A world-first report from Australia's science agency, CSIRO, has found that scientists are adopting artificial intelligence (AI) at an unprecedented rate. Analysing the impact of AI on scientific discovery, 'Artificial intelligence for science' draws insight from millions of peer-reviewed scientific papers published over 63 years and identifies key issues ahead for the sector. The report found that artificial intelligence is now implemented in 98 per cent of scientific fields, and by September 2022 approximately 5.7% of all peer-reviewed research worldwide was on the topic. "AI is no longer just the domain of computer scientists or mathematicians; it is now a significant enabling force across all fields of science, which is something we live every day at CSIRO, where digital technologies are accelerating the pace and scale of our research in fields ranging from agriculture to energy to manufacturing and beyond," says CSIRO Chief Scientist Professor Bronwyn Fox. The report uses a bibliometric analysis – statistical methods analysing trends in peer-reviewed research – to determine what percentage of the 333 research fields studied were publishing on artificial intelligence between 1960-2022. Analysing all disciplines of natural science, physical science, social science and the arts and humanities, the report found that only 14% of fields were publishing on artificial intelligence in 1960.

Turkey gets all the attention. Cranberry sauce ruffles feathers. But pumpkin pie is the lovable staple many Americans crave on Thanksgiving. And many don't want to make it themselves. That's why Sam's Club, a retail and grocery warehouse owned by Walmart, is using artificial intelligence to predict how much pie each of its nearly 600 stores needs to make for the holidays.Subscribe to The Post Most newsletter for the most important and interesting stories from The Washington Post. Their model use

Pong: a game so simple a bundle of lab-grown brain cells could play it. This might sound like a low blow, but it's true – last month, Australia-based startup Cortical Labs challenged its creation DishBrain, a biological computer chip that uses a combination of living neurons and silicon, to play the early console classic. The game – a 2D version of table tennis where players control a rectangle "paddle", moving it up and down to rally a ball – ran in the background, wired up to the DishBrain. Electrical stimulations were fed into the cells to represent the placement of the paddle and feedback was pinged when the ball was hit or missed. The scientists then measured the DishBrain's response, observing that it expended more or less energy depending on the position of the ball.

This week, craft beer company Bira landed new funding to expand its geographic reach while Torr FoodTech grabbed $12 million for its unusual and tech-centric approach to snack bars. In agtech, Clarifruit also raised $12 million while more layoffs struck the food delivery sector. Craft beer maker Bira 91 lands $70 million round led by beer company Kirin. Bira will use the funding to build more breweries and expand geographical reach of the Bira line of beverages. Sustainable grocery startup Modern Milkman raises £50 million ($60 million) after Series C close.

Four University of South Australia researchers have been awarded ARC Discovery grants collectively worth $1.8 million, for projects starting in 2023. The project will investigate the ways in which existing Australian induction policies support "precariously employed" early career teachers – those on casual and short-term contracts – to effectively manage student classroom behaviour. "We hope to propose alternative policy and practice recommendations to support the transition of insecure replacement teachers within the profession," Prof Sullivan says. "The benefits of this research include improving teachers' classroom management practices; the retention of new teachers; improving teacher workforce development; and building a healthier education system." Australia's 117,000 km of concrete sewer pipes are currently internally corroding at a depth rate of 1-3 mm per annum.

Combining multi-spectral satellite data and machine learning has been suggested as a method for monitoring plastic pollutants in the ocean environment. Recent studies have made theoretical progress regarding the identification of marine plastic via machine learning. However, no study has assessed the application of these methods for mapping and monitoring marine-plastic density. As such, this paper comprised of three main components: (1) the development of a machine learning model, (2) the construction of the MAP-Mapper, an automated tool for mapping marine-plastic density, and finally (3) an evaluation of the whole system for out-of-distribution test locations. The findings from this paper leverage the fact that machine learning models need to be high-precision to reduce the impact of false positives on results. The developed MAP-Mapper architectures provide users choices to reach high-precision ($\textit{abbv.}$ -HP) or optimum precision-recall ($\textit{abbv.}$ -Opt) values in terms of the training/test data set. Our MAP-Mapper-HP model greatly increased the precision of plastic detection to 95\%, whilst MAP-Mapper-Opt reaches precision-recall pair of 87\%-88\%. The MAP-Mapper contributes to the literature with the first tool to exploit advanced deep/machine learning and multi-spectral imagery to map marine-plastic density in automated software. The proposed data pipeline has taken a novel approach to map plastic density in ocean regions. As such, this enables an initial assessment of the challenges and opportunities of this method to help guide future work and scientific study.

Recent years have witnessed substantial growth in adaptive traffic signal control (ATSC) methodologies that improve transportation network efficiency, especially in branches leveraging artificial intelligence based optimization and control algorithms such as reinforcement learning as well as conventional model predictive control. However, lack of cross-domain analysis and comparison of the effectiveness of applied methods in ATSC research limits our understanding of existing challenges and research directions. This chapter proposes a novel unified view of modern ATSCs to identify common ground as well as differences and shortcomings of existing methodologies with the ultimate goal to facilitate cross-fertilization and advance the state-of-the-art. The unified view applies the mathematical language of the Markov decision process, describes the process of controller design from both the world (problem) and solution modeling perspectives. The unified view also analyses systematic issues commonly ignored in existing studies and suggests future potential directions to resolve these issues.

Misleading information spreads on the Internet at an incredible speed, which can lead to irreparable consequences in some cases. It is becoming essential to develop fake news detection technologies. While substantial work has been done in this direction, one of the limitations of the current approaches is that these models are focused only on one language and do not use multilingual information. In this work, we propose Multiverse -- a new feature based on multilingual evidence that can be used for fake news detection and improve existing approaches. The hypothesis of the usage of cross-lingual evidence as a feature for fake news detection is confirmed, firstly, by manual experiment based on a set of known true and fake news. After that, we compared our fake news classification system based on the proposed feature with several baselines on two multi-domain datasets of general-topic news and one fake COVID-19 news dataset showing that in additional combination with linguistic features it yields significant improvements.

In federated learning (FL) systems, e.g., wireless networks, the communication cost between the clients and the central server can often be a bottleneck. To reduce the communication cost, the paradigm of communication compression has become a popular strategy in the literature. In this paper, we focus on biased gradient compression techniques in non-convex FL problems. In the classical setting of distributed learning, the method of error feedback (EF) is a common technique to remedy the downsides of biased gradient compression. In this work, we study a compressed FL scheme equipped with error feedback, named Fed-EF. We further propose two variants: Fed-EF-SGD and Fed-EF-AMS, depending on the choice of the global model optimizer. We provide a generic theoretical analysis, which shows that directly applying biased compression in FL leads to a non-vanishing bias in the convergence rate. The proposed Fed-EF is able to match the convergence rate of the full-precision FL counterparts under data heterogeneity with a linear speedup. Moreover, we develop a new analysis of the EF under partial client participation, which is an important scenario in FL. We prove that under partial participation, the convergence rate of Fed-EF exhibits an extra slow-down factor due to a so-called ``stale error compensation'' effect. A numerical study is conducted to justify the intuitive impact of stale error accumulation on the norm convergence of Fed-EF under partial participation. Finally, we also demonstrate that incorporating the two-way compression in Fed-EF does not change the convergence results. In summary, our work conducts a thorough analysis of the error feedback in federated non-convex optimization. Our analysis with partial client participation also provides insights on a theoretical limitation of the error feedback mechanism, and possible directions for improvements.

If we want to train robots in simulation before deploying them in reality, it seems natural and almost self-evident to presume that reducing the sim2real gap involves creating simulators of increasing fidelity (since reality is what it is). We challenge this assumption and present a contrary hypothesis -- sim2real transfer of robots may be improved with lower (not higher) fidelity simulation. We conduct a systematic large-scale evaluation of this hypothesis on the problem of visual navigation -- in the real world, and on 2 different simulators (Habitat and iGibson) using 3 different robots (A1, AlienGo, Spot). Our results show that, contrary to expectation, adding fidelity does not help with learning; performance is poor due to slow simulation speed (preventing large-scale learning) and overfitting to inaccuracies in simulation physics. Instead, building simple models of the robot motion using real-world data can improve learning and generalization.