amilein7minutes

I might be the only one, but reading the article and the discussion here, I can't help but ask a bigger picture question on whether it is worth putting so many pilots through the torture of flying F-16's and F-35's. G-locking sounds extremely unpleasant. Does it have any longer term effects on the pilot? Despite the development on the software, aircraft technology, and the fundamental aerodynamics side that the F-35 and F-16 programs have brought about, they still seem inhuman to the aviators.

Why isn't anyone questioning whether these high-maneuverability fighter aircraft should be used at all, especially given the huge budget of these programs?

Did the same person write all these books, which altogether amount to upwards of 10k pages? From his website, he is quite an active research mathematician too. While just writing so many textbooks is a huge accomplishment in itself, the author has also made them available for free; I am lost for words at how prolific as well as generous the author is. I really want to know how this was possible, how long it took, and more about their motivation.

I had never about Ruth Kluger before this article, and I think it is, in general, very hard to come by her viewpoint as a layperson, who is not European and/or Jewish, because the popular media (e.g. movies like Schindler's list) have "primed us for redemption". Overall, I think her opinions, though strong, make a lot of sense and urge one to consider our present treatment of survivors of tragedies, as a society.

This article was the first time I ever encountered the view that it is impertinent to ask the survivors of a tragedy to forgive because people generally want redemption and positivity. It also makes sense to me that as a society, we expect too much from survivors of tragedies; we expect them to be more moral but they have every reason to be less so.

Bernd Heinrich is a naturalist and professor who has written plenty of uniquely insightful and highly accessible books on the intelligence of birds, among other species. One of his authoritative books is the "Mind of the raven", which analyzes the question of consciousness in ravens. From the description on Amazon: "Mind of the Raven follows an exotic journey;from New England to Germany, and from Montana to Baffin Island in the high Arctic;offering dazzling accounts of how science works in the field, filtered through the eyes of a passionate observer of nature. Each new discovery and insight into raven behavior is thrilling to read, at once lyrical and scientific."

Another of his books that particularly sheds light on the unimaginably mature navigation skills and the ultraendurance of our avian brethren: https://www.barnesandnoble.com/w/the-homing-instinct-bernd-h...

The course teaches through computation in Julia, 4 seemingly random submodules that are both mathematical and computational. The course information including the instructors (who are MIT professors Alan Edelman, David Sanders, MIT researcher James Schloss, and Youtube educator Grant Sanderson) is on the title URL. To get a flavor, check out the first lecture (after the introductions and installations lecture) on convolutions here: https://mitmath.github.io/18S191/Fall20/lecture2/

The instructors are all prolific open-source contributors and the course is definitely worth looking at, if you are interested in hands-on computations of cool and useful mathematics. It's also open to everyone!

I think the questions asked by researchers in #2 are very different from those by that of #1. The questions mostly surround the why's and how's of AI, i.e, mathematical questions. To take examples from deep learning, #2 might ask about the robustness and generalization of deep neural networks, applying dynamics/ODE theory to certain types of neural networks such as ResNets etc.

#1 might ask about the performance of a deep neural network in approximating a given model in a specific application. Alphafold, on the front page currently, is an example of #1.

What strikes about the theory Lippmann develops in this piece back in 1919, is that the entire argument remains coherent and powerful just as is, a hundred years later in 2019. In the meantime, various implementations of democracy across the world (differing in the way the elections are held, the distribution of power between the legislature, executive and the judiciary and the elections of those etc) have all pretty much encountered the same faultlines emerging.

By that I mean that the majority votes based on a confused jumble of propaganda and misinformation and this voting pattern has not produced the best outcome for 1) the protection of the minorities and 2) solving problems that impact every individual, that also require collective effort, such as climate change and poverty.

While Lippmann blames the impossibility of gleaning facts from the "inconceivable confusion" that is presented to the common person -- and this problem has only become much worse in 2019, given the ease of access to opinions in the internet age -- is it possible that fake news to undermine democracy is an inevitable feature? In other words, is it possible that even if everyone was presented with crystal clear facts, they would not become rational agents? That the opinion of the masses will still converge to something based on external variables rather than facts?

The article gives the impression that once the problem of misinformation is eliminated, the democratic model will truly achieve liberty, as Lippmann defines it, but it does not seem rational to subscribe to this belief, simply because there is no historical precedent (fake news and propaganda are as old as democracy itself and the information age has merely provided infrastructural strength for its spread) and if we were being factual/Bayesian, there is no reason to believe in the redemption of democracy in the absence of misinformation.

I have a theory as to why Engelbart (and in general, some other thinkers far ahead of their time) have trouble being appreciated and why "serious" computer scientists (in general, academics) of their time met them with a "thundering silence".

Quoting from the article, Engelbart's 1963 work has these lines:

"This hypothetical writing machine permits you to use a new process for composing text. For instance, trial drafts can rapidly be composed from rearranged excerpts of old drafts, together with new words or passages which you insert by hand typing. Your first draft may represent a free outpouring of thoughts in any order, with the inspection of foregoing thoughts continuously stimulating new considerations and ideas to be entered. If the tangle of thoughts represented by the draft becomes too complex, you can compile a reordered draft quickly. It would be practical for you to accommodate more complexity in the trails of thought you might build in search of the path that suits your needs."

The year was 1963 and the first word processors such as WordPerfect, came in 1979, 16 years later, and the mouse demo only took place 5 years later. Basically, Engelbart talked about word processors in the punch card era when there was no human-computer interaction, only programmer-computer interaction, and that too, in minimal amounts.

In comparison, Turing was taken seriously by serious mathematicians of his time, although he wrote in the 1940s about the idea of stored programs, in an era where the punch cards themselves didn't exist. This might be because his central idea was purely theoretical, and only required a construction of a hypothetical machine (a Turing machine), and thus, academics could interpret it as a thought experiment. Thought experiments are and were always considered "serious".

On the other hand, when the central idea is not theoretical, but involves construction of machines, it is interpreted as philosophical or useless. I'm saying ideas that essentially rest on engineering constructions that do not yet exist, are often dismissed in the academic space, but purely theoretical ones are found to be interesting, although they also require imagination of the non-existent.

Just a theory, I may be wrong. I just got to wondering if Engelbart had presented the idea of breaking down complexity with a more theoretical framework (e.g., constructed a mathematical model for augmented memory or something like that), would he have found NSF funding sooner?

This article makes some excellent hypotheses about music that I think everyone can anecdotally confirm.

To explain the attributes of the brain some of these hypotheses allude to, in terms of programming, imagine you have a class. It consists of a list of data members and some member functions that are called by the user (the brain) in order to parse given (sensory) inputs.

For each sensory input, a separate object is created, and so the returned outputs, after some computation, are inherently associated with the object. So, it happens that when you see a blue circle, the member functions are crunched using the .shape and .color data members, that are set to circle and blue respectively, say in the constructor of the ``blue circle`` object.

Suppose this model for processing all kinds of sensory inputs; we might have to imagine complicated classes, lots of member functions so that all kinds of sensory inputs can be parsed. The author makes the interesting claim that there are additional arguments in the member functions that are influenced by whether there is music at the time of sensory object creation.

These arguments distort the outputs (the parsed sensory inputs). The member functions are black boxes, but common experience gives us some insight, based on the observed correlations of inputs and outputs (eg. "sad story" as a sensory input without music is parsed as "sad story", with Bach's Chaconne, is parsed as "extremely depressing, weirdly poignant story").

One other claim that is made in the article, that is very interesting, is that there is a feedback, through evolution. Suppose the brains of human beings have performed Bayesian updates on the outputs of the black box member functions. When the Bayesian brain creates music, does it harness its updated function so as to amplify desired effects?

Could you explain why the restriction over selling user data would be a violation of first amendment rights?

Overall, isn't your last point also the source of problems? If the whole idea is to be able to exert regulatory control, to protect users, over companies that seem to have a huge share on decision-making in this space due to their unmatched wealth, how is their parting with a minuscule portion of their wealth so they can potentially influence a decision made on the topic of restricting them, solve any problems in the first place? It seems like a stretch to suggest that privacy advocacy groups need money from tech giants to support themselves.

I agree that every organization needs the money but the problem at hand is that every organization should not ideally depend on tech company donations because they have amassed the largest sums. Since this is the problem these laws are trying to address in the first place, I am not sure taking donations from tech giants would solve the problem, even if it doesn't exacerbate it (assuming for a moment that the tech giants are actually being a force of good here).

The author notes "Let us hope there are no real-world applications" when describing the Navier-Stokes regularity and smoothness problem. It looks like he meant to write the opposite and this was an error. However, in some sense, the statement may also be true. That is, people have been doing fluid experiments and numerical experiments (numerical fluid mechanics is a mature discipline) without relying on whether the conjecture is true. I mean, flight development was not waiting for an answer!

My point is engineering moved on without an answer to this particular question. At the same time, answering questions such as these (and the ones on number theory two of which were mentioned in the article) that occupy pure mathematicians provide more than artistic pleasure -- they provide the giant leaps that spur new technology. The often quoted example from Tao's research is compressed sensing but even bigger splashes like the idea of computers and the stored program, that can be traced back to Turing's seminal paper, comes from pure mathematics.

Charles Fefferman, mentioned in the article, makes 3 very cogent arguments[1] for the applicability of pure mathematics to the society, and in fact, says very pertinently that the line between pure and applied math is very blurry in the first place: [1]: https://www.youtube.com/watch?v=3LgjMjVA4sY

1. that unanticipated applications show up from purely theoretical questions

2. a rigourous study of math provides a way of thinking that prepares students to work in a range of fields that require quantitative or analytical thinking.

3. math is capable of revealing those ground breaking discoveries that happen rarely

I haven't thought about this carefully but is it correct to say the probability of finding n as the leading digits for a bounded m (but both very large) is a decreasing function of n?

Very cool! Is there a pattern in the sequence of m's? It feels as though there must be some bounds on the terms of the sequence on m's given that the probability of appearance of n as the first digits decreases with n, in the sequence of factorials.

Sorry for the naive question but can anyone explain why hosting an IPv6-only server can help with decentralization? As a provider of some service, say you have a website where you publish some work, is there any advantage of setting it up as an IPv6 server?

Bertand Russell, ahead of his time in his socio-political musings, as with foundations of mathematics.

Although the essay begins as though he is advocating for leisure for some privileged section of the society -- and one might especially suspect his position is thus given he himself was part of the old aristocracy by birth -- he explains not only that idleness being a collective right will elevate the society but also that it can be achieved. The time to have achieved it, in my opinion, has already passed and yet we continue to be "foolish", as he puts it.

His argument is so cogent that the following lines seem completely within reach for the society and paint the idea of paradise"

>>> In a world where no one is compelled to work more than four hours a day every person possessed of scientific curiosity will be able to indulge it, and every painter will be able to paint without starving, however excellent his pictures may be. Young writers will not be obliged to draw attention to themselves by sensational pot-boilers, with a view to acquiring the economic independence needed for monumental works, for which, when the time at last comes, they will have lost the taste and the capacity. Men who in their professional work have become interested in some phase of economics or government will be able to develop their ideas without the academic detachment that makes the work of university economists lacking in reality. Medical men will have time to learn about the progress of medicine. Teachers will not be exasperatedly struggling to teach by routine things which they learned in their youth, which may, in the interval, have been proved to be untrue.

I completely agree with you. When it comes to academic writing, the content transforms itself into a more coherent entity as I write. Oftentimes the big picture seems laughably inadequate and even inaccurate when I look at the final product, so much so that writing itself seems like research and thinking beforehand, almost redundant.

Hamming agrees in his famous speech (see the Q&A section on this page - https://www.cs.virginia.edu/~robins/YouAndYourResearch.html) that 50% of the time is just spent writing/presenting research.

>> ''In contrast to its international competitors, the United States brings a worldview that favors early involvement of industry in technology development and a greater appetite for tackling technology risk if it leads to commercial viability.''

As an aside, fusion technology feels like a good testbed for what economic model is best for the democratization of a public good. Is government investment in the ITER countries a better accelerator than the American model of private companies being allowed to take advantage of research in govt institutions? I suppose we will know in a few decades when fusion contributes to the power grid.

Their data might be small steps in the right direction though. My field is not neuroscience but from the outside, it seems that neuroimaging techniques are considered the best way to noninvasively understand more about functioning. Primarily because so little is understood about thought mechanisms these kinds of studies are definitely first steps. May be there will always be great empiricism and subjectivity involved in this complex question, like many sociological questions, but it seems like a worthy goal to understand how and why gender influences a person's choice of field. While trying to make progress on this question -- the societal conditioning/sociological factors that influence behaviors play a huge role which may not be quantifiable -- understanding the contribution of neurochemistry seems pertinent.

To understand differences in another field, such as athletics, the answers seem to be far more attainable (e.g. research on hormonal activity and its effect on strength is also making progress) today than in a field like math where its reliance on abstract thinking necessitates better understanding of neural mechanisms -- a hard problem.

There is certainly only one math (if you subscribe to this idea -- it is also the truth) but many ways of doing it. Given doing math (at least at the research level) requires a staggering variety in thought and being able to make connections through training (some might call this intuition), trying to understand the sensitivity of ways of thinking about abstractions, to neurochemistry, is definitely a nontrivial question. Now whether or not one is interested in answering this question, it is definitely complex enough to investigate.