One drone, four microphones and a loudspeaker: nothing more is needed to determine the position of walls and other flat surfaces within a room. This has been mathematically proved by Prof. Gregor Kemper of the Technical University of Munich and Prof. Mireille Boutin of Purdue University in Indiana, USA. (1)
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.
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…
The problem with solving problems. You
are always changing your view on the problem as time passes by. So much that
sometimes you do not realize that you have solved the problem. (1)
We spend all our lives in solving
At work. At our personal
But why try to solve problems in the
Why not move backwards in time and go
back to an era where there were no problems at all? Be a kid. No, you weren’t
stupid back then. You are now. Because no matter how many things you’ve
learned, you have forgotten the most important non-knowledge you ever had: That
in the beginning you didn’t know anything at all*…
* And, thus, all “knowledge” (and related problems) gained afterwards is based on that nothing. Difficult? Never mind. You will solve it. Eventually…