Tremendous research effort has been dedicated over the years to thoroughly investigate non-monotonic reasoning. With the abundance of non-monotonic logical formalisms, a unified theory that enables comparing the different approaches is much called for. In this paper, we present an algebraic graded logic we refer to as LogAG capable of encompassing a wide variety of non-monotonic formalisms. We build on Lin and Shoham's argument systems first developed to formalize non-monotonic commonsense reasoning. We show how to encode argument systems as LogAG theories, and prove that LogAG captures the notion of belief spaces in argument systems. Since argument systems capture default logic, autoepistemic logic, the principle of negation as failure, and circumscription, our results show that LogAG captures the before-mentioned non-monotonic logical formalisms as well. Previous results show that LogAG subsumes possibilistic logic and any non-monotonic inference relation satisfying Makinson's rationality postulates. In this way, LogAG provides a powerful unified framework for non-monotonicity.

From automated eye scans to analysing the cries of new-born babies, faster drug development to personalised medicine, artificial intelligence (AI) promises huge advances in the field of healthcare. At the recent AI for Good Summit in Geneva, Switzerland, we were told how AI could speed up the development of new drugs, lead to personalised medicine informed by our genomes, and help diagnose diseases in countries suffering from underdeveloped health services and a chronic shortage of doctors. But there are two main obstacles preventing access to this utopian destination. One is that the AI being applied to the world's health problems isn't quite good enough yet. The other related issue is the lack of good quality digital data - less than 20% of the world's medical data is available in a form that AI machine learning algorithms can ingest and learn from, the WHO estimates.

WASHINGTON/DUBAI, UNITED ARAB EMIRATES - Iran said it had seized a British oil tanker in the Strait of Hormuz on Friday but denied Washington's assertion that the U.S. Navy had downed an Iranian drone nearby this week, as tensions in the Gulf region rose again. Britain said it was urgently seeking information about the Stena Impero tanker, which had been heading to a port in Saudi Arabia and suddenly changed course after passing through the strait at the mouth of the Gulf. The tanker's operator, Stena Bulk, said in a statement the ship was no longer under the crew's control and could not be contacted. Iran's state news agency IRNA quoted a military source as saying the vessel had turned off its tracker, ignored warnings from the Revolutionary Guard and was sailing in the wrong direction in a shipping lane. "We will respond in a way that is considered but robust and we are absolutely clear that if this situation is not resolved quickly there will be serious consequences," British Foreign Secretary Jeremy Hunt told reporters.

In the early 1500s Lorenzo Lotto and Giovan Battista Moroni began the psychological analysis through portraiture. From this moment, the focus on the sitter's identity becomes the leitmotif in the history of portrait. Portraits interpret the external beauty, social status, and then go beyond our body and face. A portrait becomes a psychological analysis and a deep reflection on our existence. AI Portraits Ars uses Artificial Intelligence to reproduce artistic human portraits, with different styles and levels of abstraction.

From improving customer service to automating work processes and providing predictive analysis, artificial intelligence (AI) is transforming the way organisations operate. AI is also bringing significant advantage to cybersecurity in uncovering vulnerabilities and responding to threats. Security correspondent Daniel Bardsley speaks to Paul O'Brien, Director of AI, Service, Security and Operations Lab Applied Research, BT Technology and Professor Nader Azarmi, Emirates ICT Innovation Centre (EBTIC) director and head of BT Global Research Centres to discuss how advancements in AI spells the future of security in the Middle East. There is no shortage of money being invested in cybersecurity research as the threats from attackers appear to grow. Microsoft, for example, spends more than $1 billion annually in cybersecurity research and development, with the firm having said that the amount is increasing as activity migrates to the cloud.

According to BlueWeave Consulting, The Global Artificial Intelligence (AI) Robots Market is expected to grow with a significant rate during the forecast period 2019-2025, owing to Increased efficiency and effectiveness demands of robots coupled with proliferated increasing usage of robotics in various industries. The increasing developments in the field of artificial intelligence, there has been an increasing shift towards providing autonomy to the machine will foster the global Artificial Intelligence (AI) Robots Market in the forecast period. Furthermore, organizations are seeking cheaper and more efficient labor given mounting labor costs, particularly in highly-specialized fields where employers have to bid up for top talent will accelerate the growth of the Artificial Intelligence (AI) Robots market. On the basis of Robot Type, the Artificial Intelligence (AI) Robots market has been segmented into Service and Industrial. Service dominates the global Artificial Intelligence (AI) Robots on account of increasing application use outside of a manufacturing facility within a professional setting like intended to interact with people, typically deployed in retail, hospitality, healthcare, warehouse or fulfillment set.

The success of generative modeling in continuous domains has led to a surge of interest in generating discrete data such as molecules, source code, and graphs. However, construction histories for these discrete objects are typically not unique and so generative models must reason about intractably large spaces in order to learn. Additionally, structured discrete domains are often characterized by strict constraints on what constitutes a valid object and generative models must respect these requirements in order to produce useful novel samples. Here, we present a generative model for discrete objects employing a Markov chain where transitions are restricted to a set of local operations that preserve validity. Building off of generative interpretations of denoising autoencoders, the Markov chain alternates between producing 1) a sequence of corrupted objects that are valid but not from the data distribution, and 2) a learned reconstruction distribution that attempts to fix the corruptions while also preserving validity. This approach constrains the generative model to only produce valid objects, requires the learner to only discover local modifications to the objects, and avoids marginalization over an unknown and potentially large space of construction histories. We evaluate the proposed approach on two highly structured discrete domains, molecules and Laman graphs, and find that it compares favorably to alternative methods at capturing distributional statistics for a host of semantically relevant metrics.

"The increasing amount of available data, mainly due to the proliferation of access to the internet in countries where peacekeeping missions take place, has caused a technology-driven transformation of the operational environment. This comes at a time of significant developments in the fields of artificial intelligence and particularly machine learning, most of whose applications still rely on massive amounts of data. As such these developments have produced some promising individual initiatives to exploit this new and growing potential for United Nations operations." At least as early as 1996 researchers have used machine learning (ML) to predict conflicts[1]. Today, mainly due to significantly higher amounts of available data[2], advancements in computing power and the progress made in natural language processing, several artificial intelligence (AI) tools have been added to the peacekeeping arsenal.

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SINGAPORE/KUALA LUMPUR - The stench of curdled milk wafted from a shipping container of waste at Malaysia's Port Klang as Environment Minister Yeo Bee Yin told a group of journalists in May she would send the maggot-infested rubbish back where it came from. Yeo was voicing a concern that has spread across Southeast Asia, fueling a media storm over the dumping of rich countries' unwanted waste. About 5.8 million tons of trash was exported between January and November last year, led by shipments from the U.S., Japan and Germany, according to Greenpeace. Now governments across Asia are saying no to the imports, which for decades fed mills that recycled waste plastic. As more and more waste came, the importing countries faced a mounting problem of how to deal with tainted garbage that couldn't be easily recycled.