Big data… Plants… Planets… Universe…

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A group of Florida Museum of Natural History scientists has issued a “call to action” to use big data to tackle longstanding questions about plant diversity and evolution and forecast how plant life will fare on an increasingly human-dominated planet.

In a commentary published today in Nature Plants, the scientists urged their colleagues to take advantage of massive, open-access data resources in their research and help grow these resources by filling in remaining data gaps.

“Using big data to address major biodiversity issues at the global scale has enormous practical implications, ranging from conservation efforts to predicting and buffering the impacts of climate change,” said study author Doug Soltis, a Florida Museum curator and distinguished professor in the University of Florida department of biology. “The links between big data resources we see now were unimaginable just a decade ago. The time is ripe to leverage these tools and applications, not just for plants but for all groups of organisms”. (1)

Trying to understand the big picture.

By analyzing it all.

But you can never judge a book by reading all its pages.

You just read one. And then throw it away. Since you will already filled with the undying spirit of the author’s inspiration.

You can never judge a bottle of wine by drinking it all.

You just get a sip. And then spit it out. For you will be already full with the perfection of its taste and the distinctiveness of its aroma.

We cannot judge the cosmos by knowing everything about it. But only by sensing it to the point of remembering nothing about it.

Just see a butterfly fly.

Watch it die.

Sense eternity in its every dying breath…

Replicability of results. A problem we choose to ignore.

Can companies rely on the results of one or two scientific studies to design a new industrial process or launch a new product? In at least one area of materials chemistry, the answer may be yes – but only 80 percent of the time.

The replicability of results from scientific studies has become a major source of concern in the research community, particularly in the social sciences and biomedical sciences. But many researchers in the fields of engineering and the hard sciences haven’t felt the same level of concern for independent validation of their results.

A new study that compared the results reported in thousands of papers published about the properties of metal organic framework (MOF) materials – which are prominent candidates for carbon dioxide adsorption and other separations – suggests the replicability problem should be a concern for materials researchers, too.

One in five studies of MOF materials examined by researchers at the Georgia Institute of Technology were judged to be “outliers,” with results far beyond the error bars normally used to evaluate study results. The thousands of research papers yielded just nine MOF compounds for which four or more independent studies allowed appropriate comparison of results. (1)

We like to believe science and data are reliable.

But real life has nothing to do with science and data.

Everything changes. There is chaos everywhere.

And yet, we see order. Order not existing out there. But inside us.

There is nothing to replicate.

Nothing stays the same.

It was us from the very beginning.

Seeing similarities in dissimilar situations.

Because deep inside us we know…

That this is the only thing we should be seeing…

Learning machines. Unlearning humans. Void. Arkanoid.

“We present the first deep learning model to successfully learn control policies directly from high-dimensional sensory input using reinforcement learning. The model is a convolutional neural network, trained with a variant of Q-learning, whose input is raw pixels and whose output is a value function estimating future rewards. We apply our method to seven Atari 2600 games from the Arcade Learning Environment, with no adjustment of the architecture or learning algorithm. We find that it outperforms all previous approaches on six of the games and surpasses a human expert on three of them”. (1, 2, 3)

Scientists who created a learning algorithm which managed to successfully learn how to play video games. Like a baby learning how to live, machines can do that too. Everything can be learned.

We are all eager to learn. We like it when something/ someone learns. We like to teach someone/ something how to learn. But is life about learning? Is life about adding more data, more information into our brain? Or is it about removing everything from it? If a machine can learn, could it be that humans should not? Consciousness should be free from limitations. And every piece of knowledge poses a new limitation.

Listen to the void.

This is where all knowledge lies…

Unlearn what you have learnt.

Fail to do so and you will soon be inseparable from a machine playing Arcanoid…

Big data analysis. Infinity. One.

Hoping to tame the torrent of data churning out of biology labs, the National Institutes of Health (NIH) today announced $32 million in awards in 2014 to help researchers develop ways to analyze and use large biological data sets.

The awards come out of NIH’s Big Data to Knowledge (BD2K) initiative, announced last year after NIH concluded it needed to invest more in efforts to use the growing number of data sets—from genomics, proteins, and imaging to patient records—that biomedical researchers are amassing. For example, in one such “dry biology” project, researchers mixed public data on gene expression in cells and patients with diseases to predict new uses for existing drugs.

ENIGMA project on the other hand collects thousands of brain images to allow researchers to better understand nervous system wiring. (1)

Analyze too much data. And you will reach the same conclusion as you would have if you analyzed nothing at all.

How can everything behave differently from One? Parts exist just because something from which they derived existed in the first place. And how can the parts have different behaviour than that thing from which they were created?

Infinity. Nothing. Something.
So different.
And so similar at the same time…

From One to Many to Infinity and back again.
The only way out leads directly to where you started from…

Universe as a computer. Humans as… ?

A future form of computing could see information stored on clusters of microscopic particles suspended in liquid.

Clusters of spheres can arrange themselves around a central sphere in a limited number of ways, similar to how a Rubik’s cube can only be twisted in certain ways around the central point. Sharon Glotzer at the University of Michigan and her team realised these states could represent information.

To test the idea, the team created a cluster of five spheres in a liquid and watched them naturally switch between two states, like the 0s and 1s of traditional computing bits. “It’s really just the first baby steps,” says Glotzer. Next, they plan to create clusters that can be locked into a particular state to store a bit of data, and unlocked again to rewrite it, using a central sphere made from gel that can swell and shrink. (1)

The whole universe full of bits.
The whole universe a giant computer.

Are we just part of it’s RAM?
Or are we the ones who program it?

Do you feel like a god a… bit?

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