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The journal Human Computation provides an international and interdisciplinary forum for the electronic publication and print archiving of high-quality scholarly articles in all areas of human computation. There are no author fees and all published papers are freely available online.
Via Complexity Digest 
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Visualcomplexity.com is a unified resource space for anyone interested in the visualization of complex networks. Great source of information for projects and presentations. Via Baiba Svenca
bricoleuric's curator insight,
March 11, 2013 12:18 PM
Nice work for generating simple insights from complex systems
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In cognitively demanding fields, there are no naturals. Nobody walks into an operating room, straight out of a surgical rotation, and does world-class neurosurgery.
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There has been much speculation about the future of humanity in the face of super-humanly intelligent machines. Most of the dystopian scenarios seem to be driven by plain fear that entities arise that could be smarter and stronger than us. After all, how are we supposed to know which goals the machines will be driven by? Is it possible to have “friendly” AI? If we attempt to turn them off, will they care? Would they care about their own survival in the first place? There is no a priori reason to assume that intelligence necessarily implies any goals, such as survival and reproduction. But, in spite of being rather an optimist otherwise, some seemingly convincing thoughts led me to the conclusion that there is a reason and that we can reasonably expect those machines to be a potential threat to us. The reason is, as I will argue, that the evolutionary process that has created us and the living world will continue to be valid for future intelligent machines. Just as this process has installed the urge for survival and reproduction in us, it will do so in the machines as well. Via Louie Helm
Louie Helm's curator insight,
September 9, 2013 12:14 PM
How come the average newcomer publishing in my field apparently feels compelled to read 0 literature before trying to contribute their ideas? The author of "Will super-human artificial intelligence be subject to evolution?" is:
And therefore:
Dear newcomers: Up your game! There's a protocol for not publishing trivially wrong things. It's called READING THE LITERATURE IN THE NEW FIELD YOU'RE PUBLISHING IN.
Isaac Deboer's curator insight,
March 27, 2014 12:36 AM
Arthur Franz has discussed the argument that will AI mechanics be able to evolve by there own means. the main idea in this article is that computer start to evolve by the own means and get smarter and smarter over time. this is important to my topic as it describes the fact that computers could become smarter and better than humans |
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A fitness landscape is a genetic space -- with two genotypes adjacent if they differ in a single locus -- and a fitness function. Evolutionary dynamics produce a flow on this landscape from lower fitness to higher; reaching equilibrium only if a local fitness peak is found. I use computational complexity to question the common assumption that evolution on static fitness landscapes can quickly reach a local fitness peak. I do this by showing that the popular NK model of rugged fitness landscapes is PLS-complete for K >= 2; the reduction from Weighted 2SAT is a bijection on adaptive walks, so there are NK fitness landscapes where every adaptive path from some vertices is of exponential length. Alternatively -- under the standard complexity theoretic assumption that there are problems in PLS not solvable in polynomial time -- this means that there are no evolutionary dynamics (known, or to be discovered, and not necessarily following adaptive paths) that can converge to a local fitness peak on all NK landscapes with K = 2. Applying results from the analysis of simplex algorithms, I show that there exist single-peaked landscapes with no reciprocal sign epistasis where the expected length of an adaptive path following strong selection weak mutation dynamics is $e^{O(n^{1/3})}$ even though an adaptive path to the optimum of length less than n is available from every vertex. The technical results are written to be accessible to mathematical biologists without a computer science background, and the biological literature is summarized for the convenience of non-biologists with the aim to open a constructive dialogue between the two disciplines.
Complexity of evolutionary equilibria in static fitness landscapes http://arxiv.org/abs/1308.5094 Via Complexity Digest
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After mapping humans' intricate social networks, Nicholas Christakis and colleague James Fowler began investigating how this information could better our lives. Now, he reveals his hot-off-the-press findings: These networks can be used to detect epidemics earlier than ever, from the spread of innovative ideas to risky behaviors to viruses (like H1N1). Via nrip
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SFI's free online course, Introduction to Complexity, will be offered starting Sept. 30, 2013. For more information: http://t.co/TijIRjkgMU
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