Networks  

Modern stock market trading networks and systems have become so fast that the speed of light is now their key limiting factor. Alex Wissner-Gross has determined how high frequency trading may best overcome this hurdle.

The performance of a wide variety of globally-distributed online activities is increasingly limited by the finite speed of light. Dr. Alexander Wissner-Gross recently introduced technology for partially mitigating the impact of this limitation on the coordination of geographically distributed activities, such as virtual worlds, currency exchanges, telepresence, and remote surgery.

His solution involves positioning computer servers in optimal intermediate locations such as oceans and other network-sparse regions, opening the possibility of geographic remoteness becoming a new form of natural resource for developing countries in the next decade.

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According to Wissner-Gross, networks can use intermediate level networks, just as the body uses reflex arcs.  Such is the case when your hand touches a hot surface; the neural signals do not travel all the way to the brain and back to tell you to lift your arm, the nerves process and act to some degree on their own.

In particular, Wissner-Gross applied this problem to high frequency securities trading. Below is his map outlining where optimal trading node locations should be placed (in blue) to conduct trades at the various global exchanges (in red). He calls these nodes, "global reflex arcs."

Previously discussing this research, Wissner-Gross commented, “I see this work as one possible justification for making the entire surface of the planet more computationally capable… and in effect, making the whole planet smarter.”

Image Source - Wissner-Gross

Wissner-Gross is an award-winning scientist, inventor, and entrepreneur. He serves as an Institute Fellow at the Harvard University Institute for Applied Computational Science and as a Research Affiliate at the MIT Media Laboratory.

SOURCE  TEDx Talks

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 Artificial Intelligence  

Explaining how intelligence may be an emergent property rooted in the urge to take control of all possible futures, Alex Wissner-Gross has developed an equation that may explain intelligence itself, and in doing so, may dramatically impact the world of AI.

Richard Feynman once wrote that if human civilization were destroyed and you could pass on only a single concept to our descendants to help them rebuild civilization, that concept should be that all matter around us is made out of tiny elements that attract each other when they are far apart, but repel each other when they are close together.

For Alex Wissner-Gross, the equivalent statement to pass on to descendants to help them build artificial intelligence or to help them understand human intelligence is "That it should be viewed as a physical process that tries to maximize future freedom of action and to avoid constraints in its own future."

This grand view of intelligence has emerged as a guiding principle for Wissner-Gross's own work in development of Entropica, an artificial intelligence platform that has been tested for use in robotic motion planning, computer game play and financial market trading maximization.

Wissner-Gross began his work in artificial intelligence by first trying to understand the fundamental physical mechanisms that underlay intelligence.

In a paper published earlier this year, Wissner-Gross detailed how intelligent behavior stems from the impulse to seize control of future events in the environment. This is the exact opposite of the classic science-fiction scenario in which computers or robots become intelligent, then set their sights on taking over the world.

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The work led Wissner-Gross to also develop a single equation to explain intelligence. Wissner-Gross calls it, "the closest thing to an E=MC² for intelligence."  In the equation, F is a force that acts so to maximize future freedom of action, to keep options open with some strength, T with S, an amount of diversity to possible accessible futures, up to some future time horizon, Tau.

In the video above, Wissner-Gross shows how the equation can be applied to artificial intelligence. As the demonstration of Entropica suggests, intelligent behavior doesn't just correlate with the production of long term control of future events, or entropy, it actually emerges from it.

With the equation as a fundamental algorithm, Entropica was able to pass multiple animal intelligence tests, play games and even earn money trading stocks, all without being instructed to do so.

Wissner-Gross is an American scientist, inventor, and entrepreneur. In 2003, he became the last person in MIT history to receive a triple major, with bachelors degrees in physics, electrical engineering, and mathematics, while graduating first in his class from the MIT School of Engineering. In 2007, he completed his Ph.D. in Physics at Harvard, where his research on programmable matter, ubiquitous computing, and machine learning was awarded the Hertz Doctoral Thesis Prize. He currently holds academic appointments as an Institute Fellow at the Harvard University Institute for Applied Computational Science and as a Research Affiliate at the MIT Media Lab.

SOURCE  TEDx Talks

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