According to global consulting firm Accenture, intelligent automation powered by machine learning processes is 2016's biggest tech trend. For tech giants like Amazon and Google, machine learning has long been central to their operations - the most famous example being Amazon's recommendation engine, which many see as having been the key to its success. Such technology has huge implications for organizations across all industries though, and the wealth of data that companies now hold, along with the rise in affordable products like Microsoft Azure ML and IBM Watson, mean that they are rushing to adopt it.

The global cognitive systems market will generate revenues worth 50 billion between 2016 and 2020, aided by development of self-learning IT systems, new research from Technavio reports. The report, Global Cognitive Systems Market 2016-2020, shows that cognitive applications are being increasingly integrated with machine learning, which helps real-time communications between people and IT systems. The applications also help draw independent conclusions as well as data processing capabilities. These are developed to mimic the human thinking process or learning, understanding and evaluating different situations. Predictive analytics are driving the growth, as well as the potential for modelling techniques and the potential data from the Internet of Things (IoT), says Amit Sharma, lead analyst, ICT at Technavio Research.

Twitter Inc. agreed to acquire a London-based artificial intelligence startup to make tweeted live videos look more professional. In a blog post Monday, Twitter Chief Executive Officer Jack Dorsey said he was buying Magic Pony Technology "so Twitter can continue to be the best place to see what's happening and why it matters, first." Seeking to shore up slowing growth, the social media company has in recent months begun emphasizing video on its site. Magic Pony uses machine learning, a way of teaching software to perform tasks without explicit programming instructions based on pattern recognition, a technology that's "increasingly at the core of everything we build at Twitter," Dorsey said. Twitter paid about 150 million for Magic Pony, according to a person familiar with the matter.

Google is diving deeper into artificial intelligence, with the company opening a dedicated machine learning research center in its Zurich office, the search company announced on Thursday, June 16. The Google Research Europe center will focus on three areas: Machine intelligence, natural language processing and understanding, and machine perception. The research center aims to deliver machine learning that can be put into practical use, to improve the machine learning infrastructure, and to assist the research community overall. "Google's ongoing research in machine intelligence is what powers many of the products being used by hundreds of millions of people a day -- from Translate to Photo Search to Smart Reply for Inbox," Emmanuel Mogenet, head of Google Research Europe, wrote in the blog post announcing the center. Mogenet noted machine learning software engineers and researchers will be able to develop products and conduct research at the Zurich center, which also holds the largest Google engineering office outside of the US.

Intel's chip arsenal appears to have some glaring weaknesses. One of them is the lack of a high-end graphics processor, which is important for gaming, virtual reality and machine learning. However, the company does have powerful alternatives: two monster chips that will be ammunition to take on GPUs and rival chips in the areas of machine learning and supercomputing, which are important to the company. In 2018, Intel will likely release a faster and more power-efficient Xeon Phi, a supercomputing chip that is already used in some of the world's fastest computers. Intel is also looking beyond CPUs to FPGAs (field programmable gate arrays), which can be faster at key tasks.

In a Monday blog post, the leadership of artificial intelligence (AI) research company OpenAI said that the group wants to modify'off-the-shelf' robots so they can perform common household tasks. "We're working to enable a physical robot (off-the-shelf; not manufactured by OpenAI) to perform basic housework," the group said in a blog post authored by Research Director Ilya Sutskever, Chief Technology Officer Greg Brockman, Sam Altman and Elon Musk. This futuristic target is second only to the primary goal laid out in the organization's blog post, which is to develop AI that could learn to improve its ability over time. Meeting such a goal would provide an underpinning for the perhaps more glamorous concept of robots that can clean your home, but the post goes onto say that domestic robots themselves would provide a solid foundation for approaching other problems in AI. "There are existing techniques for specific tasks, but we believe that learning algorithms can eventually be made reliable enough to create a general-purpose robot. More generally, robotics is a good testbed for many challenges in AI," the blog post reads.

SYDNEY: Marketers are looking toward artificial intelligence (AI) to boost capability in measurement and targeting, according to an expert in the field. Karen Nelson-Field, Associate Professor at the University of South Australia and the author of Viral Marketing: The Science of Sharing, addressed this topic at the AdNews Media Summit in Sydney. And she outlined potentially significant opportunities for advertisers in the areas of viewability, ad avoidance, audience measurement and contextual programmatic targeting in real-time. While biometrics and similar technology have been used before to track people's responses to ads in a laboratory setting, Nelson-Field argued this is too removed from how people interact with advertising in real life. She suggested that the next step for marketers is in biometrics with vision AI behind it, a phase that will harness subconscious recollection and provide a more accurate picture of how consumers interact with advertising in real life.

Pedro Domingos speaks on the future of the Information Age. Machine learning not only affects computers, but it will also change our lives. Pedro asks "what will the ultimate learning algorithm look like?" and discusses how future technology will change how we model many parts of our lives. Pedro Domingos is a professor of computer science at the University of Washington and the author of "The Master Algorithm". He is a winner of the SIGKDD Innovation Award, the highest honor in data science.

"The tagline is, Do you want to be a machine learning ninja?" says Christine Robson, a product manager for Google's internal machine learning efforts, who helps administer the program. "So we invite folks from around Google to come and spend six months embedded with the machine learning team, sitting right next to a mentor, working on machine learning for six months, doing some project, getting it launched and learning a lot." For Holgate, who came to Google almost four years ago after with a degree in computer science and math, it's a chance to master the hottest paradigm of the software world: using learning algorithms ("learners") and tons of data to "teach" software to accomplish its tasks. For many years, machine learning was considered a specialty, limited to an elite few. That era is over, as recent results indicate that machine learning, powered by "neural nets" that emulate the way a biological brain operates, is the true path towards imbuing computers with the powers of humans, and in some cases, super humans.

They are currently in the NYC Data Science Academy 12 week full time Data Science Bootcamp program taking place between January 11th to April 1st, 2016. This post is based on their fourth class project - Machine learning(due on the 8th week of the program). The Higgs Boson Challenge, hosted by Kaggle, asked the data scientist community to utilize machine learning to accurately predict if a particle was a Higgs-Boson particle or not; more specifically if a signal detected was either a'tau tau decay of a Higgs boson' or just'background'. The datasets provided were the training and test set with 250,000 and 550,000 observations, respectively. The training set contained all the same features as the test with two additional columns of'Label' and'Weight' that gave the accurate classifiers to help train our models.