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.

Artificial intelligence developed by a group of Australian research teams has replicated a complex experiment which won the Nobel Prize for Physics in 2001. The intelligent machine learned how to run a Bose-Einstein condensation – isolating an extremely cold gas inside a beam of laser light – in under an hour, something the team "didn't expect". Results have been published in the Scientific Reports journal. The algorithm has also been uploaded to GitHub for other researchers working on "quantum experiments". "A simple computer program would have taken longer than the age of the universe to run through all the combinations and work this out," said Paul Wigley, co-lead researcher of the study and professor at the School of Physics and Engineering at the Australian National University.

Law firms are embracing artificial intelligence (AI) in a bid to improve efficiency. Developed in conjunction with tech start-up RAVN, BLP's'contract robot' can complete legal work which would take a team of paralegals and associates months to do within seconds. It is currently assisting the firm's real estate team. Called LONald, the robot extracts data from Land Registry documents and enters it into a spreadsheet in the same way staff would do. It cross-checks data points to remove duplicates and then uses the spreadsheet to send queries out.

I've been skim-reading articles in The Economist, The Sunday Times and The Evening Standard lately about the coming wave of robotics and automation. Those articles are all the same. They talk vaguely about how robots will replace lots of white collar workers within ten years. And they skim over the question of how the technology works. Ten years is a nice time scale for a prediction: far enough out that you can make amazing claims, close enough that they seem vaguely urgent.