Many large enterprises realize that their data can give them rapid, useful insights for implementing changes that will benefit their business. But as the volume of data grows, it becomes more difficult for these companies to extract meaning from it. The solution is industrial machine learning (IML), which can consistently produce data-driven insights at enterprise scale. IML can ingest data, build algorithms, deploy them into production and generate continuous insights into ongoing business problems. IML is a modern take on a very old idea: the scientific method.

More frequently we read news alerts about cyber attacks. The scary notion is that the cyber world has become a dangerous arena where most anything goes. Businesses and governments are trying to secure their online systems as quickly and effectively as possible against these digital risks. Unfortunately, the infiltrations these parties fear is not just enemies and hackers, but competitors and others looking to read confidential emails, steal secrets, or simply commit fraud. I was recently asked to share my thoughts on what could help mitigate risk in a technology driven world.

Marketers are usually the ones responsible for creating the buzzwords that follow us everywhere we go; that is, until AI turned the tables and made the whole marketing industry buzz with excitement. Your email address will not be published.

Apparently Google Translate, the company's popular machine-translation service, had suddenly and almost immeasurably improved. Rekimoto visited Translate himself and began to experiment with it. He had to go to sleep, but Translate refused to relax its grip on his imagination. Rekimoto wrote up his initial findings in a blog post. First, he compared a few sentences from two published versions of "The Great Gatsby," Takashi Nozaki's 1957 translation and Haruki Murakami's more recent iteration, with what this new Google Translate was able to produce.

MUMBAI: Airlines and airports can save an estimated USD 25 billion annually from flight disruptions by harnessing artificial intelligence, cognitive computing, predictive analytics and other progressive technical capabilities, says an industry report. According to a report by Sita, a technology services provider to air transport industry, predictive tools using artificial intelligence and cognitive computing are likely to be adopted by half of airlines and airports over the next decade, which can help them save up to USD 25 billion. Predictive technologies can provide passengers with more relevant information about their journey to help them create more seamless and personal experiences, Nigel Pickford, director market insight at Sita said. "Airlines and airports are focusing on technologies that will make them more responsive to issues in their operations. This will enable them to improve their performance and customer services," Pickford said.

Generally speaking, Computer Science degrees tend to be a little bit behind the times. Many schools require syllabuses to be planned months, if not years in advance, meaning that both undergraduates and postgraduates miss out on the cutting edge of tech. But I've noticed that many schools are catching up and are now teaching in-vogue topics like artificial intelligence and machine learning, even at an undergraduate level. For the Los Angeles-based Cloudsight, this represents a golden opportunity to put its product front-and-center to a new generation of aspirant computer scientists. As a result, it has announced that its computer vision API will soon be free for educational institutions.

The Google Brain project is working on an Artificial Intelligence that is capable of creating Artificial Intelligence. They are not the only ones, OpenAI, MIT or Berkeley are also exploring in that direction. The thought that this line of work of Google takes to me is that the Artificial Intelligence will transform massively the barriers to entry of multiple markets. It is something that has already happened to the Internet, and what many could not adapt at the time. Joshua Browder, 18, an Englishman, Stanford student, shortly after his driver's license, received more than 30 traffic tickets for parking his motorcycle.

In less than a year, Amazon's combination of the Echo speaker system and the Alexa voice-controlled digital assistant has come close to delivering on the elusive promise of easy-to-use technology that can control gadgets in the home with a few spoken words. Yet Amazon's surprise success sets up a long-term battle with Apple and Alphabet Google for primacy in the connected household. And the contours of that competition are following a classic tech industry dynamic. Amazon's surprise success with its Echo speakers and Alexa smart assistant sets up a long-term battle with Apple and Alphabet Google for primacy in the connected household. Amazon is pursuing an open-systems approach that allows quick development of many features, while Apple is taking a slower route, asserting more control over the technology in order to assure security and ease-of-use.

Engineers at the University of California, Riverside (UCR) have developed a new online energy management system (EMS) that they say can improve PHEV fuel efficiency by more than 30%. In "Development and Evaluation of an Evolutionary Algorithm-Based Online Energy Management System for Plug-In Hybrid Electric Vehicles," published in IEEE Transactions on Intelligent Transportation Systems, Xuewei Qi and colleagues explain that improving the efficiency of current PHEVs is limited by shortfalls in their energy management systems (EMS), which control the power split between engine and battery. In general, existing EMS for PHEVs are either rule-based systems, which use a set of predefined rules, or optimization-based, which can adapt according to driving conditions and driver behavior. However, notes the UCR team, most systems have only limited adaptability to real-time information. "In reality, drivers may switch routes, traffic can be unpredictable, and road conditions may change, meaning that the EMS must source that information in real-time," Qi said.

Chainer is a deep learning framework that's designed on the principle of define-by-run. Unlike frameworks that use the define-and-run approach, Chainer lets you modify networks during runtime, allowing you to use arbitrary control flow statements. It is simple, efficient, and can run and learn state-of-the-art CNNs. Deeplearning4J integrates with Hadoop and Spark and runs on several backends that enable use of CPUs and GPUs.