Physicists Alexey Bezryadin, Alfred Hubler, and Andrey Belkin from the University of Illinois at Urbana-Champaign, have demonstrated the emergence of self-organized structures that drive the evolution of a non-equilibrium system to a state of maximum entropy production. The authors suggest MEPP underlies the evolution of the artificial system’s self-organization, in the same way that it underlies the evolution of ordered systems (biological life) on Earth. The team’s results are published in Nature Publishing Group’s online journal Scientific Reports.
MEPP may have profound implications for our understanding of the evolution of biological life on Earth and of the underlying rules that govern the behavior and evolution of all nonequilibrium systems. Life emerged on Earth from the strongly nonequilibrium energy distribution created by the Sun’s hot photons striking a cooler planet. Plants evolved to capture high energy photons and produce heat, generating entropy. Then animals evolved to eat plants increasing the dissipation of heat energy and maximizing entropy production.
In their experiment, the researchers suspended a large number of carbon nanotubes in a non-conducting non-polar fluid and drove the system out of equilibrium by applying a strong electric field. Once electrically charged, the system evolved toward maximum entropy through two distinct intermediate states, with the spontaneous emergence of self-assembled conducting nanotube chains.
In the first state, the “avalanche” regime, the conductive chains aligned themselves according to the polarity of the applied voltage, allowing the system to carry current and thus to dissipate heat and produce entropy. The chains appeared to sprout appendages as nanotubes aligned themselves so as to adjoin adjacent parallel chains, effectively increasing entropy production. But frequently, this self-organization was destroyed through avalanches triggered by the heating and charging that emanates from the emerging electric current streams. (Watch the video.)
“The avalanches were apparent in the changes of the electric current over time,” said Bezryadin.
I frequently talk to groups of managers on the nature of systems thinking and its radical implications to management. In doing so I use several case studies involving prominent American corporations. At the end of the presentation I am almost alwaysasked, "If this way of thinking is as good as you say it is, why don't more organizations use it?" It is easy to reply by saying that organizations naturally resist change. This of course is a tautology. I once asked a vice president of marketing why consumers used his product. He answered, "Because they like it." I then asked him how he knew this. He answered, "Because the use it." Our answer to the question about failure of organizations to adopt systems thinking is seldom any better then this. There be many reasons why any particular organization fails to adopt systems thinking but I believe there are two that are the most important, one general and one specific. By a general reason I mean one that is responsible for organizations failing to adopt any transforming idea, let alone systems thinking. By a specific reason I mean one responsible for the failure to adopt systems thinking in particular.
The gamer punches in play after endless play of the Atari classic Space Invaders. Though an interminable chain of failures, the gamer adapts the gameplay strategy to reach for the highest score. But this is no human with a joystick in a 1970s basement. Artificial intelligence is learning to play Atari games. The Atari addict is a deep-learning algorithm called DQN.
This algorithm began with no previous information about Space Invaders—or, for that matter, the other 48 Atari 2600 games it is learning to play and sometimes master after two straight weeks of gameplay. In fact, it wasn't even designed to take on old video games; it is general-purpose, self-teaching computer program. Yet after watching the Atari screen and fiddling with the controls over two weeks, DQN is playing at a level that would humiliate even a professional flesh-and-blood gamer.
Volodymyr Mnih and his team of computer scientists at Google, who have just unveiled DQN in the journal Nature, says their creation is more than just an impressive gamer. Mnih says the general-purpose DQN learning algorithm could be the first rung on a ladder to artificial intelligence.
"This is the first time that anyone has built a single general learning system that can learn directly from experience to master a wide range of challenging tasks," says Demis Hassabis, a member of Google's team. The algorithm runs on little more than a powerful desktop PC with a souped up graphics card. At its core, DQN combines two separate advances in machine learning in a fascinating way. The first advance is a type of positive-reinforcement learning method called Q-learning. This is where DQN, or Deep Q-Network, gets its middle initial. Q-learning means that DQN is constantly trying to make joystick and button-pressing decisions that will get it closer to a property that computer scientists call "Q." In simple terms, Q is what the algorithm approximates to be biggest possible future reward for each decision. For Atari games, that reward is the game score.
Knowing what decisions will lead it to the high scorer's list, though, is no simple task. Keep in mind that DQN starts with zero information about each game it plays. To understand how to maximize your score in a game like Space Invaders, you have to recognize a thousand different facts: how the pixilated aliens move, the fact that shooting them gets you points, when to shoot, what shooting does, the fact that you control the tank, and many more assumptions, most of which a human player understands intuitively. And then, if the algorithm changes to a racing game, a side-scroller, or Pac-Man, it must learn an entirely new set of facts. That's where the second machine learning advance comes in. DQN is also built upon a vast and partially human brain-inspired artificial neural network. Simply put, the neural network is a complex program built to process and sort information from noise. It tells DQN what is and isn't important on the screen.
When Stephen Hawking, Bill Gates and Elon Musk all agree on something, it’s worth paying attention.All three have warned of the potential dangers that artificial intelligence or AI can bring. The world’s foremost physicist, Hawking said that the full development of AI could “spell the end of the human race.” Musk, the tech entrepreneur who brought us PayPal, Tesla and SpaceX described artificial intelligence as our “biggest existential threat” and said that playing around with AI was like “summo
FORBES gets an exclusive look at SourcePin, a search technology powered by Artificial Intelligence that forms part of Memex, DARPA's project to shine a light on the darker parts of the web. It's already in use by law enforcement agencies tracking sex trafficking, but could be of use to all kinds of organisation. And it's about to go open source.
A new search engine being developed by Darpa aims to shine a light on the dark web and uncover patterns and relationships in online data to help law enforcement and others track illegal activity. The project, dubbed Memex, has been in the works for a year and is being developed by 17 different contractor teams…
Does the word cyber sound dated to you?Like the phrases Information Superhighway and surfing the Web, something about the word calls one back to the early era of the Internet, not unlike when you ask a person for a URL and they start to read off, ...
Research has demonstrated laser control of quantum states in an ordinary silicon wafer and observation of these states via a conventional electrical measurement. The findings—published in the journal Nature Communications by a UK-Dutch-Swiss team from the University of Surrey, University College London, ...
Neurorobotics engineers from the Human Brain Project have recently taken the first steps towards building a "virtual mouse" by placing a simplified computer model of the mouse brain into a virtual mouse body. This new kind of tool will be made available to scientists worldwide.
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