We propose a novel method for automatic reasoning on knowledge graphs based on debate dynamics. The main idea is to frame the task of triple classification as a debate game between two reinforcement learning agents which extract arguments -- paths in the knowledge graph -- with the goal to promote the fact being true (thesis) or the fact being false (antithesis), respectively. Based on these arguments, a binary classifier, called the judge, decides whether the fact is true or false. The two agents can be considered as sparse, adversarial feature generators that present interpretable evidence for either the thesis or the antithesis. In contrast to other black-box methods, the arguments allow users to get an understanding of the decision of the judge. Since the focus of this work is to create an explainable method that maintains a competitive predictive accuracy, we benchmark our method on the triple classification and link prediction task. Thereby, we find that our method outperforms several baselines on the benchmark datasets FB15k-237, WN18RR, and Hetionet. We also conduct a survey and find that the extracted arguments are informative for users.

In recent years, computer scientists have increasingly com e to recognize that artificial intelligence (AI) systems have the potential to create harmful consequences. Especially within machine learning, there have been numerous efforts to formally characterize various not ions of fairness and develop algorithms to satisfy these criteria. However, most of this research has proceede d without any nuanced discussion of ethical foundations. Partly as a response, there have been several r ecent calls to think more broadly about the ethical implications of AI (Barabas et al., 2018; Hu and Chen, 2018b; Torresen, 2018; Green, 2019). Among the most prominent approaches to ethics within philos ophy is a highly influential position known as consequentialism. Roughly speaking, the consequentialist believes that out comes are all that matter, and that people should therefore endeavour to act so as to produce the best consequences, based on an impart ial perspective as to what is best . Although there are numerous difficulties with consequentia lism in practice (see §4), it nevertheless provides a clear and principled foundation from which to critiq ue proposals which fall short of its ideals. In this paper, we analyze the literature on fairness within mac hine learning, and show how it largely depends on assumptions which the consequentialist perspective rev eals immediately to be problematic. In particular, we make the following contributions: - We provide an accessible overview of the main ideas of conseq uentialism ( §3), as well as a discussion of its difficulties ( §4), with a special emphasis on computational limitations. 1 - We review the dominant ideas about fairness in the machine le arning literature ( §5), and provide the first critique of these ideas explicitly from the perspectiv e of consequentialism ( §6). - We conclude with a broader discussion of the ethical issues r aised by learning and randomization, highlighting future direction for both AI and consequentia lism ( §7).

Artificial intelligence is increasingly playing a role in companies' hiring decisions. Algorithms help target ads about new positions, sort through resumes, and even analyze applicants' facial expressions during video job interviews. But these systems are opaque, and we often have no idea how artificial intelligence-based systems are sorting, scoring, and ranking our applications. It's not just that we don't know how these systems work. Artificial intelligence can also introduce bias and inaccuracy to the job application process, and because these algorithms largely operate in a black box, it's not really possible to hold a company that uses a problematic or unfair tool accountable.

It's easy to get cynical about artificial intelligence (AI). China is using facial recognition against the Uighurs. NYT: 'One Month, 500,000 Face Scans: How China Is Using A.I. to Profile a Minority' Google's participating in the development of autonomous weapons. The Intercept: 'Google Continues Investments in Military and Police AI Technology Through Venture Capital Arm' And facial recognition programmes are still struggling to recognise black faces. But last year I also saw another side.

Beijing: China's telecommunications giant Huawei on Tuesday thanked the Indian government for permitting it to participate in the upcoming trials for 5G networks, a major boost to the company amidst a US clampdown on it citing national security risks. The 5G is the next generation cellular technology with download speeds stated to be 10 to 100 times faster than current 4G networks. The 5G networking standard is seen as critical because it can support the next generation of mobile devices in addition to new applications like driverless cars and gadgets made out of artificial intelligence (AI). Huawei rivals western equipment makers, such as Ericsson, and is banned in the US. India on Monday indicated its unwillingness to keep any company out of the 5G trials.

Artificial intelligence has been touted as the "next big thing in cyber" for some time, even though the concept is as old as the first email viruses. The clamour around the technology, which started in late 2015 / early 2016, was quickly amplified as it became a tool in heavy use with analysts, sales teams and marketers. AI adoption continues to accelerate, and according to Capgemini's Reinventing Cybersecurity with Artificial Intelligence report, 48 per cent of respondents said budgets for AI in cybersecurity will increase by an average of 29 per cent in 2020. However, it's important to note that potentially only a few vendors exist with the R&D budget to pour tens or hundreds of billions of dollars required into building pure AI for cybersecurity. Typically, instead of AI, what people are usually talking about when it comes to uses in cybersecurity is machine learning and its associated subfields: Supervised, Unsupervised, Reinforcement and Deep Learning.

The US National Highway Traffic Safety Administration is investigating a crash involving a speeding Tesla that killed two people in a Los Angeles suburb, the agency said on Tuesday. Spokesman Sean Rushton would not say whether the Tesla Model S was on Autopilot when it crashed on 29 December in Gardena. That system is designed to automatically change lanes and keep a safe distance from other vehicles. The black Tesla had left a freeway and was moving at a high rate of speed when it ran a red light and slammed into a Honda Civic at an intersection, police said. A man and woman in the Civic died at the scene.

A select few organisations are already making use of the AI-powered tools of tomorrow, but 2020 is likely to bring broader innovation in the way organisations use artificial intelligence and software automation technology. This year, we've seen continued strong growth in cloud computing and AI as more businesses have embarked on their digital transformation journeys. Still, Gartner has estimated that by 2021, demand for application development will grow five times faster than tech teams can deliver. Ironically, software has automated nearly every business process except the writing of software itself. In the past few years, this has started to change.

Deeptech has become a buzzword in the Indian startup ecosystem. The emergence of technologies such as artificial intelligence (AI), machine learning (ML), automation blockchain and drones, among others, has opened up plethora of opportunities. While the full potential of these technologies are yet to be unlocked, companies are already witnessing efficiencies going up. "The journey that began with bringing businesses to the cloud, adapting to e-commerce and building mobile apps to bring a seamless experience came a full cycle in the last decade. The year 2020 and the following decade are going to be about making the interactions or transactions even more seamless, and in a way that replicates human behavior," said Aakrit Vaish, co-founder and CEO, Haptik, a start-up that offers AI-powered chatbots. Quoting NASSCOM data, many reports said about 18 per cent of 1,300 Indian start-ups launched in 2019 are leveraging deep-tech.

Cyberlaw as a discipline saw some massive advances in 2019. These advances were seen in different thrust areas of this discipline. The first significant element of 2019 was the determined focus of sovereign governments across the world, to come up with strong national cybersecurity legislations and legislative frameworks. Consequently, different countries and sovereign governments started moving in the direction of trying to regulate cybersecurity. These regulations normally took two distinctive manifestations.