Convolutional Neural Networks which are the backbones of most of the Computer Vision Applications like Self-Driving Cars, Facial Recognition Systems etc are a special kind of Neural Network architectures in which the basic matrix-multiplication operation is replaced by a convolution operation. They specialize in processing data which has a grid-like topology. Examples include time-series data and image-data which can be thought of as a 2-D grid of pixels. The Convolutional Neural Networks was first introduced by Fukushima by the name Neocognitron in 1980. It was inspired by the hierarchical model of the nervous system as proposed by Hubel and Weisel.

A research team from MIT, Adobe Research, and Shanghai Jiao Tong University have introduced a novel method for reducing the cost and size of Conditional GAN generators. Generative Adversarial Networks (GAN) excel at synthesizing photorealistic images. Conditional GANS, or cGANs, provide more controllable image synthesis and enable many computer vision and graphics applications, for example motion transfer of a dance video to a different person, creating VR facial animations for remote social interaction, etc. The problem is, cGANs are notoriously computationally intensive, and this prevents them from being deployed on edge devices like mobile phones, tablets or VR headsets with insufficient hardware resources, memory or power. GAN Compression, the general-purpose compression method the team presents in their paper, has proven effective across different supervision settings (paired and unpaired), model architectures, and learning methods (e.g.

The European Commission launches an initiative to collect ideas about deployable Artificial Intelligence (AI) and Robotics solutions as well as information on other initiatives that could help face the ongoing COVID-19 crisis. The initiative aims to create a unique repository that is easily accessible to all citizens, stakeholders and policymakers and become part of the common European response to the outbreak of COVID-19.

Artificial intelligence has become an intricate part of our everyday lives. We encounter it consciously and subconsciously -- at the grocery store, when we call customer service, and even in our homes and cars. With an increasing reliance on a technology designed to constantly collect our data – one that is programmed to be "smarter" than the human brain – are we leaving ourselves open to significant issues such as data breaches or information misuse in the future? How can we mitigate the potential challenges posed by artificial intelligence in healthcare and other industries? The emergence of artificial intelligence in healthcare has brought about countless opportunities for improved patient care outcomes, machine learning-assisted care, and deep learning technological advancements. Although there is no question that artificial intelligence brings added value to the healthcare industry, we also must pause to evaluate the potential challenges that technology-driven patient care poses to patients, providers and healthcare organizations.

The US presidential election campaign is in its final days. Donald Trump is behind in the polls and the pundits are predicting a win for his Democrat challenger, former vice president Joe Biden. He boasts that he will win again. With two weeks to go, his campaign unleashes an offensive in the crucial swing states: adverts, Facebook posts, WhatsApp groups and tweets. They warn of violent crime and civil unrest driven by immigrants and gangs, playing up Trump's endorsement by evangelicals and smearing Biden as a closet atheist. The initiative works and Trump snatches another unlikely victory.

Despite attempts to contain COVID-19 spread, as of March 26 more than 530,000 people had been infected worldwide and the number of new cases continues to grow at an alarming rate. AI tools have already joined the fight, guiding UAVs to automatically disinfect public areas, tracking disease spread vectors, diagnosing patients, etc. A new project from researchers with the UN Global Pulse Data Science Team, the World Health Organization and the Mila – Quebec AI Institute looks at current studies and programs that are using AI to tackle the COVID-19 crisis and suggests some promising future research directions. The team categorizes the AI applications in three areas; medical, which includes individual patient diagnosis and treatment; molecular, comprising drug discovery-related research; and societal. Most clinical applications of AI during the COVID-19 pandemic response have been in medical imaging diagnosis, amid growing interest in using medical imaging for screening and diagnosis.

Artificial intelligence and robotics experts in Edinburgh are working to create what they hope will be the first healthcare robots to hold a conversation with more than one person at a time. It is a project designed to help older people, but it could one day be used to help handle virus outbreaks like the coronavirus pandemic. "It's not something we had actually considered while designing the project," says Heriot-Watt's professor of computer science Oliver Lemon. "But as it turns out it's quite relevant to what's going on today. "You can imagine in the future that when you walk into a hospital waiting room, instead of encountering a human you encounter a robot who's able to help you.

Researchers at the University of Notre Dame are using artificial intelligence to develop an early warning system that will identify manipulated images, deepfake videos and disinformation online. The project is an effort to combat the rise of coordinated social media campaigns to incite violence, sew discord and threaten the integrity of democratic elections. The scalable, automated system uses content-based image retrieval and applies computer vision-based techniques to root out political memes from multiple social networks. "Memes are easy to create and even easier to share," said Tim Weninger, associate professor in the Department of Computer Science and Engineering at Notre Dame. "When it comes to political memes, these can be used to help get out the vote, but they can also be used to spread inaccurate information and cause harm."

Software-defined wide-area networking (SD-WAN) and AIOps are both red-hot technologies. SD-WANs increase application availability, reduce costs and in some cases improve performance. AIOps infuses machine learning into IT operations to increase the level of automation. This reduces errors and enables businesses to make changes at digital speeds. Most think of these as separate technologies, but the two are on a collision course and will give rise to what I'm calling the AI-WAN.

Spending on artificial intelligence in Asia Pacific is set to soar over the next five years as the demand for deep learning ramps up. By 2024, the APAC region is estimated to account for about 30 per cent of the global AI platform revenue at approximately U.S.$97.5 billion, according to research firm GlobalData. But that figure is expected to increase with businesses and the rising number of start-ups specialising in the technology and advancement in the space supporting higher computational capabilities. Sunil Kumar Verma, lead ICT analyst at GlobalData, said the APAC market is already deploying deep learning-based AI technology for offline automation, safety and security for businesses and assets. "In addition, AI hardware optimisation with increased computing speed on small devices will result in the cost reduction and drive deep learning adoption across the region," Verma said.