Deep learning (DL) is a branch of machine learning that learns multiple levels of representations from data [1, 2]. DL has been successfully applied in pattern recognition and classification tasks such as image recognition and language processing. Recently, the application of DL to physics research is rapidly growing, such as in particle physics [3-7], nuclear physics [8], and condensed matter physics [9-14]. DL is shown to be very powerful in extracting pertinent features especially for complex nonlinear systems with high-order correlations that conventional techniques are unable to tackle. This suggests that it could be utilized to unveil hidden information from the highly implicit data of heavy-ion experiments.

Traditional Recurrent Neural Networks assume vectorized data as inputs. However many data from modern science and technology come in certain structures such as tensorial time series data. To apply the recurrent neural networks for this type of data, a vectorisation process is necessary, while such a vectorisation leads to the loss of the precise information of the spatial or longitudinal dimensions. In addition, such a vectorized data is not an optimum solution for learning the representation of the longitudinal data. In this paper, we propose a new variant of tensorial neural networks which directly take tensorial time series data as inputs. We call this new variant as Tensorial Recurrent Neural Network (TRNN). The proposed TRNN is based on tensor Tucker decomposition.

Specifically, you can tell the robot what flavor you like, such as "chewy candy," "sweet chocolate" or "hard mint." The robot then processes your instructions via voice recognition and natural language processing, recommends a particular kind of candy and uses image recognition to recognize and select that recommendation. The entire demo is powered by deep-learning technology running on Cloud Machine Learning Engine (the fully-managed TensorFlow runtime from Google Cloud) and Cloud machine learning APIs. This demo is intended to serve as a microcosm of a real-world machine learning (ML) solution. For example, Kewpie, a major food manufacturer in Japan, used the same Google Cloud technology to build a successful Proof of Concept (PoC) for doing anomaly detection for diced potato in a factory.

Artificial intelligence can accurately identify objects in an image or recognize words uttered by a human, but its algorithms don't work the same way as the human brain--and that means that they can be spoofed in ways that humans can't. New Scientist reports that researchers from Bar-Ilan University in Israel and Facebook's AI team have shown that it's possible to subtly tweak audio clips so that a human understands them as normal but a voice-recognition AI hears something totally different. The approach works by adding a quiet layer of noise to a sound clip that contains distinctive patterns a neural network will associate with other words. The team applied its new algorithm, called Houdini, to a series of sound clips, which it then ran through Google Voice to have them transcribed. Her bearing was graceful and animated she led her son by the hand and before her walked two maids with wax lights and silver candlesticks.

All organizations that did not originate this moment have a pre-existing culture as well as legacy technology and processes that can be more or less amenable to DevOps implementation. That organizational culture is influenced by the personalities and management styles of Executive Management, the wider culture in which the organization is situated, and the personalities of key team members at all levels of the organization. This culture and entrenched interests usually throw a wrench in the works because of misaligned incentives. In his session at @DevOpsSummit 20th Cloud Expo, Greg Bledsoe, a managing consultant at Accenture, discussed an algorithmic method that even someone with no positional power who desires to be an agent of change can implement to achieve cultural transformation and smooth the transition to overcome these obstacles and transform the culture until it has the re-aligned silos that are characteristic of a mature DevOps implementation. Speaker Bio Greg Bledsoe is a managing consultant at Accenture in the DevOps architecture practice and regularly advises and leads the implementation of DevOps principles and practices at the Fortune 100.

The result, after some work by crack film editor Alex MacKenzie, was the 45 minute documentary Singularity or Bust -- a uniquely edgy, experimental Singularitarian road movie, featuring perhaps the most philosophical three-foot-tall humanoid robot ever, a glance at the fast-growing Chinese research scene in the late aughts, and even a bit of a real-life love story. The film was screened in theaters around the world, and won the Best Documentary award at the 2013 LA Cinema Festival of Hollywood and the LA Lift Off Festival. And now it is online, free of charge, for your delectation. Singularity or Bust is a true story pertaining to events occurring in the year 2009. It captures a fascinating slice of reality, but bear in mind that things move fast these days.

Over the past few months, CSAIL's (MIT Computer Science and Artificial Intelligence Laboratory) self-driving wheelchair has become a familiar sight around the MIT halls. It's a nice little rolling advertisement for the lab's work -- and more importantly, it's a great opportunity to test the mobility device in a real world setting. As students wander by in groups, deep in conversation or face down in their smartphones, their paths are unpredictable and collisions are a very real possibility. This kind of real world testing is exactly why the chair exists in the first place. It's hard to test autonomous cars in out on the streets.

The rapid development of artificial intelligence (AI) technology means it is quickly becoming suitable for a wide range of commercial applications, and civil engineering is one of the sectors where it could soon make a big impact. Currently one of the least digitized industries, there is huge scope for AI to transform processes across the entire project life cycle. Such a significant transition may not be entirely smooth, but as the benefits become clear it is surely inevitable - so lets take a look at what the future holds. In order to plan new civil engineering projects, we first have to fully understand the nature of an urban environment and how it is used; a process that normally involves'spatial network analysis'. And as those environments become ever more convoluted, the software tools that perform that analysis are beginning to incorporate AI to handle the complexity; tasks which would have once taken humans days can now be completed in a matter of seconds.

Facebook has shut down two artificial intelligences that appeared to be chatting to each other in a strange language only they understood. The two chatbots came to create their own changes to English that made it easier for them to work – but which remained mysterious to the humans that supposedly look after them. The bizarre discussions came as Facebook challenged its chatbots to try and negotiate with each other over a trade, attempting to swap hats, balls and books, each of which were given a certain value. But they quickly broke down as the robots appeared to chant at each other in a language that they each understood but which appears mostly incomprehensible to humans. The robots had been instructed to work out how to negotiate between themselves, and improve their bartering as they went along.

AXA, the large global insurance company, has used machine learning in a POC to optimize pricing by predicting "large-loss" traffic accidents with 78% accuracy. The TensorFlow machine-learning framework has been open source since just 2015, but in that relatively short time, its ecosystem has exploded in size, with more than 8,000 open source projects using its libraries to date. This increasing interest is also reflected by its growing role in all kinds of image-processing applications (with examples including skin cancer detection, diagnosis of diabetic eye disease and even sorting cucumbers), as well as natural-language processing ones such as language translation. We're also starting to see TensorFlow used to improve predictive data analytics for mainstream business use cases, such as price optimization. For example, in this post, I'll describe why AXA, a large, global insurance company, built a POC using TensorFlow as a managed service on Google Cloud Machine Learning Engine for predicting "large-loss" car accidents involving its clients.