The flu vaccine is getting a boost from AI. The flu vaccine isn't perfect, but Australian scientists are trying to make it work better. Researchers at Flinders University in South Australia have developed a way to use artificial intelligence to create a "turbocharged" flu vaccine. A vaccine created with the computer program -- Smart Algorithms for Medical Discovery, or Sam for short -- started clinical trials in the US about a week ago, Flinders University Professor Nikolai Petrovsky said in an email to CNET. Petrovsky told the Australian Broadcasting Corporation that Sam can be trained and can then learn to create new drugs.

Dozens of databases of people's faces are being compiled without their knowledge by companies and researchers, with many of the images then being shared around the world, in what has become a vast ecosystem fueling the spread of facial recognition technology. The databases are pulled together with images from social networks, photo websites, dating services like OkCupid and cameras placed in restaurants and on college quads. While there is no precise count of the data sets, privacy activists have pinpointed repositories that were built by Microsoft, Stanford University and others, with one holding over 10 million images while another had more than two million. The face compilations are being driven by the race to create leading-edge facial recognition systems. This technology learns how to identify people by analyzing as many digital pictures as possible using "neural networks," which are complex mathematical systems that require vast amounts of data to build pattern recognition.

The current crop also includes applications of on-demand manufacturing, augmented reality, music-assisted learning, interactive video, and online music creation. The Aussie team got the nod for their knack at using AI to separate, classify and up-res audio "for the purpose of audio search, discovery, recommendation, personalization, and quality enhancement," which works with studio, UGC audio and, maybe, live mobile communication, a statement from Techstars Music reads. Software engineer and chief of Mawson and Popgun Stephen Phillips paid tribute to his fellow Brisbanites with a tweet. Two more AI startups from Mawson are ready for take off. Both Replica and SuperRes have joined the Techstars Music 2019 program in LA. White says she's "excited and super grateful" to be a member of Techstars' 2019 class of music-based startups.

LG Electronics has tapped Dr. Darin Graham as the head of the LG Artificial Intelligence (AI) Lab in Toronto. Graham is an expert on AI networks and will be responsible for strengthening LG's North American AI capacity through strategic partnerships with industry and academia in Canada. Previously, Graham helped establish founding operations of the Vector Institute, a world-renowned artificial intelligence research organization. Further, he brings over 20 years of experience in leading innovative projects that bring creative ideas to the market. Graham has led several international research and development programs and was pivotal in building Canada's AI ecosystem.

The promise of autonomous cars has no doubt been an exciting and intoxicating one for technophiles everywhere, not to mention those who see driving as a chore that must be endured, rather than enjoyed. There's no shortage of autonomy evangelists - Tesla CEO Elon Musk is a famous example, Ford reckons it will have its first truly self-driving car ready by 2021 and BMW and Mercedes have similar timelines - while ridesharing companies and Silicon Valley tech giants like Uber and Google are also bullish on fully-autonomous vehicles. And why wouldn't they be? Without a driver taking a cut of fares, profit margins for those businesses would explode, while also giving the public cheaper rides and potentially freeing them from the financial burden of owning a car. But according to one of the world's biggest manufacturers of the advanced sensors that make autonomous cars possible, the utopian vision of completely hands-off, point-to-point driving is highly unlikely for the foreseeable.

Businesses are increasing the pace of investment in AI systems to defend against the next generation of cyberattacks, a new study from the Capgemini Research Institute has found. Two thirds (69%) of organizations acknowledge that they will not be able to respond to critical threats without AI. With the number of end-user devices, networks, and user interfaces growing as a result of advances in the cloud, IoT, 5G and conversational interface technologies, organizations face an urgent need to continually ramp up and improve their cybersecurity. AI-enabled cybersecurity is now an imperative: Over half (56%) of executives say their cybersecurity analysts are overwhelmed by the vast array of data points they need to monitor to detect and prevent intrusion. Accordingly, almost half (48%) said that budgets for AI in cybersecurity will increase in FY2020 by nearly a third (29%).

Despite recent doom-and-gloom anecdotal reporting, a nationwide survey of 1,001 workers in the United States (U.S.) finds that 70% have an upbeat attitude toward new workplace technologies involving artificial intelligence (AI), such as chatbots, robots and augmented reality. Only 5% say they dislike new technology for putting their jobs at risk today. In fact, 32% of U.S. respondents feel AI will have a positive impact on their job in the next five years, increasing from 26% today. Just 19% of those surveyed express fear that AI/bots could swallow their jobs within the next decade. These findings stem from new research by Genesys (www.genesys.com),

By bridging deep networks and physics, the programme of geometrization of deep networks was proposed as a framework for the interpretability of deep learning systems. Following this programme we can apply two key ideas of physics, the geometrization of physics and the least action principle, on deep networks and deliver a new picture of deep networks: deep networks as memory space of information, where the capacity, robustness and efficiency of the memory are closely related with the complexity, generalization and disentanglement of deep networks. The key components of this understanding include:(1) a Fisher metric based formulation of the network complexity; (2)the least action (complexity=action) principle on deep networks and (3)the geometry built on deep network configurations. We will show how this picture will bring us a new understanding of the interpretability of deep learning systems.

Something stood out of the ordinary during a speech by China's president, Xi Jinping, in January 2018. Behind Xi, on a bookshelf, were two books on artificial intelligence (AI). Why were those books there? Similar to 2015, when Russia "accidentally" aired designs for a new weapon, the placement of the books may not have been an accident. Was China sending a message?

CHIEF financial officers (CFOs) and their finance teams need to understand how to anticipate and respond to artificial intelligence (AI), as it will be a key frontier technology to grow Singapore's economy in the years ahead, CPA Australia said on Thursday. The accounting professional body also urged professionals in the sector to upskill, particularly in terms of data mining, extraction and faster interpretation of big data. To help them navigate this digital journey, CPA Australia has published a resource titled Charting the Future of Accountancy with AI, in collaboration with Singapore Management University's School of Accountancy. The practical guide looks at how AI will reshape the accounting and finance sector in the coming years, and what the profession can do to continue to operate alongside the evolving technology and their changing roles. It draws on insights from professional services firms Accenture, Deloitte, EY, KPMG and PwC, as well as the Singapore Management University.