Tokyo, April 8, 2016 - Hitachi, Ltd. (TSE: 6501) today announced the development of "EMIEW3," a humanoid robot, and its "remote brain"*1 robotics IT platform. EMIEW3, capable of autonomously approaching customers requiring assistance, was developed to provide necessary services and guidance in stores and public facilities. Enhanced by the "remote brain" consisting of a robotics IT platform connected to cloud-based intelligent processing systems and a remote operation system to monitor and control multiple robots at various locations, EMIEW3 is able to provide high quality services. Since the announcement of "EMIEW" in 2005, Hitachi has continued to develop human symbiotic robots that can safely co-exist with humans, providing robot-based services with advanced communication capabilities. Using EMIEW2, first announced in 2007, Hitachi developed functions necessary for customer and guidance services, and demonstrated capabilities which include autonomous mobility at a brisk human walking pace, isolation of human voice from background noise, accessing information from the Web to identify objects and using indoor network cameras as "eyes" to locate objects.

Nvidia chief executive Jen-Hsun Huang announced that the company has created a new chip, the Tesla P100, with 15 billion transistors for deep-learning computing. It's the biggest chip ever made, Huang said. Huang made the announcement during his keynote at the GPUTech conference in San Jose, California. He unveiled the chip after he said that deep-learning artificial intelligence chips have already become the company's fastest-growing business. "We are changing so many things in one project," Huang said.

I tend to think of myself as a futurist, I immediately see the possibilities of technologies as part of a much larger ecosystem than the one it is intended for. I look for ways to test and assess "How we might Live" with the technology and how it will adapt our lives, our cultures and move humanity onwards to greater things. In our modern societies we have relieved ourselves of the burdens of the industrial age and are in the process of doing the same to the digital age. We had digitized the same old processes, making them easier to do, involving less time so we could use that time on other things, but we had not thought to remove them. That is the next stage in human and machine evolution, removing pointless interactions and processes.

I built deepjazz in 36 hours for HackPrinceton, Spring 2016. It uses Keras & Theano, two deep learning libraries, to generate jazz music. Specifically, it builds a two-layer LSTM, learning from the given MIDI file. It uses deep learning, the AI tech that powers Google's AlphaGo and IBM's Watson, to make music -- something that's considered as deeply human.

An Outlier is an observation or point that is distant from other observations/points. But, how would you quantify the distance of an observation from other observations to qualify it as an outlier. Outliers are also referred to as observations whose probability to occur is low. But, again, what constitutes low?? There are parametric methods and non-parametric methods that are employed to identify outliers.

He may not be as smart as Watson or Lieutenant Commander Data. He's definitely not as strong as the new FANUC M-2000iA/1700L that can lift 1.7 tons -- the equivalent of two small cars or 24 people. And he is certainly not as pretty as the robot "clones" on Orphan Black. Little did I know though that my favorite robot would be the precursor of the current AI robotic automation wave. The 60s TV series Lost in Space featured a cast member that aided and protected teenager Will Robinson and his family on their comical misadventures in space.

We study the relationship between the sentiment levels of Twitter users and the evolving network structure that the users created by @-mentioning each other. We use a large dataset of tweets to which we apply three sentiment scoring algorithms, including the open source SentiStrength program. Specifically we make three contributions. Firstly we find that people who have potentially the largest communication reach (according to a dynamic centrality measure) use sentiment differently than the average user: for example they use positive sentiment more often and negative sentiment less often. Secondly we find that when we follow structurally stable Twitter communities over a period of months, their sentiment levels are also stable, and sudden changes in community sentiment from one day to the next can in most cases be traced to external events affecting the community. Thirdly, based on our findings, we create and calibrate a simple agent-based model that is capable of reproducing measures of emotive response comparable to those obtained from our empirical dataset.

For the mind does not require filling like a bottle, but rather, like wood, it only requires kindling to create in it an impulse to think independently and an ardent desire for the truth. The impulse to ingest more data is our first and most powerful instinct. Born with billions of neurons, as babies we begin developing complex synaptic networks by taking in massive amounts of data - sounds, smells, tastes, textures, pictures. It's not always graceful, but it is an effective way to learn. As data scientists, the trick is to encode similar learning instincts into applications, banking more on the volume of data that will flow through the system than on the elegance of the solution (see also these discussions of the Netflix prize and the "unreasonable effectiveness of data").

AYLIEN, a natural language processing and data services provider, has launched a machine learning and natural language processing (NLP)-driven news API that allows users to search, source, and index news and blog content from across the Web in real time. This is just the latest machine learning and NLP-driven service offered by the company. Last year, AYLIEN formed a partnership with Imagga to offer a hybrid text and image analysis service. The service uses computer vision (an application of machine learning) and NLP to analyze text and images on a Web page simultaneously in order to better understand the content of that page. The AYLIEN News API allows developers to build content-driven applications with access to an enriched, real-time news data source.

Hewlett Packard Enterprise (HPE) has announced a range of workload-optimised compute platforms and solutions to help boost its customers' innovation as they flock to high-performance computing applications. It means organisations using high-performance computing (HPC) solutions are able to use big data workloads to aid their modeling, simulation, high frequency trading and deep learning efforts. At the centre of its new offering, sits HPE's GPU-accelerated deep learning platform - Apollo 6500 - which runs on up to eight high performance NVIDIA GPU cards to offer improved learning systems to organisations that need to quickly model results without dramatically increased costs. It will be especially useful for those working in the financial sector, where scalable, big data applications are in demand to help process high volumes of data generated by real-time trading. The Apollo 4520 system is a dual-node system designed to lower costs for organisations that need to support parallel file system architectures as part of their HPC implementation. It can be used in conjunction with Lustre solutions either via an HPE supported Lustre solution, based on the Intel Enterprise Edition for Lustre software or Open Source Lustre with community support.