As the world is continuing to develop artificial intelligence and machine learning software, India is also keeping up with the growth. The government of India has also started to focus on developing their own plan for AI. Software development companies in India are now focusing on creating artificially intelligent computer programs that may be used to assist human intelligence in fields like healthcare, weather and climate, crowd management, space research, and education. App developers and many app development companies in India, agencies are now coming up with the application of machine learning and AI in their apps to gather public attention and to provide new and customized experiences and new services to their customers through their apps.

Singapore's first robo-advisor unveils next level in customer experience, becomes first pure-play robo-advisor to integrate MyInfo Crossbridge Capital ("Crossbridge"), the leading independent global wealth manager with over US$4.5 billion under advisement, today announced significant updates to its CONNECT by Crossbridge digital platform – further enhancing the premium customer experience for accredited investors. Launched in late 2016, CONNECT by Crossbridge is Singapore's first, and largest, robo-advisor withver US$300 million in assets under management is invested on the CONNECT platforms. The new enhancements come as Crossbridge Capital celebrates its 10th anniversary and CONNECT its second. CONNECT by Crossbridge will become the first pure-play robo-advisor in Singapore to integrate MyInfo, a digital data vault developed by the Government Technology Agency of Singapore to facilitate online transactions. CONNECT by Crossbridge has also optimised its existing user interface based on extensive user testing and analysis to provide anenhanced wealth management experience.

Across the technology industry, rank-and-file employees are demanding greater insight into how their companies are deploying the technology that they built. At Google, Amazon, Microsoft and Salesforce, as well as at tech start-ups, engineers and technologists are increasingly asking whether the products they are working on are being used for surveillance in places like China or for military projects in the United States or elsewhere. That's a change from the past, when Silicon Valley workers typically developed products with little questioning about the social costs. It is also a sign of how some tech companies, which grew by serving consumers and businesses, are expanding more into government work. And the shift coincides with concerns in Silicon Valley about the Trump administration's policies and the larger role of technology in government.

In recent years, deep neural networks have demonstrated outstanding performance in many machine learning tasks. However, researchers have discovered that these state-of-the-art models are vulnerable to adversarial examples: legitimate examples added by small perturbations which are unnoticeable to human eyes. Adversarial training, which augments the training data with adversarial examples during the training process, is a well known defense to improve the robustness of the model against adversarial attacks. However, this robustness is only effective to the same attack method used for adversarial training. Madry et al.(2017) suggest that effectiveness of iterative multi-step adversarial attacks and particularly that projected gradient descent (PGD) may be considered the universal first order adversary and applying the adversarial training with PGD implies resistance against many other first order attacks. However, the computational cost of the adversarial training with PGD and other multi-step adversarial examples is much higher than that of the adversarial training with other simpler attack techniques. In this paper, we show how strong adversarial examples can be generated only at a cost similar to that of two runs of the fast gradient sign method (FGSM), allowing defense against adversarial attacks with a robustness level comparable to that of the adversarial training with multi-step adversarial examples. We empirically demonstrate the effectiveness of the proposed two-step defense approach against different attack methods and its improvements over existing defense strategies.

I'm still driving my own car and visit a human doctor when I feel sick. I still haven't surrendered my job to a lifeless robot, and I don't think Alexa or Siri is my best friend. And no, we haven't manufactured our AI-powered robot overlords yet. Nonetheless, just like last year, this year saw some interesting developments in the field of artificial intelligence. As I watched the landscape, I can describe the developments as a shift from hype and craze to reality checks and more focus on the social and political repercussions of this fast moving domain.

This past April a new video of Barack Obama surfaced on the Internet. Against a backdrop that included both the American and presidential flags, it looked like many of his previous speeches. Wearing a crisp white shirt and dark suit, Obama faced the camera and punctuated his words with outstretched hands: "President Trump is a total and complete dipshit." Without cracking a smile, he continued. "Now, you see, I would never say these things. The view shifted to a split screen, revealing the actor Jordan Peele. Obama hadn't said anything--it was a real recording of an Obama address blended with Peele's impersonation. Side by side, the message continued as Peele, like a digital ventriloquist, put more words in the former president's mouth. In this era of fake news, the video was a public service announcement produced by BuzzFeed News, showcasing an application of new artificial-intelligence (AI) technology that could do for audio and video what Photoshop has done for digital images: ...

As businesses struggle to combat increasingly sophisticated cybersecurity attacks, the severity of which is exacerbated by both the vanishing IT perimeters in today's mobile and IoT era, coupled with an acute shortage of skilled security professionals, IT security teams need both a new approach and powerful new tools to protect data and other high-value assets. Increasingly, they are looking to artificial intelligence (AI) as a key weapon to win the battle against stealthy threats inside their IT infrastructures, according to a new global research study conducted by the Ponemon Institute on behalf of Aruba, a Hewlett Packard Enterprise company. The Ponemon Institute study, entitled "Closing the IT Security Gap with Automation & AI in the Era of IoT," surveyed 4,000 security and IT* professionals across the Americas, Europe and Asia to understand what makes security deficiencies so hard to fix, and what types of technologies and processes are needed to stay a step ahead of bad actors within the new threat landscape. The research revealed that in the quest to protect data and other high-value assets, security systems incorporating machine learning and other AI-based technologies are essential for detecting and stopping attacks that target users and IoT devices. The majority of respondents from India agree that security products with AI functionality will help to: * Reduce false alerts (69 percent) * Increase their team's effectiveness (65 percent) * Provide greater investigation efficiencies (56 percent) * Advance their ability to more quickly discover and respond to stealthy attacks that have evaded perimeter defense systems (66 percent).

Launched in 2015, Made in China 2025(MIC 2025) is the Chinese government's ten-year plan to update China's manufacturing base by focussing on the country's ten high tech industries. The strategy focuses to outline Beijing's aspirations to dominate the global economy of the future, in pivot areas like new energy vehicles, advanced robotics, next-generation information technology (IT) and telecommunications, robotics and artificial intelligence. In the domain of industrial robots, China has earmarked new development plans and is making impressive progress. Conditions are ideal in China for building a thriving robotics industry, serving both the domestic and overseas market. First, the Chinese government's efforts to bring the country into the global map by offering generous tax breaks and subsidies to robotics startups.

Google's India-born chief executive Sundar Pichai quietly paid a visit to the Pentagon to ease tensions that erupted after employee outrage prompted the tech giant to sever a controversial defence contract to analyse drone video, according to a media report. Pichai met with a group of civilian and military leaders mostly from the office of the Under Secretary of Defence for Intelligence, the Defense Department directorate that oversees the artificial-intelligence drone system known as Project Maven, according to the people, who spoke on the condition of anonymity to speak freely about the meeting, The Washington Post reported. Google had worked with the Defense Department to develop Project Maven, which uses AI to automatically tag cars, buildings and other objects in videos recorded by drones flying over conflict zones. But in June, the tech giant said it would not renew its contract following an uprising from employees, who criticized the work as helping the military track and kill with greater efficiency, the report said. A Defense Department spokesperson said, "We do not comment on the details of private meetings. Department leaders routinely meet with industry partners to discuss innovative technologies. These meetings support continuing dialogue aimed at solving future technology challenges."

As society trusts more of its operations to autonomous systems, increasingly companies are making it a requirement that humans can understand how exactly a machine has reached a certain conclusion. The research efforts behind Explainable AI (XAI) is gaining traction as technology giants like Microsoft, Google and IBM, agree that AI should be to explain its decision making. XAI, sometimes called transparent AI, has the backing of the Defense Advanced Research Projects Agency (DARPA) an agency of the US Department of Defense, which is funding a large program develop the state of the art explainable AI techniques and modelling. Dr Brian Ruttenberg was formerly the senior scientist at Charles River Analytics (CRA) in Cambridge, where he was the principal investigator for CRA's effort on DARPA's XAI program. He argues XAI helps to identify bias or errors in algorithms and engenders trust in the technology.