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An Australian Femtech company with US headquarters in San Francisco announced new technology to help couples get pregnant via artificial intelligence-assisted in vitro fertilization (IVF). Life Whisperer is the fertility arm of Presagen, a global artificial intelligence company. The company, whose US headquarters is in San Francisco, announced in a press release new women's health technology applying artificial intelligence to the IVF embryo selection process. IVF clinics around the world can add an artificial intelligence platform to help doctors select the healthiest embryos with the best chance of success. Embryo selection is an important part of the IVF process, where the healthiest embryos are chosen for implantation.

In 2017-18, only six per cent of Australian soft plastic waste was recycled. The rest added to the growing mountain of plastic in landfills around the country. The biggest problem is the lack of an automatic solution to sort soft plastic waste from co-mingled recycling. Vucetic, an Engineers Australia Fellow, explained that this is because soft plastics like bread bags and cling wrap get tangled in machinery, causing equipment failures and contaminating other waste streams. Sydney-based recycling provider iQRenew invited Vucetic's team at the University of Sydney's Centre for IoT and Telecommunications to see the problem first hand, and potentially help them automate their processes.

The University of South Australia will lead a world-first study, using artificial intelligence, to map the risks of the most fatal reproductive cancer in women worldwide so it can be detected and treated earlier. Internationally-renowned nutritional epidemiologist Professor Elina Hypponen and a team from UniSA's Australian Centre for Precision Health have been awarded $1.2 million by the Federal Government to map the genetic and physical risks of ovarian cancer, based on the health records of 273,000 women from the UK Biobank database. A machine learning model, which automatically analyses the data to identify patterns of risk, is expected to accurately predict which women will develop ovarian cancer in the next 15 years. Ovarian cancer is usually diagnosed very late due to vague symptoms and few known causes, with a five-year survival rate of less than 30 per cent for women with late-stage cancer. Genes, diet and lifestyle come into play and the researchers say a computational approach will narrow down those most at risk. "With an early diagnosis, we can notably improve survival rates from ovarian cancer," Prof Hypponen says.

Previous works indicate that the glyph of Chinese characters contains rich semantic information and has the potential to enhance the representation of Chinese characters. The typical method to utilize the glyph features is by incorporating them into the character embedding space. Inspired by previous methods, we innovatively propose a Chinese pre-trained representation model named as GlyphCRM, which abandons the ID-based character embedding method yet solely based on sequential character images. We render each character into a binary grayscale image and design two-channel position feature maps for it. Formally, we first design a two-layer residual convolutional neural network, namely HanGlyph to generate the initial glyph representation of Chinese characters, and subsequently adopt multiple bidirectional encoder Transformer blocks as the superstructure to capture the context-sensitive information. Meanwhile, we feed the glyph features extracted from each layer of the HanGlyph module into the underlying Transformer blocks by skip-connection method to fully exploit the glyph features of Chinese characters. As the HanGlyph module can obtain a sufficient glyph representation of any Chinese character, the long-standing out-of-vocabulary problem could be effectively solved. Extensive experimental results indicate that GlyphCRM substantially outperforms the previous BERT-based state-of-the-art model on 9 fine-tuning tasks, and it has strong transferability and generalization on specialized fields and low-resource tasks. We hope this work could spark further research beyond the realms of well-established representation of Chinese texts.

This letter introduces a novel framework to optimize the power allocation for users in a Rate Splitting Multiple Access (RSMA) network. In the network, messages intended for users are split into different parts that are a single common part and respective private parts. This mechanism enables RSMA to flexibly manage interference and thus enhance energy and spectral efficiency. Although possessing outstanding advantages, optimizing power allocation in RSMA is very challenging under the uncertainty of the communication channel and the transmitter has limited knowledge of the channel information. To solve the problem, we first develop a Markov Decision Process framework to model the dynamic of the communication channel. The deep reinforcement algorithm is then proposed to find the optimal power allocation policy for the transmitter without requiring any prior information of the channel. The simulation results show that the proposed scheme can outperform baseline schemes in terms of average sum-rate under different power and QoS requirements.

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.