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As part of Australia's Artificial Intelligence (AI) Action Plan, the Australian Government is investing $53.8 million over four years to establish the National AI Centre and four AI and Digital Capability Centres to lay the foundations for an Australian AI and digital ecosystem. As the national science agency, and housing some of the country's leading capability in AI research and technology development, we're well positioned to act as lead in establishing the National AI Centre. We're bringing together partners from government, industry and the research sector to boost exploration and adoption of AI in Australia. We are uniquely positioned to represent the interests and capability of Australia's AI sector internationally, and to grow awareness of our AI capacity with global leaders in the field. By partnering with like-minded organisations, we aim to drive a new level of understanding, technology development, and adoption of AI in Australia in the years to come.

To fight the invasive fish, the international team, composed of biologists and engineers from Australia, the U.S., and Italy, turned to its natural predator -- the largemouth bass (Micropterus salmoides) -- for inspiration. They crafted a robotic fish that mimics the appearance and simulates the movements of the real predator. Aided by computer vision, the robot strikes when it spots the mosquitofish approaching tadpoles of an Australian species (Litoria moorei), which is threatened by mosquitofish in the wild. Scared and stressed, the mosquitofish showed fearful behaviors and experienced weight loss, changes in body shape, and a reduction in fertility, all of which impair their survival and reproduction. "Mosquitofish is one of the 100 world's worst invasive species, and current methods to eradicate it are too expensive and time-consuming to effectively contrast its spread," says first author Giovanni Polverino (@GioPolverino) of the University of Western Australia.

It may be smart, but it's not that clever. Artificial intelligence is nothing without human input. The algorithms that drive AI rely on the expertise of programmers and it's still no more than a tool – albeit a powerful one – that scientists and engineers can use to solve problems. Yet this is not to say that AI isn't the fastest-growing deep technology in the world, with the potential to transform people's lives and boost nations' economies. Facilitating AI innovation has even become a priority for the UK government, as laid out in the National AI Strategy it published in September.

Artificial intelligence (AI) technology can generate plausible, entertaining, and scientifically interesting titles for potential research articles, finds a study in the Christmas issue of The BMJ. A study of The BMJ's most popular Christmas research articles--which combine evidence based science with light hearted or quirky themes--finds that AI generated titles were as attractive to readers but that, as in other areas of medicine, performance was enhanced by human input. As such, the researchers say AI could have a role in generating hypotheses or directions for future research. AI is already used to help doctors diagnose conditions, based on the idea that computer systems can learn from data and identify patterns. But can AI be used to generate worthwhile hypotheses for medical research?

The United States on Thursday unleashed a volley of actions to censure China's treatment of the Uyghur minority, with lawmakers voting to curb trade and new sanctions slapped on the world's top consumer drone maker. The United States has been ramping up pressure on China amid a crop of disputes, with President Joe Biden's administration a day earlier targeting producers of painkillers that have contributed to America's addiction crisis. The U.S. Senate unanimously voted to make the United States the first country to ban virtually all imports from China's northwestern Xinjiang region over concerns of the prevalence of forced labor. "We know it's happening at an alarming, horrific rate with the genocide that we now witness being carried out," said Senator Marco Rubio, a driver behind the act, which already passed the House of Representatives and which the White House says Biden will sign. After prolonged negotiations to secure its passage, Rubio lifted objections and the Senate confirmed veteran diplomat Nicholas Burns as ambassador to China.

Despite of achieving great success in real life, Deep Reinforcement Learning (DRL) is still suffering from three critical issues, which are data efficiency, lack of the interpretability and transferability. Recent research shows that embedding symbolic knowledge into DRL is promising in addressing those challenges. Inspired by this, we introduce a novel deep reinforcement learning framework with symbolic options. This framework features a loop training procedure, which enables guiding the improvement of policy by planning with action models and symbolic options learned from interactive trajectories automatically. The learned symbolic options alleviate the dense requirement of expert domain knowledge and provide inherent interpretability of policies. Moreover, the transferability and data efficiency can be further improved by planning with the action models. To validate the effectiveness of this framework, we conduct experiments on two domains, Montezuma's Revenge and Office World, respectively. The results demonstrate the comparable performance, improved data efficiency, interpretability and transferability.

Urbanization has a strong impact on the health and wellbeing of populations across the world. Predictive spatial modeling of urbanization therefore can be a useful tool for effective public health planning. Many spatial urbanization models have been developed using classic machine learning and numerical modeling techniques. However, deep learning with its proven capacity to capture complex spatiotemporal phenomena has not been applied to urbanization modeling. Here we explore the capacity of deep spatial learning for the predictive modeling of urbanization. We treat numerical geospatial data as images with pixels and channels, and enrich the dataset by augmentation, in order to leverage the high capacity of deep learning. Our resulting model can generate end-to-end multi-variable urbanization predictions, and outperforms a state-of-the-art classic machine learning urbanization model in preliminary comparisons.

A key challenge of online news recommendation is to help users find articles they are interested in. Traditional news recommendation methods usually use single news information, which is insufficient to encode news and user representation. Recent research uses multiple channel news information, e.g., title, category, and body, to enhance news and user representation. However, these methods only use various attention mechanisms to fuse multi-view embeddings without considering deep digging higher-level information contained in the context. These methods encode news content on the word level and jointly train the attention parameters in the recommendation network, leading to more corpora being required to train the model. We propose an Event Extraction-based News Recommendation (EENR) framework to overcome these shortcomings, utilizing event extraction to abstract higher-level information. EENR also uses a two-stage strategy to reduce parameters in subsequent parts of the recommendation network. We train the Event Extraction module by external corpora in the first stage and apply the trained model to the news recommendation dataset to predict event-level information, including event types, roles, and arguments, in the second stage. Then we fuse multiple channel information, including event information, news title, and category, to encode news and users. Extensive experiments on a real-world dataset show that our EENR method can effectively improve the performance of news recommendations. Finally, we also explore the reasonability of utilizing higher abstract level information to substitute news body content.

Spotify has bought another audio platform, and this time it's hoping to bring radio into the modern era. The streaming company has acquired Whooshkaa, an Australia-based firm that offers a tool to convert radio broadcasters' shows into podcasts. Spotify plans to integrate the tech into its Megaphone suite for podcasters with a clear goal -- stations could further profit from shows by offering ad-supported podcast episodes. Whooshkaa might also boost some of Spotify's other efforts. Founder Rob Loewenthal noted Whooshkaa also had speech-to-text (and text-to-speech) technology, smart home integration and "enterprise grade" podcasting tools.