A wealth of data can be powerful, as long as that data delivers actionable insights. UK-based airline easyJet implemented artificial intelligence (AI) technology to make sense of its data and streamline areas of its business, such as stocking airplanes with the right amount of food. Alberto Rey Villaverde, easyJet's head of data science, spoke to eMarketer's Maria Minsker about why the airline turned to AI and the different ways the information AI provides is being used. Alberto Rey Villaverde: We've been using artificial intelligence for a few years. For the last year, we've been exploring new areas where these technologies could give us a boost. For example, we've been trying to be less wasteful with the food in our planes.

Amazon is extending its big data cloud platform across Europe, and the online retailer has machine learning research groups in Bangalore, Seattle, Palo Alto and Berlin, The Wall Street Journal reported. CEO Jeff Bezos is obsessed with predictive analytics and artificial intelligence -- anything that can help Amazon to better forecast customer wants and needs before the trends ever become obvious to the casual observer. The Amazon Machine Learning platform also is available for customer use via Amazon Web Services. Facebook's Artificial Intelligence Research project (FAIR) focuses on "giving people better ways to communicate." Some of Facebook's top AI researchers are Microsoft veterans, and those experts have a habit of sharing Facebook's knowledge.

Enterprises today rely on data as the foundation of business success, whether the goal is to better understand customers, build new or better products and services, or manage cost and risk. Data is now the prime raw material for creating value; across all industries, it's the norm to hold vast stores of data. An issue that remains unresolved, however, is how well and how efficiently data can be applied. Firms are still wrestling with the challenge of making big data work for them, in use cases ranging from enterprise analytics, customer 360, and product personalization to revenue assurance and fraud detection. All the data in the world has no value unless it's accessible and actionable.

Toon said that Graphcore currently stands at 40 employees and that the $30 million raised in the recently announced Series A (see Graphcore gets big backing for machine learning) would be used to complete the first design and for some limited expansion. "We could have taken more but this is sufficient to get product out," said Toon. "We will keep the engineering based here in Bristol but there is scope for some customer support and business development roles in Silicon Valley, Seattle and China," he added. Toon acknowledged that there is one other major technology company, besides Samsung and Robert Bosch, that contributed to the Series A funding . He said that company has chosen not to go public on the investment. With regard to the Intelligent Processor Unit (IPU) Knowles commented: "We will release our technology in the second half of 2017. It is a brand new, from-scratch design."

Symantec Corp. (SYMC) recently announced its latest Symantec Endpoint Protection 14 technology. "Artificial intelligence fused with critical endpoint technologies deliver the most complete endpoint security on the planet," boasts the cybersecurity industry pioneer. As headline data breaches and cybercrime news reach organizations worldwide, Symantec is positioned well to benefit from its cuttingedge integrated cyberdefense prevention and detection. The firm's Endpoint Protection 14 marks the lightest and strongest endpoint protection Symantec has on the market, "providing everything from file reputation and behavioral analysis to advanced machine learning Al," the company says. Endpoint Protection 14 stands as the only solution on the market merging essential endpoint technologies with advanced machine learning and memory exploit mitigation in a single agent.

The World Intellectual Property Organization has developed a ground-breaking new "artificial intelligence"-based translation tool for patent documents, handing innovators around the world the highest-quality service yet available for accessing information on new technologies. WIPO Translate now incorporates cutting-edge neural machine translation technology to render highly technical patent documents into a second language in a style and syntax that more closely mirrors common usage, out-performing other translation tools built on previous technologies. WIPO has initially "trained" the new technology to translate Chinese, Japanese and Korean patent documents into English. Patent applications in those languages accounted for some 55% of worldwide filings in 20141. Users can already try out the Chinese-English translation facility on the public beta test platform.

His relevant research work also includes: multimodal traffic control (assisted with machine learning and computer vision techniques), integration with decentralized route choice models and dynamic congestion pricing protocols, vehicle-to-infrastructure (V2I) communication with connected vehicles, energy efficiency optimization, and data-driven self-learning and active congestion management based on performance measurement.

The scientific community has made tremendous strides in developing neural networks, computer systems that are built to operate like the human brain. Researchers have managed to get these systems to beat the world's best Go players, identify images and shrink their file sizes. Heck, we've even taught them to write like Philip K Dick. Most incredibly, Google recently taught two nets to design their own encryption algorithm. The problem, however, is that even the researchers that designed these systems aren't particularly sure how they actually work.

Today, Microsoft Research is publicly releasing its effort to tackle just one of the problems plaguing natural language understanding -- knowledge. The company believes that background knowledge is one of the key separators between the way humans and machines understand language. Probase, a knowledge database Microsoft has been working on for quite some time, is serving as the base for a new public tool called Microsoft Concept Graph. Probase brings 5.4 million concepts to the table, beating other knowledge databases like Cyc, which offers 120,000 concepts. The goal of all the connected information is to support text analysis by mixing interpretations with probabilities -- this is very similar to the way humans use rapid process of elimination to accomplish the same task.

Turns out, the inner workings of neural networks really aren't any easier to understand than those of the human brain. But thanks to research coming out of MIT's Computer Science and Artificial Intelligence Laboratory (CSAIL), that could soon change. They've devised a means of making these digital minds not just provide the correct answer, classification or prediction, but also explain the rationale behind its choice. And with this ability, researchers hope to bring a new weapon to bear in the fight against breast cancer. The scientific community has made tremendous strides in developing neural networks, computer systems that are built to operate like the human brain.