In April of 2021, the U.S. Federal Trade Commission -- in its "Aiming for truth, fairness, and equity in your company's use of AI" report -- issued a clear warning to tech industry players employing artificial intelligence: "Hold yourself accountable, or be ready for the FTC to do it for you." Likewise, the European Commission has proposed new AI rules to protect citizens from AI-based discrimination. These warnings, and impending regulations, are warranted. Machine learning (ML), a common type of AI, mimics patterns, attitudes and behaviors that exist in our imperfect world, and as a result, it often codifies inherent biases and systemic racism. Unconscious biases are particularly difficult to overcome, because they, by definition, exist without human awareness.

The Queensland government has announced it wants to widen its 15% tax rebate under the post-production, digital, visual effects (PDV) incentive to include digital games, alongside film makers who do post-production and post-visual effects in the state. Premier Annastacia Palaszczuk said the move would incentivise more games projects and game developers to bring their business to the state. "By adding this new games' incentive, local and international studios will be further motivated to outsource the creation of valuable game titles to Queensland, while our own locally based developers will be supported to create original games content right here," she said. In addition, the state government said it would move to lower the threshold to access the PDV and games incentive from AU$500,000 to AU$250,000. "Across the state, we have a growing base of talented specialists and studio facilities in the post-production, visual effects, animation and games space, and supporting this continued growth, particularly during this recently prolific time for the screen industry, is exactly what this incentive was devised to do," Screen Queensland CEO Kylie Munnich said.

In kicking off its annual 11.11 global shopping event, now in its 13th year, Alibaba Group CMO Chris Tung took the opportunity to highlight that the main focus for this year's shopping event would be sustainability. "We are making sustainability at the heart of the festival," he told media during a virtual press event. "We believe that behavioural change is essential to ensuring a sustainable future, which is why as the creator and leader of the 11.11 festival, Alibaba aims to play an important role in driving those positive changes." Arguably the world's largest shopping event, the 11.11 event has more than 290,000 participating brands, over 14 million deals, and more than 900 million consumers in China. For the first time, the Chinese tech giant has created a dedicated vertical to showcase energy-efficient and low-impact products. This will entail issuing 100 million yuan worth of "green vouchers" to customers, which Tung believes will "incentivise shopping decisions that are environmentally friendly".

The Department of Defense's Office of the Chief Information Officer, or DoD CIO, is pursuing several efforts to make sure the U.S. combatant commands have the fundamental tools to enable artificial intelligence and machine learning to aid their operational command and control. The DoD CIO's efforts naturally hinge on data and data management, an appropriate transport layer and future cloud capabilities, solutions that will benefit a broad range of warfighters not just at the commands, said Kelly Fletcher, who is performing the duties of the department's chief information officer on behalf of John Sherman, the nominated CIO who is currently going through his confirmation process for the position and testifying tomorrow in front of the U.S. Senate. A senior executive service official, Fletcher has been working in the office since 2020. She presented a keynote address during AFCEA International's TechNet Cyber conference in Baltimore on October 27. Fletcher emphasized that the DoD CIO's office supports more than 40 major combatant commands, services and agencies, "and they all have unique requirements," she said.

The Army recently scanned 7,200 km across four states on the eastern seaboard and used artificial intelligence to find and destroy specific simulated targets in an area the size of a 10-square-foot box. It was all part of the Army's XVIII Airborne Corp artificial intelligence-enabled live-fire target identification exercise on Thursday that used nearly 20 platforms and units from each of the other branches. The event was the fourth of its kind for the Scarlet Dragon program, which began in 2020. The Corps, assisted by elements of the Navy, Air Force and Marine Corps, worked with various platforms in all domains. But a key ingredient was the National Geospatial-Intelligence Center, which provided satellite imagery for software to sift through and find targets.

The U.S. Centers for Disease Control and Prevention estimates that one in seven children in the United States experienced abuse or neglect in the past year. Child protective services agencies around the nation receive a high number of reports each year (about 4.4 million in 2019) of alleged neglect or abuse. With so many cases, some agencies are implementing machine learning models to help child welfare specialists screen cases and determine which to recommend for further investigation. But these models don't do any good if the humans they are intended to help don't understand or trust their outputs. Researchers at MIT and elsewhere launched a research project to identify and tackle machine learning usability challenges in child welfare screening.

The U.S. Centers for Disease Control and Prevention estimates that one in seven children in the United States experienced abuse or neglect in the past year. Child protective services agencies around the nation receive a high number of reports each year (about 4.4 million in 2019) of alleged neglect or abuse. With so many cases, some agencies are implementing machine learning models to help child welfare specialists screen cases and determine which to recommend for further investigation. But these models don't do any good if the humans they are intended to help don't understand or trust their outputs. Researchers at MIT and elsewhere launched a research project to identify and tackle machine learning usability challenges in child welfare screening.

As a hereditary disease, breast cancer has affected hundreds of families throughout the state. Annually, an average of 1,190 women are diagnosed with breast cancer in Hawaiʻi. As October approaches in recognition of National Breast Cancer Awareness Month, new public impact research from the University of Hawaiʻi Cancer Center is using artificial intelligence (AI) to improve risk assessment for breast cancer to aid in prevention and early detection, improving breast cancer outcomes for women all over the world. To reduce unnecessary imaging for breast cancer and costs associated with it, UH Cancer Center Researcher John Shepherd and his colleagues found that AI is able to distinguish between the mammograms of women who are more likely to develop breast cancer later on, and those who are not. The study was published in Radiology.

Imagine computer programs that use precise knowledge of molecules' electronic structure to create new designs; imagine robots that make and test these molecules. And imagine the software and robots working together--testing molecules, tweaking designs, and testing again--until they produce a material with the properties we're looking for. Actually executing it is another matter. The structures of molecules are mind-bogglingly complex, and chemical synthesis is often more art than science, defying efforts to automate the process. But advances in AI, robotics, and computing are bringing new life to the vision.

A new rapid screening test for glaucoma could help advance early detection of the disease, a leading cause of irreversible blindness. Developed by a research team of engineers and ophthalmologists led by RMIT University in Melbourne, Australia, the test uses infrared sensors to monitor eye movement and can produce accurate results within seconds. About 80 million people worldwide have glaucoma, with more than 111 million expected to be living with the disease by 2040. The loss of sight is usually gradual and 50% of people with glaucoma do not know they have it. Currently, glaucoma is diagnosed through a 30-minute eye pressure test delivered by an ophthalmologist.