In order to keep up with the ever-increasing and changing needs of the digital world, organizations do need an innovative talent platform. Artificial Intelligence (AI) and Machine Learning (ML) are transforming the way all platforms across the world function, and there is no reason why the same must not be employed to improve the efficiency of Human Resource Management (HRM). Applying AI and ML to human resource management not only reduces the time and cost required to hire, but also improves the quality of hiring. Talent Intelligence Platform (TIP) aims to solve the fundamental problems regarding hiring using AI and machine learning, hence addressing productivity loss due to open/unfilled positions, wrong hires and employee attrition. The talent acquisition platform has scoured several profiles to build a'Talent Graph' that allows them to accurately match and predict what's next for an individual, says Dave Ghosh, VP, Channel Sales and Country Head of Eightfold.ai.

Mr. Pichai, speaking Thursday at a weekly all-hands meeting in Mountain View, Calif., was responding to criticism from employees, human rights groups and others who in recent days have voiced concerns over the Alphabet Inc. unit's work with the Chinese government. Google is developing services for Chinese citizens, including a search engine that could adhere to China's strict censors, The Wall Street Journal and others reported last week. At the meeting, Google co-founder and Alphabet president Sergey Brin sounded optimistic about doing more business in China, cautioning that progress in the country is "slow-going and complicated." Mr. Brin was instrumental in Google's decision in 2010 to withdraw its search engine from China to protest the government's censorship regime and attempts to hack into the Gmail accounts of Chinese human rights activists. At the time, he described the government as having the "earmarks of totalitarianism" of the Soviet Union, where he was born.

HONG KONG/BEIJING/SAN FRANCISCO – China's regulators have frozen approvals of game licenses amid a government shake-up, according to people familiar with the matter, throwing the world's biggest gaming market into disarray. The halt follows a restructuring of power among departments, said the people, who asked not to be named because they don't have approval to discuss the issue publicly. Regulators have also been concerned about violence and gambling in some games, according to one person. Online, mobile and console games have all been affected. The whole sector has been rattled as gaming companies from online giant Tencent Holdings Ltd. to small developers await approvals.

Google's workforce is demanding answers over the company's secretive plans to build a search engine that will comply with censorship in China. More than 1,000 employees have signed a letter demanding more transparency over the project so they do not unwittingly suppress freedom of speech. In a version of the letter obtained by the New York Times, the employees say they lack the "information required to make ethically-informed decisions about our work, our projects, and our employment." China's censorship requirements "raise urgent moral and ethical issues," it adds. The letter, which has circulated through Google's internal communications, has gained more than 1,400 signatures, according to the Times.

One of the documents she submitted to prove citizenship, and shown to Reuters by the family, was an affidavit saying her name had been wrongly recorded as "Sabahan Bibi" in the 1951 citizenship registry, the first one drawn up in the state after India's independence in 1947. The affidavit also said she was named as "Sahajadi Begum" in her school certificate, and that she changed her name to "Sajida Bibi" from "Sajida Begum" after her marriage.

Google employees, upset over reports of a secretive search engine project for China, have signed a petition asking for more transparency from company leaders. SAN FRANCISCO -- Hundreds of Google employees have signed a petition protesting a secret project to develop a search engine for China, the latest example of tech workers rebelling against corporate policies that push moral boundaries. The letter, which was posted by Buzzfeed and first reported by The New York Times, says Google's decision to work with China raises "urgent moral and ethical issues," and that employees "do not have the information required to make ethically-informed decisions" about their work, projects and employment. The employees, who represent a fraction of parent company Alphabet's workforce of 89,000, also were upset by the secrecy of the project and in the petition demanded more transparency about the company's myriad ventures, which range from self-driving cars to advanced artificial intelligence. Google was scheduled to have a regular company-wide meeting between senior leadership and global employees late Thursday, during which in-person and remote staffers can ask any question they want.

In giving the diagnosis, the doctor combines the information obtained through such processes with his or her own knowledge and experience. No matter how reputed a physician may be, the chances of them making a wrong diagnosis can never be zero. With the recent progress in artificial intelligence, there has been much speculation that artificial intelligence could very well surpass a human doctor's ability to make diagnoses and write prescriptions. In August 2016, the Institute of Medical Science at the University of Tokyo released the outcome of a case study to show how powerful AI can be. IBM's Watson AI program was fed with information contained in nearly 20 million medical articles related to cancer research and more than 17 million pieces of information related to pharmaceuticals.

China has been making massive investments to create a huge pool of AI experts. The Chinese government wants to overtake the United States and be the global leader in the field by 2030. Artificial Intelligence or AI, simply described as the machine intelligence, has come to apply itself in several different sectors across countries in recent years, including healthcare, finance, education and security. But it has also increasingly become inserted into wider geopolitical conversations about the capabilities of major powers, including the United States and China. Within that aspect of the ongoing conversation, in terms of market share within the industry, the leadersin the field include the United States, with around 40 percent of the global market by some accounts, with countries like China, Israel, Germany, Canada and Russia fast catching up.

Watch out humans, robots could soon compete for your acting roles. Director Tony Kaye is looking to cast an artificial intelligence robot for his upcoming film '2nd Born,' the sequel to the yet-to-be-released '1st Born.' Kaye, who is behind the 1998 crime drama'American History X,' would employ the'real robot' as the lead in 2nd Born, according to Deadline. Artificial Intelligence,' director Tony Kaye wouldn't use a computer-generated robot for the role; instead, it would be an android trained in various acting methods and techniques Artificial Intelligence,' Kaye wouldn't use a computer-generated robot for the role; instead, it would be an android trained in various acting methods and techniques. What's more, Kaye and producer Sam Khoze hope that the robot will be recognized by the Screen Actors Guild. This would make the robot eligible for awards consideration.

Deep neuroevolution, that is evolutionary policy search methods based on deep neural networks, have recently emerged as a competitor to deep reinforcement learning algorithms due to their better parallelization capabilities. However, these methods still suffer from a far worse sample efficiency. In this paper we investigate whether a mechanism known as "importance mixing" can significantly improve their sample efficiency. We provide a didactic presentation of importance mixing and we explain how it can be extended to reuse more samples. Then, from an empirical comparison based on a simple benchmark, we show that, though it actually provides better sample efficiency, it is still far from the sample efficiency of deep reinforcement learning, though it is more stable.