DUBAI, UNITED ARAB EMIRATES – A flurry of diplomatic visits and meetings crisscrossing the Persian Gulf have driven urgent efforts in recent days to defuse the possibility of all-out war after the U.S. killed Iran's top military commander. Global leaders and top diplomats are repeating the mantra of "de-escalation" and "dialog," yet none has publicly laid out a path to achieving either. The United States and Iran have said they do not want war, but fears have grown that the crisis could spin out of Tehran's or Washington's control. Tensions have careened from one crisis to another since President Donald Trump withdrew the U.S. from Iran's nuclear deal with world powers. The U.S. drone strike that killed Revolutionary Guard Gen. Qassem Soleimani and a senior Iraqi militia leader in Baghdad on Jan. 3 was seen as a major provocation.

Artificial intelligence doesn't need'agile' governance, it needs firm regulation

Chinese search engine giant Baidu is looking to work with Indian institutes as the company looks for local expertise in AI. As India is the second-largest home in terms of the internet user base, with nearly 12 percent in the world, Baidu Co-Founder, CEO and Chairman Robin Li is seeing the country as an opportunity for areas such as artificial intelligence. On his first-ever visit to India, Robin talked here at IIT Madras Tech Fest, Shaastra 2020, titled Innovation in the Age of Artificial Intelligence (AI). He said the company is looking to work with Indian institutions in the future to make a better world through innovation. India is one of the fastest-growing smartphone markets in the world, and a very large developing country, right next to China.

A N -way array or N -th order tensor T is a multidimensional array in the product R I 1 ... I N of the vector spaces R I i for i 1, 2,...,N . A vector x R I 1 is a first-order tensor, and a matrix M R I 1 I 2 is a second-order tensor. The goal of NTF is to approximate a tensor T by a structured tensor X . Using the squared Frobenius norm as a distance metric, defined as nullXnull 2 F null j 1,j 2,...j NX 2 j 1j 2...j N, NTF is the following optimization problem: min a (i) p 0, 1 i N, 1 p r null null null null null nullT r null p 1 N null i 1a (i) p null null null null null null 2 F, (1) This work was supported by the Fonds de la Recherche Scientifique - FNRS and the Fonds Wetenschappelijk Onderzoek - Vlanderen (FWO) under EOS Project no O005318F-RG47, and by the European Research Council (ERC starting grant no 679515).

Recent advancements in Artificial Intelligence (AI) have brought new capabilities to behavioural analysis (UEBA) for cyber-security consisting in the detection of hostile action based on the unusual nature of events observed on the Information System.In our previous work (presented at C\&ESAR 2018 and FIC 2019), we have associated deep neural networks auto-encoders for anomaly detection and graph-based events correlation to address major limitations in UEBA systems. This resulted in reduced false positive and false negative rates, improved alert explainability, while maintaining real-time performances and scalability. However, we did not address the natural evolution of behaviours through time, also known as concept drift. To maintain effective detection capabilities, an anomaly-based detection system must be continually trained, which opens a door to an adversary that can conduct the so-called "frog-boiling" attack by progressively distilling unnoticed attack traces inside the behavioural models until the complete attack is considered normal. In this paper, we present a solution to effectively mitigate this attack by improving the detection process and efficiently leveraging human expertise. We also present preliminary work on adversarial AI conducting deception attack, which, in term, will be used to help assess and improve the defense system. These defensive and offensive AI implement joint, continual and active learning, in a step that is necessary in assessing, validating and certifying AI-based defensive solutions.

A merger between humans and machines is coming, and it's not what you may have thought. Something mysterious flickered into reality when our ancestors first learned to extract knowledge from their heads and embed it in tools. Now, millions of years later, our tools are fusing with us and, in so doing, bringing about something that is part biological and part technological. We are incubating this new intelligence in our organizations, but it is also true that it represents an extension of ourselves. Humanity is like a seed in an enigmatic womb made up of artificial intelligence and automation.

A merger between humans and machines is coming, and it's not what you may have thought. Something mysterious flickered into reality when our ancestors first learned to extract knowledge from their heads and embed it in tools. Now, millions of years later, our tools are fusing with us and, in so doing, bringing about something that is part biological and part technological. We are incubating this new intelligence in our organizations, but it is also true that it represents an extension of ourselves. Humanity is like a seed in an enigmatic womb made up of artificial intelligence and automation.

For all the giant leaps promised by artificial intelligence, when it comes to business, what we've seen so far amounts to just small steps. In fact, a number of very smart people advise companies to start small with AI: Use it to improve your customer service bots, for example, before you try to deploy it to cure cancer. So, yes, that appears to be a sensible approach. But it can also be a dangerous trap. When you think small, notes this week's guest, you get small results.

It's difficult to make predictions, especially about the future. But one fairly safe prediction is that data will continue eating the world in 2020 and the coming decade. The most important tech trend since the 1990s will no doubt accentuate its presence in our lives, for better or for worse. At the beginning of the last decade, IDC estimated that 1.2 zettabytes (1.2 trillion gigabytes) of new data were created in 2010, up from 0.8 zettabytes the year before. The amount of the newly created data in 2020 was predicted to grow 44X to reach 35 zettabytes (35 trillion gigabytes).

WIRE)--Cambridge Mobile Telematics (CMT), the world's leading mobile telematics and analytics provider, has launched its latest product line, Claims Studio. Through a lightweight smartphone solution, Claims Studio gives claims adjusters access to robust, unbiased telematics and contextual crash data after an impact occurs. CMT's ability to detect crashes has been in the market since 2015, but now has expanded to support the end-to-end claims process. Claims Studio uses telematics and artificial intelligence to reproduce the true story of a crash, creating a data-driven narrative to accelerate the claims process. By accessing key details like speed, severity, and vehicle impact location early in the process, insurers can spend less time collecting information from drivers and third parties, and more time confirming facts and accurately assessing loss.