The rise of the robots has plagued the imagination of science-fiction since its inception. Whether it involved Asimov's three rules of robotics or the time travelling robotic psychopaths in the Terminator franchise, the world has worried about the role of robotics. The world has prepared itself for the robotic invasion with many low skilled jobs in retail, customer services and industrial industries like factories all replacing humans with robotic counterparts to some extent; many of which improve efficiency, productivity and costs. Shockingly, San Francisco-based company, Atrium, believe that robots have the potential to supersede and complete the job of some high-paid legal service workers. As young as 14 months, Atrium, along with the $65 million investments from adventure capitalists, are set to revolutionise the legal sector by using artificial intelligence to work alongside legal service professionals, and in some cases, replace them.

SAP has released its guiding principles for artificial intelligence (AI). Recognizing the significant impact of AI on people, our customers, and wider society, SAP designed these guiding principles to steer the development and deployment of our AI software to help the world run better and improve people's lives. For us, these guidelines are a commitment to move beyond what is legally required and to begin a deep and continuous engagement with the wider ethical and socioeconomic challenges of AI. We look forward to expanding our conversations with customers, partners, employees, legislative bodies, and civil society; and to making our guiding principles an evolving reflection on these discussions and the ever-changing technological landscape. We recognize that, like with any technology, there is scope for AI to be used in ways that are not aligned with these guiding principles and the operational guidelines we are developing.

SAP has created an AI ethics panel to guide its use of machine-learning technology. If only it had a similar committee for fraud allegations: it might have avoided the corruption scandal engulfing it in South Africa. The German ERP giant – which is accused of kicking back $2m to secure state contracts – claimed it is the first European biz to create a external artificial intelligence ethics board: a five-person committee that includes technical experts and specialists in public policy, ethics, and bioethics. However, while several of them possess solid IT credentials, there's no one with a background in AI. Rather, expertise in the evolving field will come from inside SAP.

If you work on IT security in a large enterprise, chances are you're overwhelmed. Just as the capacity of your systems and networks grows and the amount of data going through them mushrooms, the number and complexity of threats grows as well. And if things weren't hard enough, government regulations put time pressure on security personnel to identify breaches and report them promptly. Even if throwing people at the problem were the answer--and it might not help at all--you probably don't have the budget for it. To deal with a large number of potential real-time threats, what you need is a lot of real-time analytical power.

Google's secret prototype search engine for China reportedly links users' mobile phone numbers to what search terms they've used. This feature would allow the Chinese government to simply associate searches with individuals, thereby putting Chinese citizens at increased risk of government repression if they search for topics that their government deems politically sensitive, according to the Intercept. The mobile-focused search engine prototype, code-named Dragonfly, was first revealed last month by the Intercept. Dragonfly is said to have been conceptualized as a joint venture between Google and a Chinese-based company. Both would have the ability to update a list of verboten search words, which could include expected subjects like "human rights" and "student protest" but could also extend to search terms such as "Nobel Prize", according to the Intercept's story.

We present an effective technique for training deep learning agents capable of negotiating on a set of clauses in a contract agreement using a simple communication protocol. We use Multi Agent Reinforcement Learning to train both agents simultaneously as they negotiate with each other in the training environment. We also model selfish and prosocial behavior to varying degrees in these agents. Empirical evidence is provided showing consistency in agent behaviors. We further train a meta agent with a mixture of behaviors by learning an ensemble of different models using reinforcement learning. Finally, to ascertain the deployability of the negotiating agents, we conducted experiments pitting the trained agents against human players. Results demonstrate that the agents are able to hold their own against human players, often emerging as winners in the negotiation. Our experiments demonstrate that the meta agent is able to reasonably emulate human behavior.

The growing influence and decision-making capacities of Autonomous systems and Artificial Intelligence in our lives force us to consider the values embedded in these systems. But how ethics should be implemented into these systems? In this study, the solution is seen on philosophical conceptualization as a framework to form practical implementation model for ethics of AI. To take the first steps on conceptualization main concepts used on the field needs to be identified. A keyword based Systematic Mapping Study (SMS) on the keywords used in AI and ethics was conducted to help in identifying, defying and comparing main concepts used in current AI ethics discourse. Out of 1062 papers retrieved SMS discovered 37 re-occurring keywords in 83 academic papers. We suggest that the focus on finding keywords is the first step in guiding and providing direction for future research in the AI ethics field.

The three college-age defendants behind the creation of the Mirai botnet--an online tool that wreaked destruction across the internet in the fall of 2016 with unprecedentedly powerful distributed denial of service attacks--will stand in an Alaska courtroom Tuesday and ask for a novel ruling from a federal judge: They hope to be sentenced to work for the FBI. Josiah White, Paras Jha, and Dalton Norman, who were all between 18 and 20 years old when they built and launched Mirai, pleaded guilty last December to creating the malware that hijacked hundreds of thousands of Internet of Things devices, uniting them as a digital army that began as a way to attack rival Minecraft video game hosts, and evolved into an online tsunami of nefarious traffic that knocked entire web hosting companies offline. At the time, the attacks raised fears amid the presidential election targeted online by Russia that an unknown adversary was preparing to lay waste to the internet. The original creators, panicking as they realized their invention was more powerful than they had imagined, released the code--a common tactic by hackers to ensure that if and when authorities catch them, they don't possess any code that isn't already publicly known that can help finger them as the inventors. That release in turn lead to attacks by others throughout the fall, including one that made much of the internet unusable for the East Coast of the United States on an October Friday.

Automatic judgment prediction aims to predict the judicial results based on case materials. It has been studied for several decades mainly by lawyers and judges, considered as a novel and prospective application of artificial intelligence techniques in the legal field. Most existing methods follow the text classification framework, which fails to model the complex interactions among complementary case materials. To address this issue, we formalize the task as Legal Reading Comprehension according to the legal scenario. Following the working protocol of human judges, LRC predicts the final judgment results based on three types of information, including fact description, plaintiffs' pleas, and law articles. Moreover, we propose a novel LRC model, AutoJudge, which captures the complex semantic interactions among facts, pleas, and laws. In experiments, we construct a real-world civil case dataset for LRC. Experimental results on this dataset demonstrate that our model achieves significant improvement over state-of-the-art models. We will publish all source codes and datasets of this work on \urlgithub.com for further research.

Anti-Trump Senator Jeff Flake, a member of the Senate Judiciary Committee, said in an interview Sunday evening that until he learns more about the sexual assault allegation regarding Brett Kavanaugh, he is "not comfortable voting yes" on Kavanaugh. It's Flakes last chance to poke President Trump and the country in the eye before he rides retires and likely finds a job in the liberal media. Jeff Flake becomes the first Republican senator to call for a pause on the Kavanaugh hearings until the Judiciary Committee hears from his accuser. Kavanaugh's accuser is a far left anti-Trump activist. Over the past few days, what appeared at first to be a merely token resistance to the nomination of Trump SCOTUS pick Brett Kavanaugh has morphed into something entirely more menacing.