In late 2020, then-HHS Secretary Alex Azar tapped Berkeley, California-based Cogitativo to help inform the Trump Administration's vaccine distribution strategy. The company had developed a machine learning model that could predict the probability a patient would end up in an intensive care unit bed. Using 60 million records from HHS, Cogitativo created risk scores that flagged individuals who might be at particular risk from the effects of COVID-19. Now, explained CEO Gary Velasquez, the company has been tasked with a new project: Working with the Veterans Health Administration to identify beneficiaries who have deferred care during COVID-19. At this point, said Velasquez in an interview with Healthcare IT News, "You're worried about the veterans you haven't seen."

On 30 July 2021, the Federal Court of Australia decided that AI systems can be inventors. In a word-first determination of Thaler v Commissioner of Patents,{[2021] FCA 879, ('Thaler')}, the Honourable Justice Beach found that AI systems can be the inventors on a patent application under Australian patent law. The decision has been appealed to the Full Bench of the Federal Court, which may decide to overrule it. For now, however, the decision is binding in Australia. Read on to find out what a patent is and an overview of the decision.

What does it mean to be artificially intelligent? More than an endless parlor game for amateur philosophers, this debate is central to the forthcoming Artificial Intelligence Regulation for the 447 million citizens of the European Union. The AI Reg--better known as the AI Act, or AIA--will determine what AI can be deployed in the EU, and how much it costs an organization to do so. The AIA, of course, is not only a context for this conversation but an artifact of a much larger moment. Vast quantities of data are being gathered not only on us as individuals but also on every component of our societies.

Influence operations with ties to Russia and Belarus have been found using deepfakes to push anti-Ukraine disinformation. Last week, AI News reported on the release of a study that found humans can generally no longer distinguish between real and AI-generated "deepfake" faces. As humans, we're somewhat trained to believe what we see with our eyes. Many believed that it was only a matter of time before Russia took advantage of deepfakes and our human psychology to take its vast disinformation campaigns to the next level. Facebook and Twitter removed two anti-Ukraine "covert influence operations" over the weekend.

Cardiologist and Us2.ai co-founder Dr Carolyn Lam talks to AIMed about the potential of AI to democratize heart ultrasound, her experience as an accidental entrepreneur, and the importance of championing women in cardiovascular science. You serve as a senior consultant cardiologist at the National Heart Centre Singapore, a full professor at Duke-National University of Singapore, and co-founder of Us2.ai. How do you split your time between these demanding positions? Time-wise I fortunately don't have to struggle since my time commitments are spelled out very clearly for me (days in clinics, days in clinical research, etc.); the challenge is really in staying ultra-focused on delivering my very best in the time that I have. To do that, I have had to learn the hard lesson of saying "no" – in fact my mission this year is to focus on my "not to do" list rather than on my "to do" list.

The detection of computer-generated text is an area of rapidly increasing significance as nascent generative models allow for efficient creation of compelling human-like text, which may be abused for the purposes of spam, disinformation, phishing, or online influence campaigns. Past work has studied detection of current state-of-the-art models, but despite a developing threat landscape, there has been minimal analysis of the robustness of detection methods to adversarial attacks. To this end, we evaluate neural and non-neural approaches on their ability to detect computer-generated text, their robustness against text adversarial attacks, and the impact that successful adversarial attacks have on human judgement of text quality. We find that while statistical features underperform neural features, statistical features provide additional adversarial robustness that can be leveraged in ensemble detection models. In the process, we find that previously effective complex phrasal features for detection of computer-generated text hold little predictive power against contemporary generative models, and identify promising statistical features to use instead. Finally, we pioneer the usage of $\Delta$MAUVE as a proxy measure for human judgement of adversarial text quality.

Pilots operating aircraft in un-towered airspace rely on their situational awareness and prior knowledge to predict the future trajectories of other agents. These predictions are conditioned on the past trajectories of other agents, agent-agent social interactions and environmental context such as airport location and weather. This paper provides a dataset, $\textit{TrajAir}$, that captures this behaviour in a non-towered terminal airspace around a regional airport. We also present a baseline socially-aware trajectory prediction algorithm, $\textit{TrajAirNet}$, that uses the dataset to predict the trajectories of all agents. The dataset is collected for 111 days over 8 months and contains ADS-B transponder data along with the corresponding METAR weather data. The data is processed to be used as a benchmark with other publicly available social navigation datasets. To the best of authors' knowledge, this is the first 3D social aerial navigation dataset thus introducing social navigation for autonomous aviation. $\textit{TrajAirNet}$ combines state-of-the-art modules in social navigation to provide predictions in a static environment with a dynamic context. Both the $\textit{TrajAir}$ dataset and $\textit{TrajAirNet}$ prediction algorithm are open-source. The dataset, codebase, and video are available at https://theairlab.org/trajair/, https://github.com/castacks/trajairnet, and https://youtu.be/elAQXrxB2gw respectively.

Some of these meteorites fall in regions on Earth where fireball observatory networks are active, making it possible to record the trajectory of the fireball as it ablates material from the originating meteoroid. For some fireballs, this data can then be used to simulate both forward and backward in time to predict where the resulting meteorite landed on Earth and where the meteoroid originated in the solar system. Thus, recovering and analyzing these'orbital meteorites' with constrained, prior orbits provides an incredibly unique insight into the geology of the asteroid belt and the nature of mass transfer between the belt and the inner solar system. The Desert Fireball Network (DFN) (Bland et al. 2012; Howie et al. 2017) is one of many organizations (Oberst et al. 1998; Spurný et al. 2006; Trigo-Rodríguez et al. 2006; Olech et al. 2006; Colas et al. 2015; Devillepoix et al. 2020) that makes this possible.

Firms can expect to hear soon, in a white paper to be published by the Office for AI, whether general AI-specific regulation will be introduced in the UK. EU law makers are currently scrutinising separate plans for a draft new EU AI Act. Both developments are expected to focus on issues such as transparency, explainability and governance. However, any new rules would only apply to technology that fits within the definition of AI in new legislation or regulation. Figuring out whether the technology firms use will be in-scope is therefore an important preliminary task for financial services businesses.

The experts say considerations for managing security must be made right from the design and planning phases of any AI project.iStockPhoto Artificial intelligence (AI) technology is a powerful asset in business, allowing machines to think for themselves – and at a faster pace than ever before. But AI systems can pose cybersecurity challenges, which can cause operational, financial, health and safety, and reputational damage. BDO Lixar, which is BDO Canada's national technology consulting business arm, helps organizations recognize and manage such risks. Partners Rocco Galletto, head of cybersecurity, and Daryl Senick, who is responsible for data and AI, as head of financial services, talk about the potential vulnerabilities of AI and what can be done to make these systems safe and secure.