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PROGRAMMING INTRONS. "Emergence of Introns in GP. These components are unnecessary since they can be removed from the program without altering the solution the program represents. The ubiquity of redundant constructs suggests a more basic truth about their nature. Introducing introns into a genetic algorithm’s representation improves the success rate as much as a factor of 10." (GP = genetic programming). http://scholar.google.es/scholar_url?url=http://citeseerx.ist.psu.edu/viewdoc/download%3Fdoi%3D10.1.1.15.5594%26rep%3Drep1%26type%3Dpdf&hl=en&sa=X&scisig=AAGBfm0J-z_xL5_kjwEhLP0aVOrhbAtTfA&nossl=1&oi=scholarr&ved=0ahUKEwi3wpKxxs_MAhXG2B4KHXRODt0QgAMICigBMAA View in LinkedIn

CONTEXTUAL RESOURCES. "Using emergent intelligence allows the removal of explicit knowledge that is a natural consequence of the problem solving process interacting with the task environment. By allowing the task environment to be an integral component of the problem solving algorithm, all the natural constraints, including those too subtle for a knowledge engineer to extract, are available to the algorithm and emerge at appropriate moments while solving problems." http://scholar.google.es/scholar_url?url=http://citeseerx.ist.psu.edu/viewdoc/download%3Fdoi%3D10.1.1.15.5594%26rep%3Drep1%26type%3Dpdf&hl=en&sa=X&scisig=AAGBfm0J-z_xL5_kjwEhLP0aVOrhbAtTfA&nossl=1&oi=scholarr&ved=0ahUKEwi3wpKxxs_MAhXG2B4KHXRODt0QgAMICigBMAA View in LinkedIn

TABLAR RAZAR. "Emergent intelligence (EI) is a method of reducing or removing the reliance on explicit knowledge in a problem solver by favoring instead the emergence of problem specific constraints as a direct consequence of the action of problem solving." http://scholar.google.es/scholar_url?url=http://citeseerx.ist.psu.edu/viewdoc/download%3Fdoi%3D10.1.1.15.5594%26rep%3Drep1%26type%3Dpdf&hl=en&sa=X&scisig=AAGBfm0J-z_xL5_kjwEhLP0aVOrhbAtTfA&nossl=1&oi=scholarr&ved=0ahUKEwi3wpKxxs_MAhXG2B4KHXRODt0QgAMICigBMAA View in LinkedIn

OPERATIONS. "All evolutionary computations perform a simple SELECT-COPY-MODIFY loop over the population each generation. SELECT identifies the number of copies each member of the current population produces in the next population based on a relative ranking by the fitness function. COPY makes identical copies of the specified numbers for each member. Finally, MODIFY applies the reproductive operators." http://scholar.google.es/scholar_url?url=http://citeseerx.ist.psu.edu/viewdoc/download%3Fdoi%3D10.1.1.15.5594%26rep%3Drep1%26type%3Dpdf&hl=en&sa=X&scisig=AAGBfm0J-z_xL5_kjwEhLP0aVOrhbAtTfA&nossl=1&oi=scholarr&ved=0ahUKEwi3wpKxxs_MAhXG2B4KHXRODt0QgAMICigBMAA View in LinkedIn

STRONG AND WEAK METHODS. "AI problem solving methods divides into two distinct categories: weak and strong. A strong method is one that contains a significant amount of task specific knowledge in order to solve it. In contrast, a weak method requires little or no knowledge about a task. As the name implies, strong methods are more powerful algorithms than weak methods." http://scholar.google.es/scholar_url?url=http://citeseerx.ist.psu.edu/viewdoc/download%3Fdoi%3D10.1.1.15.5594%26rep%3Drep1%26type%3Dpdf&hl=en&sa=X&scisig=AAGBfm0J-z_xL5_kjwEhLP0aVOrhbAtTfA&nossl=1&oi=scholarr&ved=0ahUKEwi3wpKxxs_MAhXG2B4KHXRODt0QgAMICigBMAA View in LinkedIn

"THE ORIGINS of the the TechnoCore can be traced back to the experiments of the Old Earth scientist Thomas S. Ray, who attempted to create self-evolving artificial life on a virtual computer." https://www.reddit.com/r/todayilearned/comments/3d3vct View in LinkedIn

PROMISCUOUS BINDING. "Of particular interest in the context of protein-protein interaction networks are findings demonstrating that proteins with many or large intrinsically disordered segments often have the ability to bind promiscuously, with promiscuous proteins or protein segments being those that can bind to many different targets." http://www.biochemj.org/content/454/3/361?sa=X&ved=0CDkQ9QEwEWoVChMIzdjAo5n2xgIVovRyCh0HSAum View in LinkedIn

DECEITFUL BEHAVIOR. “After 500 generations, 60 percent of the robots had evolved to keep their light off when they found the good resource, hogging it all for themselves. Even more telling, a third of the robots evolved to actually look for the liars by developing an aversion to the light; the exact opposite of their original programming!" http://www.popsci.com/scitech/article/2009-08/evolving-robots-learn-lie-hide-resources-each-other View in LinkedIn

THREE MODULES. "There appear to be three different modules within intrinsically disordered regions responsible for their functionally promiscuous behaviour: MoRFs (molecular recognition features), SLiMs (small linear motifs) and LCRs (low complexity regions)." http://www.biochemj.org/content/454/3/361?sa=X&ved=0CDkQ9QEwEWoVChMIzdjAo5n2xgIVovRyCh0HSAum View in LinkedIn

DESIGN FOR LYING TEST. "The experiment involved 1,000 robots divided into 10 different groups. Each robot had a sensor, a blue light, and its own 264-bit binary code "genome" that governed how it reacted to different stimuli. The first generation robots were programmed to turn the light on when they found the good resource, helping the other robots in the group find it." https://lnkd.in/g_45Y2T View in LinkedIn