Today, a fresh generation of technologies, fuelled by advances in artificial intelligence based on machine learning, is opening up new opportunities to reassess the upper bounds of operational excellence across these sectors. To stay one step ahead of the pack, businesses not only need to understand machine learning complexities but be prepared to act on it and take advantage. After all, the latest machine learning solutions can determine weeks in advance if and when assets are likely to degrade or fail, distinguishing between normal and abnormal equipment and process behaviour by recognising complex data patterns and uncovering the precise signatures of degradation and failure. They can then alert operators and even prescribe solutions to avoid the impending failure, or at least mitigate the consequences. The leading software constructs are autonomous and self-learning.

That was the conclusion of a recent study published in academic journal Marketing Science in which researchers analyzed field data from outbound sales calls between bots or sales reps and 6,200 randomized customers of an anonymous Asia-based financial services company. They found that the customers tended to grow curt when informed upfront of the bot's presence, and that such disclosures led to an 80% drop in sales. "They perceive the disclosed bot as less knowledgeable and less empathetic," the study authors wrote. "The negative disclosure effect seems to be driven by a subjective human perception against machines, despite the objective competence of AI chatbots." The paper raises a moral dilemma for businesses looking to deploy chatbots.

In recent years, robots play an active role in everyday life: medical robots assist in complex surgeries; search-and-rescue robots are employed in mining accidents; and low-cost commercial robots clean houses. There is a growing need for sophisticated algorithmic tools enabling stronger capabilities for these robots. One fundamental problem that robotic researchers grapple with is motion planning--which deals with planning a collision-free path for a moving system in an environment cluttered with obstacles.13,29 To a layman, it may seem the wide use of robots in modern life implies that the motion-planning problem has already been solved. This is far from true.

The use of multiple digital devices to support people's daily activities has long been discussed.11 Multi-device experiences (MDXs) spanning multiple devices simultaneously are viable for many individuals. Each device has unique strengths in aspects such as display, compute, portability, sensing, communications, and input. Despite the potential to utilize the portfolio of devices at their disposal, people typically use just one device per task; meaning they may need to make compromises in the tasks they attempt or may underperform at the task at hand. It also means the support that digital assistants such as Amazon Alexa, Google Assistant, or Microsoft Cortana can offer is limited to what is possible on the current device.

Modern cars bear little resemblance to their early ancestors, but the basic action of steering a vehicle has always remained the same. Whether you're behind the wheel of a Tesla or a vintage Model T, turning the wheel dictates the direction of movement. This simple premise, which places humans at the center of control, may be ripe for disruption as tech giants and car companies race toward a future that would render human-controlled vehicles obsolete. How does this next generation of self-driving cars "see" the road? Today's video from TED-Ed explains one of the mind-bending innovations making autonomous vehicles a reality.

This month, the NHS signed its first deals with Google. Five NHS trusts have agreed contracts with Google Health, after it swallowed up its UK sister firm DeepMind Health, nearly a year after signalling its intention to do so. New Scientist first revealed the extent of DeepMind's access to the sensitive data of more than a million National Health Service patients back in 2016, in a deal that the UK's data watchdog later found breached the law. The partnership has yielded interesting research, including using artificial intelligence to detect eye disease from scans with an accuracy that matches or exceeds human experts. But is there a material difference now the deals are with the US tech giant rather than DeepMind, and should people who use the NHS be concerned at the change?

With an aging population and a growing shortage of drivers, Japan is a country where autonomous transportation services would seem to have a bright future. Demand is particularly high for self-driving trucks in regions with few alternatives to hauling freight by road, such as Hokkaido. Among truck manufacturers, UD Trucks Corp., a Japanese unit of Sweden's AB Volvo, has teamed up with an agricultural cooperative in the northern prefecture that is increasingly concerned about the declining number of delivery truck drivers. The company has been testing its autonomous heavy-duty trucks on a 1.5-km-long (about 1 mile) designated route in and around a sugar factory in Shari, eastern Hokkaido. The truck is capable of Level 4 self-driving, meaning it performs all driving tasks without human intervention within a limited area, even in emergencies.

This week, leading AI lab OpenAI released their latest project: an AI that can play hide-and-seek. It's the latest example of how, with current machine learning techniques, a very simple setup can produce shockingly sophisticated results. The AI agents play a very simple version of the game, where the "seekers" get points whenever the "hiders" are in their field of view. The "hiders" get a little time at the start to set up a hiding place and get points when they've successfully hidden themselves; both sides can move objects around the playing field (like blocks, walls, and ramps) for an advantage. The results from this simple setup were quite impressive.

Chatbots have emerged as a great option for providing a 24/7 self-service solution to address a host of customer support requirements. They enable customers to get their questions answered in real-time and they free up support staff from having to field high volumes of repetitive inquiries. And with advances in artificial intelligence and machine learning, chatbots are becoming extremely effective at providing a cognitive and conversational experience that your customers will love. As chatbot adoption has increased, various types have entered the market to address different requirements. For example, chatbots built for specific B2C or B2B support use cases, called transactional bots, are very different from those built for more wide-ranging applications, referred to as knowledge bots.

The Cabinet on Friday approved rules for operating partially self-driving vehicles, paving the way for the use of autonomous vehicles on public roads. Autonomous driving technology is classified into five levels, ranging from Level 1, which allows either steering, acceleration or braking to be automated, to fully automated Level 5. The government plans to enforce an ordinance defining violations and setting penalties by May next year as it envisions the use of Level 3 vehicles, which allow conditionally automated driving, on expressways in 2020. The newly decided penalties apply to the inappropriate use of Level 3 autonomous driving technologies, which require users to switch to manual operations when preset conditions regarding road type, driving speed, weather, time of day and other factors are no longer met. Violators of the ordinance will face fines of up to ¥12,000 ($110) depending on vehicle size.