Laakeri

I'd say it instead makes it more precise, indicating that they are uncertain but the range is from several hours to days.

There are such things, they are called "arxiv overlay journals". Couple of examples from my research area: https://www.advancesincombinatorics.com/, https://theoretics.episciences.org/

But he made a good living out of it, so in the end it was a good idea?

I completely agree with you. My point was that the P vs NP distinction matters in practice, but of course also the subquadratic vs quadratic time distinction matters a lot.

What are examples of natural graph related problems in P with absurd exponents? I think the reason they don't get attention is not that the algorithms are not practical, but that the problems are not natural. Really, the only examples of such problems I can think of are something like "Finding a clique of size 222222 is in P because we can try all possibilities in n^222222 time".

I'm not sure if its really misunderstanding, when in 99% cases it has turned out that if a problem is in P, then it has a polynomial-time algorithm with a quite small exponent.

"Luck favors the prepared mind"

For binary-encoded input it is in 2-EXPTIME by trying all graphs of size exponential in the input number and testing all subsets of the given size. Would be surprising if any hardness result for complexity classes would be known.

I read the essay, and my takeaway to "There are for example four fundamental forces. Are we going to find another four?" would be:

Maybe not, but we could find out that the model of fundamental forces wasn't really the final answer, and explain the universe much better with some alternative theory. In your comment you assume that science is more or less settled, and all there is to do is to tweak the present understanding a bit and fill some gaps. The same sentiment was shared by many prominent physicists already in the end of 1800s, who were obviously proved wrong by Einstein and others in the 1900s.

It's enough to just take one random element and use it as a pivot.

For example Google hires mostly generalists and lots of people who care about income and career progression work there.

Red Bull is not a sponsor but the owner of the team

Well, it is also not proved to not be NP-hard, so it could also be NP-hard to our current knowledge. But you are right that researchers believe that it's not NP-hard.

I don't know, he was a co-founder of computer science.

arxiv.org

Is there some fair comparison of D-Wave annealer vs classical methods on optimization problems? I remember seeing papers where it was compared to some naive methods or the runtime of D-Wave approximation algorithm was compared to the runtime of classical exact algorithm -- obviously apples vs oranges.

The sycamore is really on the border of what current classical methods can simulate. You also need to consider that the funding used for building sycamore was probably orders of magnitudes larger than the funding used to build classical competitors.

Quantum computing is a prime example of a technology in phase 2, definitely not in "commercialization".

You may not like it, but this is what peak performance looks like.

I think you could anyway get it from eBay with quite small overhead.

>There's no real big theoretical problems in the quantum computer building space

The current quantum computers are just on the edge of what we can simulate classically, so we can't yet rule out the possibility that realizing a quantum computation requires an exponential amount of energy in the number of qubits. (Though it should be noted that quantum mechanics predicts that this will not happen.)

I think by Theorem 4.2 in https://math.dartmouth.edu/~pw/math100w13/haddadan.pdf the number of such shuffles required to have a positive probability on all permutations is 6.

To me, the Jeff Dean's email sounds like this paper failed to cite some research and as a result made Google look less good than Google actually is. This sounds like somewhat minor academic mistake, but at the same time a major PR mistake.

One useful piece of advice that I have received is that there are tradeoffs in giving shit at work. This article mentions downsides of not giving a shit, but doesn't mention downsides of giving too much shit, that is, getting too emotionally attached to the work you are doing. Especially in big companies projects get cancelled or requirements change or managers assign work that doesn't even make sense, and if you are emotionally attached to your work when this happens it can cause interpersonal issues, low morale or unhappiness in general.

Ah, that explains it perfectly!

Most research is government funded

All Turing-complete programming languages/models of computation can simulate each other with polynomial overhead. What I think is weird is that the article specifically mentions that programming languages are built on single tape Turing machines (as opposed to Turing machines in general, or random access machines), because single tape Turing machines are highly impractical and programming them would feel completely different than programming with any real programming language. For example as basic operation as array access takes a linear number of operations in the size of the array in the single tape model.

Oh, that's nice, I didn't know that! How big is your office? What I know is that Google does not hire software engineers in Helsinki. They have a small office, but it is only for sales people.

Edit: Also by https://www.amazon.jobs/en/location/helsinki-finland it seems that Amazon is not hiring in Helsinki. (If you actually are, maybe you should update the page?)

>Further, it has been exacerbated by recent computing mechanisms, such as multithreading, NUMA, GPGPU, and distributed computing, which have moved away from the "single tape" Turing Machine model of computation that most programming languages are founded upon.

Stopped reading here. No programming language with arrays resembles single tape Turing machines. The correct model would be random-access machine (RAM).

Yeah, many people from Helsinki who are good enough to get a job at Amazon/Google/etc are actually moving abroad to work for them, so it doesn't make sense to move to Helsinki if you could get a job like that. Though there are some good cities in Europe with tech giants and their good pay, for example London and Zurich.