Walking around without being constantly identified by AI could soon be a thing of the past, legal experts have warned. The use of facial recognition software could signal the end of civil liberties if the law doesn't change as quickly as advancements in technology, they say. Software already being trialled around the world could soon be adopted by companies and governments to constantly track you wherever you go. Shop owners are already using facial recognition to track shoplifters and could soon be sharing information across a broad network of databases, potentially globally. Previous research has found that the technology isn't always accurate, mistakenly identifying women and individuals with darker shades of skin as the wrong people.

Bears can exactly mimic another bear's facial expressions, casting doubt on humans and other primates being the only mammals able to express their emotions. Sun bears have been observed opening their mouths to match their playmates when they are interacting face-to-face. Researchers claim that such facial mimicry has not been seen in primates outside humans and gorillas. Dogs can also use mimic each other to reinforce bonds. In the behavioural study, they found that bears can use facial expressions to communicate with others in a similar way to humans and apes. This'strongly suggests' that other mammals could also perform this complex social skill and, in addition, have a degree of social sensitivity.

Select commuters in New York City and Fairfield, California will have a chance to pioneer a fleet of autonomous vehicles slated to begin serving the cities later this year. Boston-based, Optimus Ride, announced that in the second quarter of 2019 it will deploy a fleet of autonomous cars at New York City's Brooklyn Navy Yard, an up-and-coming modern industrial and business park, as well as Paradise Valley Estates, a senior community in Fairfield, California. For New York, the introduction of Optimus' fully autonomous vehicles will mark the first-ever commercial self-driving car to tread in New York State where it will have a chance to offer rides to thousands of commuters. According to the company, the New York self-driving cars will help serve passengers on the Navy Yards private roads as well as'providing a loop shuttle service to connect NYC Ferry passengers to Flushing Avenue outside the Yard's perimeter.' In Paradise Valley, the cars will serve to provide potential residents of the community their own tours of the neighborhood and in the later phases of its deployment, be able to serve current residents looking to travel to destinations within the gated community.

Food waste could become a thing of the past thanks to an AI powered smart bin that let's you know the type of items you throw away most regularly. The system uses a camera, a set of smart scales and the same type of machine learning technology found in self-driving cars. It comes pre-programmed with common items and learns to recognise different foods being thrown away regularly. It uses this information to calculate the financial and environmental cost of this wasted food, so that you can tailor your next food order accordingly. The smart bin is currently aimed at commercial kitchens but could one day be a common feature in people's homes, the firm hopes. Food waste could become a thing of the past thanks to an AI powered smart bin that let's you know the type of items you throw away most regularly.

An army of'killer robots' that will assist infantry on the battlefield has been unveiled in propaganda footage released by Russia The video, released by the Kremlin, appears to showcase the state's latest drone technology. That includes and AI-controlled driverless tank that follow the aim of a soldier's rifle to obliterate targets with its own weaponry. Russia's Advanced Research Foundation (ARF) said the ultimate goal is to have an army of robots entirely controlled by Artificial Intelligence algorithms. Currently the drones are deployed alongside infantry who remotely control the vehicles, but in the future the tech will be fully autonomous. That means the military hardware will be able to target and kill enemies without any human intervention.

Vampire: The Masquerade – Bloodlines is almost unique in video game history. It suffered a deeply troubled development involving turbulent clashes between developer Troika and publisher Activision, and was eventually released incomplete and deeply broken. And yet, the 2004 financial disaster of a game is adored and celebrated for its innovative dialogue, astounding characters and banquet of choice. Kept alive ever since by a dedicated group of fans who have patched it up and improved it multiple times, it's a game now remembered for its huge ambition rather than its colossal failure. Fifteen years later, a sequel has just been announced.

Information Retrieval (IR) aims at retrieving documents that are most relevant to a query provided by a user. Traditional techniques rely mostly on syntactic methods. In some cases, however, links at a deeper semantic level must be considered. In this paper, we explore a type of IR task in which documents describe sequences of events, and queries are about the state of the world after such events. In this context, successfully matching documents and query requires considering the events' possibly implicit, uncertain effects and side-effects. We begin by analyzing the problem, then propose an action language based formalization, and finally automate the corresponding IR task using Answer Set Programming.

In this study, we establish a basis for selecting similarity measures when applying machine learning techniques to solve materials science problems. This selection is considered with an emphasis on the distinctiveness between materials that reflect their nature well. We perform a case study with a dataset of rare-earth transition metal crystalline compounds represented using the Orbital Field Matrix descriptor and the Coulomb Matrix descriptor. We perform predictions of the formation energies using k-nearest neighbors regression, ridge regression, and kernel ridge regression. Through detailed analyses of the yield prediction accuracy, we examine the relationship between the characteristics of the material representation and similarity measures, and the complexity of the energy function they can capture. Empirical experiments and theoretical analysis reveal that similarity measures and kernels that minimize the loss of materials distinctiveness improve the prediction performance.

The paper studies binary classification and aims at estimating the underlying regression function which is the conditional expectation of the class labels given the inputs. The regression function is the key component of the Bayes optimal classifier, moreover, besides providing optimal predictions, it can also assess the risk of misclassification. We aim at building non-asymptotic confidence regions for the regression function and suggest three kernel-based semi-parametric resampling methods. We prove that all of them guarantee regions with exact coverage probabilities and they are strongly consistent.

Pulmonary nodule detection using low-dose Computed Tomography (CT) is often the first step in lung disease screening and diagnosis. Recently, algorithms based on deep convolutional neural nets have shown great promise for automated nodule detection. Most of the existing deep learning nodule detection systems are constructed in two steps: a) nodule candidates screening and b) false positive reduction, using two different models trained separately. Although it is commonly adopted, the two-step approach not only imposes significant resource overhead on training two independent deep learning models, but also is sub-optimal because it prevents cross-talk between the two. In this work, we present an end-to-end framework for nodule detection, integrating nodule candidate screening and false positive reduction into one model, trained jointly. We demonstrate that the end-to-end system improves the performance by 3.88\% over the two-step approach, while at the same time reducing model complexity by one third and cutting inference time by 3.6 fold. Code will be made publicly available.