Superbugs capable of everything from curing diseases to mopping up pollution have come a step closer after scientists created an artificial lifeform in a lab. The new bacterial cell, nicknamed Synthia 3.0, has fewer genes than any other bacterium, making it the most basic form of life on Earth. Its creation paves the way for microbes that can be customised with genes so they churn out clean biofuels, soak up carbon dioxide from the atmosphere or pump out vaccines in industrial quantities. Researchers have designed and synthesized a minimal bacterial genome, containing only the 473 genes necessary for life. Dr Craig Venter who led the research team, said: 'I think it's the start of a new era.'

Charles Darwin's theory of evolution offers an explanation for why biological organisms seem so well designed to live on our planet. This process is typically described as "unintelligent" – based on random variations with no direction. But despite its success, some oppose this theory because they don't believe living things can evolve in increments. Something as complex as the eye of an animal, they argue, must be the product of an intelligent creator. I don't think invoking a supernatural creator can ever be a scientifically useful explanation.

As a proof of concept, the researchers set up a publicly available web site called Regex Generator at http://regex.inginf.units.it/ You do so by entering a piece of text and then highlighting the segments to be extracted. After the minimum requirements in the length of text and the number of matches to extract (requires a minimum of 25 highlighted items) are satisfied,the'Evolve!' button becomes enabled. By pressing it you start a run and let the engine come up with the regular expression suitable for the task.

Even as I begin the long process of writing about all of the games I saw and the people I spoke to at GDC, I've found a new distraction. The San Andreas Streaming Deer Cam is a live feed of a modded GTA V [official site] that "creates and follows a deer wandering through the fictional state of San Andreas". The deer "is autonomous and will wander and respond to it's surroundings, interacting with the existing GTA V artifical intelligence". Over the weekend, the deer wandered through a gunfight between two gangs, caused a traffic jam during rush hour and evaded the police. This is the best version of GTA V.

Last Friday, we posted a bunch of videos from the AAAI Video Competition. There are lots of good videos (really, they're all good), and we didn't want to play favorites or otherwise influence your votes, so we didn't add much in the way of commentary or anything like that. But it's been almost a week, and a few of those videos are certainly worth taking a closer look at. First, we have a video accompanying "Evolution of Collective Behaviors for a Real Swarm of Aquatic Surface Robots," by Miguel Duarte, Vasco Costa, Jorge Gomes, Tiago Rodrigues, Fernando Silva, Sancho Moura Oliveira, and Anders Lyhne Christensen, from the BioMachines Lab and Institute of Telecommunications, in Lisbon, Portugal. This video is fantastic because, among other reasons, I HAD THAT EXACT SAME PLAYMOBIL PIRATE SHIP WHEN I WAS A KID.

Studies on computational neuroscience through functional magnetic resonance imaging (fMRI) and following biological inspired system stated that human action recognition in the brain of mammalian leads two distinct pathways in the model, which are specialized for analysis of motion (optic flow) and form information. Principally, we have defined a novel and robust form features applying active basis model as form extractor in form pathway in the biological inspired model. An unbalanced synergetic neural net-work classifies shapes and structures of human objects along with tuning its attention parameter by quantum particle swarm optimization (QPSO) via initiation of Centroidal Voronoi Tessellations. These tools utilized and justified as strong tools for following biological system model in form pathway. But the final decision has done by combination of ultimate outcomes of both pathways via fuzzy inference which increases novality of proposed model. Combination of these two brain pathways is done by considering each feature sets in Gaussian membership functions with fuzzy product inference method. Two configurations have been proposed for form pathway: applying multi-prototype human action templates using two time synergetic neural network for obtaining uniform template regarding each actions, and second scenario that it uses abstracting human action in four key-frames. Experimental results showed promising accuracy performance on different datasets (KTH and Weizmann).

Perception is often described as a predictive process based on an optimal inference with respect to a generative model. We study here the principled construction of a generative model specifically crafted to probe motion perception. In that context, we first provide an axiomatic, biologically-driven derivation of the model. This model synthesizes random dynamic textures which are defined by stationary Gaussian distributions obtained by the random aggregation of warped patterns. Importantly, we show that this model can equivalently be described as a stochastic partial differential equation. Using this characterization of motion in images, it allows us to recast motion-energy models into a principled Bayesian inference framework. Finally, we apply these textures in order to psychophysically probe speed perception in humans. In this framework, while the likelihood is derived from the generative model, the prior is estimated from the observed results and accounts for the perceptual bias in a principled fashion.

The paper presents an application of non-linear stacking ensembles for prediction of Go player attributes. An evolutionary algorithm is used to form a diverse ensemble of base learners, which are then aggregated by a stacking ensemble. This methodology allows for an efficient prediction of different attributes of Go players from sets of their games. These attributes can be fairly general, in this work, we used the strength and style of the players.

We consider integrated process planning and scheduling for remanufacturing. Two potentially conflicting objective functions are considered simultaneously. A simulation-based genetic algorithm approach is developed. Key parameters of the algorithm have been fine-tuned. Extensive computational experiments and evaluations have been performed. Remanufacturing has attracted growing attention in recent years because of its energy-saving and emission-reduction potential.

According to E.T. Jaynes and E.P. Wigner, entropy is an anthropomorphic concept in the sense that in a physical system correspond many thermodynamic systems. The physical system can be examined from many points of view each time examining different variables and calculating entropy differently. In this paper we discuss how this concept may be applied in information entropy; how Shannon's definition of entropy can fit in Jayne's and Wigner's statement. This is achieved by generalizing Shannon's notion of information entropy and this is the main contribution of the paper. Then we discuss how entropy under these considerations may be used for the comparison of password complexity and as a measure of diversity useful in the analysis of the behavior of genetic algorithms.