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P-NP··on 1931: Kurt Gödel shows limits of math, logic, computing, AI
You also write that Turing's work "paved the way for modern general computers." According to the text, however, this practical part really started with Konrad Zuse's patent application of 1936:

"The formal models of Gödel (1931-34), Church (1935), Turing (1936), and Post (1936) were theoretical pen & paper constructs that cannot directly serve as a foundation for practical computers. Remarkably, Konrad Zuse's patent application[ZU36-38][Z36][RO98] for the first practical general-purpose program-controlled computer also dates back to 1936. It describes general digital circuits (and predates Claude Shannon's 1937 thesis on digital circuit design[SHA37]). Then, in 1941, Zuse completed Z3, the world's first practical, working, programmable computer (based on the 1936 application). Ignoring the inevitable storage limitations of any physical computer, the physical hardware of Z3 was indeed universal in the "modern" sense of Gödel, Church, Turing, and Post—simple arithmetic tricks can compensate for Z3's lack of an explicit conditional jump instruction.[RO98]"

P-NP··on 1931: Kurt Gödel shows limits of math, logic, computing, AI
You write that he is "trying to say that Turing did nothing new, his work is a mere extension of Godel's (!), and he only accounted for practical computational reality. That is not true." However, Schmidhuber's text on Gödel/Church/Turing/Post/Zuse is much more nuanced than that:

"In 1935, Alonzo Church derived a corollary / extension of Gödel's result by showing that Hilbert & Ackermann's famous Entscheidungsproblem (decision problem) does not have a general solution.[CHU] To do this, he used his alternative universal coding language called Untyped Lambda Calculus, which forms the basis of the highly influential programming language LISP.

In 1936, Alan Turing introduced yet another universal model which has become perhaps the most well-known of them all (at least in computer science): the Turing Machine.[TUR] He rederived the above-mentioned result.[T20](Sec. IV) Of course, he cited both Gödel and Church in his 1936 paper[TUR] (whose corrections appeared in 1937). In the same year of 1936, Emil Post published yet another independent universal model of computing,[POS] also citing Gödel and Church. Today we know many such models. Nevertheless, according to Wang,[WA74-96] it was Turing's work (1936) that convinced Gödel of the universality of both his own approach (1931-34) and Church's (1935).

What exactly did Post[POS] and Turing[TUR] do in 1936 that hadn't been done earlier by Gödel[GOD][GOD34] (1931-34) and Church[CHU] (1935)? There is a seemingly minor difference whose significance emerged only later. Many of Gödel's instruction sequences were series of multiplications of number-coded storage contents by integers. Gödel did not care that the computational complexity of such multiplications tends to increase with storage size. Similarly, Church also ignored the spatio-temporal complexity of the basic instructions in his algorithms. Turing and Post, however, adopted a traditional, reductionist, minimalist, binary view of computing—just like Konrad Zuse (1936).[ZU36] Their machine models permitted only very simple elementary instructions with constant complexity, like the early binary machine model of Leibniz (1679).[L79][LA14][HO66] They did not exploit this back then—for example, in 1936, Turing used his (quite inefficient) model only to rephrase the results of Gödel and Church on the limits of computability. Later, however, the simplicity of these machines made them a convenient tool for theoretical studies of complexity. (I also happily used and generalized them for the case of never-ending computations.[ALL2])"

P-NP··on Submarine communication: an aquatic device extracting energy from ambient noise
A device by Fadel Adib of MIT not only broadcasts and receives sound. It is also powered by sound.
P-NP··on Landmark math proof clears hurdle in top Erdős conjecture
Very promising, although the jury is still out: "The new paper is 77 pages long, and it will take time for mathematicians to check it carefully. But many feel optimistic that it is correct. “It really looks the way a proof of this result should look,” said Katz, whose earlier work laid much of the groundwork for this new result."
P-NP··on Fastest star ever seen is moving at 8% the speed of light
But what is the fastest known UNBOUND star that does NOT orbit a black hole? As of November 2019, it was S5-HVS1. Its speed is over 1,700 km/s or 1,056 mi/s. That's about 0.57% of light speed. S5-HVS1 will be flicked out of the Milky Way. The previous record-holder was US708, a white dwarf travelling at 1,200 km/s or 746 mi/s.
P-NP··on Verifying Schmidhuber's Critique of Turing Award for Bengio and Hinton and LeCun
1. Schmidhuber's Sec. XVIII points out that the basic convnet architecture with convolutional and downsampling layers is actually due to Fukushima (1979). In 1987, NNs with convolutions were combined by Waibel with weight sharing and backpropagation, before LeCun.

3. Sec. D and XIV say that the convnet was scaled up already in 2011 through the DanNet from Schmidhuber's lab. DanNet was the first convnet to win a computer vision contest. It also was the first convnet to achieve superhuman performance and had a "monopoly on winning CV competitions." It won 4 CV contests before Hinton's AlexNet won ILSVRC.

It's true. I checked the references.

P-NP··on Verifying Schmidhuber's Critique of Turing Award for Bengio and Hinton and LeCun
I agree. It’s about accomplishments.

And let's not forget the accomplishments of the other pioneers: Lainnainmaa, the inventor of backpropagation 1970. Ivakhnenko, the inventor of the first deep learning networks that learn internal representations 1965. Fukushima, the inventor of the convolutional neural network architecture 1979. Waibel, who combined backpropagation with convolutions 1987. They are prominently mentioned in the critique in Sec. XII, XIX, XXI, XVIII, XX and others.

P-NP··on Verifying Schmidhuber's Critique of Turing Award for Bengio and Hinton and LeCun
PS: Here the direct link to the critique: http://people.idsia.ch/~juergen/critique-turing-award-bengio...
P-NP··on Verifying Schmidhuber's Critique of Turing Award for Bengio and Hinton and LeCun
But Hinton also republished lots of stuff first published by Schmidhuber, without citing it. See Sec. XVII (5-8), I, II. Hinton also used methods invented by other authors without citation. Same for LeCun. See Sec. XII, XIX, XXI, XVIII. One could argue that Schmidhuber should have received the full award, without sharing it with anybody, except maybe with his student Hochreiter.
P-NP··on Critique of 2018 Turing Award for Drs. Bengio and Hinton and LeCun
Schmidhuber's critique has over 200 references. I have spent several hours on verifying them. It helped that I already knew many of his papers and earlier posts i.e. "Deep Learning: Our Miraculous Year 1990-1991" http://people.idsia.ch/~juergen/deep-learning-miraculous-yea... and "Critique of Honda Prize for Dr. Hinton" http://people.idsia.ch/~juergen/critique-honda-prize-hinton....

As far as I can tell, all of Schmidhuber's claims are correct. Apparently the post even went through some sort of peer review. He is thanking "several expert reviewers for useful comments."

Have a look at the astonishing Sec. XVII which lists eight of his direct priority disputes with Bengio and Hinton.

This is not a glorious chapter in the annals of the Turing award.

P-NP··on Advertising anonymous conference submissions on Twitter (NeurIPS)
Many disagree with this. Others defend such ads, claiming that the process of blind peer review is broken anyway.
P-NP··on Discovering Dennis Ritchie’s Lost Dissertation
> So there is a chance he did appreciate your thank you but couldn't reply back properly.

> I tend to take the most optimistic interpretation.

Yes, always take the most optimistic interpretation compatible with the known facts. Best way to stay happy.

P-NP··on Apple is building an operating system for health
Many studies show that modern machine learning could revolutionize healthcare. However, many hospitals and governments are extremely protective of patient data. And many hospitals don't even know how to exploit their data through deep learning. Enter Apple and Google and others with big machine learning departments. They are finding new ways of collecting health-relevant data. For example:

Through Apple Heart Study, Stanford Medicine researchers show wearable technology can help detect atrial fibrillation

https://med.stanford.edu/news/all-news/2019/11/through-apple...

https://www.apple.com/healthcare/docs/site/Apple_Watch_Arrhy...

An OS with a focus on health seems to make a lot of sense.

P-NP··on IBM no longer offers general purpose facial recognition or analysis software
A striking difference to what's happening in China where facial recognition software made SenseTime the world's most valuable AI unicorn: "However, facial recognition does not seem to have been making the company much money, if any. To be fair the technology is really in its infancy and there are few applications where an enterprise vendor like IBM makes sense."
P-NP··on Bill Thurston's answer to “What's a mathematician to do?” (2010)
I like the Feynman quote in the comments: "You keep on learning and learning, and pretty soon you learn something no one has learned before."
P-NP··on Da Vinci – Overrated?
Many don't believe that the rather unimpressive 'Salvator Mundi' was painted by Leonardo. Nevertheless, it sold for 450 million bucks. His most famous paintings such as the Mona Lisa might bring hundreds of billions if they were on the market. Probably more than what Steve Jobs ever owned. So much for 'shipping products to the consumer market.'
P-NP··on There’s plenty of room at the Top: computer performance after Moore’s law
It would become very hot indeed. But that does not necessarily imply a runaway explosion. Just like the center of the sun won't explode anytime soon. Nevertheless, I'll take the much cooler model with only 10^44 operations per second.
P-NP··on There’s plenty of room at the Top: computer performance after Moore’s law
There is even more room at the top. The physical limits are as follows. A computer "with a mass of one kilogram confined to a volume of a liter" can perform at most 2:71 * 10^50 operations per second on 2.13*10^31 bits. See:

Lloyd, S. Ultimate physical limits to computation. Nature volume 406, 1047–1054 (2000)

Bremermann, H. J. Minimum energy requirements of information transfer and computing. Int. J. Theor. Phys. 21, 203–217 (1982)

Bekenstein, J. D. Energy cost of information transfer. Phys. Rev. Lett. 46, 623–626 (1981)

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