Chief Justice of India, Sharad Arvind Bobde on Saturday hinted at the possibility of Artificial Intelligence being developed for the court system while making it clear that it will never replace human discretion. Speaking at an event here, Bobde said, "We have a possibility of developing Artificial Intelligence for the court system. Only for the purpose of ensuring that the undue delay in justice is prevented." "I must make it clear at the outset as there are times when even judges have asked this. AI is not going to replace human judges or human discretion", he added.

Former Obama administration advisors say the White House regulatory AI principles announced this week are a good start in many ways, but they're incorrect in their oversimplified mandate to avoid overregulation of private business use, and that the Trump administration could face an uphill battle in its appeal to the rest of the world. Though the Trump administration has developed a reputation for blaming the Obama administration when things go wrong or trying to erase Obama-era policy, on artificial intelligence policy, at times the Trump administration has remained strikingly similar to its predecessor. This was evident in the AI research and development strategy plan for federal agencies released in summer 2019. In some instances, like with White House deputy CTO and assistant director of AI at the White House Office of Science and Technology Policy (OSTP) Dr. Lynne Parker who also served in the Obama administration, the same people drive White House AI policy. The list of 10 AI principles are meant to guide US federal agencies as they consider making rules that regulate AI. White House CTO Michael Kratsios said he wants other countries around the world to adopt similar policies.

New Delhi: When it comes to programming languages in India, Python is most popular among the students for its role in Artificial Intelligence (AI) applications, data science, Machine Learning (ML) and data analytics, US-based online education company Coursera has said. Python dominated the top 10 list with courses like'Programming for Everybody', 'Python Data Structures', 'Python for Data Science and AI' and more. Python is also easy to get started with, offers a lot of flexibility and is versatile. "Its open source nature makes it easy to learn. A large number libraries for tasks like web development, text processing, calculations add to its appeal," the repor said.

For better or for worse, the White House's new set of principles for artificial intelligence will set the tone for its development in the United States, experts …

After languishing for years, enterprise imaging appears ready to enter the mainstream of health care. At least a small part of that may involve the use of artificial intelligence (AI) to make the transmission, storage, display and analysis of the many different types of images easier and more efficient. At RSNA 2019, GE Healthcare addressed the crossover of AI and enterprise imaging with the latest version of its AI-enabled Centricity Universal Viewer. Version 7 "consumes" and displays AI findings revealed by the Centricity PACS, said Veena Haravu, Senior Product Manager for Centricity PACS at GE Healthcare. This Version 7, which is pending FDA clearance, is integrated with the company's Edison Open AI Orchestrator to display the results of smart algorithms embedded in it.

"We're seeing a lot of pressure in today's market as passenger expectations continue to grow and this is making airlines think differently about the passenger experience, which must become personal, highly engaging and seamless across their travel journey", says Rob Ranieri, VP, Travel & Transportation Industries at IBM. Aegean, the Greek airline company and Star Alliance member, is using IBM's public cloud and artificial intelligence programs to "transform its internal business processes" as it seeks to improve its customer experience. This agreement builds on a previous multi-year agreement for the implementation of core applications and the delivery of IBM Cloud Hosting Services to help accelerate the airline's digital transformation strategy. "We feel confident that IBM is the strategic provider that will support our determination and willingness to innovate and achieve excellence at both an organizational and business process level," said Aegean Chief Information Officer Aristeidis Kamvysis. "By utilizing IBM's most innovative technologies, such as cloud and AI, proven industry expertise and strong corporate culture, we will continue to provide even greater value for our customers, personnel and shareholders." IBM's tech will cover both Aegean, and its subsidiary, Olympic Air.

Google is everywhere at CES 2020. With the world's largest consumer electronics showcase under way here in Las Vegas, the search giant has dispatched an army of people clad in white uniforms to spread the gospel about the Google Assistant, the company's digital concierge software. The company built a massive fun house with slides and a ball pit. The words "Hey Google," the wake phrase for the software, are plastered all over buildings and the monorail in Las Vegas, the tech show's host city. It's a classic corporate marketing blitz, but it's also an apt metaphor for Google's grand ambition: to get its software all around you -- to fill up every inch of your life, from your commute to work to your Saturday morning vacuuming the house.

WASHINGTON – Defense Secretary Mark Esper explicitly said Sunday that he had seen no hard evidence that four American embassies had been under possible threat when President Donald Trump authorized the targeting of Iran's top commander, raising questions about the scale of the threat described by Trump last week. As the administration struggled with its justification for the drone strike that killed Gen. Qassem Soleimani, Esper and other officials tried to refocus attention on voices of dissent in Iran. Esper said street protests in Tehran show the Iranian people are hungry for a more accountable government after leaders denied, then admitted shooting down a Ukrainian passenger plane. The plane was downed shortly after Iran launched strikes against U.S. bases in Iraq in retaliation for Soleimani's killing. "You can see the Iranian people are standing up and asserting their rights, their aspirations for a better government -- a different regime," Esper said.

The widespread use of neural networks across different scientific domains often involves constraining them to satisfy certain symmetries, conservation laws, or other domain knowledge. Such constraints are often imposed as soft penalties during model training and effectively act as domain-specific regularizers of the empirical risk loss. Physics-informed neural networks is an example of this philosophy in which the outputs of deep neural networks are constrained to approximately satisfy a given set of partial differential equations. In this work we review recent advances in scientific machine learning with a specific focus on the effectiveness of physics-informed neural networks in predicting outcomes of physical systems and discovering hidden physics from noisy data. We will also identify and analyze a fundamental mode of failure of such approaches that is related to numerical stiffness leading to unbalanced back-propagated gradients during model training. To address this limitation we present a learning rate annealing algorithm that utilizes gradient statistics during model training to balance the interplay between different terms in composite loss functions. We also propose a novel neural network architecture that is more resilient to such gradient pathologies. Taken together, our developments provide new insights into the training of constrained neural networks and consistently improve the predictive accuracy of physics-informed neural networks by a factor of 50-100x across a range of problems in computational physics. All code and data accompanying this manuscript are publicly available at \url{https://github.com/PredictiveIntelligenceLab/GradientPathologiesPINNs}.

In order to collaborate safely and efficiently, robots need to anticipate how their human partners will behave. Some of today's robots model humans as if they were also robots, and assume users are always optimal. Other robots account for human limitations, and relax this assumption so that the human is noisily rational. Both of these models make sense when the human receives deterministic rewards: i.e., gaining either $100 or $130 with certainty. But in real world scenarios, rewards are rarely deterministic. Instead, we must make choices subject to risk and uncertainty--and in these settings, humans exhibit a cognitive bias towards suboptimal behavior. For example, when deciding between gaining $100 with certainty or $130 only 80% of the time, people tend to make the risk-averse choice--even though it leads to a lower expected gain! In this paper, we adopt a well-known Risk-Aware human model from behavioral economics called Cumulative Prospect Theory and enable robots to leverage this model during human-robot interaction (HRI). In our user studies, we offer supporting evidence that the Risk-Aware model more accurately predicts suboptimal human behavior. We find that this increased modeling accuracy results in safer and more efficient human-robot collaboration. Overall, we extend existing rational human models so that collaborative robots can anticipate and plan around suboptimal human behavior during HRI.