Zachary Ulissi (right) explores how surface chirality affects chemical reactions.Credit: Materials Science and Engineering Department/Carnegie Mellon University Materials scientists are increasingly turning to machine learning and other computational techniques to discover new materials. From corrosion resistant aeroplane components and better batteries to new drugs or novel catalysts, big data can help to find them. "The problem is that the number of possible materials is infinite," says Matthias Scheffler, a computational materials scientist at the Fritz-Haber Institute in Berlin, Germany. "With high-throughput screening, you can screen thousands of systems, and a thousand is nothing compared to infinite." Along with physicist Claudia Draxl, of Humboldt University Berlin, Scheffler launched the Novel Materials Discovery Laboratory (NOMAD) at Fritz-Haber, a data repository for a wide variety of information about chemical compounds.

In a move to increase soft plastics recycling, engineering researchers at the University of Sydney are creating a smart, automated robotic system that uses artificial intelligence to sort recyclable waste. Soft plastics lack adequate recycling methods because they easily entangle in waste-separation machinery, which often leads to mechanical failure and contamination of other recyclable materials such as paper. Because of this problem, current recycling methods rely on the manual sorting of soft plastics. Despite an improvement in plastic recycling in recent years, landfill is a growing issue. Soft plastics like cling wrap and plastic bags are a major contributor to the problem, with 94% going to landfill in 2016–17.

The Australian Government has released Australia's Artificial Intelligence (AI) Action Plan. The plan sets out a vision for Australia to be a global leader in the development and adoption of trusted, secure and responsible AI. It includes actions the Australian Government is taking to realise this vision and ensure all Australians share the benefits of an AI-enabled economy. This includes progressing the work we have been doing to promote ethical approaches to AI. A key feature of the Australian Government's Digital Economy Strategy, the action plan will help deliver a modern and leading digital economy by 2030.

Climate change is the most pressing threat that the human species faces today. Artificial intelligence is the most powerful tool that humanity has at its disposal in the twenty-first century. Can we deploy the second to combat the first? A group of promising startups has emerged to do just that. Both climate change and artificial intelligence are sprawling, cross-disciplinary fields. Both will transform literally every sector of the economy in the years ahead. There is therefore no single "silver bullet" application of AI to climate change. Instead, a wide range of machine learning use cases can help in the race to decarbonize our world. Nearly every major activity that humanity engages in today contributes to our carbon footprint to some extent: building things, moving things, powering things, eating things, computing things.

We leverage state-of-the-art machine learning methods and a decade's worth of archival data from the Canada-France-Hawaii Telescope (CFHT) to predict observatory image quality (IQ) from environmental conditions and observatory operating parameters. Specifically, we develop accurate and interpretable models of the complex dependence between data features and observed IQ for CFHT's wide field camera, MegaCam. Our contributions are several-fold. First, we collect, collate and reprocess several disparate data sets gathered by CFHT scientists. Second, we predict probability distribution functions (PDFs) of IQ, and achieve a mean absolute error of $\sim0.07''$ for the predicted medians. Third, we explore data-driven actuation of the 12 dome ``vents'', installed in 2013-14 to accelerate the flushing of hot air from the dome. We leverage epistemic and aleatoric uncertainties in conjunction with probabilistic generative modeling to identify candidate vent adjustments that are in-distribution (ID) and, for the optimal configuration for each ID sample, we predict the reduction in required observing time to achieve a fixed SNR. On average, the reduction is $\sim15\%$. Finally, we rank sensor data features by Shapley values to identify the most predictive variables for each observation. Our long-term goal is to construct reliable and real-time models that can forecast optimal observatory operating parameters for optimization of IQ. Such forecasts can then be fed into scheduling protocols and predictive maintenance routines. We anticipate that such approaches will become standard in automating observatory operations and maintenance by the time CFHT's successor, the Maunakea Spectroscopic Explorer (MSE), is installed in the next decade.

In the big data and AI era, context is widely exploited as extra information which makes it easier to learn a more complex pattern in machine learning systems. However, most of the existing related studies seldom take context into account. The difficulty lies in the unknown generalization ability of both context and its modeling techniques across different scenarios. To fill the above gaps, we conduct a large-scale analytical and empirical study on the spatiotemporal crowd prediction (STCFP) problem that is a widely-studied and hot research topic. We mainly make three efforts:(i) we develop new taxonomy about both context features and context modeling techniques based on extensive investigations in prevailing STCFP research; (ii) we conduct extensive experiments on seven datasets with hundreds of millions of records to quantitatively evaluate the generalization ability of both distinct context features and context modeling techniques; (iii) we summarize some guidelines for researchers to conveniently utilize context in diverse applications.

In light of the COVID-19 pandemic, it is an open challenge and critical practical problem to find a optimal way to dynamically prescribe the best policies that balance both the governmental resources and epidemic control in different countries and regions. To solve this multi-dimensional tradeoff of exploitation and exploration, we formulate this technical challenge as a contextual combinatorial bandit problem that jointly optimizes a multi-criteria reward function. Given the historical daily cases in a region and the past intervention plans in place, the agent should generate useful intervention plans that policy makers can implement in real time to minimizing both the number of daily COVID-19 cases and the stringency of the recommended interventions. We prove this concept with simulations of multiple realistic policy making scenarios.

Where does your enterprise stand on the AI adoption curve? Take our AI survey to find out. As AI proliferates, researchers are beginning to call for technologies that might foster trust in AI-powered systems. According to a survey conducted by KPMG, across five countries -- the U.S., the U.K., Germany, Canada, and Australia -- over a third of the general public says that they're unwilling to place trust in AI systems in general. And in a report published by Pega, only 25% of consumers said they'd trust a decision made by an AI system regarding a qualification for a bank loan, for example.

The Sliced-Wasserstein distance (SW) is being increasingly used in machine learning applications as an alternative to the Wasserstein distance and offers significant computational and statistical benefits. Since it is defined as an expectation over random projections, SW is commonly approximated by Monte Carlo. We adopt a new perspective to approximate SW by making use of the concentration of measure phenomenon: under mild assumptions, one-dimensional projections of a high-dimensional random vector are approximately Gaussian. Based on this observation, we develop a simple deterministic approximation for SW. Our method does not require sampling a number of random projections, and is therefore both accurate and easy to use compared to the usual Monte Carlo approximation. We derive nonasymptotical guarantees for our approach, and show that the approximation error goes to zero as the dimension increases, under a weak dependence condition on the data distribution. We validate our theoretical findings on synthetic datasets, and illustrate the proposed approximation on a generative modeling problem.

Current model-based reinforcement learning methods struggle when operating from complex visual scenes due to their inability to prioritize task-relevant features. To mitigate this problem, we propose learning Task Informed Abstractions (TIA) that explicitly separates reward-correlated visual features from distractors. For learning TIA, we introduce the formalism of Task Informed MDP (TiMDP) that is realized by training two models that learn visual features via cooperative reconstruction, but one model is adversarially dissociated from the reward signal. Empirical evaluation shows that TIA leads to significant performance gains over state-of-the-art methods on many visual control tasks where natural and unconstrained visual distractions pose a formidable challenge.