The World Health Organisation has classified stress as the "health epidemic of the 21st century." As employees face greater pressures at their workplace along with an'always on' work culture, stress is the single biggest reason for most health issues today. This also comes with an economic cost where depression and anxiety disorders cost the global economy about $1 trillion each year. When Mudit Dandwate wanted to do something more meaningful in life, the IIT engineer got in touch with Gaurav Parchani, a colleague he met at Altair Engineering in Bengaluru. The duo, post toying with many ideas, zeroed on the healthcare sector for its immense transformative nature for the masses and that's how Dozee, a contactless health monitoring device, came into being in 2015.

My iPhone screen time is over four hours every day. Over the last month I've booked restaurant reservations and doctor's appointments, received motorcycle maintenance records, loaded new applications and ordered clothes. All of these actions involved the sort of data exchanges that today's information-based tech companies crave. Applying machine learning tools to personal data can uncover valuable knowledge and generate tremendous business value. With data increasingly seen as "the new oil," many economists, politicians, and others are suggesting people should be paid for the data they produce.

Someday, humanity might build conscious machines--machines that not only seem to think and feel, but really do. But how could we know for sure? How could we tell whether those machines have genuine emotions and desires, self-awareness, and an inner stream of subjective experiences, as opposed to merely faking them? In her new book, Artificial You (which Nautilus has excerpted), philosopher Susan Schneider proposes a practical test for consciousness in artificial intelligence. If her test works out, it could revolutionize our philosophical grasp of future technology.

As a driver, have you ever asked yourself whether to make left turns? Unprotected left turns, that is, left turns with oncoming traffic, are among the most difficult and dangerous driving maneuvers. Although the risk of each individual left turn is negligible, if you are designing the behavior of a large fleet of self-driving cars, small individual risks add up to a significant number of expected injuries in the aggregate. Whether a fleet of cars should make left turns is a question that any developer of self-driving cars and any designer of mapping and routing applications faces today. A more general issue is at stake here: the decision of whether to make left turns involves a trade-off between safety and mobility (the time it takes to get to a destination).

Originally published in 1968 by Hart, Nilsson, and Raphael,2 the well-known A* search algorithm is a foundational pathfinding algorithm in computer science and artificial intelligence (AI) for traversing trees and graphs. The method provides the optimal path from the initial state to the target goal state, given the use of an admissible heuristic (must not overestimate the remaining distance to the goal). The A* algorithm is included in nearly all AI textbooks and courses worldwide. Given its widespread fame, however, there is no reliably documented evidence as to the origin of the name "A*": What does it really stand for and what does it mean? This Communications Viewpoint answers the question.

We know how artificial intelligence works in our lives: it helps in picking movies, choosing dates, and correcting misspellings. But what does it mean in policing? Is AI replacing traditional police tasks? Does the police use of AI present novel challenges? Should increasing police reliance on AI concern us?

Samuel Greengard is an author and journalist based in West Linn, OR, USA.

Machine learning is progressing at an astounding rate, powered by complex models such as ensemble models and deep neural networks (DNNs). These models have a wide range of real-world applications, such as movie recommendations of Netflix, neural machine translation of Google, and speech recognition of Amazon Alexa. Despite the successes, machine learning has its own limitations and drawbacks. The most significant one is the lack of transparency behind their behaviors, which leaves users with little understanding of how particular decisions are made by these models. Consider, for instance, an advanced self-driving car equipped with various machine learning algorithms does not brake or decelerate when confronting a stopped firetruck. This unexpected behavior may frustrate and confuse users, making them wonder why. Even worse, the wrong decisions could cause severe consequences if the car is driving at highway speeds and might ultimately crash into the firetruck. The concerns about the black-box nature of complex models have hampered their further applications in our society, especially in those critical decision-making domains like self-driving cars. Interpretable machine learning would be an effective tool to mitigate these problems. It gives machine learning models the ability to explain or to present their behaviors in understandable terms to humans,10 which is called interpretability or explainability and we use them interchangeably in this article. Interpretability would be an indispensable part for machine learning models in order to better serve human beings and bring benefits to society. For end users, explanation will increase their trust and encourage them to adopt machine learning systems. From the perspective of machine learning system developers and researchers, the provided explanation can help them better understand the problem, the data and why a model might fail, and eventually increase the system safety. Thus, there is a growing interest among the academic and industrial community in interpreting machine learning models and gaining insights into their working mechanisms.

Healthcare technology, aka healthtech, is rapidly transforming the way healthcare services are accessed and delivered across the world, particularly to the vulnerable populations in the low and middle-income countries. Health technologies and interventions are critical elements that expand access to effective and affordable health services whilst simultaneously catalyzing efforts to achieve the goal of Universal Health Coverage (UHC). With the advent of electronic health records or digital records, concerns regarding the security and ownership of the sensitive health data have also arisen. For the medical data to be stored and accessed safely, healthcare providers and consumers are utilizing blockchain, the technology behind cryptocurrencies that significantly increases transparency and security by storing and distributing data to all participants across the entire supply chain. Besides data security, the distributed ledger technology is also being used to curb the menace of drug counterfeiting.

The number of industrial robots in operation around the world has grown rapidly in recent years, but nowhere more so than in China, where some 30% of the world's robots are in operation. This growth has prompted many to ponder whether humans are being pushed out of the workforce in favor of their robotic brethren. This isn't really born out by the evidence however. For instance, a study from a few years ago suggests that such fears of widespread job displacement may be somewhat overblown. The study saw over 300 occupations examined over a 33 year time-scale from 1980 to try and examine the impact of automation.