Even a stopped clock is right twice a day.

Wait, what was I doing?

People who are deeply partisan or utterly in love with a single politician don't understand people who despise a politician specifically because of their actions and policies. It's particularly confusing for them when this is baised towards one party or the other because parties tend to stick to similar sorts of things in the short to medium term.

The blind partisans can't tell the difference between that and being blindly partisan like themselves.

So, they assume that like themselves you don't judge the actual actions of the person.

A stopped clock is right twice a day.

That's as simple as it gets. I'm no fan of the orange turd, but it's virtually impossible for him to be wrong on every issue.

Normally during bubbles, things like this keep working fine until the bubble bursts because when the stocks and other assets keep going up in value it is hard to see if someone is making reasonable investments or not. That Situational Awareness fell apart even in the middle of this may mean they were overly risky even for ventures like this, or it may be evidence that we're in less of a bubble than many (including myself) think. One curious detail is that at least some people (such as many of the people who seem to be fans of Ed Zitron) see he collapse of SA as a sign that the bubble is in the process of bursting. The collapse of SA occurred about a month ago, so it is still recent enough to see if they are correct. But Zitron and his fans have been predicting an imminent collapse for years now, so I'm not sure I'd read too much into it.

Except time and willpower. However, the McDonald's analogy does hold up -- nobody is ever going to get rich flipping burgers at McDonald's, precisely because it requires so little skill to do that job, making McDonald's workers completely fungible. Since anyone can man a grill, no one is going to command a salary much more than what a high schooler will agree to do it for.

Nvidia's Jetson Orin isn't subject to export controls. It's basically like Nvidia's version of the Raspberry Pi with a few tensor cores bolted on so they can call it "AI".

The economic problem with having an AI produce music for you is, you never get the opportunity to learn how to produce it for yourself. At the end of the day, you're paying them $200/month for the privilege of remaining unskilled, and you're competing against everyone else who knows how to enter "write a catchy song for me" into a prompt box. You can't make money that way, because the moment you do, the AI companies (who own all of the talent and all of the production machinery) can simply appropriate your income stream for themselves.

It's the difference playing Guitar Hero and taking guitar lessons -- both let you feel like you're a competent musician, but only the latter will ever get you a career as a musician.

You'll find a considerable number of elder geeks spent years gainfully employed working on 4 bit MCUs.

The key details here are the number of qubits steadily improving, and that throwaway comment about needing millions of qubits should be disregarded as utter nonsense. Sure, it's gonna be a while before "gcc -march=quantum" is an option, but Grover's algorithm has a space complexity of O(n); Shor's is O((log n)^2). Hand-turned implementations of those will be gutting virtually all of modern cryptography long before the script kiddies start trying to fork-bomb the entire universe.

... find a rhyme for "booty". Job done.

I'm in complete agreement that none of these sorts of issues are at all likely with Xinuous (thankfully). My comment was to add some detail to how the system functions in general.

I'm not expecting quantum computers to happen tomorrow. Are you at least capable of understanding that your claim about "28" being the largest number factored was wrong for the basic reason that Shor's algorithm only factors odd numbers?

There are some complications. Sometimes SCOTUS will rule against a specific motion or part of a ruling, or refuse to hear something, even as another piece of an appeal can occur. One can also file in some circumstances a petition for the Supreme Court to reconsider its own ruling. But this is basically never successful. You also can in some circumstances ask an appeals court to change a prior ruling based on a recent Supreme Court ruling, and this has some success. The circumstances where you can do this sort of thing are complicated. Jean Carroll's lawsuit against Trump is an example of this https://en.wikipedia.org/wiki/E._Jean_Carroll_v._Donald_J._Trump where Trump tried basically everything to try and prevent having to pay out even some ver low percentage strategies. Frankly, if someone other than Trump had done this, I suspect the court response would have been more negative.

Most of this is wrong or missing important context.

Scaling in terms of what? The other user was talking about energy usage. This is pretty obviously not the case. Most forms of quantum computers have to be kept incredibly cold, often in miliKelvin. If there were exponential energy use, then adding even in a few more quantum gates would make that energy total massive. But we don't see that. So by what metric are you claiming exponential scaling effort?

I don't know what "QC effort" is, but the obvious metric for this is just false. For example, Shor's algorithm, which is the algorithm for factoring using a quantum computer, has a number of qubits which scales slightly worse than the square of the number of digits https://en.wikipedia.org/wiki/Shor's_algorithm.

This is highly garbled. First, of all the record for factoring cannot be 28, and it cannot be 28 for a pretty obvious reason. Shor's algorithm only works for *odd numbers*. This is a very basic part of how the algorithm functions. The number you are thinking of is 21, not 28. https://arxiv.org/abs/1111.4147. Your point about that these have been "no-decision algorithm" is not completely accurate, but is approximately so. They did use a compiled circuit which used prior knowledge of the solution to optimize the arrangement of the qubits.

But there are also good reasons that factorization records have not expanded. First, Shor's algorithm has high overhead if you trace out the number of gates, and requires very high coherence to even start getting used. It isn't a useful metric of where things are going. This is like looking at the number of people going to space in the 1950s when no one has gone to space and ignoring that rockets had been steadily improving since the 1930s. The coherence time for quantum computers continues to improve. For a while it was improving at a rate of a factor of 10 roughly every 3 years https://en.wikipedia.org/wiki/Quantum_computing_scaling_laws#Schoelkopf's_law . That has slowed down in the last decade or so, so it is now improving by about a factor of 3 to 5 every 3 years. Similar remarks apply to other metrics like number of gates. And we know that if you can get error levels down and coherence times long then quantum error correcting codes https://en.wikipedia.org/wiki/Quantum_error_correction works. In particular, the threshold theorem https://en.wikipedia.org/wiki/Threshold_theorem says that once your physical error rate is low enough, the logical error rate can be as low as you want, regardless of the size of the computation.

It isn't unreasonable to have this gut reaction to how quantum computers are described frequently to the general public. But one important thing to realize is that they don't let you do magic parallel information processing. There's this way of describing them as "trying all solutions at once," or things like that. But a quantum computer cannot in general do that, since if one has all potential "solutions" one needs some way of making sure that the non-solutions cancel out. In that context. there are some problems where we can do that, such as factoring numbers via Shor's algorithm https://en.wikipedia.org/wiki/Shor's_algorithm. Even there, what is going on is more subtle than just trying all factorizations, as noted by the number of quantum gates needed scaling at slightly worse than the number of binary digits of the number. If it were just trying everything in parallel this would scale close to linear with the total number of gates.

A different related situation to look at which may also be useful is how we strongly suspect that NP problems cannot be efficiently solved on a quantum computer. In particular, that NP is not contained in BQP https://en.wikipedia.org/wiki/BQP, the set of problems which can be done efficiently on a quantum computer. If a quantum computer could do "magic parallel information processing" then we'd expect NP to be contained in BQP.