Google claimed that TensorFlow's distributed architecture gives it a high level of flexibility in how coders define models that train the software. "To make TensorFlow easier to use, we have included Python libraries that make it easy to write a model that runs on a single process and scales to use multiple replicas for training".Distributed computing allows neural networks to learn much faster than the network running on one computer. Engineering leader of TensorFlow Rajat Monga said the reason why TensorFlow's multi-server version was delayed for release because they found it hard to adapt the open-source software to be usable outside of the highly customized data centers of Google. But for many researchers, its expense might as well place it in outer space.TensorFlow comes in a branch of artificial intelligence called deep learning, it works the same way human brain cells interact together.Equally, having access to the combined power of even a small cluster of computers, rather than relying on one machine, means that the overall data throughput of machine learning models and the speed at which they deliver accurate results can be accelerated.Regardless of the advanced feature, TensorFlow has already gained popularity for its software.The Verge has a report covering some of the more compelling projects that developers have created using TensorFlow.

Meetup's Machine Learning and Data Engineering team is pleased to announce we're sponsoringMLConf NYC2016.In the spirit of MLConf, we'll be hosting a pre-conference meetup whereData Scientists and Machine Learning Engineering experts will speak on topics related to Applied Machine Learning. Come talk tech and mingle over food and drinks with this community! You don't have to be a conference attendee to join us -- we look forward to seeing you soon! Try this 18% discount on us!

DigitalGenius is announcing its Human AI customer service platform today, along with a 4.1 million seed investment. Machine learning is an approach or set of techniques where you use massive data sets to train machines in semi-supervised ways…Deep learning is a step below machine learning in the tree. The company includes what they call a "confidence threshold" in their AI/customer interactions, and if it drops below a certain point, a human customer service agent steps in or approves the messaging. This follows a growing trend in the industry, as rapid evolution in NLP (natural language processing) and machine learning techniques like deep learning are pushing the technology forward.

The crowdsourcing platform combines human insights with machine learning techniques to untangle and promote wider analytics use of unstructured data. To improve accuracy, the company's "Reputation Engine" applied machine-learning techniques to rate each individual's performance by domain. The resulting combination of human insights and machine learning can then be used to organize unstructured data into "clean," labeled data. Spare5 asserted limitations in current data quality tools leave much unstructured data unused.

"Advanced cyberlearning environments that involve Virtual Reality and Artificial Intelligence innovations are becoming powerful tools that can facilitate the explorations and conversations needed to solve society's "wicked challenges," said Winslow Burleson, PhD, MSE, an engineer by training and currently associate professor, New York University Rory Meyers College of Nursing. The researchers posit that the use of technology, specifically a bundled and ever-evolving fluid set of integrated cyber tools, will connect disparate groups and individuals, converging them in both a real and an imagined cyber-social-physical environment, called the Holodeck, that Burleson's NYU-X Lab is currently advancing in prototype form, in close collaboration with colleagues at NYU Courant, Tandon, Steinhardt, and Tisch, "The "Holodeck" will support a broad range of transdisciplinary collaborations, integrated education, research, and innovation by providing a networked software/hardware infrastructure that can synthesize visual, audio, physical, social, and societal components," said Burleson. NYU-X Lab's Holodeck prototype harnesses the collective power of shared computation, integrated distributed data, immersive visualization, and social interaction to make possible large-scale synthesis of learning, research, and innovation, that will dramatically accelerate the Rittel and Webber iterative mode of problem solving. The goal is to create a networked infrastructure and communication environment where "wicked challenges" can be iteratively explored and re-solved, utilizing visual, acoustic, and physical sensory feedback, human dynamics with and social collaboration.

The 15th International Conference on AI and Law (ICAIL 2015) will be held in San Diego, California, USA, June 8-12, 2015, at the University of San Diego, at the Kroc Institute, under the auspices of the International Association for Artificial Intelligence and Law (IAAIL), an organization devoted to promoting research and development in the field of AI and law with members throughout the world. The conference is held in cooperation with the Association for the Advancement of Artificial Intelligence (AAAI) and with ACM SIGAI (the Special Interest Group on Artificial Intelligence of the Association for Computing Machinery).

We review the First International Competition on Computational Models of Argumentation (ICMMA'15). The competition evaluated submitted solvers performance on four different computational tasks related to solving abstract argumentation frameworks. Each task evaluated solvers in ways that pushed the edge of existing performance by introducing new challenges. Despite being the first competition in this area, the high number of competitors entered, and differences in results, suggest that the competition will help shape the landscape of ongoing developments in argumentation theory solvers.

If the artificial intelligence research community is to have a challenge problem as an incentive for research, as many have called for, it behooves us to learn the principles of past successful inducement prize competitions. Those principles argue against the Turing test proper as an appropriate task, despite its appropriateness as a criterion (perhaps the only one) for attributing intelligence to a machine.

The annual International Web Rule Symposium (RuleML) is an international conference on research, applications, languages and standards for rule technologies. RuleML is a leading conference to build bridges between academe and industry in the field of rules and its applications, especially as part of the semantic technology stack. It is devoted to rule-based programming and rule-based systems including production rules systems, logic programming rule engines, and business rule engines/business rule management systems; semantic web rule languages and rule standards; rule-based event processing languages (EPLs) and technologies; and research on inference rules, transformation rules, decision rules, production rules, and ECA rules. The 9th International Web Rule Symposium (RuleML 2015) was held in Berlin, Germany, August 2-5.

As a challenge problem for AI systems, I propose the use of hand-constructed multiple-choice tests, with problems that are easy for people but hard for computers. Specifically, I discuss techniques for constructing such problems at the level of a fourth-grade child and at the level of a high-school student. For the fourth grade level questions, I argue that questions that require the understanding of time, impossible or pointless scenarios, of causality, of the human body, or of sets of objects, and questions that require combining facts or require simple inductive arguments of indeterminate length can be chosen to be easy for people, and are likely to be hard for AI programs, in the current state of the art. For the high-school level, I argue that questions that relate the formal science to the realia of laboratory experiments or of real-world observations are likely to be easy for people and hard for AI programs.