Using data analytics and advanced algorithms, DXC and the Singapore General Hospital (SGH) are working on an AI solution that can guide a doctor in prescribing antibiotics. This solution has recently been recognized as one of the most impactful projects, by the Ministry of Health, Singapore at the "National Health Tech Challenge" awards.Humanoid Robotics for Digital Collaboration – Using advanced mobile applications and humanoid robotics technologies, this DXC solution highlights the future of digital collaboration between enterprises, customers and other stakeholders relevant to the insurance industry today. Supported by the application of machine learning/artificial intelligence (ML/AI), the humanoid robot agent helps banks or wealth managers recommend and sell insurance solutions personalized for each customer. It also allows insurers to interact and complete the insurance solutions sale process through this platform. The interaction is directly with the customer, thereby giving transparent and authentic data based on vital image statistics from the robot.

Veteran perfumer David Apel works on the AI-designed fragrance.IBM and Symrise Artificial intelligence, a big buzzword across different sectors, may be about to shake up the fragrance industry. IBM Research and Symrise, a major global producer of flavors and fragrances that counts clients including Estee Lauder, Victoria's Secret parent L Brands and Coty, have created what they described as the industry's first AI-designed perfume for sale after the two parties came together over a year ago. The AI tool (named Philyra) uses machine learning algorithm to study Symrise's database of some 1.7 million formulas and can identify "white space" and come up with formula suggestions that not only may resonate with consumers but also combinations where perfumers may not have thought of before. For instance, when asked to come up with the "most creative" interpretation of a fragrance created 12 years, one formula the system generated removed an outdated material and upped the dosage of popular sandalwood scent. It also unexpectedly introduced to the mix cedar wood, another ingredient popular with today's consumers, said David Apel, Symrise's VP and senior perfumer of fine fragrance.

Variable speed limits (VSL) control is a flexible way to improve traffic condition,increase safety and reduce emission. There is an emerging trend of using reinforcement learning technique for VSL control and recent studies have shown promising results. Currently, deep learning is enabling reinforcement learning to develope autonomous control agents for problems that were previously intractable. In this paper, we propose a more effective deep reinforcement learning (DRL) model for differential variable speed limits (DVSL) control, in which the dynamic and different speed limits among lanes can be imposed. The proposed DRL models use a novel actor-critic architecture which can learn a large number of discrete speed limits in a continues action space. Different reward signals, e.g. total travel time, bottleneck speed, emergency braking, and vehicular emission are used to train the DVSL controller, and comparison between these reward signals are conducted. We test proposed DRL baased DVSL controllers on a simulated freeway recurrent bottleneck. Results show that the efficiency, safety and emissions can be improved by the proposed method. We also show some interesting findings through the visulization of the control policies generated from DRL models.

We present a statistical-modelling method for piano reduction, i.e. converting an ensemble score into piano scores, that can control performance difficulty. While previous studies have focused on describing the condition for playable piano scores, it depends on player's skill and can change continuously with the tempo. We thus computationally quantify performance difficulty as well as musical fidelity to the original score, and formulate the problem as optimization of musical fidelity under constraints on difficulty values. First, performance difficulty measures are developed by means of probabilistic generative models for piano scores and the relation to the rate of performance errors is studied. Second, to describe musical fidelity, we construct a probabilistic model integrating a prior piano-score model and a model representing how ensemble scores are likely to be edited. An iterative optimization algorithm for piano reduction is developed based on statistical inference of the model. We confirm the effect of the iterative procedure; we find that subjective difficulty and musical fidelity monotonically increase with controlled difficulty values; and we show that incorporating sequential dependence of pitches and fingering motion in the piano-score model improves the quality of reduction scores in high-difficulty cases.

Volocopter is preparing to run inner-city tests of its autonomous air taxis in Singapore, starting in the second half of 2019. The company and the city-state's civil aviation authority are determining the scope of the tests, which Volocopter plans to conclude with public demo flights. The vertical take-off and landing (VTOL) vehicles look like a cross between a helicopter and a drone, and have 18 rotors working to get you from one place to another. Volocopter claims its machine can fly two people up to 30 kilometers, while it can account for micro turbulences close to skyscrapers to keep your rides smooth. "We are getting ready to start implementing the first fixed routes in cities," Volocopter CEO Florian Reuter said in a press release.

An experiment has investigated human morality and ranked countries based on who they would save in the event of a certain death situation. The findings reveal the value of life varies according to different countries, with French people, for example, were far more likely to save women than men. The four most spared characters in the game are a baby, a little girl, a little boy and a pregnant woman. The game posed difficult ethical decisions such as choosing between the lives of a family of four crossing the road and a group of pensioners going the other way. Quandaries like this will one day be faced by autonomous vehicles that will be programmed with algorithms that place a value on human life.

With the rise of artificial intelligence (AI), many of us non androids have become fearful of massive job displacement, and for good reason. We know that AI already powers many of our favorite apps and websites and that, in the not so distant future, AI will also be operating our cars, managing our work portfolios, and manufacturing the things we buy. Fear of the toll that AI might take on job security is substantiated. As I point out in my new book, AI Superpowers: China, Silicon Valley, and the New World Order, about 50% of our jobs will, in fact, be taken over by AI and automation within the next 15 years. Accountants, factory workers, truckers, paralegals, and radiologists--just to name a few--will be confronted by a disruption akin to that faced by farmers during the industrial revolution.

OK, tell me if you've heard this one before. A trolley, a diverging track, a fat man, a crowd, a broken brake. Let the trolley continue to speed the way it's going, and it will smash into the crowd, obliterating the people in its way. Hit the switch, and the trolley will careen into the fat man, KOing him--permanently--on impact. That is, of course, the classic trolley problem, devised in 1967 by the philosopher Philippa Foot.

Animals and the old should be sacrificed in autonomous vehicle crashes, according to a major new study. When self-driving cars arrive, they will be forced to decide who should die when they collide with members of the public. When something goes wrong, they will have to be programmed to opt for one group or another when deciding where to crash, an issue that has become a central ethical problem for those designing the cars. Researchers asked more than 2 million people in an attempt to establish who the public thinks should be sacrificed in those crashes. They were told to imagine a situation where a deadly crash was going to occur and the car had to choose between two sets of people – and asked to decide which of those groups would die.

China is the world leader in facial recognition technology.