Only past life regression can tell

 

Have you ever wondered if Einstein after his rebirth (with the caveat that he did not attain Moksha) struggled with the equations he himself created, or if he struggled to understand General Theory of Relativity after his rebirth? Indeed, did he even remain interested in scientific pursuit in his new life?

We keep seeing and hearing about musical prodigies who sing difficult ragas like a walk in the park. And, we keep hearing from people, 'Oh! She must have carried this talent from her previous birth.' 

So, does this work for scientists, athletes, mathematicians, painters, sculptors, actors, military men and women...meaning, across the board?

Quoting from Wolfram MathWorld:

Fermat's last theorem is a theorem first proposed by Fermat in the form of a note scribbled in the margin of his copy of the ancient Greek text Arithmetica by Diophantus. The scribbled note was discovered posthumously, and the original is now lost. However, a copy was preserved in a book published by Fermat's son. In the note, Fermat claimed to have discovered a proof that the Diophantine equation x^n+y^n=z^n has no integer solutions for n>2 and x,y,z!=0.

Fermat's last theorem has still not be proven. Shouldn't Fermat in his next birth (assuming, he didn't attain Moksha), or in a later birth, have solved this theorem for us?

 Erriyon Knighton (born January 29, 2004) is an American sprinter specializing in the 100 meters and 200 meters. At the age of 18, he won the bronze medal in the 200 m at the 2022 World Athletics Championships, becoming the youngest ever individual sprint medalist in Championships history. Bob Hayes, the 10 seconds wonder, at Tokyo Olympics, died in 2002. On the other hand, Knighton was born in 2004. Is Erriyon Knighton reincarnation of Bob Hayes?

Only past life regression can tell

Richter Scale dumbs down the ferocity of earthquakes

 

The Myanmar-Thailand earthquake yesterday was horrific. But do we know the scale of the horror? We generally gloss over the Richter Scale. We say it was 7.7, on the Richter Scale. But the Richter Scale dumbs down the ferocity of the earthquakes. Why? Because it is on the logarithmic scale. So, if there was an earlier earthquake that was 5.7 on the Richter Scale, the one that happened in Myanmar - Thailand would be 100 times more ferocious. Because 7.7 - 5.7 = 2. And, 10 to the power of 2 is 100. 

This is explained in a Khan Academy video here:

https://youtu.be/RFn-IGlayAg

Do we have more such scales in everyday life? Yes. Decibels, the scale with which we measure sound's loudness is in logarithmic scale.

Selecting Candidates for an interview: e reasoning

 

This conversation with OpenAI tells us how 'e' the transcendental number, helps us to choose a candidate from many that come for an interview:

Me:

Everyday problem like secretary problem seems to be interesting, if it is not too complicated!

OpenAI:

Great choice! The secretary problem is a famous example of optimal stopping theory, and it has a surprising connection to Euler’s number $e$. Here’s how it works:

The Problem Statement

Imagine you’re hiring a secretary (or choosing a house, a partner, etc.).

• You have $n$ candidates who arrive one by one in a random order.

• After each interview, you must decide immediately whether to hire that candidate.

• If you reject someone, you can’t go back.

• Your goal: maximize the chance of picking the best candidate.

The Best Strategy & How $e$ Appears

The optimal strategy turns out to be:

1. Reject the first n/e​ (≈ 37%) of candidates—just observe their quality.

2. Then, pick the next candidate who is better than all you’ve seen so far.

Mathematically, this gives you a $\frac{\mathrm{1/}}{e}\approx$ chance of picking the best person—which is the best you can do!

Why Does This Work?

• The first $\frac{n}{e}$ helps you estimate the true quality range.

• Once you reach the decision phase, you’re more likely to find the best candidate before running out of options.

This strategy applies to real-life decisions:

• Dating: Don't settle too early! Meet ~37% of potential partners before deciding.

• House hunting: Visit ~37% of the listings, then choose the best after that.

• Job offers: If you have multiple interviews, wait until ~37% are done before accepting the first great one.

Me:

Before we delve into n/e, if I were a candidate for the interview, should I come in after the 37th of 100 people congregated for interview? If every candidate thinks the same, which candidate would come in the bracket of 1 to 37?

OpenAI:

Ah! You’re flipping the problem on its head—thinking from the candidate’s perspective instead of the interviewer’s. Brilliant! 

Let’s break it down logically.

Should You Aim to Interview After 37% Have Gone?

If you were a candidate and knew the interviewer was following the $\frac{\mathrm{1/}}{e}$ strategy, then yes, you’d ideally want to show up after the first 37% have been seen because that’s when they actually start hiring.

If you come too early (1-37%), you’ll definitely be rejected since the interviewer is just "observing."
If you come too late, they might have already picked someone.

So your sweet spot is somewhere in the selection phase (after 37%).

What Happens If Everyone Knows This?

Now, suppose all 100 candidates are aware of this strategy and try to "game" the system by arriving after 37% have been interviewed.

• Then, who would show up in the first 37%?

• If nobody comes in that period, then the interviewer has no reference point for comparison.

• If everyone tries to come later, some will still end up in the first 37% randomly due to scheduling constraints.

This means that even in a system where everyone is strategic, the first 37% will still get filled by candidates who either weren’t aware of the strategy or had no choice.

Like the stranger in a strange land

 

The wind of a land

asked a stranger,

'Why did you come here?'

The stranger's replied,

'Do you like to caress my hair?,

If you like, I'll stay,

Else, I'll go away.'

Like the wind,

You asked me, 

Why I've made your heart my home.

Like the stranger I replied,

'Ask your heart 

If it would let me stay,

Or, else, I'll go away.'

Pothayanar and Pythagoras

The following proposition in italics in going around in the Internet for sometime now. It is another proposition of solving the hypotenuse length problem which is different from what was given by Pythagoras. This has been interpreted from a quatrain of Pothayanar, a Tamil poet. 

The proposition could be true for some values, but I found that it is not true for all values of the sides of a right angled triangle. I worked out one way to contradict the proposition in the image below the text in italics:

It is a simple task to find the square of a number, but finding the square root of a number is not so easy. There is no simple formula to find the square root of a number.

An ancient Tamil mathematician/poet Pothayanar, who lived 800 years before the Pythagoras had given a quatrain of four lines articulating the method of finding the length of the hypotenuse of a right-angle triangle without the need to find the square or the square-root, only using the length of the sides, and simple fractions. 

Here is the English translation of the quatrain:

Divide the horizontal into eight,           

Delete one portion, and add the remaining,

to half of vertical to result you’ve got.

The answer would be hypotenuse of the triangle.  

The Tamil poem by poet Pothayanar is :

ஓடும் நீளம் தனை ஒரேஎட்டுக்

கூறு ஆக்கி கூறிலே ஒன்றைத்

தள்ளி குன்றத்தில் பாதியாய்ச் சேர்த்தால்

வருவது கர்ணம் தானே. – போதையனார்

The advantage of the ancient theorem is that  there is no need to use a square / square root function.

But before we jump to conclusions let us see how this ancient and simple formula works :

Let us take the three sides of the right-angle triangle to be A, B, and C, where C be the hypotenuse. 

Let us take A and B to be the horizontal and perpendicular 

sides respectively. 

If we are to divide A into eight parts and takeaway one eight, it would be 7/8A. 

The half of the vertical side will be 1/2B. 

Thus, the result should be :

C= 7/8A + 1/2B

Let us give some numbers and try : 

 Firstly Say A=8 and B=6

By Pythagoras theorem, C equals √ (8x8+6x6) Which is √ (64+ 36) = √100 =10.

Now, according to the quatrain :

C should be 7/8 A+ ½ B 

 7/8 of A (8) = 7 and ½ of B (6) =3 

Together they add up to give hypotenuse to be 7+3=10  

 Second let us try with taking A=28 and B=21 then 

by Pythagoras theorem C= √ (21x21+28x28)

C = √ (441+784)  

which is =√1225 = 35

According to quatrain : hypotenuse becomes 7/8A + 1/2 B.

7/8 A=7/8 (28) = 24.5 and 1/2B= 1/2 (21) = 10.5

Thus 24.5 + 10.5= 35.

 Third let us try with taking A= 12 and B= 5 then 

By Pythagoras theorem C= √ (12x12) + (5x5) = (144+ 25) √169 =13.

According to the ancient Tamil quatrain :  the hypotenuse becomes 7/8A + 1/2B

7/8(12) = 10.5 1/2 (5) = 2.5

Thus 10.5 +2.5 =13 

Pothayanar must have been a great mathematician, who got lost like fruit hidden in the foliage of the tree. 

The discoveries of the Greek scientists and mathematicians spread far and wide along with their conquests in the world.  

Unfortunately, in ancient India,  many great intellectuals, and their knowledge / findings were  lost to the world owing to various reasons and events. 

Our schools teach the  Pythagoras Theorem to our children. They should also teach Pothayanar's theorem as an alternate and easier method, as explained above.

Love of our teacher

 

Yesterday (22nd March 2025), our incredible Chemistry teacher from our school showered us with his special love on his 80th. He invited us, his students of 1974 batch from Kendriya Vidyalaya, Chandigarh, for a get together that was being organised by his wonderful family. We were all so overwhelmed. In his own words, 'The relationship of teacher and student is only after relationship with one's parents. It never ends.' And may it never end.

Tax free

 

Where the mind is without fear,

Though the financial year's end is near,

Where the mind is agnostic of tax slab reduction,

Where the mind cares little

About 80C and standard deduction,

Where we don't have a system:

Where one percent pay income tax,

And the ninety nine percent relax,

Where citizens earn to make ends meet,

And not for government's freebies sake,

Into that income-tax free regime

May my country awake.

Welcome Home!

 

Have you noticed?

When an astronaut returns to earth after some days in space people say to her, 'Welcome Home!'

When a person returns to her country after a sojourn to a different country, her countrymen and women say to her, 'Welcome home.'

When a person returns to her hometown after working in a different city in her country, people say to her, 'Welcome home.'

In other words, the notion of 'Home' depends upon from where one is returning from.

Your orbital ways

 

I agree

Every day is not the same:

Someday it is a solstice,

Someday an equinox.

At days you are farthest from me,

Tomorrow you’ll be halfway home,

But still,

With your orbital ways,

You never will be home.

Planned obsolescence

I saw a video on 'planned obsolescence'. It said that companies are deliberately early ageing products. It all started, it seems, with the life of an electric bulb deliberately reduced from 2500 hours to 1000 hours by the Phoebus cartel:

https://en.wikipedia.org/wiki/Phoebus_cartel

And now, companies routinely do planned obsolescence for most of their products.

'What about humans?' I asked myself. Isn't retirement age which is mandated by governments and companies planned obsolescence or 'forced obsolescence', if you will? I mean, the Indian Army, till only a few years ago, retired servicemen at the bottom of the hierarchical pyramid as young as 35. Couldn't blame the Army, though. It wanted to keep the Army young. Similarly companies got fresh blood when they fill the gaps, mostly with lesser salary than the outgoing retiree. Sometimes, they don't even fill up the void because of advancement of technology or perceived or actual cost cutting. In that sense, from the point of view of the upstream manufactured product - in this case some college and university students - they will fall through the cracks, and never make it as a replacement to the 'planned obsolescence'.

But not all folks in the service of community or God cross over to planned obsolescence. For example, Supreme Court Justices of America choose the time they want to retire; or, the Pope at Vatican can retire when he chooses; or, a King/Queen can choose when s/he wants to abdicate.

Then I asked myself, 'What about God?' Has He got some obsolescence plan for humans? When someone unfortunately gets Alzheimer's, is it planned obsolescence of Providence? A thing we often call Karma? When a person becomes a cripple after an accident, or a vegetable after a stroke, is it God's planned obsolescence?

What I have noticed though is that Government's and companies' planned obsolescence often times do not coincide with God's plan of obsolescence.

 

Snow and first rain are emotions

 

Snow is an emotion,

As is the first rain:

One awaits the reindeer sleigh,

One awaits petrichor.

Some with colours, some sans colours

 

The colour sentient,

The vision sentient,

The colour blind,

And the blind,

We wish them all

Happy Holi:

Some with colours,

Some sans colours.

Life was still a strife

 

A life so ordinary:

That it was never the topper,

Never the go-getter,

Never joined the Ivy League,

Never played the Premier League,

Was still a life,

Was still a strife.

Some mobilephones have gone silent

 

Some mobilephones

Have gone silent

Since last Holi.

Captain Elkay's had gone silent,

Now his dear wife's has gone too.

Captain Elkay

Taught me the ropes

When I joined the Army,

Bucked me up

When I sat in a lonely planet

Called picquet Khajoor.

This Holi there shall be

One less wish coming my way

On my mobilephone -

And one less wish

Is one less too many.

*Captain L K Singh(Elkay) was my senior subaltern in 19 MADRAS. He was one of the finest.

Long time since we met at the fence

 

We have drifted apart

Like continental plates.

It's been a long time

Since we met,

Even at the fence.

Tall grass has since grown

At the fence.

But some flowers

Are blooming now

At the fence,

Did you visit the fence lately?

Our meeting place

Does smell of you.

And the dewdrops on the grass?

They are so much like

Your tear drops.

Decoding the ancient Indus Valley script

 

I received this link today from a friend with a teaser whether anyone could crack the code of ancient Indus Valley script:

https://www.cnn.com/2025/02/28/asia/indus-valley-script-india-prize-intl-hnk-dst

I asked chatgpt if Google Willow be able to crack the code, or will chatgpt itself be able to crack the code. Google Willow promises to solve ten septillion year problem in 5 minutes. Still, chatgpt says neither of them can crack the code. It is an interesting conversation that I had with chatgpt. Please go through it. Will be worth your while.

https://chatgpt.com/share/6763ce8a-6fa8-8013-825a-697bce53ee96

*Start from the statement: There is a a million dollar award waiting for anyone who can crack the code of ancient Indus valley script.