On lengths of yarn stretched between chairs, sixth-grade math students were placing small yellow squares of paper, making number lines--including everything from fractions to negative decimals--in a classroom at Walsh Middle School. Their teacher, Michele O'Connor, had assigned the number lines in previous years, but this year was different. She, personally, hadn't spent much time leading students through practice problems or introducing the basic math concepts they would use in the project. That had largely been relegated to online math lessons, part of separate periods of learning time when students were free to work through computer-based lessons in any subject they chose, at their own pace. The change at Walsh, located in Framingham, Massachusetts, is part of a nationwide pilot program, one that could indicate just how deeply and how quickly the personalized-learning trend will penetrate the average classroom. Indeed, despite the buzz around personalized learning, there's no simple recipe for success, and the common ingredients -- such as adaptive-learning technology and student control over learning -- can backfire if poorly implemented. A looming question is whether personalized learning that works in, say, a tight-knit, mission-driven charter school can be reliably translated into traditional district schools with many more students, less flexible schedules, keener standardized-test worries and cultures steeped in established ways of teaching and learning.

Amid a mounting tide of concerns over the implications of AI (artificial intelligence) on society, a new fund backed by well-known tech industry figures along with Harvard and MIT has been formed. The Ethics and Governance of Artificial Intelligence fund will start out with $27 million to fund work that "advances the development of ethical AI in the public interest." Machine learning, task automation and robotics are already widely used in business. These and other AI technologies are about to multiply, and we look at how organizations can best take advantage of them. EBay founder Pierre Omidyar's charitable foundation Omidyar Network, LinkedIn founder Reid Hoffman, the Knight Foundation and others are contributors to the fund.

NEW DELHI: Artificial intelligence (AI) will play fundamental role in the success of an organisation's strategy but stringent ethical standards are also needed to ensure the success of the new technology, a report by Infosys today said. The report said organisations that have already deployed or have plans to deploy AI technologies expect to see a 39 per cent increase on an average in their revenues by 2020, alongside a 37 per cent reduction in costs. Also, companies in India and China are much more likely to state that they are ahead of industry competitors when it comes to AI use, followed by Germany, the US, the UK, France, it added. About 76 per cent respondents cited AI as fundamental to the success of their organisation's strategy, and 64 per cent said future growth of the company is dependent on large-scale AI adoption. The report also highlighted the ethical and job related concerns with 62 per cent respondents saying stringent ethical standards are needed to ensure the success of AI. "However, most respondents seem optimistic about redeploying displaced employees with higher value work. The majority, 85 per cent, plan to train employees about the benefits and use of AI," the report said.

At the annual J.P. Morgan Healthcare conference that concluded last week, the majority of panelists on a panel discussing digital health dismissed artificial intelligence in healthcare as overhyped. Deborah DiSanzo, general manager of IBM Watson Health, who presented her company's vision at the conference in San Francisco and Vinod Khosla, founder of investment firm Khosla Ventures, who spoke at a satellite conference beg to differ. IBM Watson Health, with its acquisition of Merge Healthcare, has amassed a vast volume of imaging data – 30 billion to be exact -- and is putting its machine learning power to work. In a question-and-answer session following her presentation, DiSanzo explained that Watson can actually prioritize the image stack in a CT scan for example, and tell radiologists which images to see first and not waste time in viewing images that have nothing in them. What's more, the technology can also pull in unstructured text data from an electronic health record to provide context and decision-making support to the radiologists, DiSanzo explained.

An example of whistleblower behaviour taken from Harry McLaren's slides Workplace surveillance is nothing new, but this slide from Harry McLaren's talk on Machine Learning for Threat Detection illustrates particularly well the challenges facing journalists wishing to protect whistleblowers. McLaren is talking about malicious threats, and the way that machine learning can be used to identify suspicious patterns of behaviour. But the example given above is equally useful in illustrating the way that similar behaviour might be used to identify an employee intending to whistleblow on illegal, unethical or dangerous behaviour by his or her organisation. Data Loss Prevention (DLP), network forensics, and content management technologies are already being used to prevent such leaks, but machine learning adds a new dimension to the field. The point for journalists is that collections of small actions – including those which protect the whistleblower – can be just as compromising as obvious oversights like a lack of information security.

Microsoft Corp. and its competitors should eschew artificial intelligence systems that replace people instead of maximizing their time, Chief Executive Officer Satya Nadella said in an interview Monday. "The fundamental need of every person is to be able to use their time more effectively, not to say, 'let us replace you'," Nadella said in an interview at the DLD conference in Munich. "This year and the next will be the key to democratizing AI. The most exciting thing to me is not just our own promise of AI as exhibited by these products, but to take that capability and put it in the hands of every developer and every organization." Nadella is pushing Microsoft into consumer and industrial applications of software that can make inferences about its environment.

Amazon Alexa, the voice assistant of Amazon, was everywhere on CES 2017. Integrated in cars, refrigerators, assistant devices and more, there's an emerging trend which requires us to talk to devices. But are we actually going to talk to machines on a structural level? The first sales forecasts of the Amazon echo are good. Google's voice assistant also had significant sales numbers last Christmas.

This is an invitation to collaborate. In particular, it is an invitation to collaborate in framing how we look at and develop machine intelligence. Even more specifically, it is an invitation to collaborate in the construction of a Periodic Table of AI. Thinking about Artificial Intelligence has proven to be difficult for us. We argue constantly about what is and is not AI.

When humans interact and collaborate with each other, they coordinate their turn-taking behaviors using verbal and nonverbal signals, expressed in the face and voice. In this article, I give an overview of several studies that show how humans in interaction with a humanlike robot make use of the same coordination signals typically found in studies on human-human interaction, and that it is possible to automatically detect and combine these cues to facilitate real-time coordination. The studies also show that humans react naturally to such signals when used by a robot, without being given any special instructions. They follow the gaze of the robot to disambiguate referring expressions, they conform when the robot selects the next speaker using gaze, and they respond naturally to subtle cues, such as gaze aversion, breathing, facial gestures and hesitation sounds.

This issue of AI Magazine brings together a collection of articles on challenges, mechanisms, and research progress in turn-taking and coordination between humans and machines.