Open-ending algorithms… The end as the beginning…

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Photo by Enric Cruz López from Pexels

Evolution allows life to explore almost limitless diversity and complexity. Scientists hope to recreate such open-endedness in the laboratory or in computer simulations, but even sophisticated computational techniques like machine learning and artificial intelligence can’t provide the open-ended tinkering associated with evolution. Here, common barriers to open-endedness in computation and biology were compared, to see how the two realms might inform each other, and ultimately enable machine learning to design and create open-ended evolvable systems. (1)

Looking for an end.

By accepting that there is none.

How could there be one?

The end is defined by the beginning.

And this definition is also the end.

One can never pass through the walls he raised.

Achilles will never reach the turtle.

Mathematicians will never prove everything.

Humans will never find the meaning of life.

Unless they stop looking for meaning.

Unless mathematicians stop trying to prove things.

Unless Achilles stops trying to pass the turtle and just runs.

No, there is no end. There are just beginnings…

Be careful with that first step…

No, it is not just a first step.

It is also your last…

Feathers. Flying. Crawling on the dirt…

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Photo by Rafael Paul from Pexels

New research suggests that feathers arose 100 million years before birds — changing how we look at dinosaurs, birds, and pterosaurs, the flying reptiles. (1)

What is not shall be.

What is was not.

There is nothing that can be done that will not be done. For the date of the universe is written from its birth. And yet, with feathers or no feathers. It will always be up to you.

Look at the golden sky.

Do you want to fly?

One day you will.

Only because you can’t…

Only because you already have…

While crawling on the dirt. You did dream of the stars…

Aeons from now you will reach them.

And you will find yourself waiting…

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