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.

There are two main approaches to machine learning – supervised and unsupervised – and each has specific applications in the context of healthcare. And even though their impact has not yet sent shockwaves through the industry, the potential of each is enormous, according to John Guttag, head of the Data Driven Inference Group at MIT's Computer Science and Artificial Intelligence Laboratory. At its basic level, machine learning involves looking at data, and from that data finding information that is not readily visible. Example: Applying machine learning to data about patients infected with Zika or another virus and using what we can learn about what happens to those people to inform care decisions regarding the best ways to treat people who get infected in the future. "Typically we use machine learning to build inference tools, where we find patterns in existing data that allow us – when presented with new data – to infer something interesting about that data," said Guttag.

When John Guttag keynotes the HIMSS and Healthcare IT News Big Data and Healthcare Analytics Forum in Boston on October 24, the MIT professor will describe the unique challenges of applying machine learning to healthcare – as well as the huge potential for efficiencies and quality improvements as these data techniques become more widespread across the industry. Guttag, who heads the Data Driven Inference Group at the MIT's Computer Science and Artificial Intelligence Laboratory, and his MIT students are currently working closely with Mass General on integrating machine learning into clinical workflows, specifically with the aim of reducing healthcare-associated infections. "I want to actually see things change in the system, not just write papers saying things could change," Guttag said. "The goal here is to have something good happen. I hope a year from now I'm able to say, 'Guess what, we've lowered the rate of nosocomial infections at MGH – and more importantly put together a description of how we've done it that is exportable to other organizations.'"

What will the future look like, and how will be it be shaped by new and emerging technologies? Well, according to new research from Nesta, 60% of people in the UK believe new technological innovations will improve their future wellbeing. In the run up to Nesta's FutureFest event, which will take place on 17th and 18th September in London's Tabacco Dock, the innovation foundation commissioned market research agency, ComRes, to explore how consumers in the UK feel about emerging technologies, such as augmented reality, driverless cars and artificial intelligence, and whether they believe the future innovations that will be developed within the next 20 years will be beneficial to their lives. Many of the responses were upbeat: almost half of respondents (49%) said DNA sequencing and editing represents a future opportunity for the healthcare sector, while 68% think future technologies will help to improve food production. Around a third (36%) of those surveyed in London said they would be willing to be microchipped in order to secure access to their work devices and homes, while 28% were open to eating meals in pill form and 27% were up for dating a partner they were paired up with through pheromone or DNA-matching.

Japanese investment in startup companies is surging, driven by interest in a number of powerful new technological trends. In the first six months of 2016, unlisted startups in the country raised a record 92.8 billion yen ( 897 million) of capital, up 21% from the same period the previous year. Total investment in entrepreneurial ventures in 2016 is likely to reach the highest level since data started being compiled in 2006. The trend has been led by companies keen to invest in ventures which own cutting-edge technology in areas like artificial intelligence. Japan Venture Research, a Tokyo-based research company, has estimated the amount of money raised by some 8,600 unlisted companies by analyzing data concerning their capital.

The HR Technology Conference is in Chicago this year from October 4-7 and I'll once again be blogging live from the show. As I'm preparing and scheduling meetings with various vendors one thing have become perfectly clear, I'll be doing a lot of talking about "Artificial Intelligence"(AI). The stuff we see in movies in the future where computers and robots begin the think for themselves then very quickly understand that humans are inadequate so they'decide' humans are no longer needed and only machines should run the world. Yeah, That AI! Sounds like the perfect HR replacement! The actual definition of Artificial Intelligence is simply, intelligence exhibited by machines.

Benevolent.ai, a British artificial intelligence (AI) healthcare company, has hired IBM's AI expert Jérôme Pesenti, ex-VP of Watson Core Technology, to head up its technology division. Founded in 2013, benevolent.ai was spun out of the management team working at Proximagen, a pharmaceutical company, who were frustrated with the slow pace of drug discovery. Data from patient databases and scientific papers is constantly expanding, said Ken Mulvany, chairman of benevolent.ai. Some of the information might be true, some of it will be speculative, and some of it will be false. The human brain just doesn't have the capacity to keep up," Mulvany told The Register. Benevolent.ai aims to analyse data and form connections through a knowledge graph, allowing researchers to observe patterns they might have missed. "The data might show that a protein upregulates a particular gene.

This post is Part 4 of the Deep Learning in a Nutshell series, in which I'll dive into reinforcement learning, a type of machine learning in which agents take actions in an environment aimed at maximizing their cumulative reward. Deep Learning in a Nutshell posts offer a high-level overview of essential concepts in deep learning. The posts aim to provide an understanding of each concept rather than its mathematical and theoretical details. While mathematical terminology is sometimes necessary and can further understanding, these posts use analogies and images whenever possible to provide easily digestible bits that make up an intuitive overview of the field of deep learning. Previous posts covered core concepts in deep learning, training of deep learning networks and their history, and sequence learning. Remember how you learned to ride a bike? More than likely an adult stood or walked behind you and encouraged you to make the first moves on your bike, and helped you get going again when you stumbled or fell. But it is very difficult to explain to a child how to ride a bike, and even a good explanation makes little sense to someone who has never ridden before: you have to get the feel for it. So how did you learn to ride a bike if it could not be clearly explained?

Microsoft is in an extraordinary situation right now. Unlike most of its competitors, Microsoft's business is incredibly diversified as Redmond transitions from its heyday of the PC era into the mobile first, cloud first paradigm. Microsoft's flexibility allows it to not be wed to any single revenue master in the same way that Apple (the iPhone) and Google (advertising) are with their primary income drivers. Microsoft has three primary business units--devices, cloud and productivity--that are all growing and all tied together like the most expensive Venn diagram of all time. About half of all of Microsoft's business in the last quarter ( 12.7 billion) was from its "More Personal Computing" reporting group which includes Windows, Bing and its own hardware like the Surface tablets and Xbox. Microsoft's Office-based "Productivity And Business" segment made 6.6 billion in revenue while its "Intelligent Cloud" made 6.34 billion.

This robot dives to depths humans dare not attempt - and it can bring people along for the ride without them getting wet. The Stanford-built OceanOne is filled with compressible oil to offset the crushing pressures experienced when 100 metres underwater, and AI-assisted navigation steers it clear of obstacles. Its operators remain on land, observing on screen everything the robot captures, using joysticks to drive it and guiding its hands through a feedback mechanism that relays tactile sensations. "It's impossible to let a robot act alone in such an environment: it will fail," says Professor Oussama Khatib, OceanOne's creator. "The only way you can guarantee success is connecting a worker through a haptic device to the robot.