The University of Western Australia (UWA) and Rio Tinto Iron Ore (RTIO) have entered into a four-year, $6.1 million research partnership to develop innovative data science solutions (artificial intelligence) for automated geological logging to improve mining practice. The partnership, which follows more than 10 years of collaboration between UWA's data science team and RTIO, will employ five full-time researchers and provide training opportunities for a number of industry-driven PhD programmes. Dr Daniel Wedge, from (CDG) in UWA's School of Geosciences, said UWA's expertise will be resorted to help RTIO's mine geology team tackle the challenge of objective well geological materials. "Until recently, geologist's specialists had to manually interpret and document material found in core samples, a process that was time-consuming and challenging," Dr Wedge said. "Our project can use artificial intelligence: machine learning, pc vision, spacial modelling and improvement techniques to integrate disparate borehole information, together with analysis, imagery, geochemical and natural science informationalong side chemical analysis, imagery, geochemical and earth science info, to."RTIO head Dr. Angus McFarlane said the past partnership between UWA and RTIO has led to the commercialisation of UWA's automated downhole image analysis software and three joint patent applications for RTIO-driven machine learning-based geological modelling.

Since 2002, Quantium have combined the best of human and artificial intelligence to power possibilities for individuals, organisations and society. Whether it be building forecasting engines that are driving down food wastage or creating mapping tools to support targeted measures in combatting human trafficking, Quantium believes in better goods, services, experiences, and championing the benefits of data for a brighter future. Q-Telco is the new joint venture between Quantium and Telstra to unlock the full potential of data and AI for Telstra and its customers. We'll do this by combining our market leading data science and AI capabilities with Telstra's customer, product and network data assets. This new partnership will not only provide personalised and data-enabled products and offers for Telstra's customers, but it will also embed proactive and predictive AI and machine learning across Telstra's core business.

Communications' Inside Risks columns have long stressed the importance of total-system awareness of riskful situations, some of which may be very difficult to identify in advance. Specifically, the desired properties of the total system should be specified as requirements. Those desired properties are called emergent properties, because they often cannot be derived solely from lower-layer component properties, and appear only with respect to the total system. Unfortunately, additional behavior of the total system may arise--which either defeats the ability to satisfy the desired properties, or demonstrates that the set of required properties was improperly specified. In this column, I consider some cases in which total-system analysis is of vital importance, but generally very difficult to achieve with adequate assurance.

This is my last editorial as Editor-in-Chief of Communications,a so it is a moment to share learnings and, of course, to reflect on accomplishments. First, we launched the Regional Special Sections (RSS) in November 2018 with a spotlight on computing in the China Region. With 40 pages of articles, spanning tech idols to gaming to computing culture to fintech and "superAI," the first RSS created an excitement that inspired and challenged co-hosts of the Europe, India, East Asia and Oceania, Latin America, and Arabia Regions. In just three years, we have circumnavigated the globe,b and with the second Europe Region Section (April 2022) and India Region Section (November 2022), a new circuit is well under way! The RSS are an exciting read for the ACM community (great job by the co-hosts and authors), delivering news insights and perspectives into how computing is shaping and being shaped around the world.

New software developed by Peter Mac and collaborators is helping patients diagnosed with acute lymphoblastic leukemia (ALL) to determine what subtype they have. ALL is the most common childhood cancer in the world, and also affects adults. "Thirty to forty percent of all childhood cancers are ALL, it's a major pediatric cancer problem," says Associate Professor Paul Ekert from Peter Mac and the Children's Cancer Institute, who was involved in this work. More than 300 people are diagnosed with the disease in Australia each year, and more than half of those are young children under the age of 15. Determining what subtype of ALL a patient has provides valuable information about their prognosis, and how they should best be treated.

Author summary Interest in machine learning as applied to challenges in medicine has seen an exponential rise over the past decade. A key issue in developing machine learning models is the availability of sufficient high-quality data. Another related issue is a requirement to validate a locally trained model on data from external sources. However, sharing sensitive biomedical and clinical data across different hospitals and research teams can be challenging due to concerns with data privacy and data stewardship. These issues have led to innovative new approaches for collaboratively training machine learning models without sharing raw data. One such method, termed ‘federated learning,’ enables investigators from different institutions to combine efforts by training a model locally on their own data, and sharing the parameters of the model with others to generate a central model. Here, we systematically review reports of successful deployments of federated learning applied to research problems involving biomedical data. We found that federated learning links research teams around the world and has been applied to modelling in such as oncology and radiology. Based on the trends we observed in the studies reviewed in our paper, we observe there are opportunities to expand and improve this innovative approach so global teams can continue to produce and validate high quality machine learning models.

The novelty cheque has long been a mainstay of the political "photo op" but a Guardian Australia analysis of photos posted during the 2022 and 2019 election campaigns suggests giant cheques are on the way out, while hi-vis workwear and photos of dogs are on the rise. During our work building the automated systems behind the pork-o-meter, which tracks election campaign pork barrelling as it occurs, the Guardian's data team found ourselves asking an important question. Could we teach a robot to spot photos of novelty cheques? We were already using machine learning to flag text from politicians' Facebook posts as likely grant announcements and election promises, but having another model in place to find big cheques and certificates in photos might pick up things we'd missed in the text.

If you spend too much time on your smartphone, scientists have a list of 10 solutions that can help you cut back on screen time. The small but effective changes can help curb smartphone addiction and mental health issues such as depression, say experts at McGill University in Canada. In experiments, people following the strategies reduced their screen time, felt less addicted to their phone and improved their sleep quality, the experts report. Among the 10 strategies are changing the phone display to'greyscale' so the display appears black and white, and disabling facial recognition as a method of unlocking the screen. A black and white screen makes smartphones'less gratifying' to look at compared to the bright colours offered by app icons such as TikTok and Instagram.

Continual learning - learning new tasks in sequence while maintaining performance on old tasks - remains particularly challenging for artificial neural networks. Surprisingly, the amount of forgetting does not increase with the dissimilarity between the learned tasks, but appears to be worst in an intermediate similarity regime. In this paper we theoretically analyse both a synthetic teacher-student framework and a real data setup to provide an explanation of this phenomenon that we name Maslow's hammer hypothesis. Our analysis reveals the presence of a trade-off between node activation and node re-use that results in worst forgetting in the intermediate regime. Using this understanding we reinterpret popular algorithmic interventions for catastrophic interference in terms of this trade-off, and identify the regimes in which they are most effective.

This work identifies a common flaw of deep reinforcement learning (RL) algorithms: a tendency to rely on early interactions and ignore useful evidence encountered later. Because of training on progressively growing datasets, deep RL agents incur a risk of overfitting to earlier experiences, negatively affecting the rest of the learning process. Inspired by cognitive science, we refer to this effect as the primacy bias. Through a series of experiments, we dissect the algorithmic aspects of deep RL that exacerbate this bias. We then propose a simple yet generally-applicable mechanism that tackles the primacy bias by periodically resetting a part of the agent. We apply this mechanism to algorithms in both discrete (Atari 100k) and continuous action (DeepMind Control Suite) domains, consistently improving their performance.