Trials for AI lie detection at border patrol checkpoints are set to begin soon in the EU. The program, called iBorderCtrl, will run for six months at four border crossing points in Hungary, Latvia and Greece with countries outside the European Union, as reported by Gizmodo. The system has users fill out an online application and upload some documents, like their passport, before a virtual border guard takes over to ask questions. According to New Scientist, some of these questions include "What's in your suitcase?" If iBorderCtrl determines the traveler is telling the truth, then they receive a QR code that will let them pass the border.

Philosopher René Descartes' quote "cogito, ergo sum" became well known after being translated into English as "I think, therefore I am." It was the result of his attempt to figure out which of the things he thought he knew were impervious to doubt. Long before Descartes uttered these words, however, humans were telling stories about artificial beings and thinking about what defines humanity. A new book by Adrienne Mayor, entitled Gods and Robots: The Ancient Quest for Artificial Life, explores the history of robots from as far back as Ancient Greece. In the book, Mayor asks whether ancient myths such as the story of Pandora and her famous box of misery have influenced the finest technological minds of the 20th and 21st centuries.

Artificial intelligence (AI), in its many forms including self-driving cars, robo-advisors, mechanical baristas, and state-run facial recognition campaigns will go ahead and continue to redefine how individuals work and have their views about school, work, government, and daily life. AI startups have been marching ahead in the technology bandwagon gaining funding in a world where supergiant rounds are now quite common. The top well-funded AI startup to date, SenseTime has brought a funding in a total of $1.6 billion. Most recently, in May 2018 the company raised $620 million in a Series C round led by Tiger Global Management and Fidelity International. This round raised SenseTime's valuation to more than $4.5 billion, making it the world's most valuable artificial intelligence startup.

Over the past 100 years new industries and new ways of working have divided the UK's towns and cities into haves and have-nots. Some have managed to make the transition, while others have been unable to recreate the boom they enjoyed in the early 20th Century. Why have some cities managed to stride ahead, while others have fallen by the wayside? Funding, infrastructure and opportunity all play a part, marking the difference between the places able to adapt and those forced into a cycle of low-paid, low-skilled jobs. In the decades after World War Two, London's economy fell into long-term decline.

After 12 years of construction and £15m in funding, a giant computer designed to mimic the human brain is finally ready to be switched on. Built by the University of Manchester, the SpiNNaker machine is made up of one million processors capable of 200 trillion actions per second – meaning it can model more biological neurons in real time than any other machine ever built. Unlike traditional computers, it does not communicate by sending large amounts of information from point A to point B via a standard network. Instead, it mimics the parallel communication architecture of the brain by sending small amounts of information to different destinations simultaneously. The I.F.O. is fuelled by eight electric engines, which is able to push the flying object to an estimated top speed of about 120mph.

We provide frequentist estimates of aleatoric and epistemic uncertainty for deep neural networks. To estimate aleatoric uncertainty we propose simultaneous quantile regression, a loss function to learn all the conditional quantiles of a given target variable. These quantiles lead to well-calibrated prediction intervals. To estimate epistemic uncertainty we propose training certificates, a collection of diverse non-trivial functions that map all training samples to zero. These certificates map out-of-distribution examples to non-zero values, signaling high epistemic uncertainty. We compare our proposals to prior art in various experiments.

We propose a multi-label multi-task framework based on a convolutional recurrent neural network to unify detection of isolated and overlapping audio events. The framework leverages the power of convolutional recurrent neural network architectures; convolutional layers learn effective features over which higher recurrent layers perform sequential modelling. Furthermore, the output layer is designed to handle arbitrary degrees of event overlap. At each time step in the recurrent output sequence, an output triple is dedicated to each event category of interest to jointly model event occurrence and temporal boundaries. That is, the network jointly determines whether an event of this category occurs, and when it occurs, by estimating onset and offset positions at each recurrent time step. We then introduce three sequential losses for network training: multi-label classification loss, distance estimation loss, and confidence loss. We demonstrate good generalization on two datasets: ITC-Irst for isolated audio event detection, and TUT-SED-Synthetic-2016 for overlapping audio event detection.

Automated planning is one of the foundational areas of AI. Since a single planner unlikely works well for all tasks and domains, portfolio-based techniques become increasingly popular recently. In particular, deep learning emerges as a promising methodology for online planner selection. Owing to the recent development of structural graph representations of planning tasks, we propose a graph neural network (GNN) approach to selecting candidate planners. GNNs are advantageous over a straightforward alternative, the convolutional neural networks, in that they are invariant to node permutations and that they incorporate node labels for better inference. Additionally, for cost-optimal planning, we propose a two-stage adaptive scheduling method to further improve the likelihood that a given task is solved in time. The scheduler may switch at halftime to a different planner, conditioned on the observed performance of the first one. Experimental results validate the effectiveness of the proposed method against strong baselines, both deep learning and non-deep learning based.

Personal electronic devices such as smartphones give access to a broad range of behavioral signals that can be used to learn about the characteristics and preferences of individuals. In this study we explore the connection between demographic and psychological attributes and digital records for a cohort of 7,633 people, closely representative of the US population with respect to gender, age, geographical distribution, education, and income. We collected self-reported assessments on validated psychometric questionnaires based on both the Moral Foundations and Basic Human Values theories, and combined this information with passively-collected multi-modal digital data from web browsing behavior, smartphone usage and demographic data. Then, we designed a machine learning framework to infer both the demographic and psychological attributes from the behavioral data. In a cross-validated setting, our model is found to predict demographic attributes with good accuracy (weighted AUC scores of 0.90 for gender, 0.71 for age, 0.74 for ethnicity). Our weighted AUC scores for Moral Foundation attributes (0.66) and Human Values attributes (0.60) suggest that accurate prediction of complex psychometric attributes is more challenging but feasible. This connection might prove useful for designing personalized services, communication strategies, and interventions, and can be used to sketch a portrait of people with similar worldviews.

Due to big data analysis ability, machine learning (ML) algorithms are becoming popular for several applications in autonomous vehicles. However, ML algorithms possessinherent security vulnerabilities which increase the demand for robust ML algorithms. Recently, various groups have demonstrated how vulnerabilities in ML can be exploited to perform several security attacks for confidence reduction and random/targeted misclassification, by using the data manipulation techniques. These traditional data manipulation techniques, especially during the training stage, introduce the random visual noise. However, such visual noise can be detected during the attack or testing through noise detection/filtering or human-in-the-loop. In this paper, we propose a novel methodology to automatically generate an "imperceptible attack" by exploiting the back-propagation property of trained deep neural networks (DNNs). Unlike state-of-the-art inference attacks, our methodology does not require any knowledge of the training data set during the attack image generation. To illustrate the effectiveness of the proposed methodology, we present a case study for traffic sign detection in an autonomous driving use case. We deploy the state-of-the-art VGGNet DNN trained for German Traffic Sign Recognition Benchmarks (GTSRB) datasets. Our experimental results show that the generated attacks are imperceptible in both subjective tests (i.e., visual perception) and objective tests (i.e., without any noticeable change in the correlation and structural similarity index) but still performs successful misclassification attacks.