At this point in the film, with artificial intelligence (AI) creating art, music and chronicles, directing ships and planes, creating deepfakes that put the most experienced of views to the test and passing almost as human --or without the almost, that depending on who you ask--the question seems quite pertinent: why not let it set the course for governments? It sounds bizarre, but in the Kingdom of Denmark there are those who believe that it would be a barbaric idea. So much so, in fact, that he has already set in motion the administrative machinery to achieve it. At the end of May, the artist collective Computer Lars launched Det Syntetiske Parti, which translated into Spanish would be equivalent to El Partido Sintético. The name may be somewhat cryptic, but it captures part of the essence of the Danish formation: its artificial character and its effort to synthesize ideas, something that it has achieved by using precisely the possibilities offered by AI.

Robotic process automation (RPA) has been well received and is making a significant difference to business processes across organisations. At its next level, RPA is being enhanced by artificial intelligence (AI) to transform business smartly. This is according to speakers at a roundtable hosted by UiPath in Cape Town, where executives discussed AI, automation the future of work. Michael Law, country manager at UiPath, told delegates: "RPA alone was last year. It has transformed areas such as finance and HR. UiPath is now bringing AI and automation together across the organisation."

Artificial intelligence and automation are seeping into our daily working lives -- and female office workers are among those whose jobs are being taken over by machines. That's according to new research provided to ABC News by an Australian teaching organisation that's urging people to upskill so they don't find themselves out of work. Pearson's research looks at roles that are likely to be automated as technology advances. Historically, much of the conversation when it comes to automation has been about robots taking over factory jobs, or even replacing retail assistants in the form of self-service check-outs at supermarkets. Pearson's data also shows the less obvious pictures of automation encroaching into office environments, including for medical receptionists, accountants and personal assistants.

Soon the turn of the year has arrived, bringing us the most unique technological solutions to rule over the outdated ones. One sector which is sure to see new techniques is that of software testing! New approaches to testing are being introduced in the IT industry due to the emergence of development technologies like DevOps and Agile. Therefore, the need to keep up and transform your own testing techniques according to the new ones is very important. For this reason, we have created a list of the important software testing techniques that you have to learn. The'Internet of Things is a technology that has brought with it a radical change in the way communication between multiple devices took place traditionally.

Runtime enforcement refers to the theories, techniques, and tools for enforcing correct behavior with respect to a formal specification of systems at runtime. In this paper, we are interested in techniques for constructing runtime enforcers for the concrete application domain of enforcing safety in AI. We discuss how safety is traditionally handled in the field of AI and how more formal guarantees on the safety of a self-learning agent can be given by integrating a runtime enforcer. We survey a selection of work on such enforcers, where we distinguish between approaches for discrete and continuous action spaces. The purpose of this paper is to foster a better understanding of advantages and limitations of different enforcement techniques, focusing on the specific challenges that arise due to their application in AI. Finally, we present some open challenges and avenues for future work.

To track the target in a video, current visual trackers usually adopt greedy search for target object localization in each frame, that is, the candidate region with the maximum response score will be selected as the tracking result of each frame. However, we found that this may be not an optimal choice, especially when encountering challenging tracking scenarios such as heavy occlusion and fast motion. To address this issue, we propose to maintain multiple tracking trajectories and apply beam search strategy for visual tracking, so that the trajectory with fewer accumulated errors can be identified. Accordingly, this paper introduces a novel multi-agent reinforcement learning based beam search tracking strategy, termed BeamTracking. It is mainly inspired by the image captioning task, which takes an image as input and generates diverse descriptions using beam search algorithm. Accordingly, we formulate the tracking as a sample selection problem fulfilled by multiple parallel decision-making processes, each of which aims at picking out one sample as their tracking result in each frame. Each maintained trajectory is associated with an agent to perform the decision-making and determine what actions should be taken to update related information. When all the frames are processed, we select the trajectory with the maximum accumulated score as the tracking result. Extensive experiments on seven popular tracking benchmark datasets validated the effectiveness of the proposed algorithm.

Automated Machine Learning with ensembling (or AutoML with ensembling) seeks to automatically build ensembles of Deep Neural Networks (DNNs) to achieve qualitative predictions. Ensemble of DNNs are well known to avoid over-fitting but they are memory and time consuming approaches. Therefore, an ideal AutoML would produce in one single run time different ensembles regarding accuracy and inference speed. While previous works on AutoML focus to search for the best model to maximize its generalization ability, we rather propose a new AutoML to build a larger library of accurate and diverse individual models to then construct ensembles. First, our extensive benchmarks show asynchronous Hyperband is an efficient and robust way to build a large number of diverse models to combine them. Then, a new ensemble selection method based on a multi-objective greedy algorithm is proposed to generate accurate ensembles by controlling their computing cost. Finally, we propose a novel algorithm to optimize the inference of the DNNs ensemble in a GPU cluster based on allocation optimization. The produced AutoML with ensemble method shows robust results on two datasets using efficiently GPU clusters during both the training phase and the inference phase. Deep Neural networks (DNNs) are notoriously difficult to tune, train, and ensemble to achieve state-of-the-art results. Automatic machine learning with ensembling or "AutoML+ensembling" tools provide a simple interface to train and evaluate many ensembles of DNNs to achieve high accuracy by reducing overfitting. Nowadays, multiple researchers and practitioners have well understood the benefit of ensembling DNNs. Further, several winners and top performers on challenges routinely use ensembles to improve accuracy. However, ensembles of DNNs suffer from three main limitations to be widely deployed in research and industrial applications.

A big convergence of language, vision, and multimodal pretraining is emerging. In this work, we introduce a general-purpose multimodal foundation model BEiT-3, which achieves state-of-the-art transfer performance on both vision and vision-language tasks. Specifically, we advance the big convergence from three aspects: backbone architecture, pretraining task, and model scaling up. We introduce Multiway Transformers for general-purpose modeling, where the modular architecture enables both deep fusion and modality-specific encoding. Based on the shared backbone, we perform masked "language" modeling on images (Imglish), texts (English), and image-text pairs ("parallel sentences") in a unified manner. Experimental results show that BEiT-3 obtains state-of-the-art performance on object detection (COCO), semantic segmentation (ADE20K), image classification (ImageNet), visual reasoning (NLVR2), visual question answering (VQAv2), image captioning (COCO), and cross-modal retrieval (Flickr30K, COCO).

Structural health monitoring (SHM) has been an active research area for the last three decades, and has accumulated a number of critical advances over that period, as can be seen in the literature. However, SHM is still facing challenges because of the paucity of damage-state data, operational and environmental fluctuations, repeatability issues, and changes in boundary conditions. These issues present as inconsistencies in the captured features and can have a huge impact on the practical implementation, but more critically, on the generalisation of the technology. Population-based SHM has been designed to address some of these concerns by modelling and transferring missing information using data collected from groups of similar structures. In this work, vibration data were collected from four healthy, nominally-identical, full-scale composite helicopter blades. Manufacturing differences (e.g., slight differences in geometry and/or material properties), among the blades presented as variability in their structural dynamics, which can be very problematic for SHM based on machine learning from vibration data. This work aims to address this variability by defining a general model for the frequency response functions of the blades, called a form, using mixtures of Gaussian processes.

Camera traps have revolutionized the animal research of many species that were previously nearly impossible to observe due to their habitat or behavior. They are cameras generally fixed to a tree that take a short sequence of images when triggered. Deep learning has the potential to overcome the workload to automate image classification according to taxon or empty images. However, a standard deep neural network classifier fails because animals often represent a small portion of the high-definition images. That is why we propose a workflow named Weakly Object Detection Faster-RCNN+FPN which suits this challenge. The model is weakly supervised because it requires only the animal taxon label per image but doesn't require any manual bounding box annotations. First, it automatically performs the weakly-supervised bounding box annotation using the motion from multiple frames. Then, it trains a Faster-RCNN+FPN model using this weak supervision. Experimental results have been obtained with two datasets from a Papua New Guinea and Missouri biodiversity monitoring campaign, then on an easily reproducible testbed.