When you hand over your credit card for a new washing machine or refrigerator, would you pay an extra fee to receive alerts about how well it's working, or if you need to call a service technician? Manufacturers of consumer goods are banking on you saying "yes" to the extra cost, just like enterprises do today for service on hardware investments like jet engines and assembly-line technology. This Everything-as-a-Service (XaaS) business model--one which has helped companies in the B2B space generate continuous revenue from their products--is being eyed by consumer companies hungry for income that lasts beyond the initial product purchase. Through "servitization"--combining products with services--businesses can innovate faster and deepen their relationships with customers by providing more value. That value includes data insights derived from IoT-powered devices--from thermostats to wind turbines.

LONDON, Oct 1: Using machine learning – a type of artificial intelligence (AI) – could help develop better predictions of why children struggle at school, scientists say. The researchers from the University of Cambridge in the UK used AI and data from hundreds of children who struggle at school to identify clusters of learning difficulties which did not match the previous diagnosis the children had been given. The finding, published in the journal Developmental Science, reinforces the need for children to receive detailed assessments of their cognitive skills to identify the best type of support. The researchers recruited 550 children who were referred to a clinic because they were struggling at school. Much of the previous research into learning difficulties has focussed on children who had already been given a particular diagnosis, such as attention deficit hyperactivity disorder (ADHD), an autism spectrum disorder, or dyslexia, they said.

The idea of artificial intelligence, first coined in 1956, has dominated popular film (think the Matrix Trilogy or Stanley Kubrick's 2001: A Space Odyssey) and ethical debate -- which, in the UK, is addressed by the National Centre for Data Ethics and Innovation, which aims to position the UK as a world-leading force for the future of AI. This public body can't address the potential problem of ethical AI alone. To ensure that AI develops as a force for good, industry collaboration is required. Digital Catapult has released its first Ethics Framework as a means to integrate ethical practice into the development of artificial intelligence and machine learning technologies. The organisations has invited AI companies to test this framework.

Digital healthcare specialist Babylon has announced plans to invest $100 million to create a multi-disciplinary team dedicated to building next-generation, AI-powered healthcare technologies. The move forms part of a long-term product and service strategy to apply AI to chronic disease management. It builds on recent partnerships with the likes of Tencent, Samsung, Bupa, Prudential, The Gates Foundation, and TELUS. As Babylon scales its operations internationally, it is increasing its focus on chronic conditions – which affect half the US population. Twenty-five percent of the populace in developed countries suffer from mental health issues, while diabetes and anti-obesity treatments cost the UK's NHS an estimated £10 billion and £5 billion each year, respectively.

The modern workplace has already embraced advanced technology with smart devices, paperless workplaces, cloud services and wearable tech that tracks employee productivity. Research collected by flexible workspace specialist Instant Offices shows office workers believe tech integration improves working conditions, efficiency and communication with co-workers. Wearable tech is becoming a part of everyday life, with more and more people relying on devices like smart watches and fitness trackers to help them make more informed lifestyle decisions. In fact, the international market for wearables reached a new high in 2017 with 16.9 per cent growth year on year. Fitbit, Jawbone and Bellabeat have become household names and forward-thinking employers have been keeping a close eye on the rising trend of wearable tech.

The researchers from the Medical Research Council (MRC) Cognition and Brain Sciences Unit at the University of Cambridge say this reinforces the need for children to receive detailed assessments of their cognitive skills to identify the best type of support. The study, published in Developmental Science, recruited 550 children who were referred to a clinic--the Centre for Attention Learning and Memory--because they were struggling at school. The scientists say that much of the previous research into learning difficulties has focussed on children who had already been given a particular diagnosis, such as attention deficit hyperactivity disorder (ADHD), an autism spectrum disorder, or dyslexia. By including children with all difficulties regardless of diagnosis, this study better captured the range of difficulties within, and overlap between, the diagnostic categories. Dr. Duncan Astle from the MRC Cognition and Brain Sciences Unit at the University of Cambridge, who led the study said: ...

Researchers have used machine learning to more accurately identify children with learning difficulties who, until now, have either been misdiagnosed, or have gone under the radar of education authorities. Scientists at the Medical Research Council (MRC) Cognition and Brain Sciences Unit at the University of Cambridge said by using data from hundreds of children who struggle at school, they were able to identify new clusters of learning difficulties that did not match the previous diagnoses some children had been given. The study, published in Developmental Science, recruited 550 children who were referred to a clinic – the Centre for Attention Learning and Memory – because they were experiencing problems at school. The team build up a machine learning algorithm with a range of cognitive testing data from each child, including measures of listening skills, spatial reasoning, problem-solving, vocabulary, and memory. Based on this data, the algorithm suggested that the children best fitted into four clusters of difficulties.

Pics Images generated by AI have always been pretty easy to spot since they are always slightly odd to the human eye, but it's getting harder to differentiate what's real and fake. Researchers from DeepMind and Heriot-Watt University in the UK have managed to significantly boost the quality of images simulated by a generative adversarial network (GAN) by increasing the size of the machine learning model, which they dubbed BigGANs. The best results, including pictures of a brown dog with floppy ears, a island landscape, a butterfly, and a cheeseburger, look like real photos at first glance. Keep staring, however, and you will begin to see some slight inconsistencies. The dog's eyes are glazed over and there is a weird patch that doesn't belong to the butterfly's wing.

Reinforcement learning has shown great potential in generalizing over raw sensory data using only a single neural network for value optimization. There are several challenges in the current state-of-the-art reinforcement learning algorithms that prevent them from converging towards the global optima. It is likely that the solution to these problems lies in short- and long-term planning, exploration and memory management for reinforcement learning algorithms. Games are often used to benchmark reinforcement learning algorithms as they provide a flexible, reproducible, and easy to control environment. Regardless, few games feature a state-space where results in exploration, memory, and planning are easily perceived. This paper presents The Dreaming Variational Autoencoder (DVAE), a neural network based generative modeling architecture for exploration in environments with sparse feedback. We further present Deep Maze, a novel and flexible maze engine that challenges DVAE in partial and fully-observable state-spaces, long-horizon tasks, and deterministic and stochastic problems. We show initial findings and encourage further work in reinforcement learning driven by generative exploration.

Buried landmines and unexploded remnants of war are a constant threat for the population of many countries that have been hit by wars in the past years. The huge amount of human lives lost due to this phenomenon has been a strong motivation for the research community toward the development of safe and robust techniques designed for landmine clearance. Nonetheless, being able to detect and localize buried landmines with high precision in an automatic fashion is still considered a challenging task due to the many different boundary conditions that characterize this problem (e.g., several kinds of objects to detect, different soils and meteorological conditions, etc.). In this paper, we propose a novel technique for buried object detection tailored to unexploded landmine discovery. The proposed solution exploits a specific kind of convolutional neural network (CNN) known as autoencoder to analyze volumetric data acquired with ground penetrating radar (GPR) using different polarizations. This method works in an anomaly detection framework, indeed we only train the autoencoder on GPR data acquired on landmine-free areas. The system then recognizes landmines as objects that are dissimilar to the soil used during the training step. Experiments conducted on real data show that the proposed technique requires little training and no ad-hoc data pre-processing to achieve accuracy higher than 93% on challenging datasets.