Graph neural networks (GNNs) belong to a category of neural networks that operate naturally on data structured as graphs. Despite being what can be a confusing topic, GNNs can be distilled into just a handful of simple concepts. We'll pick a likely familiar starting point: recurrent neural networks. As you may recall, recurrent neural networks are well-suited to data that are arranged in a sequence, such as time series data or language. The defining feature for a recurrent neural network is that the state of an RNN depends not only on the current inputs but also on the network's previous hidden state.

The applications of Artificial Intelligence (AI) for X-ray and CT-Scan image analysis using Convolutional neural network architectures, Generative adversarial networks, transfer learning, and data augmentation techniques are discussed. Currently, AI algorithms embedded on a mobile x-ray and CT-Scan devices for automated diagnosis, measurements, case prioritization, and quality control are most popular research area. More than 60,000 research articles have been published related to the use of deep learning in healthcare and related applications. Established architectures, such as ResNet-50 or DenseNet-161 (with 50 and 161 representing the number of layers within the respective neural network) are easy to use. Integration of the AI modules with the drug systems and the experts are the key issues of implementing AI systems in healthcare.

As DeepMind, the British artificial intelligence (AI) firm owned by Google, claims to have solved one of science's toughest and most enduring mysteries, the'protein folding problem', you can't help but think what sort of genius must be the driving forced behind such a triumph. 'Thrilled to announce our first major breakthrough in applying AI to a grand challenge in science,' writes Demis Hassabis, the company's 44-year-old founder says in reaction to the news. But was it really a surprise that Hassabis' firm had achieved such a feat? Thirty years ago, Hassabis was the world's second best 12-year-old chess player, his career as a future grandmaster set out before him. 'Thrilled to announce our first major breakthrough in applying AI to a grand challenge in science,' writes Demis Hassabis, the company's 44-year-old founder says in reaction to the news But while he loved the game and what it taught him about his own thought processes that brought such success, the youngster realised the game of chess was not what actually interested him. 'It got me into thinking about the process of thought: what is intelligence, how is my brain coming up with these ideas?' Hassabis finished his A-levels at 15, and although he was accepted into Cambridge he would have to wait until he was old enough to enrol.

A long-standing and incredibly complex scientific problem concerning the structure and behaviour of proteins has been effectively solved by a new artificial intelligence (AI) system, scientists report. DeepMind, the UK-based AI company, has wowed us for years with its parade of ever-advancing neural networks that continually trounce humans at complex games such as chess and Go. All those incremental advancements were about much more than mastering recreational diversions, however. In the background, DeepMind's researchers were seeking to coax their AIs towards solving much more fundamentally important scientific puzzles – such as finding new ways to fight disease by predicting infinitesimal but vitally important aspects of human biology. Now, with the latest version of their AlphaFold AI engine, they seem to have actually achieved this very ambitious goal – or at least gotten us closer than scientists ever have before. For about 50 years, researchers have strived to predict how proteins achieve their three-dimensional structure, and it's not an easy problem to solve.

ServiceNow Inc. is beefing up its artificial intelligence development capabilities with the acquisition today of a company called Element AI Inc. that's widely known as one of the pioneers in the field. Montreal-based Element AI launched back in 2016 as a professional services firm focused on helping traditional enterprises implement machine learning. The startup garnered significant industry attention from the outset thanks in part to its high-profile co-founder, the well-known deep learning researcher Yoshua Bengio, who won the Turing Award in 2018 for his contributions to the field. Element AI has gradually expanded its focus since its launch by creating a fund to support fellow machine learning companies and introducing ready-made AI tools. The company's offerings include Knowledge Scout, a search engine for manufacturers that speeds up the diagnosis and repair of production line issues by giving technicians relevant information about previous incidents with similar characteristics.

Alphabet-owned DeepMind has developed a piece of artificial intelligence software that can accurately predict the structure that proteins will fold into in a matter of days, solving a 50-year-old "grand challenge" that could pave the way for better understanding of diseases and drug discovery. Every living cell has thousands of different proteins inside that keep it alive and well. Predicting the shape that a protein will fold into is important because it determines their function and nearly all diseases, including cancer and dementia, are related to how proteins function. "Proteins are the most beautiful, gorgeous structures and the ability to predict exactly how they fold up is really very, very challenging and has occupied many people over many years," Professor Dame Janet Thornton from the European Bioinformatics Institute told journalists on a call. British research lab DeepMind's "AlphaFold" AI system was entered into a competition organized by a group called CASP (Critical Assessment for Structure Prediction). It's a community experiment organization with the mission of accelerating solutions to one problem: how to compute the 3D structure of protein molecules.

A trio of researchers at Johannes Kepler University has used artificial intelligence to improve thermal imaging camera searches of people lost in the woods. In their paper published in the journal Nature Machine Intelligence, David Schedl, Indrajit Kurmi and Oliver Bimber, describe how they applied a deep learning network to the problem of people lost in the woods and how well it worked. When people become lost in forests, search and rescue experts use helicopters to fly over the area where they are most likely to be found. In addition to simply scanning the ground below, the researchers use binoculars and thermal imaging cameras. It is hoped that such cameras will highlight differences in body temperature of people on the ground versus their surroundings making them easier to spot.

Current deep reinforcement learning (DRL) algorithms utilize randomness in simulation environments to assume complete coverage in the state space. However, particularly in high dimensions, relying on randomness may lead to gaps in coverage of the trained DRL neural network model, which in turn may lead to drastic and often fatal real-world situations. To the best of the author's knowledge, the assessment of coverage for DRL is lacking in current research literature. Therefore, in this paper, a novel measure, Approximate Pseudo-Coverage (APC), is proposed for assessing the coverage in DRL applications. We propose to calculate APC by projecting the high dimensional state space on to a lower dimensional manifold and quantifying the occupied space. Furthermore, we utilize an exploration-exploitation strategy for coverage maximization using Rapidly-Exploring Random Tree (RRT). The efficacy of the assessment and the acceleration of coverage is demonstrated on standard tasks such as Cartpole, highway-env.

Deep learning applied to electrocardiogram (ECG) data can be used to achieve personal authentication in biometric security applications, but it has not been widely used to diagnose cardiovascular disorders. We developed a deep learning model for the detection of arrhythmia in which time-sliced ECG data representing the distance between successive R-peaks are used as the input for a convolutional neural network (CNN). The main objective is developing the compact deep learning based detect system which minimally uses the dataset but delivers the confident accuracy rate of the Arrhythmia detection. This compact system can be implemented in wearable devices or real-time monitoring equipment because the feature extraction step is not required for complex ECG waveforms, only the R-peak data is needed. The results of both tests indicated that the Compact Arrhythmia Detection System (CADS) matched the performance of conventional systems for the detection of arrhythmia in two consecutive test runs. All features of the CADS are fully implemented and publicly available in MATLAB.

Activity recognition in smart homes is essential when we wish to propose automatic services for the inhabitants. However, it is a challenging problem in terms of environments' variability, sensory-motor systems, user habits, but also sparsity of signals and redundancy of models . Therefore, end-to-end systems fail at automatically extracting key features, and need to access context and domain knowledge. We propose to tackle feature extraction for activity recognition in smart homes by merging methods of Natural Language Processing (NLP) and Time Series Classification (TSC) domains. We evaluate the performance of our method with two datasets issued from the Center for Advanced Studies in Adaptive Systems (CASAS). We analyze the contributions of the use of embedding based on term frequency encoding, to improve automatic feature extraction. Moreover we compare the classification performance of Fully Convolutional Network (FCN) from TSC, applied for the first time for activity recognition in smart homes, to Long Short Term Memory (LSTM). The method we propose, shows good performance in offline activity classification. Our analysis also shows that FCNs outperforms LSTMs, and that domain knowledge gained by event encoding and embedding improves significantly the performance of classifiers.