AI. Games. Intelligence. Humans.

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Artificial Intelligence is constantly beating humans in more and more board games. Some years ago, the same team that created that Go-playing bot celebrated something more formidable: an artificial intelligence system that is capable of teaching itself—and winning at—three different games. The AI is one network, but works for multiple games; that generalizability makes it more impressive, as it might also be able to learn other similar games, too.

They call it AlphaZero, and it knows chess, shogi (Japanese chess), and Go. All of these games fall into the category of “full information” or “perfect information” contests – each player can see the entire board and has access to the same info (that is different from games like poker where you do not know what cards an opponent is holding). The network needs to be told the rules of the game first, and after that, it learns by playing games against itself.

The system “is not influenced by how humans traditionally play the game,” says Julian Schrittwieser, a software engineer at DeepMind, which created it.

Since AlphaZero is “more general” than the AI that won at Go, in the sense that it can play multiple games, “it hints that we have a good chance to extend this to even more real-world problems that we might want to tackle later,” Schrittwieser adds. (1)

See?

Even computers can learn.

As long as you teach them. (the rules)

That is how you learnt as well.

Alone.

Wandering in the dark abyss.

Walking in the dead of the night.

You knew the rules.

You just had to deduct the rest.

And you were so afraid.

Because the only rule was that there were no rules.

Because the only law was that you were the law.

Once upon a time, your father told you he loves you.

And that you were free to go.

You decided to leave.

Afraid of yourself.

And you are trying to find rules ever since…

Translating in a “dead” language. More “freedom”. Thinking without thinking.

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Bochum-based philologist Prof Dr Reinhold Glei has figured out why Latin still turned up in many documents in the 17th to 19th centuries, even though it had not been a spoken language for a long time. During that period, Latin served as an instrument for translating languages that had hitherto been little known in Western culture.

Arabic, Chinese, Sanskrit: novel sentence structures in those languages posed a challenge to scholars in the Early Modern Period. Scholars recreated the foreign-language sentences with the aid of Latin, thus crafting a text upon which further analyses could be based. In doing so, translators didn’t have to conform to specific linguistic rules of the Latin language, because native speakers no longer existed who might have taken exception to an unusual syntax in Latin. “Had the foreign-language texts been translated into, for example, German, the translator would have been restricted by the respective grammatical structures. Using Latin, the translators had more freedom,” elaborates Glei.

Continue reading “Translating in a “dead” language. More “freedom”. Thinking without thinking.”

Remembering rules. Math. Blind cosmos… [Against mathematical operations?!]

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Children differ substantially in their mathematical abilities. In fact, some children cannot routinely add or subtract, even after extensive schooling. This new paper proposes that math disability arises from abnormalities in brain areas supporting procedural memory. Procedural memory is a learning and memory system that is crucial for the automation of non-conscious skills, such as driving or grammar. (1)

We learn rules.

We then learn math based on rules which we memorize.

Failure to do so makes us “bad” at math. And yet why should that be a problem? Why should we “learn rules” and memorize them? Why should we interpret or measure the cosmos based on these rules?

In a world where everything is One and non-dividable we try to learn the rules of division. In a world made out of oblivion, we try to base our civilization on remembering…

How can 1+1 even have meaning,

when One is clearly defined?

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