We've seen multiple use cases where smart devices like smart watches and other smart appliances have helped people maintain excellent homes and healthy bodies. Are there any smart devices or sensors that can help individuals in improving their mental health and ensure that they lead a happy life? Mental health is imperative for physical health. To live a fully healthy lifestyle, we need to take care of our mind, just as much as our body. And for that, there are some machine learning solutions and smart devices that prioritize mental well-being.

That's a process that may sound time-consuming -- and it is, if you're doing it in a low-tech way. "If a set of quality results does not meet standards, you can build algorithms and start doing repeatability studies to predict when things might go wrong," Ravi said. "Machine learning can be used for that. If you try doing the process analysis manually, it is going to take a lot of time. Machine learning algorithms can get that information quickly and minimize bad product quality by allowing leaders to be proactive and take corrective actions."

The Salary Benchmark report for 2019 by Michael Page was released yesterday. The report predicts strong hiring activity to continue (especially in the technology sector) and a general broadening of roles. Nicolas Dumoulin, Managing Director at Michael Page India stated, "Movement among senior level professionals can be attributed to the growth of India's industries and the availability of talent. This explains the key employment activity within the mid and large manufacturing organizations specializing in chemicals, building materials as well as domestic consumer companies." He added that the injection of new funds in the private equity sector has also led to a growth in senior level hiring.

Would you ever consider purchasing diamond jewelry online? Lots of people have -- according to Research and Markets, the jewelry ecommerce market accounts for roughly 5 percent of the $257 billion overall jewelry industry, a share that's expected to triple by 2020. It's growing especially quickly in Asia, where the compound annual growth rate from 2011 to 2014 exceeded 60 percent. But if you're reluctant to dive in, you're not the only one -- there's a lot of uncertainty in the online diamond-buying market. High-resolution photos alone don't always tell the full story, particularly if they're lit unnaturally.

Candidates from fields like big data analysis, machine learning and data science will be in demand in 2019, a report by Michael Page, a Singapore-based global professional recruitment consultancy, said. The candidates will get a 20% to 25% increase in their compensation package while switching jobs within their respective industries this year, the recruitment consultancy has predicted. "There will be a major movement among senior professionals who can contribute to the growth of India's industries. There are also accountable and skilled candidates for employment opportunities within mid- and large-manufacturing industries especially in chemicals, building materials and domestic consumer companies," Nicolas Dumoulin, Managing Director, Michael Page India said. "The entrance of newly-evolved funds within the private equity sector has led to the hiring of senior professionals. The rise in India's e-commerce and financial technology business has increased the hiring opportunities for senior-level talent from banking spaces," he said.

A decade in the making, Rio Tinto's driverless train system, called AutoHaul, now manages roughly 200 locomotives that move iron ore from inland mines to coastal ports in Western Australia. The trains are operated hundreds of miles away, in an office block in Perth. Rio Tinto's network, which began formally operating in driverless mode late last month, is the first fully autonomous, long-haul freight railroad. Rail-company executives from countries including the U.S. and Canada have visited to see the technology in action, said Ivan Vella, Rio Tinto's head of iron-ore rail services. American companies say automating tasks once handled by crew will create fluid networks more akin to a model train set.

Welcome to our HR Modernization Playbook: Tomorrow's people – Why HR matters more than ever in the age of artificial intelligence. Digital transformation is happening faster than ever. The adoption of artificial intelligence (AI) and automation will redefine jobs, enhance employee productivity and accelerate workforce development. In fact, skills and culture – not technology – are the biggest barriers to business growth in the AI era. This means CEOs are looking to their CHRO to lead culture change, manage talent and drive down costs.

A new machine-learning model developed by a West Virginia University student has potential applications in the energy, environmental and health-care fields. The model, which can be used to predict adsorption energies -- i.e., adhesive capabilities in gold nanoparticles -- was developed by Gihan Panapitiya, a doctoral physics student from Sri Lanka. Gold nanoparticles have historically been used by artists to bring out vibrant colors via their interaction with light. Now they are increasingly used in high-technology applications such as electronic conductors and others. "Machine learning recently came into the spotlight, and we wanted to do something linking machine learning with gold nanoparticles as catalysts," Panapitiya said.

A new machine-learning model developed by a West Virginia University student has the potential for energy, environmental and even healthcare applications. The model, which can be used to predict the adsorption energies, i.e. adhesive capabilities in gold nanoparticles, was developed by Gihan Panapitiya, a doctoral physics student from Sri Lanka. Gold nanoparticles have historically been used by artists to bring out vibrant colors via their interaction with light. Now they are increasingly used in high technology applications, electronic conductors and others. "Machine learning recently came into the spotlight, and we wanted to do something linking machine learning with gold nanoparticles as catalysts," he said.

The problem of retrosynthetic planning can be framed as one player game, in which the chemist (or a computer program) works backwards from a molecular target to simpler starting materials though a series of choices regarding which reactions to perform. This game is challenging as the combinatorial space of possible choices is astronomical, and the value of each choice remains uncertain until the synthesis plan is completed and its cost evaluated. Here, we address this problem using deep reinforcement learning to identify policies that make (near) optimal reaction choices during each step of retrosynthetic planning. Using simulated experience or self-play, we train neural networks to estimate the expected synthesis cost or value of any given molecule based on a representation of its molecular structure. We show that learned policies based on this value network outperform heuristic approaches in synthesizing unfamiliar molecules from available starting materials using the fewest number of reactions. We discuss how the learned policies described here can be incorporated into existing synthesis planning tools and how they can be adapted to changes in the synthesis cost objective or material availability.