Before presenting employer certification-demand findings, it is necessary to describe the methodology in order to assist in interpreting the results. The certification-demand analysis was performed using the Economic Modeling Specialists International (EMSI) dataset. To populate the dataset, EMSI combs through 100,000 websites, effectively capturing job listings for more than 1.5 million companies. The same job listings regularly appear on multiple websites. To reduce duplicates, EMSI uses a machine learning-based duplicate-detection process.

The recent emergence of artificial intelligence (AI)-powered media manipulations has widespread societal implications for journalism and democracy,7 national security,1 and art.8,14 AI models have the potential to scale misinformation to unprecedented levels by creating various forms of synthetic media.21 For example, AI systems can synthesize realistic video portraits of an individual with full control of facial expressions, including eye and lip movement;11,18,34,35,36 clone a speaker's voice with a few training samples and generate new natural-sounding audio of something the speaker never said;2 synthesize visually indicated sound effects;28 generate high-quality, relevant text based on an initial prompt;31 produce photorealistic images of a variety of objects from text inputs;5,17,27 and generate photorealistic videos of people expressing emotions from only a single image.3,40 The technologies for producing machine-generated, fake media online may outpace the ability to manually detect and respond to such media. We developed a neural network architecture that combines instance segmentation with image inpainting to automatically remove people and other objects from images.13,39 Figure 1 presents four examples of participant-submitted images and their transformations. The AI, which we call a "target object removal architecture," detects an object, removes it, and replaces its pixels with pixels that approximate what the background should look like without the object.

"Life isn't fair" is perhaps one of the most frequently repeated philosophical statements passed down from generation to generation. In a world increasingly dominated by data, however, groups of people that have already been dealt an unfair hand may see themselves further disadvantaged through the use of algorithms to determine whether or not they qualify for employment, housing, or credit, among other basic needs for survival. In the past few years, more attention has been paid to algorithmic bias, but there is still debate about both what can be done to address the issue, as well as what should be done. The use of an algorithm is not at issue; algorithms are essentially a set of instructions on how to complete a problem or task. Yet the lack of transparency surrounding the data and how it is weighed and used for decision making is a key concern, particularly when the algorithm's use may impact people in significant ways, often with no explanation as to why they have been deemed unqualified or unsuitable for a product, service, or opportunity.

The UK government has published its much-awaited National AI Strategy in pursuit of "global science superpower" status. The document talks of plans for a …

I've received a lot of feedback regarding my last few columns about drones, simulations and new uses for artificial intelligence. My most recent column about creating an AI that could task the kind of swarm intelligence found in bee colonies to do some amazing things, like controlling weather satellites, in particular generated a lot of buzz. Much of the feedback I received was from federal agencies and IT companies working on futuristic AI programs. I'll probably be highlighting some of them in the near future. But there was also a note from a company called Hypergiant about how they were working with the Army's Robotic Combat Vehicle program not for some advanced combat project, but simply using AI to help with predictive maintenance tasks.

Remaining useful life (RUL) refers to the expected remaining lifespan of a component or system. Accurate RUL prediction is critical for prognostic and health management and for maintenance planning. In this work, we address three prevalent challenges in data-driven RUL prediction, namely the handling of high dimensional input features, the robustness to noise in sensor data and prognostic datasets, and the capturing of the time-dependency between system degradation and RUL prediction. We devise a highly accurate RUL prediction model with uncertainty quantification, which integrates and leverages the advantages of deep learning and nonstationary Gaussian process regression (DL-NSGPR). We examine and benchmark our model against other advanced data-driven RUL prediction models using the turbofan engine dataset from the NASA prognostic repository. Our computational experiments show that the DL-NSGPR predictions are highly accurate with root mean square error 1.7 to 6.2 times smaller than those of competing RUL models. Furthermore, the results demonstrate that RUL uncertainty bounds with the proposed DL-NSGPR are both valid and significantly tighter than other stochastic RUL prediction models. We unpack and discuss the reasons for this excellent performance of the DL-NSGPR.

An entity mention in text such as "Washington" may correspond to many different named entities such as the city "Washington D.C." or the newspaper "Washington Post." The goal of named entity disambiguation is to identify the mentioned named entity correctly among all possible candidates. If the type (e.g. location or person) of a mentioned entity can be correctly predicted from the context, it may increase the chance of selecting the right candidate by assigning low probability to the unlikely ones. This paper proposes cluster-based mention typing for named entity disambiguation. The aim of mention typing is to predict the type of a given mention based on its context. Generally, manually curated type taxonomies such as Wikipedia categories are used. We introduce cluster-based mention typing, where named entities are clustered based on their contextual similarities and the cluster ids are assigned as types. The hyperlinked mentions and their context in Wikipedia are used in order to obtain these cluster-based types. Then, mention typing models are trained on these mentions, which have been labeled with their cluster-based types through distant supervision. At the named entity disambiguation phase, first the cluster-based types of a given mention are predicted and then, these types are used as features in a ranking model to select the best entity among the candidates. We represent entities at multiple contextual levels and obtain different clusterings (and thus typing models) based on each level. As each clustering breaks the entity space differently, mention typing based on each clustering discriminates the mention differently. When predictions from all typing models are used together, our system achieves better or comparable results based on randomization tests with respect to the state-of-the-art levels on four defacto test sets.

After decades of massive digitisation, an unprecedented amount of historical documents is available in digital format, along with their machine-readable texts. While this represents a major step forward with respect to preservation and accessibility, it also opens up new opportunities in terms of content mining and the next fundamental challenge is to develop appropriate technologies to efficiently search, retrieve and explore information from this 'big data of the past'. Among semantic indexing opportunities, the recognition and classification of named entities are in great demand among humanities scholars. Yet, named entity recognition (NER) systems are heavily challenged with diverse, historical and noisy inputs. In this survey, we present the array of challenges posed by historical documents to NER, inventory existing resources, describe the main approaches deployed so far, and identify key priorities for future developments.

The steady increase in the number of vehicles operating on the highways continues to exacerbate congestion, accidents, energy consumption, and greenhouse gas emissions. Emerging mobility systems, e.g., connected and automated vehicles (CAVs), have the potential to directly address these issues and improve transportation network efficiency and safety. In this paper, we consider a highway merging scenario and propose a framework for coordinating CAVs such that stop-and-go driving is eliminated. We use a decentralized form of the actor-critic approach to deep reinforcement learning$-$multi-agent deep deterministic policy gradient. We demonstrate the coordination of CAVs through numerical simulations and show that a smooth traffic flow is achieved by eliminating stop-and-go driving. Videos and plots of the simulation results can be found at this supplemental $\href{https://sites.google.com/view/ud-ids-lab/MADRL}{site}$.

Deception is a crucial tool in the cyberdefence repertoire, enabling defenders to leverage their informational advantage to reduce the likelihood of successful attacks. One way deception can be employed is through obscuring, or masking, some of the information about how systems are configured, increasing attacker's uncertainty about their targets. We present a novel game-theoretic model of the resulting defender-attacker interaction, where the defender chooses a subset of attributes to mask, while the attacker responds by choosing an exploit to execute. The strategies of both players have combinatorial structure with complex informational dependencies, and therefore even representing these strategies is not trivial. First, we show that the problem of computing an equilibrium of the resulting zero-sum defender-attacker game can be represented as a linear program with a combinatorial number of system configuration variables and constraints, and develop a constraint generation approach for solving this problem. Next, we present a novel highly scalable approach for approximately solving such games by representing the strategies of both players as neural networks. The key idea is to represent the defender's mixed strategy using a deep neural network generator, and then using alternating gradient-descent-ascent algorithm, analogous to the training of Generative Adversarial Networks. Our experiments, as well as a case study, demonstrate the efficacy of the proposed approach.