Vinod Iyengar is Director of Marketing at California-based developer H2O.ai. H2O.ai are the makers behind H2O, the leading open source machine learning platform for smarter applications and data products. They work across a number of mission critical applications, including predictive maintenance, operational intelligence, security, fraud, auditing, credit scoring, user based insurance, ICU monitoring and more in over 5,000 organizations. And with customers including Capital One, PricewaterhouseCoopers, Comcast, Nielsen Catalina Solutions, Macy's and Aetna – to name just a select few – they are clearly in a prominent position in this space. Vinod details some key use cases of artificial intelligence in specific industries, while also sharing H2O.ai's vision for AI and the challenges we face in adopting it… Across industries and business disciplines, businesses use artificial intelligence to increase revenue or reduce costs by performing tasks more efficiently than humans could do unaided. With more than 150 million active digital wallets than 200 billion in annual payments, PayPal leads the online payments industry.

The following interview is one of many included in the report. Benjamin Recht is an associate professor in the electrical engineering and computer sciences department as well as the statistics department at the University of California at Berkeley. His research focuses on scalable computational tools for large-scale data analysis, statistical signal processing, and machine learning -- exploring the intersections of convex optimization, mathematical statistics, and randomized algorithms. David Beyer: You're known for thinking about computational issues in machine learning, but you've recently begun to relate it to control theory. Can you talk about some of that work?

Google on Wednesday revealed new products and software that uses machine learning to help users better perform simple tasks, access information and entertainment, and communicate with others. Among the specific items unveiled at the company's 10th annual conference for developers was an updated Android system, a new Watch and a virtual-reality platform. Google plans to start selling a device called Home that will answer users' questions and complete tasks for them. At the start of the conference, Chief Executive Sundar Pichai revealed new products and services that use smarter software to make decisions rather than follow instructions, part of a major push into artificial intelligence that he said would define the tech giant over the next decade. Google, a unit of Alphabet Inc. GOOG, 2.02% GOOGL, 1.95%, said it would soon start selling a device called Home that will answer users' questions and complete tasks for them, like scheduling appointments, playing music and sending emails.

Last year Microsoft and Google both showed that their image-recognition algorithms had learned to best humans. They independently created software that could exceed the average human score on a standard test that challenges software to recognize images of a thousand different objects, from mosques to mosquitoes. But to get good enough to defeat humanity, each company's software scrutinized 1.2 million labeled images. A child can learn to recognize a new kind of object or animal using only one example. Startup Geometric Intelligence said Monday that it has developed machine-learning software that is a much quicker study.

The conversations around artificial intelligence continue to evolve. From customer service to friendly social robots, AI can be used for a variety of practical uses. But can an AI system learn how to be creative? Google's new project Magenta wants to investigate just that. According to Popular Science, Google is launching a "research project to explore using artificial intelligence to create art, and make that process easier for TensorFlow users."

Zebra Medical Vision, an Israeli startup that uses machine learning to teach computers to read and diagnose imaging data, raised 12 million in its latest funding round, including a re-investment from Salesforce.com Inc. co-founder Marc Benioff. Zebra has been building a database of millions of files such as CT-scans and MRIs of real patients over the past three years, offering enough data so that machines can learn to accurately detect illnesses including breast cancer, and health problems with bones, the liver and lungs, said President and co-founder Eyal Gura. Company developers are writing specific algorithms for each health issue and three have been approved by the U.S. Food and Drug Administration, according to Gura. The company says its product can help the medical industry deal with a growing shortage of radiologists amid more chronic diseases, an aging population and an expanding middle class seeking more advanced health care. According to the World Bank, the middle class in low and middle income countries will jump from 5 percent in 2005 to 25 percent in 2030.

Just how worried should we be about killer robots? To go by the opinions of a highly regarded group of scholars, including Stephen Hawking, Max Tegmark, Franz Wilczek, and Stuart Russell, we should be wary of the prospect of artificial intelligence rebelling against its makers. "One can imagine (AI) outsmarting financial markets, out-inventing human researchers, out-manipulating human leaders, and developing weapons we cannot even understand," Hawking wrote in a 2014 article for The Independent. "Whereas the short-term impact of AI depends on who controls it, the long-term impact depends on whether it can be controlled at all." The fear that our irresponsible creations might bring about the end of humanity is a common one.

"Machine learning" has graduated into the upper echelon of business buzzwords. That hallowed level where we've all used it, but few up us really know what it means. We sort of get it, but what are the real world applications and ramifications? The impacts of machine learning are far reaching. Just look at everything IBM's Watson, a cognitive technology, has done in the past year.

Deep learning is a topic that is making big waves at the moment. It is basically a branch of machine learning (another hot topic) that uses algorithms to e.g. Scientists have used deep learning algorithms with multiple processing layers (hence "deep") to make better models from large quantities of unlabeled data (such as photos with no description, voice recordings or videos on YouTube). It's one kind of supervised machine learning, in which a computer is provided a training set of examples to learn a function, where each example is a pair of an input and an output from the function. Very simply: if we give the computer a picture of a cat and a picture of a ball, and show it which one is the cat, we can then ask it to decide if subsequent pictures are cats.

By 2018, China shall build platforms for fundamental AI resources and innovation and make breakthroughs on basic core technology, said the three-year implementation program for "Internet Plus" artificial intelligence. The plan is formulated jointly by the National Development and Reform Commission, the Ministry of Science and Technology, the Ministry of Industry and Information Technology, and the Cyberspace Administration of China. According to the website, the country shall be in line with global AI technology and industries by 2018. At key regions, the country will cultivate some global leading AI enterprises and set up an innovative, open, cooperative, green and safe AI industrial ecology. The country will cultivate and develop emerging artificial intelligence industries, promote innovation in intelligent products and enhance the intelligence level of terminal products.