Indeed. And while for a time I saw the use of Linux in CS students being small, today they almost all know at least how to run a server on it. Linux has won on the server completely. It is just so much more easy to use there and the results are so much better. I also see the difference in the enterprise: Windows servers are often problematic, while Unix/Linux work pretty well. And the Unix/Linux teams usually have much more of a clue on top of that.
Indeed. Also the reasons to use Linux in a lot of places is because it is a well-designed, versatile, flexible, reliable and secure and open OS. That it is free is a bonus. Windows just cannot compete in most of these regards. It does not even run (well) on other CPU architectures other than AMD64 and forget about putting it on mainframes or very small IoT devices.
However the problem that MS has is not that Linux is so strong. Linux is just state-of-the-art in many respects and even a little behind in some. The problem that MS has is that Windows is still so incredibly weak as an OS.
I second that. For example, many banks use Linux as replacement for Solaris and other Unix servers. They will not be moving these servers to Windows anytime soon because that would not even work.
Indeed. In a sense the halting problem says "there are no general shortcuts to describing program semantics", i.e. the code itself is the most simple general description method for code. A direct implication is that you cannot determine code semantics in general without testing it. (You can transform it into some mathematical model, but that does, in general, not simplify things and hence is useless.) Another direct implication is that you cannot, in general, fully test code.
The question whether it halts or not is just the most simple instance of this and hence very useful for proofs.
Where does this moronic idea that "AI can program itself" come from? There is zero indication this is even theoretically possible. Or rather there is strong theoretical indication this is not possible.
Only if it works at all. In actual reality, Physics has always come up with inaccuracies and limits when you go to the extremes. The same will happen here.
In actual reality, when the inputs scale, the largest thing you can tolerate is O(n log n) or the algorithm is basically irrelevant. Pretty much means that out-scaling Shor's algorithm is not a problem, the numbers just need to get a bit larger.
This is so far from a demonstration of actual usefulness as a computing device, it is pathetic. An no, there is no "Moore's Law" for QC. About 30 years ago, they were at 4 Qbits. Now they are at 72? Sounds more like a linear scaling or worse to me.
That is a question of cost, space, numbers and equipment that is already there. Sure, if there was a market for 100'000 of these per year, they would probably come as a complete replacement for the cooking station. But there is not. So a general-purpose tool gets adapted. Also, this is the first iteration, optimization will be done when it is known how large the market is.
If it finds even 10% of the bugs, it is still a huge win.
I disagree. This is 10% less opportunity for the coders to increase their skills finding bugs on more benign mistakes (software cannot find hard to find ones). As humans are able to generalize, this also reduced harder to find mistakes. In the end, whenever something like this is done, you end up with more damage per remaining mistake, which may well make matters worse overall. As a second negative effect, this will lead PHBs to decide that they can now hire even cheaper coders.
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Don't tell me, I suffered through exactly those Macs a few (not many) years later. I agree to your points.
So? And why would I assume you know what you are talking about? And how would you know what exactly I am using this for?
I highly doubt that.
You seem to be unaware how an enterprise-environment actually works.
Indeed. And while for a time I saw the use of Linux in CS students being small, today they almost all know at least how to run a server on it. Linux has won on the server completely. It is just so much more easy to use there and the results are so much better. I also see the difference in the enterprise: Windows servers are often problematic, while Unix/Linux work pretty well. And the Unix/Linux teams usually have much more of a clue on top of that.
Not quite. A hypervisor may give you better filesystem access to the host than having an NFS/SMB/whatever export.
On the other hand, the difference is small enough that it gives you the experience needed to run it bare-metal.
Ah, yes, FU-bubtu and systemd. The cancers Linux actually has. But the good news is that both can actually be avoided.
I am not so sure, see the systemd debacle. But that group will not be MS and if that group overdoes it it will find itself kicked out.
MS is evil. So no. However, not everything they do is evil, so a discussion is merited.
Indeed. Personally I use either cygwin or a full VM if I want to run something Linux on Windows.
Indeed. Also the reasons to use Linux in a lot of places is because it is a well-designed, versatile, flexible, reliable and secure and open OS. That it is free is a bonus. Windows just cannot compete in most of these regards. It does not even run (well) on other CPU architectures other than AMD64 and forget about putting it on mainframes or very small IoT devices.
However the problem that MS has is not that Linux is so strong. Linux is just state-of-the-art in many respects and even a little behind in some. The problem that MS has is that Windows is still so incredibly weak as an OS.
I second that. For example, many banks use Linux as replacement for Solaris and other Unix servers. They will not be moving these servers to Windows anytime soon because that would not even work.
The madness and the experiments in practical entanglement started back then.
Good luck with that.
Indeed. In a sense the halting problem says "there are no general shortcuts to describing program semantics", i.e. the code itself is the most simple general description method for code. A direct implication is that you cannot determine code semantics in general without testing it. (You can transform it into some mathematical model, but that does, in general, not simplify things and hence is useless.) Another direct implication is that you cannot, in general, fully test code.
The question whether it halts or not is just the most simple instance of this and hence very useful for proofs.
No, you did not. "Actuality" can well be mathematical actuality.
You have failed to even begin to understand what I said.
Actually most things do not need any tough AI problems to be solved at all.
Where does this moronic idea that "AI can program itself" come from? There is zero indication this is even theoretically possible. Or rather there is strong theoretical indication this is not possible.
Only if it works at all. In actual reality, Physics has always come up with inaccuracies and limits when you go to the extremes. The same will happen here.
In actual reality, when the inputs scale, the largest thing you can tolerate is O(n log n) or the algorithm is basically irrelevant. Pretty much means that out-scaling Shor's algorithm is not a problem, the numbers just need to get a bit larger.
That is not a general result. It just says there is one 5 digit number that can be factored this way.
This is so far from a demonstration of actual usefulness as a computing device, it is pathetic. An no, there is no "Moore's Law" for QC. About 30 years ago, they were at 4 Qbits. Now they are at 72? Sounds more like a linear scaling or worse to me.
That is a question of cost, space, numbers and equipment that is already there. Sure, if there was a market for 100'000 of these per year, they would probably come as a complete replacement for the cooking station. But there is not. So a general-purpose tool gets adapted. Also, this is the first iteration, optimization will be done when it is known how large the market is.
If it finds even 10% of the bugs, it is still a huge win.
I disagree. This is 10% less opportunity for the coders to increase their skills finding bugs on more benign mistakes (software cannot find hard to find ones). As humans are able to generalize, this also reduced harder to find mistakes. In the end, whenever something like this is done, you end up with more damage per remaining mistake, which may well make matters worse overall. As a second negative effect, this will lead PHBs to decide that they can now hire even cheaper coders.