Flames not so old…

The same picture, but two American interpretations, both partly misleading (to varying degrees):

NASA releases a photo [^] on the FaceBook, on 24 August at 14:24, with this note:

The visualization above highlights NASA Earth satellite data showing aerosols on August 23, 2018. On that day, huge plumes of smoke drifted over North America and Africa, three different tropical cyclones churned in the Pacific Ocean, and large clouds of dust blew over deserts in Africa and Asia. The storms are visible within giant swirls of sea salt aerosol (blue), which winds loft into the air as part of sea spray. Black carbon particles (red) are among the particles emitted by fires; vehicle and factory emissions are another common source. Particles the model classified as dust are shown in purple. The visualization includes a layer of night light data collected by the day-night band of the Visible Infrared Imaging Radiometer Suite (VIIRS) on Suomi NPP that shows the locations of towns and cities.

[Emphasis in bold added by me.]

For your convenience, I reproduce the picture here:

Aerosol data by NASA

Aerosol data by NASA. Red means: Carbon emissions. Blue means: Sea Salt. Purple means: Dust particles.

Nicole Sharp blogs [^] about it at her blog FYFD, on Aug 29, 2018 10:00 am, with this description:

Aerosols, micron-sized particles suspended in the atmosphere, impact our weather and air quality. This visualization shows several varieties of aerosol as measured August 23rd, 2018 by satellite. The blue streaks are sea salt suspended in the air; the brightest highlights show three tropical cyclones in the Pacific. Purple marks dust. Strong winds across the Sahara Desert send large plumes of dust wafting eastward. Finally, the red areas show black carbon emissions. Raging wildfires across western North America are releasing large amounts of carbon, but vehicle and factory emissions are also significant sources. (Image credit: NASA; via Katherine G.)

[Again, emphasis in bold is mine.]

As of today, Sharp’s post has collected some 281 notes, and almost all of them have “liked” it.

I liked it too—except for the last half of the last sentence, viz., the idea that vehicle and factory emissions are significant sources (cf. NASA’s characterization):


My comment:

NASA commits an error of omission. Dr. Sharp compounds it with an error of commission. Let’s see how.

NASA does find it important to mention that the man-made sources of carbon are “common.” However, the statement is ambiguous, perhaps deliberately so. It curiously omits to mention that the quantity of such “common” sources is so small that there is no choice but to regard it as “not critical.” We may not be in a position to call the “common” part an error of commission. But not explaining that the man-made sources play negligible (even vanishingly small) role in Global Warming, is sure an error of omission on NASA’s part.

Dr. Sharp compounds it with an error of commission. She calls man-made sources “significant.”

If I were to have an SE/TE student, I would assign a simple Python script to do a histogram and/or compute the densities of red pixels and have them juxtaposed with areas of high urban population/factory density.


This post may change in future:

BTW, I am only too well aware of the ugly political wars being waged by a lot of people in this area (of Global Warming). Since I do appreciate Dr. Sharp’s blog, I would be willing to delete all references to her writing from this post.

However, I am going to keep NASA’s description and the photo intact. It serves as a good example of how a good visualization can help in properly apprehending big data.

In case I delete references to Sharp’s blog, I will simply add another passage on my own, bringing out how man-made emissions are not the real cause for concern.

But in any case, I would refuse to be drawn into those ugly political wars surrounding the issue of Global Warming. I have neither the interest nor the bandwidth to get into it, and further, I find (though can’t off-hand quote) that several good modelers/scientists have come to offer very good, detailed, and comprehensive perspectives that justify my position (mentioned in the preceding paragraph). [Off-hand, I very vaguely remember an academic, a lady, perhaps from the state of Georgia in the US?]


The value of pictures:

One final point.

But, regardless of it all (related to Global Warming and its politics), this picture does serve to highlight a very important point: the undeniable strength of a good visualization.

Yes I do find that, in a proper context, a picture is worth a thousand words. The obvious validity of this conclusion is not affected by Aristotle’s erroneous epistemology, in particular, his wrong assertion that man thinks in terms of “images.” No, he does not.

So, sure, a picture is not an argument, as Peikoff argued in the late 90s (without using pictures, I believe). If Peikoff’s statement is taken in its context, you would agree with it, too.

But for a great variety of useful contexts, as the one above, I do think that a picture is worth a thousand words. Without such being the case, a post like this wouldn’t have been possible.


A Song I Like:
(Hindi) “dil sajan jalataa hai…”
Singer: Asha Bhosale
Music: R. D. Burman [actually, Bertha Egnos [^]]
Lyrics: Anand Bakshi


Copying it right:

“itwofs” very helpfully informs us [^] that this song was:

Inspired in the true sense, by the track, ‘Korbosha (Down by the river) from the South African stage musical, Ipi Ntombi (1974).”

However, unfortunately, he does not give the name of the original composer. It is: Bertha Egnos (apparently, a white woman from South Africa [^]).

“itwofs” further opines that:

Its the mere few initial bars that seem to have sparked Pancham create the totally awesome track [snip]. The actual tunes are completely different and as original as Pancham can get.

I disagree.

Listen to Korbosha and to this song, once again. You will sure find that it is far more than “mere few initial bars.” On the contrary, except for a minor twist here or there (and that too only in some parts of the “antaraa”/stanza), Burman’s song is almost completely lifted from Egnos’s, as far as the tune goes. And the tune is one of the most basic—and crucial—elements of a song, perhaps the most crucial one.

However, what Burman does here is to “customize” this song to “suit the Indian road conditions tastes.” This task also can be demanding; doing it right takes a very skillful and sensitive composer, and R. D. certainly shows his talents in this regard, too, here. Further, Asha not only makes it “totally, like, totally” Indian, she also adds a personal chutzpah. The combination of Egnos, RD and Asha is awesome.

If the Indian reader’s “pride” got hurt: For a reverse situation of “phoreenn” people customizing our songs, go see how well Paul Mauriat does it.

One final word: The video here is not recommended. It looks (and is!) too gaudy. So, even if you download a YouTube video, I recommend that you search for good Open Source tools and use it to extract just the audio track from this video. … If you are not well conversant with the music software, then Audacity would confuse you. However, as far as just converting MP4 to MP3 is concerned, VLC works just as great; use the menu: Media \ Convert/Save. This menu command works independently of the song playing in the “main” VLC window.


Bye for now… Some editing could be done later on.

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Caste Brahmins, classification, and ANN

1. Caste Brahmins:

First, a clarification: No, I was not born in any one of the Brahmin castes, particularly, not at all in the Konkanastha Brahmins’ caste.

Second, a suggestion: Check out how many caste-Brahmins have made it to the top in the Indian and American IT industry, and what sort of money they have made—already.

No, really.

If you at all bother visiting this blog, then I do want you to take a very serious note of both these matters.

No. You don’t have to visit this blog. But, yes, if you are going to visit this blog, to repeat, I do want you to takeĀ  matters like these seriously.

Some time ago, perhaps a year ago or so, a certain caste-Brahmin in Pune from some place (but he didn’t reveal his shakha, sub-caste, gotra, pravar, etc.) had insulted me, while maintaining a perfectly cool demeanor for himself, saying how he had made so much more money than me. Point taken.

But my other caste-Brahmin “friends” kept quiet at that time; not a single soul from them interjected.

In my off-the-cuff replies, I didn’t raise this point (viz., why these other caste-Brahmins were keeping quiet), but I am sure that if I were to do that, then, their typical refrain would have been (Marathi) “tu kaa chiDatos evhaDa, to tar majene bolat hotaa.” … English translation: Why do you get so angry? He was just joking.

Note the usual caste-Brahmin trick: they skillfully insert an unjustified premise; here, that you are angry!

To be blind to the actual emotional states or reactions of the next person, if he comes from some other caste, is a caste-habit with the caste-Brahmins. The whole IT industry is full of them—whether here in India, or there in USA/UK/elsewhere.

And then, today, another Brahmin—a Konkanastha—insulted me. Knowing that I am single, he asked me if I for today had taken the charge of the kitchen, and then, proceeded to invite my father to a Ganesh Pooja—with all the outward signs of respect being duly shown to my father.


Well, coming back to the point which was really taken:

Why have caste-Brahmins made so much money—to the point that they in one generation have begun very casually insulting the “other” people, including people of my achievements?

Or has it been the case that the people of the Brahmin castes always were this third-class, in terms of their culturally induced convictions, but that we did not come to know of it from our childhood, because the elderly people around us kept such matters, such motivations, hidden from us? May be in the naive hope that we would thereby not get influenced in a bad manner? Possible.

And, of course, how come these caste-Brahmins have managed to attract as much money as they did (salaries in excess of Rs. 50 lakhs being averagely normal in Pune) even as I was consigned only to receive “attract” psychic attacks (mainly from abroad) and insults (mainly from those from this land) during the same time period?

Despite all my achievements?

Do take matters like these seriously, but, of course, as you must have gathered by now, that is not the only thing I would have, to talk about. And, the title of this post anyway makes this part amply clear.


2. The classification problem and the ANNs:

I have begun my studies of the artificial neural networks (ANNs for short). I have rapidly browsed through a lot of introductory articles (as also the beginning chapters of books) on the topic. (Yes, including those written by Indians who were born in the Brahmin castes.) I might have gone through 10+ such introductions. Many of these, I had browsed through a few years ago (I mean only the introductory parts). But this time round, of course, I picked them up for a more careful consideration.

And soon enough (i.e. over just the last 2–3 days), I realized that no one in the field (AI/ML) was talking about a good explanation of this question:

Why is it that the ANN really succeeds as well as it does, when it comes to the classification tasks, but not others?

If you are not familiar with Data Science, then let me note that it is known that ANN does not do well on all the AI tasks. It does well only on one kind of them, viz., the classification tasks. … Any time you mention the more general term Artificial Intelligence, the layman is likely to think of the ANN diagram. However, ANNs are just one type of a tool that the Data Scientist may use.

But the question here is this: why does the ANN do so well on these tasks?

I formulated this question, and then found an answer too, and I would sure like to share it with you (whether the answer I found is correct or not). However, before sharing my answer, I want you to give it a try.

It would be OK by me if you answer this question in reference to just one or two concrete classification tasks—whichever you find convenient. For instance, if you pick up OCR (optical character recognition, e.g., as explained in Michael Nielson’s free online book [^]), then you have to explain why an ANN-based OCR algorithm works in classifying those MNIST digits / alphabets.


Hint: Studies of Vedic literature won’t help. [I should know!] OTOH, studies of good books on epistemology, or even just good accounts covering methods of science, should certainly come in handy.

I will give you all some time before I come back on that question.

In the meanwhile, have fun—if you wish to, and of course, if you are able to. With questions of this kind. (Translating the emphasis in the italics into chaste Marathi: “laayaki asali tar.” Got it?)


A song I like:
(Marathi) “ooncha nicha kaahi neNe bhagawant”
Lyrics: Sant Tukaram
Music and Singer: Snehal Bhatkar