The rapidly expanding attack surface of virtually every enterprise, with the proliferation of Internet of Things (IoT) devices and cloud systems is a leading reason why artificial intelligence (AI) has become essential in security. Add to this the fact that malicious attackers also have AI at their disposal: Their attacks have become more sophisticated and frequent – with AI allowing them to automate breach attempts. Through AI, it's now possible for a security system to be frequently updated on both global and industry-specific threats, and to prioritize them according to their local potential to occur. It's stated above that much of the problem today is the proliferation of IoT, which opens up the attack surface; AI helps manage the burgeoning ocean of devices, to navigate their firmware updates and security patches, where no human security professional could reasonably be expected to do so. With self-learning, device management, ongoing gap detection and threat exposure, the system can learn to predict the risk of breach under a wide range of scenarios, and even to prioritize those risks – making it possible for the human security professionals to focus their attention on the greatest ones. As AI offers new functionality and potential for new cybersecurity threats, how do these play out as explicit features?

The Turing Institute is the UK's national institute for data science and artificial intelligence. On 23-24 March they held an event to showcase the very best AI academic content in the UK. Here, we give a flavour of the proceedings and highlight some of the interesting sessions and panel debates that took place on the first day. As the national institute for data science and artificial intelligence, The Turing Institute brings together people from a range of disciplines, and works with universities, centres of research excellence, industry and the public sector. The Institute has three main goals: 1) to advance world-class research and apply it to real-world problems, 2) to train the leaders of the future, 3) to lead the public conversation.

BOSTON - New treatments for Alzheimer's disease are desperately needed, but numerous clinical trials of investigational drugs have failed to generate promising options. Now a team at Massachusetts General Hospital (MGH) and Harvard Medical School (HMS) has developed an artificial intelligence-based method to screen currently available medications as possible treatments for Alzheimer's disease. The method could represent a rapid and inexpensive way to repurpose existing therapies into new treatments for this progressive, debilitating neurodegenerative condition. Importantly, it could also help reveal new, unexplored targets for therapy by pointing to mechanisms of drug action. "Repurposing FDA-approved drugs for Alzheimer's disease is an attractive idea that can help accelerate the arrival of effective treatment--but unfortunately, even for previously approved drugs, clinical trials require substantial resources, making it impossible to evaluate every drug in patients with Alzheimer's disease," explains Artem Sokolov, PhD, director of Informatics and Modeling at the Laboratory of Systems Pharmacology at HMS. "We therefore built a framework for prioritizing drugs, helping clinical studies to focus on the most promising ones."

The Scottish government has outlined an artificial intelligence (AI) strategy that puts ethics and inclusion first and foremost. The strategy was put forth in a report entitled Scotland's artificial intelligence strategy: Trustworthy, ethical and inclusive. It is said to complement the country's digital strategy, an updated version of which was published earlier this month. The government said the strategy would ensure that AI-driven technologies are "used for positive effect across the economy and society" and "highlight the opportunity to become a world leader in ethical AI". It put a big emphasis on committing to the expansion of "international collaboration on AI and children", and on "upskilling and reskilling of displaced workers and people vulnerable to exclusion".

Yes, these are amazing places. I'm sure you've used one at least once. Yet, while a few types of media are clearly edited, different changes might be harder to spot. You may have heard the term "deepfake videos" recently. It originally came to fruition in 2017 to depict videos and pictures that incorporate deep learning algorithms to create videos and images that look real.

As the editor-in-chief of The Verge, I can theoretically assign whatever I want. However, there is one topic I have failed to get people at The Verge to write about for years: robotic process automation, or RPA. RPA isn't robots in factories, which is often what we think of when it comes to automation. This is different: RPA is software. Software that uses other software, like Excel or an Oracle database. On this week's Decoder, I finally found someone who wants to talk about it with me: New York Times tech columnist Kevin Roose. His new book, Futureproof: 9 Rules for Humans in the Age of Automation, has just come out, and it features a lengthy discussion of RPA, who's using it, who it will affect, and how to think about it as you design your career. What struck me during our conversation were the jobs that Kevin talks about as he describes the impact of automation: they're not factory workers and truck drivers. If you have the kind of job that involves sitting in front of a computer using the same software the same way every day, automation is coming for you. It won't be cool or innovative or even work all that well -- it'll just be cheaper, faster, and less likely to complain. That might sound like a downer, but Kevin's book is all about seeing that as an opportunity. You'll see what I mean. Okay, Kevin Roose, tech columnist, author, and the only reporter who has ever agreed to talk to me about RPAs. This transcript has been lightly edited for clarity. Kevin Roose, you're a tech columnist at The New York Times and you have a new book, Futureproof: 9 Rules for Humans in the Age of Automation, which is out now. Thank you for having me. You're ostensibly here to promote your book, which is great. But there's one piece of the book that I am absolutely fascinated by, which is this thing called "robotic process automation." And I'm gonna do my best with you on this show, today, to make that super interesting. But before we get there, let's talk about your book for a minute. What is your book about? Because I read it, and it has a big idea and then there's literally nine rules for regular people to survive. So, tell me how the book came together. So, the book is basically divided into two parts.

At this turbulent time for the economy, the viability of many companies depends on their commercial efficiency. The huge volume of data they generate is an opportunity to turn this information into knowledge that will help them come up with more efficient and competitive commercial solutions. Decision Making project is being led by UOC researcher and the Falset Marçà Agricultural Cooperative in conjunction with Centre Vinícola del Penedès and the Federation of Agricultural Cooperatives of Catalonia, and is funded by the European Agricultural Fund for Rural Development and the Ministry of Agriculture, Livestock, Fisheries and Food of the Government of Catalonia. According to Xavi Domènech, manager of the Falset Marçà Cooperative and a graduate of Computer Engineering from the UOC, "Companies today amass enough data to be able to take much more calculated commercial decisions than they are generally taking. Personalizing their commercial structure and equipping it with rigorous methods and processes is key to guaranteeing their survival. We believe that supporting this process by introducing the intelligence of analytics can be a differentiating factor."

"What is fair?" feels like a rhetorical question. But for Michigan State University's Pang-Ning Tan, it's a question that demands an answer as artificial intelligence systems play a growing role in deciding who gets proper health care, a bank loan or a job. With funding from Amazon and the National Science Foundation, Tan has been working for the last year to teach artificial intelligence algorithms how to be more fair and recognize when they're being unfair. "We're trying to design AI systems that aren't just for computer science, but also bring value and benefits to society. So I started thinking about what are the areas that are really challenging to society right now," said Tan, a professor in MSU's Department of Computer Science and Engineering.

The banning of video game Disco Elysium from sale in Australia has renewed calls for the Australian government to overhaul the classification system to move away from the "moral panic" associated with video games. On Friday afternoon, the Australian classification board announced Disco Elysium – The Final Cut was refused classification on the grounds the game was found to "depict, express or otherwise deal with matters of sex, drug misuse or addiction, crime, cruelty, violence or revolting or abhorrent phenomena" in a way that offended "against the standards of morality, decency and propriety generally accepted by reasonable adults". It ruled the game should not be classified. The post-war murder mystery role-playing game has won over a dozen industry awards since its release in 2019. The game has been available in Australia for two years through the Steam online games store, but the game's developers, ZA/UM planned to launch the game on consoles this month, meaning before it could be sold in stores in Australia, it had to go to the classification board for review.

So far, ACE has demonstrated advanced virtual AI dogfights involving both within-visual-range (WVR) and beyond-visual-range (BVR) multi-aircraft scenarios with simulated weapons, plus live flying using an instrumented jet to measure pilot physiology and trust in AI. Throughout the ACE program, which began last year, DARPA has stressed the importance of establishing human pilots' trust in AI, allowing it to conduct the actual combat maneuvers while the human concentrates on overarching battle management decisions. The process of "capturing trust data" has seen test pilots fly in an L-29 Delfin jet trainer at the University of Iowa Technology Institute's Operator Performance Laboratory. This aircraft has been adapted with cockpit sensors to measure the pilot's physiological responses, giving an insight into whether or not the pilot trusts the AI. In these missions, the L-29 has been flown by a safety pilot in the front seat, who makes flight control inputs based on AI decisions.