Artificial intelligence (AI) is here to stay and there is no turning back in the march of this technology as many devices and services are already integrated with this advanced computing power. Globally, businesses and government are exploring how AI can be used to enhance their services, and India is also not far behind in adopting the advanced practices. The advent of handsets with internet connection is one of the key factors enabling technology to make deep inroads in India. Aadhaar is a classic example of AI technology with biometric recognition being one of the key facets of its application. PwC India in its recent survey report titled "Artificial Intelligence--Hype or Reality" found that 60% of respondents believe that AI will enable people to live richer lives and further socio-economic causes such as economic growth, health and education and cybersecurity. Sudipta Ghosh, partner and leader, Data and Analytics, PwC India said, "Indian businesses, the government and individuals have, in recent years, also seen multiple use cases of AI in various facets of life.

ABU DHABI – Walking through a trade show all about military drones, Emirati officials made a point on Sunday to stop first at a stand run by Chinese officials with a mock armed drone hanging above them. Defense analysts believe that drone, the Wing Loong II, is now being used by the Emirati military while the United Arab Emirates remains barred from purchasing weaponized drones from the United States. That purchase, as well as Abu Dhabi hosting the Unmanned Systems Exhibition & Conference this week in the Emirati capital, shows the power these weapons now hold across the Middle East. Top UAE officials, including Abu Dhabi's powerful crown prince, Mohammed bin Zayed Al Nahyan, were on hand for the drone conference, which opened on Sunday. The UAE, home to skyscraper-studded Dubai, already has embraced drones.

Artificial intelligence and machine learning in cybersecurity prove to be hot topics amongst security professionals and they're looking to spend more on tools that can do those very tasks, according to the 11th Cisco 2018 Annual Cybersecurity Report. According to the report, machine learning is able to help enhance network security and defences by learning how to detect unusual traffic patterns in cloud and IoT environments. That technology is in hot demand, particularly as the volume of legitimate and malicious web traffic grows. According to Cisco statistics from October 2017, 50% of web traffic is encrypted. Over a 12-month period, Cisco researchers also spotted a threefold increase in malware samples that used encrypted network communication.

FRANKFURT (Reuters) – Rapid advances in artificial intelligence (AI) are raising risks that malicious users will soon exploit the technology to mount automated hacking attacks, cause driverless car crashes or turn commercial drones into targeted weapons, a new report warns. The study, published on Wednesday by 25 technical and public policy researchers from Cambridge, Oxford and Yale universities along with privacy and military experts, sounded the alarm for the potential misuse of AI by rogue states, criminals and lone-wolf attackers. The researchers said the malicious use of AI poses imminent threats to digital, physical and political security by allowing for large-scale, finely targeted, highly efficient attacks. The study focuses on plausible developments within five years. "We all agree there are a lot of positive applications of AI," Miles Brundage, a research fellow at Oxford's Future of Humanity Institute.

UPDATED, Feb. 5, 11:38 a.m.: Just 20 years ago, fax machines were in wide use and most people didn't have cell phones. Fast forward to today, and smartphones are inescapable and personal hotspots are tucked into everyone's pockets. The real estate industry has seen its own advances during that time, with drones debuting on construction sites and prefabricated modular apartments now being snapped together easily onsite. Today, for example, it's virtually impossible to ignore the white-hot Bitcoin craze and barrage of news about how cryptocurrencies and blockchain could revolutionize real estate. But the next big tech revolution in New York's trillion-dollar real estate business could be far more disruptive. In the near future, the industry could see smart buildings fully controlled by technology, robots taking the place of construction workers and New York City skyscrapers being made by 3D printers. Meanwhile, property leases, loans and investment transactions may be handled without any human involvement, observers say, thanks to artificial intelligence. The question everyone is now mulling: After decades of sparse innovation, is real estate finally in for a major wakeup call from the tech sphere? "I only see the pace of change accelerating," said James Barrett, chief innovation officer at Turner Construction, one of the largest construction management firms in the country. This month, on the heels of the bustling CES tech conference in Las Vegas, The Real Deal broke down some of the innovations with the greatest potential to upend the real estate industry -- from artificial intelligence and virtual reality to people-tracking beacons and blockchain.

The challenge we face today is not a "world without work" but a world with rapidly changing work. This article is part of an MIT SMR initiative exploring how technology is reshaping the practice of management. The pace of progress in AI and machine learning is accelerating rapidly. In the past month alone, these are just a few of the news items I've seen: Deep learning and neural networks have dramatically improved in effectiveness and impact, leading to human-level performance in many aspects of vision, conversational speech, and problem-solving. As a result, industries are in the midst of a major transformation and more is on the way.

Victor Schwartz is a partner in the Washington, D.C. office of the Kansas City-based law firm of Shook, Hardy & Bacon L.L.P. He chairs the firm's Public Policy Group, which seeks to be the vanguard of developing public policy issues that will help improve the civil justice system. Mr. Schwartz has been an advisor for each of the American Law Institute's (ALI) Restatement (Third) of Torts projects: Products Liability, Apportionment of Liability, and Liability for Physical and Emotional Harm. He is a life member of the ALI. Prior to entering the full time practice of law, Mr. Schwartz was a professor and dean at the University of Cincinnati College of Law.

Abstract-- Recent successes in reinforcement learning have lead to the development of complex controllers for realworld robots.As these robots are deployed in safety-critical applications and interact with humans, it becomes critical to ensure safety in order to avoid causing harm. A first step in this direction is to test the controllers in simulation. To be able to do this, we need to capture what we mean by safety and then efficiently search the space of all behaviors to see if they are safe. In this paper, we present an active-testing framework based on Bayesian Optimization. We specify safety constraints using logic and exploit structure in the problem in order to test the system for adversarial counter examples that violate the safety specifications. These specifications are defined as complex boolean combinations of smooth functions on the trajectories and, unlike reward functions in reinforcement learning, are expressive and impose hard constraints on the system. In our framework, we exploit regularity assumptions on individual functions in form of a Gaussian Process (GP) prior. We combine these into a coherent optimization framework using problem structure. The resulting algorithm is able to provably verify complex safety specifications or alternatively find counter examples. Experimental results show that the proposed method is able to find adversarial examples quickly.

As algorithms are increasingly used to make important decisions that affect human lives, ranging from social benefit assignment to predicting risk of criminal recidivism, concerns have been raised about the fairness of algorithmic decision making. Most prior works on algorithmic fairness normatively prescribe how fair decisions ought to be made. In contrast, here, we descriptively survey users for how they perceive and reason about fairness in algorithmic decision making. A key contribution of this work is the framework we propose to understand why people perceive certain features as fair or unfair to be used in algorithms. Our framework identifies eight properties of features, such as relevance, volitionality and reliability, as latent considerations that inform people's moral judgments about the fairness of feature use in decision-making algorithms. We validate our framework through a series of scenario-based surveys with 576 people. We find that, based on a person's assessment of the eight latent properties of a feature in our exemplar scenario, we can accurately (> 85%) predict if the person will judge the use of the feature as fair. Our findings have important implications. At a high-level, we show that people's unfairness concerns are multi-dimensional and argue that future studies need to address unfairness concerns beyond discrimination. At a low-level, we find considerable disagreements in people's fairness judgments. We identify root causes of the disagreements, and note possible pathways to resolve them.

Abstract--Classifiers trained using conventional empirical risk minimization or maximum likelihood methods are known to suffer dramatic performance degradations when tested over examples adversarially selected based on knowledge of the classifier's decision rule. Due to the prominence of Artificial Neural Networks (ANNs) as classifiers, their sensitivity to adversarial examples, as well as robust training schemes, have been recently the subject of intense investigation. In this paper, for the first time, the sensitivity of spiking neural networks (SNNs), or third-generation neural networks, to adversarial examples is studied. The study considers rate and time encoding, as well as rate and first-to-spike decoding. Furthermore, a robust training mechanism is proposed that is demonstrated to enhance the performance of SNNs under white-box attacks.