Far right conspiracy theorist Alex Jones is suing PayPal over claims that his InfoWars website was blocked due to political bias. PayPal is one of several technology companies that have banned the controversial site from their platform, with Twitter, Facebook and Spotify all saying that Mr Jones' promotion of hate and violence is in violation of their policies. InfoWars has previously reported that the Sandy Hook Elementary School shooting in 2012 – in which 20 students and six staff members were killed – was a hoax. In a 15-page complaint, Mr Jones' company Free Speech Systems claims that the bans are purely political. "It is at this point well known that large tech companies, located primarily in Silicon Valley, are discriminating against politically conservative entities and individuals, including banning them from social media platforms such as Twitter, based solely on their political and ideological viewpoints," the complaint states.

Racing drones has become fairly common place in engineering departments at universities, but students from the University of Florida took it to a new level by controlling the flying robots with their minds. The students controlled the drones using technology called brain-computer interface, Juan Gilbert, endowed professor and chair of UF's computer and information science and engineering program, explained in a video posted Friday. When people are hooked up to BCI technology, it gives them the ability to control an external device with their mind -- sort of like mastering the force or telekinesis. It's actually the same technology that gave a paralyzed man the ability to walk again. The students wore electroencephalogram (EEG) headbands that are able to measure electrical impulses from the brain.

Professor Martin Carroll is an Executive General Manager, Academic & Provost Manukau Institute of Technology, New Zealand. In 2017, Martin co-founded with Blackboard, a global forum for the Ethical Use of AI in higher education. Drawing upon the projections of realists and the visions of sci-fi, Martin's entertaining and thought provoking talk will leave you viewing education in an entirely new light! One clear conclusion is the need for ethical guidance for AI and AI-type technologies. Drawing upon the projections of realists and the visions of sci-fi, Martin's entertaining and thought-provoking talk will leave you viewing education in an entirely new light.

If you've been thinking about trying to learn deep learning, here's a new software library that promises to make things easy. Fast.ai, a startup co-founded by Rachel Thomas and Jeremy Howard, a professor and research scientist both working at the University of San Francisco, have released a free open source framework that works on top of PyTorch. Known as fastai (without a dot), it's aimed at budding coders that have some experience with Python. It includes some of the most popular algorithms for image classification and natural language tasks so that models can be quickly built and run in just few lines of code. No other library that we know of provides such an easy way to leverage Nvidia's latest technology, which gives two to three times better performance compared to previous approaches," the startup announced on Tuesday. Fast.ai is known for its free deep learning introductory courses that have been completed by people that don't necessarily have technical backgrounds. "Our goal is to get to a point where we don't need to teach courses any more - where we've made deep learning so easy to use, that anyone can use it (and get world-class results) without needing to take a course," Howard told The Register. "The only way to get there is for us to write software that makes deep learning easier.

This survey presents an overview of verification techniques for autonomous systems, with a focus on safety-critical autonomous cyber-physical systems (CPS) and subcomponents thereof. Autonomy in CPS is enabling by recent advances in artificial intelligence (AI) and machine learning (ML) through approaches such as deep neural networks (DNNs), embedded in so-called learning enabled components (LECs) that accomplish tasks from classification to control. Recently, the formal methods and formal verification community has developed methods to characterize behaviors in these LECs with eventual goals of formally verifying specifications for LECs, and this article presents a survey of many of these recent approaches.

Building effective, enjoyable, and safe autonomous vehicles is a lot harder than has historically been considered. The reason is that, simply put, an autonomous vehicle must interact with human beings. This interaction is not a robotics problem nor a machine learning problem nor a psychology problem nor an economics problem nor a policy problem. It is all of these problems put into one. It challenges our assumptions about the limitations of human beings at their worst and the capabilities of artificial intelligence systems at their best. This work proposes a set of principles for designing and building autonomous vehicles in a human-centered way that does not run away from the complexity of human nature but instead embraces it. We describe our development of the Human-Centered Autonomous Vehicle (HCAV) as an illustrative case study of implementing these principles in practice.

Mining dense quasi-cliques is a well-known clustering task with applications ranging from social networks over collaboration graphs to document analysis. Recent work has extended this task to multiple graphs; i.e. the goal is to find groups of vertices highly dense among multiple graphs. In this paper, we argue that in a multi-graph scenario the sparsity is valuable for knowledge extraction as well. We introduce the concept of contrasting quasi-clique patterns: a collection of vertices highly dense in one graph but highly sparse (i.e. less connected) in a second graph. Thus, these patterns specifically highlight the difference/contrast between the considered graphs. Based on our novel model, we propose an algorithm that enables fast computation of contrasting patterns by exploiting intelligent traversal and pruning techniques. We showcase the potential of contrasting patterns on a variety of synthetic and real-world datasets.

We present a developmental framework based on a long-term memory and reasoning mechanisms (Vision Similarity and Bayesian Optimisation). This architecture allows a robot to optimize autonomously hyper-parameters that need to be tuned from any action and/or vision module, treated as a black-box. The learning can take advantage of past experiences (stored in the episodic and procedural memories) in order to warm-start the exploration using a set of hyper-parameters previously optimized from objects similar to the new unknown one (stored in a semantic memory). As example, the system has been used to optimized 9 continuous hyper-parameters of a professional software (Kamido) both in simulation and with a real robot (industrial robotic arm Fanuc) with a total of 13 different objects. The robot is able to find a good object-specific optimization in 68 (simulation) or 40 (real) trials. In simulation, we demonstrate the benefit of the transfer learning based on visual similarity, as opposed to an amnesic learning (i.e. learning from scratch all the time). Moreover, with the real robot, we show that the method consistently outperforms the manual optimization from an expert with less than 2 hours of training time to achieve more than 88% of success.

In this work, we attempt to answer a critical question: whether there exists some input sequence that will cause a well-trained discrete-space neural network sequence-to-sequence (seq2seq) model to generate egregious outputs (aggressive, malicious, attacking, etc.). And if such inputs exist, how to find them efficiently. We adopt an empirical methodology, in which we first create lists of egregious output sequences, and then design a discrete optimization algorithm to find input sequences that will cause the model to generate them. Moreover, the optimization algorithm is enhanced for large vocabulary search and constrained to search for input sequences that are likely to be input by real-world users. In our experiments, we apply this approach to dialogue response generation models trained on three real-world dialogue data-sets: Ubuntu, Switchboard and OpenSubtitles, testing whether the model can generate malicious responses. We demonstrate that given the trigger inputs our algorithm finds, a significant number of malicious sentences are assigned large probability by the model, which reveals an undesirable consequence of standard seq2seq training. Recently, research on adversarial attacks (Goodfellow et al., 2014; Szegedy et al., 2013) has been gaining increasing attention: it has been found that for trained deep neural networks (DNNs), when an imperceptible perturbation is applied to the input, the output of the model can change significantly (from correct to incorrect). This line of research has serious implications for our understanding of deep learning models and how we can apply them securely in real-world applications. It has also motivated researchers to design new models or training procedures (Madry et al., 2017), to make the model more robust to those attacks.

An artificial intelligence algorithm has outperformed humans in a reading comprehension test for the first time, potentially putting millions of customer service jobs at risk of automation. The AI algorithm, developed by Chinese retail giant Alibaba, outscored humans in the Stanford Question Answering Dataset--a global reading test consisting of more than 100,000 questions. Using natural-language processing, the machine-learning model developed by Alibaba's Institute of Data Science of Technologies beat rival humans with a score of 82.44 versus 82.305, the company said. According to the researchers, the landmark result could have a significant impact in introducing the technology into roles typically performed by humans, as the AI algorithm can provide precise answers to questions when provided with vast amounts of information from resources like Wikipedia. "That means objective questions such as'What causes rain' can now be answered with high accuracy by machines," Luo Si, chief scientist for natural language processing at the Institute of Data Science of Technologies, said in a statement.