Two of Glasgow's leading scientists will develop cutting-edge Artificial Intelligence (AI) technology thanks to a £20 million UK Government cash boost. The Scottish projects, at the University of Glasgow and University of Strathclyde, are among fifteen innovative projects receiving the new Turing AI fellowships as part of the UK government's ambition to establish the UK as a world leader in AI and support researchers to scale up their innovations. Dr Antonio Hurtado, University of Strathclyde, received £1.16 million. He aims to meet the growing demand across the UK economy to process large volumes of data fast and efficiently, while minimising the energy required to do so. His AI technology will use laser light, similar to those used in supermarket checkouts, to perform complex tasks at ultrafast speed – from weather forecasting to processing images for medical diagnostics.

According to a report on the website of the National Institute of Standards and Technology on November 24, a multi-institutional team from the National Institute of Standards and Technology, the University of Maryland and the Stanford Linear Accelerator Center (SLAC) of the U.S. Department of Energy has developed a closed-loop material exploration and optimization based on artificial intelligence The system (CAMEO) algorithm aims to use the self-learning characteristics of the algorithm to discover complex new materials with specific properties through fewer experiments, to help scientists minimize the time of trial and error in experiments and improve the efficiency of new material development. The research team connected the X-ray diffraction equipment to a computer equipped with the CAMEO algorithm and imported the existing material database into the algorithm. After many iterations of learning, only a small amount of routine measurement can be used to find The best material for specific properties. Using this method, researchers discovered new nanocomposite phase change memory materials among 177 possible materials. The number of test iterations required was reduced to 1/10 of the original, and the time required was shortened from 90 hours.

In a new report released on October 27 by the United States Patent and Trademark Office (USPTO), more than 42% of all technology areas in 2018 incorporate Artificial Intelligence (AI) in their new inventions. The majority of these improvements come in knowledge processing and planning/control, which involve analyzing information to gain new insights and using those insights to manage a business process. CIOs continue to talk about how vital AI technologies are, but this new report confirms that if companies aren't already putting that talk into action, they are behind the curve. The danger of falling behind is even greater for companies that haven't started adoption since the statistics only cover till the end of 2018. In the last 18 months, the percentage of technologies that include AI has undoubtedly continued to increase. The report also confirms an increased interest by the office in this technology and a higher willingness to consider new applications that include them.

This work documents a pilot user study evaluating the effectiveness of contrastive, causal and example explanations in supporting human understanding of AI in a hypothetical commonplace human robot interaction HRI scenario. In doing so, this work situates explainable AI XAI in the context of the social sciences and suggests that HRI explanations are improved when informed by the social sciences.

In this work, we study the trade-off between differential privacy and adversarial robustness under L2-perturbations in the context of learning halfspaces. We prove nearly tight bounds on the sample complexity of robust private learning of halfspaces for a large regime of parameters. A highlight of our results is that robust and private learning is harder than robust or private learning alone. We complement our theoretical analysis with experimental results on the MNIST and USPS datasets, for a learning algorithm that is both differentially private and adversarially robust.

In recent years, the media have been paying increasing attention to adversarial examples, input data such as images and audio that have been modified to manipulate the behavior of machine learning algorithms. Stickers pasted on stop signs that cause computer vision systems to mistake them for speed limits; glasses that fool facial recognition systems, turtles that get classified as rifles -- these are just some of the many adversarial examples that have made the headlines in the past few years. There's increasing concern about the cybersecurity implications of adversarial examples, especially as machine learning systems continue to become an important component of many applications we use. AI researchers and security experts are engaging in various efforts to educate the public about adversarial attacks and create more robust machine learning systems. Among these efforts is adversarial.js,

We aim to find any correlation between a US President and its state concerning other Presidents. We use data from different sources to form a dataset that comprises different US presidents and their home states with additional facts about the state. We pull data from multiple sources and combine it to form a comprehensive dataset. We also find the most common state and ultimately plot the results on a live interactive map. We start by preparing the data to comb the datasets from different sources and perform some basic preprocessing on it.

In August, the US government announced the release of $ 1 billion in financing to ensure "that the United States continues to lead the world in artificial intelligence and quantum computing," according to US chief technology officer Michael Kratsios. It seems that the money came too late: for the first time, China surpassed the US in number of AI patents, with more than 110,000 applications filed last year. The news was given by the deputy head of the Chinese Academy of Cyberspace Studies, Li Yuxiao at a press conference on the 23rd, during the 7th World Internet Conference (WIC). "China is strengthening its independence in information technology on the Internet," he said, without mentioning how many AI patents have been registered by the United States. During the event, two studies were presented on Chinese efforts to develop the internal digital economy.

There is ample evidence that we have long-since emerged from the proverbial AI Winter. However, one often-cited datum we may wish to reconsider as irrefutable evidence is the number of "AI-powered" companies in the market. Given that roughly 40% of businesses purporting to be "AI Startups" show absolutely no evidence that the technology is material to the execution of their value proposition, it appears a prudent juncture for taking stock of exactly what role Artificial Intelligence can and does play in various industries. To state this more directly, the astonishingly alarming rate at which companies appear to be, deliberately or unintentionally, misrepresenting the role that AI plays in their business model necessitates that investors, regulators, policymakers, and consumers alike become vigilant in their detection of this technological chicanery. With Artificial Intelligence startups having received a record $26.6bn in funding in 2019, it's no wonder the demand for Machine Learning Engineers and Data Architects has skyrocketed, with entrepreneurial fervor rushing into the sector.

"These end-to-end voice translation system uses Automatic Speech Recognition (ASR), Machine Translation and Speech-to-Text to convert Mandarin to English and is designed to help armed forces, intelligence agencies and local law enforcement authorities in improving communication systems and giving substantial leeway to the Indian defense forces," the company in its statement said. The solution has a wide range of applications that includes cross border intelligence, voice surveillance, monitoring telephone/internet conversations, intercepting Radio/Satellite communication, and to bridge interactions during border meetings & joint exercises. Its unique features include noise reduction, dialect/accent detection, and support for all audio file formats. Speaking on the launch, Ananth Nagaraj, Co-founder & CTO, Gnani.ai said, "AI-based Speech Recognition technology is a necessity and is quickly making its way in becoming part of modern warfare. We believe AI has the potential to transform and improve the communication systems and will help strengthen Indian Armed forces." "Understanding linguistic nuances such as phoneme and dialects is a challenge especially when it comes to Mandarin.