Category: Analysis

Certain!

Physicists are raising doubts about the existence of an exotic subatomic particle that failed to show up in twin experiments. (1)

But by when would anyone be certain?

When can anyone know anything?

What a weird world.

Shaped by the ones who are afraid of it.

People being certain of what they know.

Full of joy within a chaos of ignorance.

At the end, they will discover all the particles.

And they will be certain of their existence.

And at that moment of certainty.

During their most triumphant moment.

Inside that world of ignorance.

The cosmos will whisper to them.

(Who is certain of you?)

Embryos. Analysis. Zero.

Researchers have created the first complete description of early embryo development, accounting for every single cell in the embryo. This ‘virtual embryo’ will help to answer how the different cell types in an organism can originate from a single egg cell. (1)

Analyzing life, cell by cell.

Atom by atom.

Until we reach zero.

One by one.

And see nothingness inside everything…

Oh, look!

What a beautiful baby!

(Are you dead?)

Big Data & Archeology…

In a recently released edition of the Journal of Field Archaeology, Brown Assistant Professor of Anthropology Parker VanValkenburgh and several colleagues detailed new research they conducted in the former Inca Empire in South America using drones, satellite imagery and proprietary online databases. Their results demonstrate that big data can provide archaeologists with a sweeping, big-picture view of the subjects they study on the ground — prompting new insights and new historical questions.

Using the data they collected, VanValkenburgh, Wernke and Saito created a comprehensive map of every known Spanish-founded colonial settlement, or reducción, stretching from Ecuador to Chile, allowing those who study the region to understand the ebb and flow of social life on a multi-country scale. (1)

People moving around. Like ants. Big Data will reveal things and details. Analysis will show patterns and will reveal motives. But it will never reveal anything for the baker who wakes up in the morning to bake bread. It will not show anything about the children playing in the dirt. Big Data will not show anything about a man dying and his wife crying next to him.

Big Data can show everything.

But at the same time they show nothing.

Why care about revealing new information for past civilizations? Will we be wiser if we know patterns which were not even consciously known even to the people at that era? Civilizations are not built on data, patterns or systems analysis. They are built on cries and laugher. They are built on blood and despair.

And Big Data will never show anything for these things.

Take a good look at the laptop running the analysis.

So clean.

So silent.

But do not be fooled by its tiny size.

It kills whole civilizations in seconds.

And as researchers laugh in excitement.

Beyond the buzz of the hard drive…

Thousands die in agony a thousand years ago…

Chaos. Numbers. Simulations.

Photo by Spiros Kakos from Pexels

Digital computers use numbers based on flawed representations of real numbers, which may lead to inaccuracies when simulating the motion of molecules, weather systems and fluids, find scientists.

The study, published today in Advanced Theory and Simulations, shows that digital computers cannot reliably reproduce the behaviour of ‘chaotic systems’ which are widespread. This fundamental limitation could have implications for high performance computation (HPC) and for applications of machine learning to HPC.

Professor Peter Coveney, Director of the UCL Centre for Computational Science and study co-author, said: “Our work shows that the behaviour of the chaotic dynamical systems is richer than any digital computer can capture. Chaos is more commonplace than many people may realise and even for very simple chaotic systems, numbers used by digital computers can lead to errors that are not obvious but can have a big impact. Ultimately, computers can’t simulate everything.”

The team investigated the impact of using floating-point arithmetic — a method standardised by the IEEE and used since the 1950s to approximate real numbers on digital computers.

Digital computers use only rational numbers, ones that can be expressed as fractions. Moreover the denominator of these fractions must be a power of two, such as 2, 4, 8, 16, etc. There are infinitely more real numbers that cannot be expressed this way. (https://www.sciencedaily.com/releases/2019/09/190923213314.htm)

An irrational universe.

Full of irrational people.

Trying to analyze it rationally.

Under the illusion that number we have invented can draw a sketch of the cosmos. And yet, nothing we have invented is anywhere to be seen but on a piece of paper. Can you limit the birth of a star on a piece of paper? Can you contain the death of the universe on an equation?

We believe we can.

And sadly, we do.

And at the moment we do, the universe indeed dies…

And a small voice will whisper in our ear…

Congratulations. You have now understood it all.

How irrationally rational everything is!

And inside the darkest night you will dance.

Laughter.

And for a brief moment the forest will look at you.

Crying.

And for a brief moment the forest will see nothing…

But an empty broken CD. Full of data. Full of life…

Old mathematics… Broken cosmos… Blurry image…

Photo by Spiros Kakos from Pexels

By combining cutting-edge machine learning with 19th-century mathematics, a Worcester Polytechnic Institute (WPI) mathematician worked to make NASA spacecraft lighter and more damage tolerant by developing methods to detect imperfections in carbon nanomaterials used to make composite rocket fuel tanks and other spacecraft structures.

Using machine learning, neural networks, and an old mathematical equation, Randy Paffenroth has developed an algorithm that will significantly enhance the resolution of density scanning systems that are used to detect flaws in carbon nanotube materials.

The algorithm was “trained” on thousands of sets of nanomaterial images and to make it more effective at making a high-resolution image out of a low-resolution image, he combined it with the Fourier Transform, a mathematical tool devised in the early 1800s that can be used to break down an image into its individual components.

“The Fourier Transform makes creating a high-resolution image a much easier problem by breaking down the data that makes up the image. Think of the Fourier Transform as a set of eyeglasses for the neural network. It makes blurry things clear to the algorithm. We’re taking computer vision and virtually putting glasses on it”, said Paffenroth. (1)

We like breaking the world into pieces.

We can see better that way.

But even the sharpest image of a tree.

Conveys nothing about the forest…

A forest that is there because of the trees.

Trees we know are there.

We remember those trees.

We once saw those trees.

Casting their shadows during the evening hours.

At a time when we used to stand within a forest.

But never really saw one…

Cause in the midst of the evening.

There was nothing else casting a shadow.

Nothing but our self!

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