As the number of fatal crashes involving truck drivers increases, one company believes artificial intelligence (AI) could help. It may look like a standard mini-tanker, but a small camera in the truck's cab could be a lifesaver. The AI device scans the driver's eyes to detect signs of fatigue and distraction, and if their eye lids close for one and a half seconds, an alarm sounds and the driver's seat will vibrate. A camera also records the moment. "What this technology is about is keeping the driver focussed on the road, and alerting them for any reason if their attention is drawn away," said Charles Dawson, the chief executive of Autosense.

A few days before racing in the 36th match for the America's Cup, the covers have been lifted on the testing and development process, using Artificial Intelligence employed by Emirates Team New Zealand, and developed in conjunction with one of worlds most prestigious consulting firms McKinsey & Company. While the team's use of simulators has been widely discussed, and one is on display at the America's Cup Village. The team has been working with McKinsey subsidiary Quantum Black to develop a "digital twin" of the team's AC75 that used a process of machine learning to perform many more iterations of a sailing situation than was possible using human crew, and to come up with options that were faster than the crew was currently achieving. AI Bots work particularly well when there is large volume of data. The Bot is programmed to self-learn from its own analysis.

We present a continual learning approach for generative adversarial networks (GANs), by designing and leveraging parameter-efficient feature map transformations. Our approach is based on learning a set of global and task-specific parameters. The global parameters are fixed across tasks whereas the task specific parameters act as local adapters for each task, and help in efficiently transforming the previous task's feature map to the new task's feature map. Moreover, we propose an element-wise residual bias in the transformed feature space which highly stabilizes GAN training. In contrast to the recent approaches for continual GANs, we do not rely on memory replay, regularization towards previous tasks' parameters, or expensive weight transformations. Through extensive experiments on challenging and diverse datasets, we show that the feature-map transformation based approach outperforms state-of-the-art continual GANs methods, with substantially fewer parameters, and also generates high-quality samples that can be used in generative replay based continual learning of discriminative tasks.

There has been a growing interest in model-agnostic methods that can make deep learning models more transparent and explainable to a user. Some researchers recently argued that for a machine to achieve a certain degree of human-level explainability, this machine needs to provide human causally understandable explanations, also known as causability. A specific class of algorithms that have the potential to provide causability are counterfactuals. This paper presents an in-depth systematic review of the diverse existing body of literature on counterfactuals and causability for explainable artificial intelligence. We performed an LDA topic modelling analysis under a PRISMA framework to find the most relevant literature articles. This analysis resulted in a novel taxonomy that considers the grounding theories of the surveyed algorithms, together with their underlying properties and applications in real-world data. This research suggests that current model-agnostic counterfactual algorithms for explainable AI are not grounded on a causal theoretical formalism and, consequently, cannot promote causability to a human decision-maker. Our findings suggest that the explanations derived from major algorithms in the literature provide spurious correlations rather than cause/effects relationships, leading to sub-optimal, erroneous or even biased explanations. This paper also advances the literature with new directions and challenges on promoting causability in model-agnostic approaches for explainable artificial intelligence.

Consensus maximisation (MaxCon), which is widely used for robust fitting in computer vision, aims to find the largest subset of data that fits the model within some tolerance level. In this paper, we outline the connection between MaxCon problem and the abstract problem of finding the maximum upper zero of a Monotone Boolean Function (MBF) defined over the Boolean Cube. Then, we link the concept of influences (in a MBF) to the concept of outlier (in MaxCon) and show that influences of points belonging to the largest structure in data would generally be smaller under certain conditions. Based on this observation, we present an iterative algorithm to perform consensus maximisation. Results for both synthetic and real visual data experiments show that the MBF based algorithm is capable of generating a near optimal solution relatively quickly. This is particularly important where there are large number of outliers (gross or pseudo) in the observed data.

We introduce the task of changing the narrative point of view, where characters are assigned a narrative perspective that is different from the one originally used by the writer. The resulting shift in the narrative point of view alters the reading experience and can be used as a tool in fiction writing or to generate types of text ranging from educational to self-help and self-diagnosis. We introduce a benchmark dataset containing a wide range of types of narratives annotated with changes in point of view from deictic (first or second person) to anaphoric (third person) and describe a pipeline for processing raw text that relies on a neural architecture for mention selection. Evaluations on the new benchmark dataset show that the proposed architecture substantially outperforms the baselines by generating mentions that are less ambiguous and more natural.

Three years ago, Customs and Border Protection placed an order for self-flying aircraft that could launch on their own, rendezvous, locate and monitor multiple targets on the ground without any human intervention. In its reasoning for the order, CBP said the level of monitoring required to secure America's long land borders from the sky was too cumbersome for people alone. To research and build the drones, CBP handed $500,000 to Mitre Corp., a trusted nonprofit Skunk Works that was already furnishing border police with prototype rapid DNA testing and smartwatch hacking technology. They were "tested but not fielded operationally" as "the gap from simulation to reality turned out to be much larger than the research team originally envisioned," a CBP spokesperson says. This year, America's border police will test automated drones from Skydio, the Redwood City, Calif.-based startup that on Monday announced it had raised an additional $170 million in venture funding at a valuation of $1 billion. That brings the total raised for Skydio to $340 million.

WHEN IT comes to using artificial intelligence (AI), intelligence agencies have been at it longer than most. In the cold war America's National Security Agency (NSA) and Britain's Government Communications Headquarters (GCHQ) explored early AI to help transcribe and translate the enormous volumes of Soviet phone-intercepts they began hoovering up in the 1960s and 1970s. Your browser does not support the audio element. Yet the technology was immature. One former European intelligence officer says his service did not use automatic transcription or translation in Afghanistan in the 2000s, relying on native speakers instead.

Joining TikTok has become something of a trend for Hollywood celebrities stuck at home like everyone else. So it wasn't necessarily surprising to see Tom Cruise on the app, sharing videos of himself playing golf and pratfalling around the house. But the strange thing is that Cruise never actually made the videos. And the account that posted them, DeepTomCruise, wore that on its sleeve: it was openly the work of a talented creator of "deepfakes", AI-generated video clips that use a variety of techniques to create situations that have never happened in the real world. Despite being open about its falseness, the account's videos are so realistic that they still prompted wild speculation.

The pandemic is a perfect storm for mental health issues. Isolation from others, economic uncertainty, and fear of illness can all contribute to poor mental health -- and right now, most people around the world face all three. New research suggests that the virus is tangibly affecting mental health. Rates of depression and anxiety symptoms are much higher than normal. In some population groups, like students and young people, these numbers are almost double what they've been in the past.