When Bill Gates floated the idea of imposing a tax on robots a couple of years ago, Lawrence Summers, a former top economic adviser to President Barack Obama, called the Microsoft co-founder "profoundly misguided." How do you even define a robot to tax it? And taxing innovation is a sure way to make a country poorer. Europe has also rejected the idea. In 2017 the European Parliament soundly defeated a draft motion, proposed by its committee on legal affairs, that recommended considering a tax on the owners of robots to fund retraining programs for workers displaced by the machines and shore up the finances of their social security system.

"We are not talking about Terminator. We're talking about much simpler technologies, which are at best a few years away, and in fact, many of which you can see under development today in every theater of the war." He spoke February 14th as part of discussion called Killer Robots: Technological, Legal and Ethical Challenges at a meeting of the American Association for the Advancement of Science. "And so these are systems that are using sensors and software processing on their own to determine what constitutes a target and then applying lethal force to that, without supervision or meaningful human control." Another speaker, Peter Asaro, co-founder of the International Committee for Robot Arms Control, has participated in U.N. talks on autonomous weapons.

With over 15% of the total organizations utilizing Artificial Intelligence (AI), it has turned into a matter of extraordinary discussion of whether AI is great or terrible. In spite of the fact that AI was initially instituted in 1950, it has seen an exponential development in the previous couple of years and individuals are worried about how is it going to influence the human life. Gossipy tidbits are drifting all around with respect to the aspects of AI. From Sophia, the bot to Alexa has gotten the eyes of individuals making them wonder how is this field going to pivot. Safety measures have expanded essentially over the most recent years, and vindictive on-screen characters have comparably propelled their procedures to keep pace, especially with advances in attack techniques, for example, fileless malware.

If you shop at Westfield, you've probably been scanned and recorded by dozens of hidden cameras built into the centres' digital advertising billboards. The semi-camouflaged cameras can determine not only your age and gender but your mood, cueing up tailored advertisements within seconds, thanks to facial detection technology. Westfield's Smartscreen network was developed by the French software firm Quividi back in 2015. Their discreet cameras capture blurry images of shoppers and apply statistical analysis to identify audience demographics. And once the billboards have your attention they hit record, sharing your reaction with advertisers.

Artificial Intelligence (AI) is a collective term for computer systems that can sense their environment, think, learn, and respond to what they are sensing. Forms of AI in use today include digital assistants, chatbots and machine learning among others. Saudi Arabia is seeking to be a global leader in the application of technology related to AI. In a 2017 global study, consulting firm PWC estimated that AI could contribute $135 billion or 12.4 percent to Saudi Arabia's gross domestic product (GDP) by 2030 -- the second-highest share in the region after the UAE. The study saw retail and the public sector, including health care and education, as particularly ripe for transformation by AI.

Humans have schematic knowledge of how certain types of events unfold (e.g. coffeeshop visits) that can readily be generalized to new instances of those events. Schematic knowledge allows humans to perform role-filler binding, the task of associating schematic roles (e.g. "barista") with specific fillers (e.g. "Bob"). Here we examined whether and how recurrent neural networks learn to do this. We procedurally generated stories from an underlying generative graph, and trained networks on role-filler binding question-answering tasks. We tested whether networks can learn to maintain filler information on their own, and whether they can generalize to fillers that they have not seen before. We studied networks by analyzing their behavior and decoding their memory states. We found that a network's success in learning role-filler binding depends on both the breadth of roles introduced during training, and the network's memory architecture. In our decoding analyses, we observed a close relationship between the information we could decode from various parts of network architecture, and the information the network could recall.

Recent studies have demonstrated that reinforcement learning (RL) agents are susceptible to adversarial manipulation, similar to vulnerabilities previously demonstrated in the supervised setting. Accordingly focus has remained with computer vision, and full observability. This paper focuses on reinforcement learning in the context of autonomous defence in Software-Defined Networking (SDN). We demonstrate that causative attacks---attacks that target the training process---can poison RL agents even if the attacker only has partial observability of the environment. In addition, we propose an inversion defence method that aims to apply the opposite perturbation to that which an attacker might use to generate their adversarial samples. Our experimental results illustrate that the countermeasure can effectively reduce the impact of the causative attack, while not significantly affecting the training process in non-attack scenarios.

Crowd-sourcing is a cheap and popular means of creating training and evaluation datasets for machine learning, however it poses the problem of `truth inference', as individual workers cannot be wholly trusted to provide reliable annotations. Research into models of annotation aggregation attempts to infer a latent `true' annotation, which has been shown to improve the utility of crowd-sourced data. However, existing techniques beat simple baselines only in low redundancy settings, where the number of annotations per instance is low ($\le 3$), or in situations where workers are unreliable and produce low quality annotations (e.g., through spamming, random, or adversarial behaviours.) As we show, datasets produced by crowd-sourcing are often not of this type: the data is highly redundantly annotated ($\ge 5$ annotations per instance), and the vast majority of workers produce high quality outputs. In these settings, the majority vote heuristic performs very well, and most truth inference models underperform this simple baseline. We propose a novel technique, based on a Bayesian graphical model with conjugate priors, and simple iterative expectation-maximisation inference. Our technique produces competitive performance to the state-of-the-art benchmark methods, and is the only method that significantly outperforms the majority vote heuristic at one-sided level 0.025, shown by significance tests. Moreover, our technique is simple, is implemented in only 50 lines of code, and trains in seconds.

You can add Microsoft to the growing list of companies whose staff are objecting to the use of their technology for some military purposes. A group of Microsoft workers has published an open letter to CEO Satya Nadella and legal chief Brad Smith asking them to end a $479 million HoloLens contract with the US Army. They contnded that Microsoft is effectively developing weapons by helping the Army create a platform that helps its soldiers train and fight using augmented reality. It not only helps kill people, but turns war "into a simulated'video game'" that disconnects infantry from the "grim stakes" of combat, the workers argued. They also asserted that Microsoft's ethics review process was "opaque" to employees and not strong enough to discourage weapon-related work.

At the opposite end of the country, in Pembroke Pines, Fla., 87-year-old Marilyn Sumkin uses an app called Join Papa to summon what the company calls "grandchildren on demand." College students show up for shopping, chores and chit-chat. Studies have found that loneliness is worse for health than obesity or inactivity, and is as lethal as smoking 15 cigarettes a day. It's also an epidemic: A recent study from Cigna Corp. found that about half of Americans are lonely. According to a recent Harvard University study, the cost of loneliness for Medicare is $6.7 billion a year.