are starting to become used to play Go, eg 'Move evaluation in Go using Deep Convolutional Neural Networks', by Maddison Huang, Sutskever and Silver, http://arxiv.org/pdf/1412.6564... Maddison et al used a 12-layer convolutional network to predict where an expect would move next with 50% accuracy:-)
Progress on convolutional networks moves forward all the time, in an incremental way. If we had one article per day about one increment it would quickly lose mass appeal though:-) The article is about one increment along the way, but does symbolize the massive progress that is being made.
Convolutional networks work well partly because they can take advantage of the massive computional capacity made available in GPU hardware.
Yes, check this out 'High Confidence Predictions for Unrecognizable Images', by Nguyen, Yosinkski and Clune, http://arxiv.org/abs/1412.1897 . It's a paper that shows an image that the net is 99.99% sure is an electric guitar, but looks nothing like:-)
For the technically minded, the paper's authors propose that the reason is that the network is using a discriminative model, rather than a generative model. That means that the network learns a mathematical boundary that separates the images that it sees, in some kind of high-dimensional transformed space. It doesn't learn how to generate such new images, ie, you cant ask it 'draw me an electric guitar':-) Maybe in a few years:-)
The authors don't compare the network too much with the human brain though, ie, are they saying that the human brain is using a generative model? Is that why the human brain doesn't see a white noise picture, and claim it's a horse?
Note that encfs is perfect for this: - encrypts using AES-256 - easy to use - works on linux:-) - and there's at least one app for Android that is compatible with the encryption protocol - each file still is stored as a single file so:
-- no issues with losing all your data at once:-)
-- replication can still be file by file - works through Fuse, doesn't need admin rights, kernel drivers and stuff:-)
The advantage of this system is: - only one password to remember - if a website gets hacked, that password can't be used on other websites, and can't realistically be used to obtain your master password, assuming they even know which algorithm you're using, which is unlikely - unlike a password safe, you don't need to handle making backups, replicating the backups around, and so on
To be fair, as far as 'we're a threat', this includes 'we could become a threat in the future'. Why wait for us to become strong enough to be troublesome to mop-up, when they could mop us up now?
It's a bit like keeping the fridge clean. You don't wait until it grows monsters that will actually attack you. You simply clean the surfaces occasionally, get rid of any traces of mold and stuff.
There are two types of deep learning currently by the way: - restricted Boltzmann machines (RBM) - sparse auto-encoders
Google / Andrew Ng use sparse auto-encoders. Hinton uses (created) deep RBM networks. They both work in a similar way: each layer learns to reconstruct the input, using a low-dimensional representation. In this way, lower layers build up for example line detectors, and higher levels build up more abstract representations.
"Restrictions: Must run in 10 hours on a 2GHz P4 with 1GB RAM and 10GB free HD"
So, even if you could write an algorithm that fits in a couple of meg, and magically generates awesome feature extraction capabilities, which is kind of what deep learning can do, you'd be excluded from using it in the Hutter prize competition.
For comparison, the Google / Andrew Ng experiment where they got a computer to learn to recognize cats all by itself used a cluster of 16,000 cores (1000 nodes * 16 cores) for 3 days. That's a lot of core-hours, and far exceeds the limitations of the Hutter prize competition.
I extended it a bit https://github.com/hughperkins/passwordbookmarklet : - longer passwords generated - the bookmarklet password field uses password characters - there's the option of using a bookmarklet with a 'confirm' field - added a console application (python) which invisibly copies the password to the clipboard, for non-web applications
I get the feeling that many of the comments here are from people who are 30-50, with just a very few exceptions. (I am somewhere in the middle of that range too in fact). Slashdot users are getting older? Where do the 20-somethings hang out?
> 2) Periodic activation every 30 days - this one seriously ticks me off after I've already activated once then wtf?
To save other people from googling, what the parent means is that if you want to play starcraft offline on a particular computer, you must have played starcraft online on that computer in the last 30 days.
I was panicing for a bit, thinking I'd just lost my battle.net profile, since I havent played sc2 for... a while...
Firstly, why is this a nightmare? Who wants extra competition?
Secondly, "technical interview" is a misnomer. They're actually "potential colleague" interviews. Who is going to pick someone who is smarter than them, or who is going to give them competition for promotion?
Those who get through technical interviews are either smart enough to bluff to the interviewer that they're not quite as smart as the interviewer, but an ok guy to hang out with; or are genuinely not as smart or talented as the interviewer, but are an ok guy to hang out with.
Quick tip: when you attend a technical interview, answering the questions correctly doesn't get you the job. Being amazed at how much the interviewer knows does.
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Absolutely. Seems like a simple, easy solution. It's a win for the guys who otherwise have to queue up for years for H1B, and it's a win for the guys that dont want to compete for jobs with people paid three times less than them.
So, why not fork wikipedia? What's to stop that? Or is it like, it's not ideal, but it mostly works ok?
On the whole, my own experiences with Wikipedia have been more or less ok:
- as a contributor: on high traffic pages, trying to edit something is not going to get very far. But... whatever
- as a contributor: on low-traffic pages, as far as I know, edits mostly go through, unless you say something that conflicts too much with someone else's agenda. Adding equations: works ok. Adding some opinion on someone's personal bio page: might not get very far
- as a reader/end-user: most stuff is approximately correct, most of the time, at least for the uses I make of it, ie looking up some scientific or mathematical concept
Well, iphones have less memory than Android devices at the same price point. iPhone 6 has 1GB memory https://en.wikipedia.org/wiki/... Even the Zuk Z1, which is 2-3 times cheaper, has 3GB ram... https://en.wikipedia.org/wiki/...
So, it's more like, ios somehow manages to run things efficiently, using less memory than its android counterparts. But it doesnt take much to push ios outside the envelope of reasonable performance, given the limited available memory of iphone devices.
I have a ZUK Z1, and after a full day, 6am to 6pm, it's somewhere around 60-80%, depending on how much I've been using it. Admittedly it weighs 180g or so, but seems not to be a downside to me.
I think it's more like, people who say AI will never happen are imagining the kind of AIs in terminator: androids walking around with lasers.
This xkcd says it quite well: http://www.xkcd.com/652/
Or more concretely:
- our primary industry, ie mines and so on, are increasingly automated
- second industry, ie factories, are heavily automated, already
- tertiary, ie services, are well under way
- military (drones and so on) is well under way
There is a quota of 80,000 H1Bs per year. This is currently filled randomly, according to a lottery. Why not simply rank the applicants by their salary, from highest first, and give an H1B to the top 80,000 salaries?
Solves two problems, win-win:
- makes sure that only the most critical employees enter
- means that those critical employees can enter easily, without having to do endless lotteries...
Have you seen the roads around Baidu headquarters, ie near Wudaokou? Jam packed with traffic, except at 4am. From around 7am in the morning, it takes 20-30 minutes to go a couple of kilometres...
Usually, you need a mixture of approaches to get things to work. Idealism in software engineering, or in engineering, works about as well as idealism in politics, ie it doesnt really, it misses key points. But, in both areas, it's much easier to create a platform on idealism, and so people who propose one single idealistic viewpoint often do quite well.
In practice, in software engineering, saying 'all tests must be automated, 100%', misses that some things are really hard to test automatically, but can be tested by hand quite simply. Similarly for creating test harnesses, mocking, which this article is the hardware-engineering equivalent for. Sometimes it's easier to mock, and do real 'unit-testing', and sometimes it isnt, and insisting that every project, and every part of every project, uses the exact uniform standard, might not always work as well as it looks like it will in the Powerpoint presentation :-P
Pale Moon is trademarked, https://github.com/MoonchildPr... If you fork it, you have to change the name, just like the original Firefox, and also Truecrypt.
this is one of those articles where what the article says, and what people think, have a discrepancy. It reminds me of articles with titles like:
"Java developers claim uses no more memory than C++"
"C++ developers claim no more bugs than python"
"Python developers claim runs as fast as C"
They're using a standard technique. Convolutional networks started to become big with LeCun's 1998 paper on learning to recognize hand-written digits http://yann.lecun.com/exdb/pub... . His lenet-5 network could identify the digit accurately 99% of the time.
Convolutional networks are starting to become used to play Go, eg 'Move evaluation in Go using Deep Convolutional Neural Networks', by Maddison Huang, Sutskever and Silver, http://arxiv.org/pdf/1412.6564... Maddison et al used a 12-layer convolutional network to predict where an expect would move next with 50% accuracy :-)
Progress on convolutional networks moves forward all the time, in an incremental way. If we had one article per day about one increment it would quickly lose mass appeal though :-) The article is about one increment along the way, but does symbolize the massive progress that is being made.
Convolutional networks work well partly because they can take advantage of the massive computional capacity made available in GPU hardware.
Yes, check this out 'High Confidence Predictions for Unrecognizable Images', by Nguyen, Yosinkski and Clune, http://arxiv.org/abs/1412.1897 . It's a paper that shows an image that the net is 99.99% sure is an electric guitar, but looks nothing like :-)
For the technically minded, the paper's authors propose that the reason is that the network is using a discriminative model, rather than a generative model. That means that the network learns a mathematical boundary that separates the images that it sees, in some kind of high-dimensional transformed space. It doesn't learn how to generate such new images, ie, you cant ask it 'draw me an electric guitar' :-) Maybe in a few years :-)
The authors don't compare the network too much with the human brain though, ie, are they saying that the human brain is using a generative model? Is that why the human brain doesn't see a white noise picture, and claim it's a horse?
"Democracy is the worst form of government, except for all the others" - Winston Churchill
Note that encfs is perfect for this: :-) :-) :-)
- encrypts using AES-256
- easy to use
- works on linux
- and there's at least one app for Android that is compatible with the encryption protocol
- each file still is stored as a single file so:
-- no issues with losing all your data at once
-- replication can still be file by file
- works through Fuse, doesn't need admin rights, kernel drivers and stuff
http://www.arg0.net/encfs
http://en.wikipedia.org/wiki/E...
You can use a single password, combined with the url of the website, to generate unique passwords for each website, via a hashing algorithm.
One implementation of this is: https://github.com/hughperkins/openpw , which is a derivative of http://angel.net/~nic/passwd.current.html There are other implementations around.
The advantage of this system is:
- only one password to remember
- if a website gets hacked, that password can't be used on other websites, and can't realistically be used to obtain your master password, assuming they even know which algorithm you're using, which is unlikely
- unlike a password safe, you don't need to handle making backups, replicating the backups around, and so on
To be fair, as far as 'we're a threat', this includes 'we could become a threat in the future'. Why wait for us to become strong enough to be troublesome to mop-up, when they could mop us up now?
It's a bit like keeping the fridge clean. You don't wait until it grows monsters that will actually attack you. You simply clean the surfaces occasionally, get rid of any traces of mold and stuff.
"Asking whether a computer can be intelligent is like asking whether a submarine can swim".
An airplane doesn't flap its wings, but flies faster than birds can.
Submarines don't swim, but they move through the water faster than dolphins.
Not everything has to copy nature exactly in order to be effective.
There's also a great tutorial by Andrew Ng's group at:
http://deeplearning.stanford.edu/wiki/index.php/UFLDL_Tutorial
There are two types of deep learning currently by the way:
- restricted Boltzmann machines (RBM)
- sparse auto-encoders
Google / Andrew Ng use sparse auto-encoders. Hinton uses (created) deep RBM networks. They both work in a similar way: each layer learns to reconstruct the input, using a low-dimensional representation. In this way, lower layers build up for example line detectors, and higher levels build up more abstract representations.
From the task description:
"Restrictions: Must run in 10 hours on a 2GHz P4 with 1GB RAM and 10GB free HD"
So, even if you could write an algorithm that fits in a couple of meg, and magically generates awesome feature extraction capabilities, which is kind of what deep learning can do, you'd be excluded from using it in the Hutter prize competition.
For comparison, the Google / Andrew Ng experiment where they got a computer to learn to recognize cats all by itself used a cluster of 16,000 cores (1000 nodes * 16 cores) for 3 days. That's a lot of core-hours, and far exceeds the limitations of the Hutter prize competition.
Check out Nic's password generator: http://angel.net/~nic/passwd.current.html
I extended it a bit https://github.com/hughperkins/passwordbookmarklet :
- longer passwords generated
- the bookmarklet password field uses password characters
- there's the option of using a bookmarklet with a 'confirm' field
- added a console application (python) which invisibly copies the password to the clipboard, for non-web applications
Whooshy-whoosh! I've always wanted to do that :-D
http://abstrusegoose.com/
Specialized content for machine learning / artificial intelligence. I chain-read them for 18 hours till I'd finished!
I get the feeling that many of the comments here are from people who are 30-50, with just a very few exceptions. (I am somewhere in the middle of that range too in fact). Slashdot users are getting older? Where do the 20-somethings hang out?
> 2) Periodic activation every 30 days - this one seriously ticks me off after I've already activated once then wtf?
To save other people from googling, what the parent means is that if you want to play starcraft offline on a particular computer, you must have played starcraft online on that computer in the last 30 days.
I was panicing for a bit, thinking I'd just lost my battle.net profile, since I havent played sc2 for... a while...
Firstly, why is this a nightmare? Who wants extra competition?
Secondly, "technical interview" is a misnomer. They're actually "potential colleague" interviews. Who is going to pick someone who is smarter than them, or who is going to give them competition for promotion?
Those who get through technical interviews are either smart enough to bluff to the interviewer that they're not quite as smart as the interviewer, but an ok guy to hang out with; or are genuinely not as smart or talented as the interviewer, but are an ok guy to hang out with.
Quick tip: when you attend a technical interview, answering the questions correctly doesn't get you the job. Being amazed at how much the interviewer knows does.