The algorithmic revolution is here, and nations are losing control of not only their understanding of the potential impact of artificial intelligence, but also the governance model that enforced accountability on the advances in science and technology over the years at all levels. While each new technology innovation claims its territory for the economic advances in the human ecosystem with significant ramifications across cyberspace, geospace and/or space (CGS), the rise of artificial intelligence (AI) has not only undermined governance, management and growth models, but it has also broken all barriers to boundaries defined by human decision makers. In addition, it is both blurring the boundaries between human intelligence and machine intelligence, and the boundaries between man and machine and real and fake. As a result, the power dynamics is shifting away from the select few across nations (and is moving away from humans entirely to algorithms)--re-defining the criteria upon which geopolitics was framed--and thereby threatening the foundations of global peace and security. Since the beginning of the technological age, each new idea, innovation and invention has helped humans across nations usher in a new era of economic growth, changing the fundamentals of respective nations and their security.

Every human needs food, water and shelter as their basic needs. Nowadays artificial intelligence is included in this list. In future, it will surely become a part of human life. In 2016 Internet access is considered as a basic human right. Even now Interruption in the connection of the Internet is considered as human right violence.

Artificial intelligence (AI) refers to computer systems that perform tasks that normally require human intelligence, such as visual perception, speech recognition, decision making or translation. More recently, AI has often revolved around the use of algorithms. An algorithm is a set of instructions that a computer can execute. Owing to such advanced technologies, farmers today can get watering assistance to make production more efficient; flu epidemics can be predicted, thereby enabling better medical preparation; intelligent surgical robotics can assist doctors; and, in Norway, a more balanced gender representation in the public media landscape is possible when AI tracks how much airtime is given to females on the Norwegian news programme'Dagsnytt 18'. These are merely a few examples of areas that benefit from AI and robot technology. Other less positive examples of AI include efforts to build applications, such as a'gaydar', which is an algorithm that researchers claim will reveal a person's sexual orientation; facial recognition systems that are biased towards white males, like Google's deep-learning algorithm, which consequently classified a picture of a black woman as a gorilla; or China's social credit system, whereby the Chinese government uses AI and big data as a tool to socially control its citizens.

The AI Ecosystem has generated a multi-billion dollar industry, and it all starts from data. Going upward in the value chain there are the Chips (GPUs) that allow the physical storing of Big Data (a dominant player is NVIDIA). That Big Data will need to be stored on platforms and infrastructures that SMEs can't afford. That is where players like Google Cloud, Amazon AWS, IBM Cloud and Microsoft Azure come into rescue. At large scale, a few corporations control the Enterprise AI market; while nations like China, USA, Japan, Germany, UK, and France have widely bet on it!

World AI Show marked its grand entry into the Indian sub-continent with another eventful show this year. Hosted by Trescon, the conference took place at The Lalit Plaza in Mumbai on the 22-23 November, to support the government's AI vision that enables enterprises and industries to implement the latest innovations across multiple domains. In support of the developing Artificial Intelligence industry in India, the event commenced with an opening keynote delivered by Shri Ajay Prakash Sawhney, Secretary for the Ministry of Electronics and Information Technology (MeitY), Government of India, followed by a special address by Shri Kaustubh Dhavse, Joint Secretary & Officer on Special Duty to the Hon. Chief Minister, Govt. of Maharashtra, who touched upon the topic of making governments ready for the advent of AI. Speaking at the event, Kaustubh said, "The key here is that when you are going to see the magic of AI being delivered, it is important to also monitor how effectively you can use it in your applications."

COMPANIES and academia often debate about the real-world implications of progressing artificial intelligence (AI) to a stage where it is all powerful and truly intelligent. While some, including global business leader Elon Musk, believe that AI is extremely dangerous -- more than nuclear weapons even, others believe that an all-encompassing AI system is inevitable and will support and improve human life in many ways. At the recent event at Davos, the Singapore government showed faith in the companies that work with AI and published a new AI governance framework that it believes will help guide them drive ethical and responsible AI deployments. While it is currently a "live document" open for public consultation, pilot adoption, and feedback, it is intended to be agile in evolving with the fast-paced changes in a Digital Economy and expected to continue to develop alongside adoptees use. "The key point to note is that this is a framework. It is not a rule or a regulation. This is basically an outcome from industry consultation that we have had," explained Singapore Minister for Communications and Information S Iswaran.

Governments and companies worldwide are investing heavily in artificial intelligence in hopes of new profits, smarter gadgets, and better health care. Financier and philanthropist George Soros told the World Economic Forum in Davos Thursday that the technology may also undermine free societies and create a new era of authoritarianism. "I want to call attention to the mortal danger facing open societies from the instruments of control that machine learning and artificial intelligence can put in the hands of repressive regimes," Soros said. He made an example of China, repeatedly calling out the country's president, Xi Jinping. China's government issued a broad AI strategy in 2017, asserting that it would surpass US prowess in the technology by 2030. As in the US, much of the leading work on AI in China takes place inside a handful of large tech companies, such as search engine Baidu and retailer and payments company Alibaba. Soros argued that AI-centric tech companies like those can become enablers of authoritarianism. He pointed to China's developing "social credit" system, aimed at tracking citizens' reputations by logging financial activity, online interactions, and even energy use, among other things. The system is still taking shape, but depends on data and cooperation from companies like payments firm Ant Financial, a spinout of Alibaba.

Approval ballot based committee formation is concerned with aggregating individual approvals of voters. Voters submit their approvals of candidates and these approvals are aggregated to arrive at the optimal committee of specified size. There are several aggregation techniques proposed in the literature and these techniques differ among themselves on the criterion function they optimize. Voters preferences for a candidate is based on his/her opinion on candidate suitability. We note that candidates have attributes that make him/her suitable or otherwise. Hence, it is relevant to approve attributes and select candidates who have the approved attributes. This paper addresses the committee selection problem when voters submit their approvals on attributes. Though attribute based preference is addressed in several contexts, committee selection problem with attribute approval has not been attempted earlier. We note that extending the theory of candidate approval to attribute approval in committee selection problem is not trivial. In this paper, we study different aspects of this problem and show that none of the existing aggregation rules satisfies Unanimity and Justified Representation when attribute based approvals are considered. We propose a new aggregation rule that satisfies both the above properties. We also present other analysis of committee selection problem with attribute approval.

In several different applications, including data transformation and entity resolution, rules are used to capture aspects of knowledge about the application at hand. Often, a large set of such rules is generated automatically or semi-automatically, and the challenge is to refine the encapsulated knowledge by selecting a subset of rules based on the expected operational behavior of the rules on available data. In this paper, we carry out a systematic complexity-theoretic investigation of the following rule selection problem: given a set of rules specified by Horn formulas, and a pair of an input database and an output database, find a subset of the rules that minimizes the total error, that is, the number of false positive and false negative errors arising from the selected rules. We first establish computational hardness results for the decision problems underlying this minimization problem, as well as upper and lower bounds for its approximability. We then investigate a bi-objective optimization version of the rule selection problem in which both the total error and the size of the selected rules are taken into account. We show that testing for membership in the Pareto front of this bi-objective optimization problem is DP-complete. Finally, we show that a similar DP-completeness result holds for a bi-level optimization version of the rule selection problem, where one minimizes first the total error and then the size.

We analyze the necessary and sufficient conditions for exact recovery of the symmetric Latent Space Model (LSM) with two communities. In a LSM, each node is associated with a latent vector following some probability distribution. We show that exact recovery can be achieved using a semidefinite programming approach.