In real-world applications, data do not reflect the ones commonly used for neural networks training, since they are usually few, unlabeled and can be available as a stream. Hence many existing deep learning solutions suffer from a limited range of applications, in particular in the case of online streaming data that evolve over time. To narrow this gap, in this work we introduce a novel and complex setting involving unsupervised meta-continual learning with unbalanced tasks. These tasks are built through a clustering procedure applied to a fitted embedding space. We exploit a meta-learning scheme that simultaneously alleviates catastrophic forgetting and favors the generalization to new tasks. Moreover, to encourage feature reuse during the meta-optimization, we exploit a single inner loop taking advantage of an aggregated representation achieved through the use of a self-attention mechanism. Experimental results on few-shot learning benchmarks show competitive performance even compared to the supervised case. Additionally, we empirically observe that in an unsupervised scenario, the small tasks and the variability in the clusters pooling play a crucial role in the generalization capability of the network. Further, on complex datasets, the exploitation of more clusters than the true number of classes leads to higher results, even compared to the ones obtained with full supervision, suggesting that a predefined partitioning into classes can miss relevant structural information.

Brisbane-based robotics company Lyro Robotics has successfully deployed its robots and world-leading picking and packing technology in commercial trials held at the avocado facility, Sunnyspot Packhouse, in Ravensbourne, Queensland. Summer harvest looms, and so does a widespread labour shortage for much of Australia's horticulture industry. The Australian Fresh Produce Alliance (AFPA) indicated in September the country's fruit and vegetable industry was facing a workforce shortage of up to 26,000 people throughout the peak summer season. This figure was reached by Ernst & Young. This is where robotics technology, such as Lyro Robotics, could provide solutions assisting farmers with seasonal and short-term farm work.

A stellar flare is a sudden flash of increased brightness on a star. Young stars are prone to these flares which can incinerate everything around them, including the atmospheres of nearby planets starting to form. Finding out how often young stars erupt can help scientists understand where to look for habitable planets. But until now, searching for these flares involved poring over thousands of measurements of star brightness variations, called'light curves', by eye. Now, an international team of scientists based in Australia and the USA have used machine learning to make the search faster and more effective.

The "Thinking, Fast and Slow" paradigm of Kahneman proposes that we use two different styles of thinking -- a fast and intuitive System 1 for certain tasks, along with a slower but more analytical System 2 for others. While the idea of using this two-system style of thinking is gaining popularity in AI and robotics, our work considers how to interleave the two styles of decision-making, i.e., how System 1 and System 2 should be used together. For this, we propose a novel and general framework which includes a new System 0 to oversee Systems 1 and 2. At every point when a decision needs to be made, System 0 evaluates the situation and quickly hands over the decision-making process to either System 1 or System 2. We evaluate such a framework on a modified version of the classic Pac-Man game, with an already-trained RL algorithm for System 1, a Monte-Carlo tree search for System 2, and several different possible strategies for System 0. As expected, arbitrary switches between Systems 1 and 2 do not work, but certain strategies do well. With System 0, an agent is able to perform better than one that uses only System 1 or System 2.

To assist human review process, we build a novel ReviewRobot to automatically assign a review score and write comments for multiple categories such as novelty and meaningful comparison. A good review needs to be knowledgeable, namely that the comments should be constructive and informative to help improve the paper; and explainable by providing detailed evidence. ReviewRobot achieves these goals via three steps: (1) We perform domain-specific Information Extraction to construct a knowledge graph (KG) from the target paper under review, a related work KG from the papers cited by the target paper, and a background KG from a large collection of previous papers in the domain. (2) By comparing these three KGs, we predict a review score and detailed structured knowledge as evidence for each review category. (3) We carefully select and generalize human review sentences into templates, and apply these templates to transform the review scores and evidence into natural language comments. Experimental results show that our review score predictor reaches 71.4%-100% accuracy. Human assessment by domain experts shows that 41.7%-70.5% of the comments generated by ReviewRobot are valid and constructive, and better than human-written ones for 20% of the time. Thus, ReviewRobot can serve as an assistant for paper reviewers, program chairs and authors.

Every day we are likely to interact with some form of artificial intelligence (AI). It works behind the scenes in everything from social media and traffic navigation apps to product recommendations and virtual assistants. AI systems can perform tasks or make predictions, recommendations or decisions that would usually require human intelligence. Their objectives are set by humans but the systems act without explicit human instructions. As AI plays a greater role in our lives both at work and at home, questions arise.

The 2020 presidential election in the United States is just around the corner. This year, the election has been particularly controversial in part because of the ongoing COVID-19 pandemic and the restrictions the virus has placed on in-person gatherings. In a world in which connected devices and IoT (Internet of Things) technologies have enabled everything from autonomous vehicles to robotic surgery, it seems like there should be other options for casting votes besides sending paper ballots in by mail or turning them in by hand. However, concerns (both legitimate and overblown) about election-outcome accuracy and voter privacy have held the election process back in many ways from the digital revolution that has permeated almost everything else. Will 2020 be a pivotal year in changing how the American people and "the powers that be" feel about advancing the voting process?

October 28, 2020 – "It has been a wild year," says Montreal-based Brainbox AI's president, Sam Ramadori. When the company launched in May 2019, it had just shy of 20 people, and worked on six development buildings totalling half a million square feet. "Today, we're in over 30 million, and with the commitment here, getting us over 40 million installed square feet." The commitment to which Ramadori is referring is that of AMP Capital in Australia, which is set to roll out Brainbox AI's energy-saving AI technology--which promises a reduction of carbon footprint by 20-40% and 15-20% in energy spend--across its entire managed real estate portfolio of central business district (CBD) office buildings, retail shopping centres and logistics facilities. Six AMP Capital-managed buildings across Australia are already trialling the technology, which will then be rolled out across the entire portfolio of over 40 buildings in Australia and New Zealand by Q1 2021. "We have a very young team, of very highly-capable technical folks, and they're all driven, not only by the truly autonomous solution, but we can really make a strong impact on climate change… but the only way to do that is to be able to scale quickly," said Ramadori, referring to the solution's ease of deployment.

Performance indicators that contributed to success at the group stage and play-off stages of the 2019 Rugby World Cup were analysed using publicly available data obtained from the official tournament website using both a non-parametric statistical technique, Wilcoxon's signed rank test, and a decision rules technique from machine learning called RIPPER. Our statistical results found that ball carry effectiveness (percentage of ball carries that penetrated the opposition gain-line) and total metres gained (kick metres plus carry metres) were found to contribute to success at both stages of the tournament and that indicators that contributed to success during the group stages (dominating possession, making more ball carries, making more passes, winning more rucks, and making less tackles) did not contribute to success at the play-off stage. Our results using RIPPER found that low ball carries and a low lineout success percentage jointly contributed to losing at the group stage, while winning a low number of rucks and carrying over the gain-line a sufficient number of times contributed to winning at the play-off stage of the tournament. The results emphasise the need for teams to adapt their playing strategies from the group stage to the play-off stage at tournament in order to be successful.

Statistical methods for the analysis and forecasting of time series data have a long history (Tsay, 2000). The well-accepted Box-Jenkins analysis and forecasting methods have been applied in a wide range of applications, from finance to medicine, and the classic book that laid out the theory is now in its fourth edition with over 55,000 citations (Box et al., 2015). In this paper, we focus on the specialized area of time series regression where the goal is to predict one time series with the help of covariates that include elements which also have a time series nature. Some authors refer to this as dynamic regression (Hyndman and Athanasopoulos, 2018), others use the term regARIMA (Gómez and Maravall, 1994; Maravall et al., 2016). Pankratz (2012) provides an excellent overview.