In a new report, called The Malicious Use of Artificial Intelligence (AI), the authors - who are made up of AI researchers and civil liberties groups - warn that if breakthroughs in AI continue at the current pace then technology will soon become so powerful that it will outmanoeuvre many digital and physical defence systems. Jack Clark, head of policy at OpenAI, San Francisco-based AI group whose backers include Elon Musk and Peter Thiel, said: "What struck a lot of us was the amount that happened in the last five years -- if that continues, you see the chance of creating really dangerous things." The report also warns that drones and driverless cars could be commandeered and used as weapons and that malevolent AI could be used to organise swarms of drones. Also, political systems could be hacked by using tools for online advertising and commerce to manipulate voters.

Drones turned into missiles, fake videos manipulating public opinion and automated hacking are just three of the threats from artificial intelligence in the wrong hands, experts have said. The Malicious Use of Artificial Intelligence report warns that AI is ripe for exploitation by rogue states, criminals and terrorists. Those designing AI systems need to do more to mitigate possible misuses of their technology, the authors said. And governments must consider new laws. Speaking to the BBC, Shahar Avin, from Cambridge University's Centre for the Study of Existential Risk, explained that the report concentrated on areas of AI that were available now or likely to be available within five years, rather than looking to the distant future.

AI could reboot industries and make the economy more productive; it's already infusing many of the products we use daily. But a new report by more than 20 researchers from the Universities of Oxford and Cambridge, OpenAI, and the Electronic Frontier Foundation warns that the same technology creates new opportunities for criminals, political operatives, and oppressive governments--so much so that some AI research may need to be kept secret. Included in the report, The Malicious Use of Artificial Intelligence: Forecasting, Prevention, and Mitigation, are four dystopian vignettes involving artificial intelligence that seem taken straight out of the Netflix science fiction show Black Mirror. An administrator for a building's robot security system spends some of her time on Facebook during the workday. There she sees an ad for a model train set and downloads a brochure for it.

The world is under threat from artificial intelligence and needs to do more to keep people safe, experts have urged. A new report compiled by 26 of the world's leading experts paints a terrifying picture of the world in the next 10 years. Physical attacks as well as those on our digital worlds and political system could drastically undermine the safety of humanity, it warns, and people must work together now if they want to keep the world safe. The use of artificial intelligence is likely to empower all kinds of people – including rogue states, criminals, and terrorists, the report warns. Boston Dynamics describes itself as'building dynamic robots and software for human simulation'.

Terrorists, rogue states and criminals could soon use artificial intelligence to undermine freedom and national security, warns a new report. Superhuman hacking, surveillance and persuasion are just some of the terrifying features of artificial intelligence in 2018. The security implications of'emerging technologies' were announced by 26 experts in the AI field. They predict rapid cybercrime growth, drone misuse and the unprecedented rise in the use of'bots' to manipulate everything from elections to the news agenda and social media. The security implications of'emerging technologies' were announced by 26 experts in the AI field, who forecasted rapid cybercrime growth, drone misuse and the unprecedented rise in the use of'bots' to manipulate everything from elections to the news agenda and social media'Artificial intelligence is a game changer and this report has imagined what the world could look like in the next five to ten years,' said Dr Seán Ó hÉigeartaigh, co-author and Executive Director of Cambridge University's Centre for the Study of Existential Risk.

In a cybersecurity context, AI is a software that perceives its environment well enough to identify events and take action against predefined purpose. It can also learn and build the rules on the go as well; actually thats the real AI. Biggest fear of today's time is the concern that hackers are getting much more smarter. These hackers will use artificial intelligence in cyberattacks that are more sophisticated and harder to detect. We do have Artificial Intelligence in our systems and business strategies.

Algorithmic collusion is an emerging concept in current artificial intelligence age. Whether algorithmic collusion is a creditable threat remains as an argument. In this paper, we propose an algorithm which can extort its human rival to collude in a Cournot duopoly competing market. In experiments, we show that, the algorithm can successfully extorted its human rival and gets higher profit in long run, meanwhile the human rival will fully collude with the algorithm. As a result, the social welfare declines rapidly and stably. Both in theory and in experiment, our work confirms that, algorithmic collusion can be a creditable threat. In application, we hope, the frameworks, the algorithm design as well as the experiment environment illustrated in this work, can be an incubator or a test bed for researchers and policymakers to handle the emerging algorithmic collusion.

These perturbations can be designed to be barely noticeable to the human eye, but can cause large classification errors in state of the art deep networks. While it is tempting to speculate that this vulnerability arises from the complex, nonlinear nature of deep networks, a more plausible explanation is that it is due to the excessive linearity of such networks [3-6]. When we take a linear combination of the components of a high-dimensional input, small, adversarially chosen, perturbations of each component can add up to a large perturbation at the output. Complex operations such as a rectified linear unit (ReLU) operating beyond its bias, or a sigmoid in its linear region, together with operations such as max pooling or average pooling, when cascaded through multiple stages, still amount to an approximately linear combination of the input. Of course, the coefficients of the linear combination exhibit some dependence on the input, but these can be viewed as on-off switches rather than a change in the value of the coefficients: for example, whether the input is such that a ReLU unit is operating in its linear region, or the identity of the argument of the maximum in a max pooling unit. This motivates us to take a step back in this paper, and study adversarial perturbations in the simplest possible setting: a linear classifier.

Many recent advances in large scale probabilistic inference rely on variational methods. The success of variational approaches depends on (i) formulating a flexible parametric family of distributions, and (ii) optimizing the parameters to find the member of this family that most closely approximates the exact posterior. In this paper we present a new approximating family of distributions, the variational sequential Monte Carlo (VSMC) family, and show how to optimize it in variational inference. VSMC melds variational inference (VI) and sequential Monte Carlo (SMC), providing practitioners with flexible, accurate, and powerful Bayesian inference. The VSMC family is a variational family that can approximate the posterior arbitrarily well, while still allowing for efficient optimization of its parameters. We demonstrate its utility on state space models, stochastic volatility models for financial data, and deep Markov models of brain neural circuits.

A recent issue of a popular computing journal asked which laws would apply if a self-driving car killed a pedestrian. This paper considers the question of legal liability for artificially intelligent computer systems. It discusses whether criminal liability could ever apply; to whom it might apply; and, under civil law, whether an AI program is a product that is subject to product design legislation or a service to which the tort of negligence applies. The issue of sales warranties is also considered. A discussion of some of the practical limitations that AI systems are subject to is also included.