LLVM/Clang builds the DragonFly world and kernel but does not yet build the boot loader. It can be brought in via dports. So it isn't 100% yet but very close. When it does get to 100% it will become one of our two officially supported compilers. Those are currently gcc-4.7 and gcc-5.2.1.
Wayland support isn't really up to us, but there is wayland support in XOrg that I think works for programs desiring to use that API. Don't quote me on it though.
The "Science" of Physics was "settled" back in the time of Issac Newton.
What bullshit! The problem with gravity, its mysterious action st a distance, continues to this day. Physicists are still looking for gravity waves/gravitons with no success yet.
Nice in theory. Not so much in practice. With crypto, the devil's in the details. Here are just a few of the hard problems:
...
"The perfect is the enemy of the good" -- Voltaire.
Yes, those are all hard problems, but at least a widespread partial solution would make mass surveillance at least an order of magnitude more difficult and push TLAs to be more focused in their data gathering.
Also, a partial solution has the chance to be improved into better solutions. This would be a much better situation than what we have now. The fact that we can't solve all those hard problems now should not be an excuse to do nothing.
Corporations decide your worthiness to participate in our society: credit ratings, insurance scores, background checks, etc. It's not as bad as jail, but if you are blacklisted in some way, it's still pretty shitty.
The AI is designed to improve/maximize its performance measure. An AI will "desire" self-preservation (or any other goal) to the extent that self-preservation is part of its performance measure, directly or indirectly, and to the extent of the AI's capabilities. For example, it doesn't sound too hard for an AI to figure out that if it dies, then it will be difficult to do well on its other goals.
Emotion in us is a large part of how we implement a value system for deciding whether actions are good/bad. Avoid actions that make me feel bad; do actions that make me feel good. For an AI, it's very similar. Avoid actions that decrease its performance measure; do actions that increase its performance measure.
The first big question is implementing a moral performance measure (no biggie, just a 2000+-year old philosophy problem). The second big question is keeping that from being hacked, e.g., by giving the AI erroneous information/beliefs. Judging by current events, we don't do very well at this, so I can't imagine much better success with AIs.
Yes, the advantage of open source is that good actors can read the code and find and fix security flaws. The disadvantage is that bad actors can also read the code and find and exploit security flaws. One would hope good actors would outweigh the bad ones, but my fear that that governments and organized crime have become bad and worse actors in a big way. Even when a particular flaw is fixed, we all know that there are still flaws to be found and exploited in any big software project, and nowadays the big-time software exploiters have the budgets and the manpower to take advantage.
That said, that doesn't mean closed-source is any better (a different tradeoff), but it would be foolish to think that open-source software is not being exploited for its open-source properties.
I can't speak for Kilobug, but my answers would be:
1. It depends on your values. E.g., how much do you value your own welfare compared to family, friends, co-workers, fellow citizens, and those other people? If you want to be conscious about it, you need to think about what you value and how you might have done things differently in that light.
2. I probably thought I was I a deotonologist, but if you carefully study your own and other people's decisions, the vast majority are consequentialists with values that tend to selfishness. WItness how many Americans are angry about the Central American children/teenagers trying to get into the US.
3. As others have commented, doing a full analysis is time-consuming and uncertain (hence "maximum expected utility"). Most of the time, one has to follow rules that generally (so one believes) that have good consequences. And generally, virtue and duty are good rules. But people make up all sorts of rules with little sense behind them. My grandmother thought opening an umbrella indoors was bad luck, but I am a little skeptical about that one.
Knuth's books are very book, but they don't get much use from me. Instead:
Introduction to Algorithms by Cormen et al.
A good statistics book. Mine is an old thing: Mathematical Statistics with Applications by Mendenhall and Scheaffer.
A good operations research book (linear programming, queueing theory, Markov models/decision processes, and the like). Another old thing: Operations Research by Hillier and Lieberman.
Other than that, it's books that are/were used often for programming reference: Common Lisp: The Language by Steele and LaTeX: A Document Preparation System by Lamport look the most worn.
Hopefully, someone will come up with something a little more recent than the "old things" I mentioned above.
Really, the "location" of these mega-corporations is a sham.
Instead, figure out (or estimate) what percentage of the shares are owned by US residents. Multiply that percentage times the corporation's profit times the corporate tax rate and that is what they should pay.
Note: Any public corporation knows who are the immediate owners, so that they can send out shareholder info. However, a shareholder might be another corporation which is owned by other corporations, etc. Hence, the need to estimate (along with following the money as much as possible).
Raw data need to be cleaned up and organized to feed into the ML algorithm.
The results of the ML algorithm need to be cleaned up and organized so that they can be used by the rest of the system.
No one (currently) can tell you which ML algorithm will work best on your problem and how its parameters should be chosen without a lot of study. Preconceived bias (e.g., that it should be biologically based, blah, blah) can be a killer here.
The best results typically come from combinations of ML algorithms through some kind of ensemble learning, so now your have the problem of choosing a good combination and choosing a lot more parameters.
All of the above need to work together in concert.
Certainly, it's not a bad idea to try to make this process better, but I wouldn't be expecting miracles too soon.
Yes, plus the fact that this kind of decision policy is already evolving with collision avoidance systems in some cars (and experimental self-driving cars). It's not going to be a sudden mystery to be solved 30 years from now.
One of the biggest issues for current MOOCs is the large attrition rate (in the 90% range). Assuming that people signing up are at least average intelligence (on average of course), this suggests that average students are unable, for whatever reasons, to complete these courses. Part of it is that the instructors come from elite universities, are used to teaching elite students, and approach the MOOC in the same way, leaving the average student in the dust. Another part is that average students lack the motivation, discipline, as well as the smarts to learn complex concepts without a real-life instruction.
The local paper will typically have recommendations of how to vote for as well. It's not ideal, but you might trust them enough to weed out the crazies.
Slashdot Mirror
Scrooge is alive and well on the Slashdot boards.
Ok, got it. No quoting.
What bullshit! The problem with gravity, its mysterious action st a distance, continues to this day. Physicists are still looking for gravity waves/gravitons with no success yet.
If you want an informative web site that actually works without Javascript, visit http://www.skyandtelescope.com...
Less than 0.1%.
Nah, that couldn't be it. Must be a CONSPIRACY.
A simpler rule would be to raise the price for a H1-B to $1M. The price should be far higher than hiring and training someone here.
Dear advertisers,
I don't mind ads so much, but I definitely do not want to run your programs, hence Noscript.
When did Slashdot turn so misogynist?
Evidence 1: The parent of this post.
Evidence 2: The moderators got it to a 5 rating.
In the end, I think the real problem is that we have unions running our schools for the benefit of the union members, rather than for the children.
Well, Mr. Evidence Guy, does evidence change your opinion or not?
http://voices.washingtonpost.c...
... instead of hoarding zero-days and working to make our hardware and software more insecure.
Nice in theory. Not so much in practice. With crypto, the devil's in the details. Here are just a few of the hard problems:
...
"The perfect is the enemy of the good" -- Voltaire.
Yes, those are all hard problems, but at least a widespread partial solution would make mass surveillance at least an order of magnitude more difficult and push TLAs to be more focused in their data gathering.
Also, a partial solution has the chance to be improved into better solutions. This would be a much better situation than what we have now. The fact that we can't solve all those hard problems now should not be an excuse to do nothing.
Corporations decide your worthiness to participate in our society: credit ratings, insurance scores, background checks, etc. It's not as bad as jail, but if you are blacklisted in some way, it's still pretty shitty.
See headsmartlabs.com and scroll down for an experiment that shows a nearly 2 psi decrease due to lower temperature and a wet football.
This leaves the question of why the Colts' footballs were still fully inflated.
You misunderstand how AIs are built.
The AI is designed to improve/maximize its performance measure. An AI will "desire" self-preservation (or any other goal) to the extent that self-preservation is part of its performance measure, directly or indirectly, and to the extent of the AI's capabilities. For example, it doesn't sound too hard for an AI to figure out that if it dies, then it will be difficult to do well on its other goals.
Emotion in us is a large part of how we implement a value system for deciding whether actions are good/bad. Avoid actions that make me feel bad; do actions that make me feel good. For an AI, it's very similar. Avoid actions that decrease its performance measure; do actions that increase its performance measure.
The first big question is implementing a moral performance measure (no biggie, just a 2000+-year old philosophy problem). The second big question is keeping that from being hacked, e.g., by giving the AI erroneous information/beliefs. Judging by current events, we don't do very well at this, so I can't imagine much better success with AIs.
but NoScript seems to block most of them anyway. I don't mind seeing a few ads, but I'm going to try to control what programs run on my machine.
Yes, the advantage of open source is that good actors can read the code and find and fix security flaws. The disadvantage is that bad actors can also read the code and find and exploit security flaws. One would hope good actors would outweigh the bad ones, but my fear that that governments and organized crime have become bad and worse actors in a big way. Even when a particular flaw is fixed, we all know that there are still flaws to be found and exploited in any big software project, and nowadays the big-time software exploiters have the budgets and the manpower to take advantage.
That said, that doesn't mean closed-source is any better (a different tradeoff), but it would be foolish to think that open-source software is not being exploited for its open-source properties.
I can't speak for Kilobug, but my answers would be:
1. It depends on your values. E.g., how much do you value your own welfare compared to family, friends, co-workers, fellow citizens, and those other people? If you want to be conscious about it, you need to think about what you value and how you might have done things differently in that light.
2. I probably thought I was I a deotonologist, but if you carefully study your own and other people's decisions, the vast majority are consequentialists with values that tend to selfishness. WItness how many Americans are angry about the Central American children/teenagers trying to get into the US.
3. As others have commented, doing a full analysis is time-consuming and uncertain (hence "maximum expected utility"). Most of the time, one has to follow rules that generally (so one believes) that have good consequences. And generally, virtue and duty are good rules. But people make up all sorts of rules with little sense behind them. My grandmother thought opening an umbrella indoors was bad luck, but I am a little skeptical about that one.
Treating it as a maxim rather than as a caution.
Knuth's books are very book, but they don't get much use from me. Instead:
Introduction to Algorithms by Cormen et al.
A good statistics book. Mine is an old thing: Mathematical Statistics with Applications by Mendenhall and Scheaffer.
A good operations research book (linear programming, queueing theory, Markov models/decision processes, and the like). Another old thing: Operations Research by Hillier and Lieberman.
Other than that, it's books that are/were used often for programming reference: Common Lisp: The Language by Steele and LaTeX: A Document Preparation System by Lamport look the most worn.
Hopefully, someone will come up with something a little more recent than the "old things" I mentioned above.
Really, the "location" of these mega-corporations is a sham.
Instead, figure out (or estimate) what percentage of the shares are owned by US residents. Multiply that percentage times the corporation's profit times the corporate tax rate and that is what they should pay.
Note: Any public corporation knows who are the immediate owners, so that they can send out shareholder info. However, a shareholder might be another corporation which is owned by other corporations, etc. Hence, the need to estimate (along with following the money as much as possible).
Raw data need to be cleaned up and organized to feed into the ML algorithm.
The results of the ML algorithm need to be cleaned up and organized so that they can be used by the rest of the system.
No one (currently) can tell you which ML algorithm will work best on your problem and how its parameters should be chosen without a lot of study. Preconceived bias (e.g., that it should be biologically based, blah, blah) can be a killer here.
The best results typically come from combinations of ML algorithms through some kind of ensemble learning, so now your have the problem of choosing a good combination and choosing a lot more parameters.
All of the above need to work together in concert.
Certainly, it's not a bad idea to try to make this process better, but I wouldn't be expecting miracles too soon.
Yes, plus the fact that this kind of decision policy is already evolving with collision avoidance systems in some cars (and experimental self-driving cars). It's not going to be a sudden mystery to be solved 30 years from now.
One of the biggest issues for current MOOCs is the large attrition rate (in the 90% range). Assuming that people signing up are at least average intelligence (on average of course), this suggests that average students are unable, for whatever reasons, to complete these courses. Part of it is that the instructors come from elite universities, are used to teaching elite students, and approach the MOOC in the same way, leaving the average student in the dust. Another part is that average students lack the motivation, discipline, as well as the smarts to learn complex concepts without a real-life instruction.
Why doesn't North Korea just use some version of Apple's walled garden? It sounds perfect for them.
The local paper will typically have recommendations of how to vote for as well. It's not ideal, but you might trust them enough to weed out the crazies.