A recent study found out that older adults feel younger when they feel that they have more control over their daily lives, regardless of stress or health concerns. However, stress and health – not a sense of control – play a significant role in how old younger adults feel. (1)
Old people try to control life.
Because they feel it will end.
Young people not caring about controlling anything.
Because they believe they will live forever.
For both they had what they seek.
But they lost it the moment they started seeking it.
Old people had control when they were still healthy and thought of everything except having control. Young men had health when they had still control and thought of everything except being healthy.
Think again for what you search for.
You will never find it ahead.
For it is already behind you.
And think for a moment.
Why did you even start?
It’s the archetypal child’s drawing – family, pet, maybe a house and garden, and the child themselves. Yet, how do children represent themselves in their drawings, and does this representation alter according to who will look at the picture? A research found that children’s expressive drawings of themselves vary according to the authority of and familiarity with the adult who will view the picture. (1)
Drawing the cosmos.
Drawing your mother.
Drawing your father.
But do you know… you?
The hardest things to draw are the ones we know the most. Because the essence of things lies not on the outside. But on the things which are left unseen. Any line on paper will not reveal more about who you are. But it will obscure the true self that lies beneath the veil of existence.
A tear staining the white surface.
Can you smile?
Can you see the cosmos behind the lines?
The happiness we feel after a particular event or activity diminishes each time we experience that event, a phenomenon known as hedonic adaptation. But giving to others may be the exception to this rule, according to research forthcoming in Psychological Science, a journal of the Association for Psychological Science.
In two studies, psychology researchers Ed O’Brien (University of Chicago Booth School of Business) and Samantha Kassirer (Northwestern University Kellogg School of Management) found that participants’ happiness did not decline, or declined much slower, if they repeatedly bestowed gifts on others versus repeatedly receiving those same gifts themselves. (1)
We like to receive. And we love to give.
But why do any of those two?
How can giving be meaningful if receiving is not?
How can receiving be meaningless if giving is not?
When you see two obvious paths in front of you…
Try and look out for the third one!
It is the goal of philosophy to question the obvious.
And here we have two very obvious options…
A wise man will never ask for anything. But neither will he give anything back. In a cosmos built of dirt, there is no point to try to reach the stars. In a cosmos full of butterflies, there is nothing you can receive. Look at the calm lake. Feel the deep dark forest inside you.
You cannot give anything to anyone. For there is only you.
There is no point in receiving anything. For it is you who will get it.
Try to clap with one hand.
You can do it.
Analog computers were used to predict tides from the early to mid-20th century, guide weapons on battleships and launch NASA’s first rockets into space. They first used gears and vacuum tubes, and later, transistors, that could be configured to solve problems with a range of variables. They perform mathematical functions directly. For instance, to add 5 and 9, analog computers add voltages that correspond to those numbers, and then instantly obtain the correct answer. However, analog computers were cumbersome and prone to “noise” – disturbances in the signals – and were difficult to re-configure to solve different problems, so they fell out of favor.
Digital computers emerged after transistors and integrated circuits were reliably mass produced, and for many tasks they are accurate and sufficiently flexible. Computer algorithms for those computers are based on the use of 0s and 1s.
Yet, 1s and 0s, pose limitations into solving some NP-hard problems. (e.g. the “Traveling Salesman” problem) The difficulty with such optimization problems, researcher Toroczkai noted, is that “while you can always come up with some answer, you cannot determine if it’s optimal. Determining that there isn’t a better solution is just as hard as the problem itself”.
[Note: NP-hardness is a theory of computational complexity, with problems that are famous for their difficulty. When the number of variables is large, problems associated with scheduling, protein folding, bioinformatics, medical imaging and many other areas are nearly unsolvable with known methods.]
That’s why researchers such as Zoltán Toroczkai, professor in the Department of Physics and concurrent professor in the Department of Computer Science and Engineering at the University of Notre Dame, are interested in reviving analog computing. After testing their new method on a variety of NP-hard problems, the researchers concluded their solver has the potential to lead to better, and possibly faster, solutions than can be computed digitally. (1)
Breaking a problem into pieces can do so many things.
But at the end you will have to look at the problem itself.
And the problem does not have any components.
But only a solution.
Visible only to those who do not see the problem.
You cannot ride the waves.
All you can do is fall into the sea and swim.
You cannot live life.
All you can do is let go and prepare to die.
Look at the big picture.
You can solve anything.
As long as you accept that you cannot…
At the end, the voltage will reach zero.
At the end, the computer will shut down.
You might see this as a sign of failure.
But it would be the first time it really solved anything…