Adi Livnat (alivnat@univ.haifa.ac.il) is a Senior Lecturer in the Department of Evolutionary and Environmental Biology, and Institute of Evolution at the University of Haifa, Israel. Christos Papadimitriou (christos@cs.berkeley.edu) is the C. Lester Hogan Professor in the Computer Science Division of the University of California at Berkeley.

We haven't figured out what Ebola virus selects as its natural host, but it's definitely not humans. Every once in a while, Ebola stumbles upon a human host, which ends up being a fatal mistake. When I say fatal, I mean for the virus. After all, Ebola is usually not highly efficient at sustaining infection or transmitting from human to human, and eventually that chain of transmission turns into a dead end. Every Ebola outbreak has ended, even the 2014-2015 West African epidemic.

Non-negative Matrix Factorization (NMF) is a popular tool for data exploration. Bayesian NMF promises to also characterize uncertainty in the factorization. Unfortunately, current inference approaches such as MCMC mix slowly and tend to get stuck on single modes. We introduce a novel approach using rapidly-exploring random trees (RRTs) to asymptotically cover regions of high posterior density. These are placed in a principled Bayesian framework via an online extension to nonparametric variational inference. On experiments on real and synthetic data, we obtain greater coverage of the posterior and higher ELBO values than standard NMF inference approaches.

This paper describes some biologically-inspired processes that could be used to build the sort of networks that we associate with the human brain. New to this paper, a 'refined' neuron will be proposed. This is a group of neurons that by joining together can produce a more analogue system, but with the same level of control and reliability that a binary neuron would have. With this new structure, it will be possible to think of an essentially binary system in terms of a more variable set of values. The paper also shows how recent research associated with the new model, can be combined with established theories, to produce a more complete picture. The propositions are largely in line with conventional thinking, but possibly with one or two more radical suggestions. An earlier cognitive model can be filled in with more specific details, based on the new research results, where the components appear to fit together almost seamlessly. The intention of the research has been to describe plausible 'mechanical' processes that can produce the appropriate brain structures and mechanisms, but that could be used without the magical 'intelligence' part that is still not fully understood. There are also some important updates from an earlier version of this paper.

This Tech Stock forecast is designed and based on stock picking strategies for investors and analysts who need predictions for the 10 best tech stocks for the whole Technology Industry (See Industry Package). Package Name: Tech Stocks Forecast Length: 1 Year (10/08/2015 – 10/08/2016) I Know First Average: 40.11% The top performing stock for the long position of the 1 year period was NeoPhotonics Corp. (NPTN), which yielded a return of 125.38%. Additionally Ebix, Inc. (EBIX), and Alpha Pro Tech, Ltd. (APT) yielded returns of 122.65% and 59.53%, respectively. The average return for I Know First's 1 year long position was 40.11%, providing investors with a 33.14% premium above the S&P 500's return of 6.97%.

AI is moving at a stellar speed and is probably one of most complex and present sciences. The complexity here is not meant as a level of difficulty in understanding and innovating (although of course, this is quite high), but as the degree of interrelation with other fields apparently disconnected. There are basically two schools of thought on how an AI should be properly built: the Connectionists start from the assumption that we should draw inspiration from the neural networks of the human brain, while the Symbolists prefer to move from banks of knowledge and fixed rules on how the world works. Given these two pillars, they think it is possible to build a system capable of reasoning and interpreting. In addition, a strong dichotomy is naturally taking shape in terms of problem-solving strategy: you can solve a problem through a simpler algorithm, which though it increases its accuracy in time (iteration approach), or you can divide the problem into smaller and smaller blocks (parallel sequential decomposition approach).

We propose Turing Learning, a novel system identification method for inferring the behavior of natural or artificial systems. Turing Learning simultaneously optimizes two populations of computer programs, one representing models of the behavior of the system under investigation, and the other representing classifiers. By observing the behavior of the system as well as the behaviors produced by the models, two sets of data samples are obtained. The classifiers are rewarded for discriminating between these two sets, that is, for correctly categorizing data samples as either genuine or counterfeit. Conversely, the models are rewarded for 'tricking' the classifiers into categorizing their data samples as genuine. Unlike other methods for system identification, Turing Learning does not require predefined metrics to quantify the difference between the system and its models. We present two case studies with swarms of simulated robots and prove that the underlying behaviors cannot be inferred by a metric-based system identification method. By contrast, Turing Learning infers the behaviors with high accuracy. It also produces a useful by-product - the classifiers - that can be used to detect abnormal behavior in the swarm. Moreover, we show that Turing Learning also successfully infers the behavior of physical robot swarms. The results show that collective behaviors can be directly inferred from motion trajectories of individuals in the swarm, which may have significant implications for the study of animal collectives. Furthermore, Turing Learning could prove useful whenever a behavior is not easily characterizable using metrics, making it suitable for a wide range of applications.

San Francisco, Calif.: Sentient Technologies, the provider of distributed artificial intelligence (AI), today announced Sentient Ascend, the first conversion rate optimization (CRO) solution that uses AI to evolve winning designs for online marketers. Improving testing speeds by as much as 100 times or more over current A/B and multivariate testing solutions, Ascend gives marketers the freedom to try many more copy, image, design and interaction changes to accelerate the path to increased revenues and conversions. Existing A/B testing tools can only evaluate the impact of one marketing idea at a time and require substantial traffic to achieve a result, and existing multivariate testing solutions require even more traffic to test a limited number of changes. With Ascend, marketers can test dozens of ideas simultaneously, and the AI determines not only which individual changes are helpful, but also which combination of changes achieves the best results. Some early Ascend customers are testing millions of potential design combinations at once.

We introduce a new artificial intelligence (AI) approach called, the 'Digital Synaptic Neural Substrate' (DSNS). It uses selected attributes from objects in various domains (e.g. chess problems, classical music, renowned artworks) and recombines them in such a way as to generate new attributes that can then, in principle, be used to create novel objects of creative value to humans relating to any one of the source domains. This allows some of the burden of creative content generation to be passed from humans to machines. The approach was tested in the domain of chess problem composition. We used it to automatically compose numerous sets of chess problems based on attributes extracted and recombined from chess problems and tournament games by humans, renowned paintings, computer-evolved abstract art, photographs of people, and classical music tracks. The quality of these generated chess problems was then assessed automatically using an existing and experimentally-validated computational chess aesthetics model. They were also assessed by human experts in the domain. The results suggest that attributes collected and recombined from chess and other domains using the DSNS approach can indeed be used to automatically generate chess problems of reasonably high aesthetic quality. In particular, a low quality chess source (i.e. tournament game sequences between weak players) used in combination with actual photographs of people was able to produce three-move chess problems of comparable quality or better to those generated using a high quality chess source (i.e. published compositions by human experts), and more efficiently as well. Why information from a foreign domain can be integrated and functional in this way remains an open question for now. The DSNS approach is, in principle, scalable and applicable to any domain in which objects have attributes that can be represented using real numbers.

EAGO is a simple framework for evolutionary computation (EC) in Go. You can use this framework to run any kind of EC experiment, simply by two steps: configure and run. Following are algorithms that are available in this framework. More will be added with future updates. These are algorithms that are not implemented yet, but are planned to be added in the future.