This paper demonstrates our vision for knowledge graphs that assist machines to reason about the cause of signals observed by sensors. We show how the approach allows for constructing smarter surveillance systems that reason about the most likely cause (e.g., an attacker breaking a window) of a signal rather than acting directly on the received signal without consideration for how it was produced.

Hail risk assessment is necessary to estimate and reduce damage to crops, orchards, and infrastructure. Also, it helps to estimate and reduce consequent losses for businesses and, particularly, insurance companies. But hail forecasting is challenging. Data used for designing models for this purpose are tree-dimensional geospatial time series. Hail is a very local event with respect to the resolution of available datasets. Also, hail events are rare - only 1% of targets in observations are marked as "hail". Models for nowcasting and short-term hail forecasts are improving. Introducing machine learning models to the meteorology field is not new. There are also various climate models reflecting possible scenarios of climate change in the future. But there are no machine learning models for data-driven forecasting of changes in hail frequency for a given area. The first possible approach for the latter task is to ignore spatial and temporal structure and develop a model capable of classifying a given vertical profile of meteorological variables as favorable to hail formation or not. Although such an approach certainly neglects important information, it is very light weighted and easily scalable because it treats observations as independent from each other. The more advanced approach is to design a neural network capable to process geospatial data. Our idea here is to combine convolutional layers responsible for the processing of spatial data with recurrent neural network blocks capable to work with temporal structure. This study compares two approaches and introduces a model suitable for the task of forecasting changes in hail frequency for ongoing decades.

A study led by Monash University and believed to be a world first has demonstrated that an Artificial Intelligence (AI) model can potentially predict the best personalized, anti-seizure medication for patients with newly diagnosed epilepsy. The predictive model, once fully developed, would spare these patients the uncertainty of not knowing when their lives would be returned to normal by taking anti-seizure medications, and possibly the harmful side-effects associated with some drugs. Professor Patrick Kwan, a neurologist and researcher from the Monash Central Clinical School's Department of Neuroscience is leading an international collaboration that is "training" the deep-learning prediction model (deep learning is a type of machine learning). Their study is published in the influential JAMA Neurology. Epilepsy affects 70 million people worldwide. Currently, choosing anti-seizure drugs for a patient is a process of trial and error with clinicians unable to predict which drug a particular patient will respond to, Professor Kwan said.

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.