ARM has acquired Apical, a U.K. designer of embedded computer vision technology, and plans to incorporate that technology into future ARM microprocessor and system-on-chip designs, it said Wednesday. The move will open up new opportunities for designers of autonomous vehicles and security systems, among other connected things, according to ARM CEO Simon Segars. Computer vision is in its early stages, and Apical is at the forefront of embedding such technology, he said. Apical's technologies is already used in 1.5 billion smartphones, according to ARM, although many of those phones may be using nothing more sophisticated than a display brightness control Apical calls Assertive Display. That technology also turned up in Samsung Electronics' new laptop, the ATIV Book 9. Assertive Camera is another of Apical's developments: It's a range of software packages and silicon-based image signal processors for reducing image noise, managing color and shooting high dynamic range images.

The experiment, developed by physicists from ANU, University of Adelaide and UNSW ADFA, created an extremely cold gas trapped in a laser beam, known as a Bose-Einstein condensate, replicating the experiment that won the 2001 Nobel Prize. The artificial intelligence system's ability to set itself up quickly every morning and compensate for any overnight fluctuations would make this fragile technology much more useful for field measurements, said co-lead researcher Dr Michael Hush from UNSW ADFA. The team cooled the gas to around 1 microkelvin, and then handed control of the three laser beams over to the artificial intelligence to cool the trapped gas down to nanokelvin. "It may be able to come up with complicated ways humans haven't thought of to get experiments colder and make measurements more precise.

Physicists are putting themselves out of a job, using artificial intelligence to run a complex experiment. The experiment, developed by physicists from ANU, University of Adelaide and UNSW ADFA, created an extremely cold gas trapped in a laser beam, known as a Bose-Einstein condensate, replicating the experiment that won the 2001 Nobel Prize. "I didn't expect the machine could learn to do the experiment itself, from scratch, in under an hour," said co-lead researcher Paul Wigley from ANU Research School of Physics and Engineering. "A simple computer program would have taken longer than the age of the universe to run through all the combinations and work this out." Bose-Einstein condensates are some of the coldest places in the Universe, far colder than outer space, typically less than a billionth of a degree above absolute zero.

Among technology categories creating sweeping change right now, cloud computing and Big Data analytics dominate the headlines, and open source platforms are making a difference in these categories. However, one of the biggest open source stories of the year surrounds newly contributed projects in the field of artifical intelligence and the closely related field of machine learning. Some of the biggest tech companies are helping to drive the trend. Google has open sourced a program called TensorFlow. It's based on the same internal toolset that Google has spent years developing to support its AI software.

These datasets are used for machine learning research and have been cited in peer-reviewed academic journals and other publications. Datasets are an integral part of the field of machine learning. Major advances in this field can result from advances in learning algorithms (such as deep learning), computer hardware, and, less-intuitively, the availability of high-quality training datasets.[1] High-quality labeled training datasets for supervised and semi-supervised machine learning algorithms are usually difficult and expensive to produce because of the large amount of time needed to label the data. Although they do not need to be labeled, high-quality datasets for unsupervised learning can also be difficult and costly to produce.[2][3][4][5]

The only way to monitor progress is to view the status of the Spark jobs, which provides no information about convergence or other statistics of interest. In this talk, we will discuss how to visualize and monitor the training of machine learning models in real-time with Spark. With this capability, you can monitor machine learning training from one iteration to the next, observe how the model converges during each iteration, visualize the characteristics of the model in real time, and decide if you wish to continue to train the model. Talk 2: SPARK TUNING FOR ENTERPRISE SYSTEM ADMINISTRATORS Speaker: Anya Bida, Rachel Warren Spark offers the promise of speed, but many enterprises are reluctant to make the leap from Hadoop to Spark. Indeed, System Administrators will face many challenges with tuning Spark performance.

As a Senior Data Scientist I participate in three activities – technical pre-sales, professional services and core technology. I engage on a day-to-day basis with potential clients as a pre-sales technical consultant supporting the sales team globally to understand client problems related to data and reporting. Pre-sales engagements involve building rapid prototypes and managing the development team on rapid prototyping of potential applications to illustrate how Arria NLG's technology can solve clients' data reporting issues. I also spend time working as part of a team delivering on multiple platform projects, performing the role of a data guru. I also work very closely with Arria's Chief Data Scientist to champion data science internally within the organization as well as define and design the articulate analytics' vision for Arria's Core Technology.

Sony Corp. said Wednesday it has invested in U.S. artificial intelligence startup Cogitai, aiming to develop new AI technologies and release products within the next three years. Sony is believed to have obtained a roughly 20 percent stake in the company that was founded in September by three AI researchers. The move could lead to the electronics giant re-entering the robot business. Sony was an AI pioneer, known for producing robotic dog AIBO and humanoid QRIO featuring AI technologies. But it withdrew from the robot business in 2006 to improve profitability and restructure its consumer electronics business.

Round the year, investors kept showing faith in innovative data driven healthcare ideas. Market and innovators continued exploring how mobile technology could be leveraged to improve user healthcare. Global commitment to better healthcare, from both the governments as well as industry giants, strengthened. I foresee 2016 to continue carrying the momentum of this year. Several ideas and solutions that received initial support from the medical community will go mainstream next year as new paradigms will keep emerging.

Agriculture is considered a prime area of potential growth in the drone industry because of the technology's ability to help survey crops and gather real-time information on farmland. Crop-spraying drones or easy-to-fly devices that are designed to spray pesticides on crops, can also capture high resolution images of whole field for further analysis. Effect of crop-spraying drone usage is massive. Drones can take off and land vertically which means unmanned aerial vehicle (UAV) sprayer does not need a runway. They are suitable for all kinds of complex terrain, crops and plantations of varying heights.