Welcome to our March 2021 monthly digest. Our digests are designed to keep you up-to-date with the latest happenings in the AI world. You can catch up with any AIhub stories you may have missed, get the low-down on recent conferences, and generally immerse yourself in all things AI. This month, our attention turned to education, and we considered both the use of AI in teaching, and the teaching of AI. Carles Sierra wrote about team formation techniques in education, describing how AI methods can be used to facilitate collaborative learning.

China enlisted surveillance firms to help draw up standards for mass facial recognition systems, researchers said on Tuesday, warning that an unusually heavy emphasis on tracking characteristics such as ethnicity created wide scope for abuse. The technical standards, published by surveillance research group IPVM, specify how data captured by facial recognition cameras across China should be segmented by dozens of characteristics -- from eyebrow size to skin color and ethnicity. "It's the first time we've ever seen public security camera networks that are tracking people by these sensitive categories explicitly at this scale," said the report's author, Charles Rollet. The standards are driving the way surveillance networks are being built across the country -- from residential developments in the capital, Beijing, to police systems in the central province of Hubei, he said. In one instance, the report cites a November 2020 tender for a small "smart" housing project in Beijing, requiring suppliers for its surveillance camera system to meet a standard that allows sorting by skin tone, ethnicity and hairstyle.

Those that climb need to be both fast and stable to avoid predation and find food. A robot made to mimic their movements has now shown how the rotation of their legs and the speed with which they move up vertical surfaces helps them climb efficiently. "Most lizards look a lot like other lizards," says Christofer Clemente at University of the Sunshine Coast, Australia. To find out why, Clemente and his team built a robot based on a lizard's body plan to explore its efficiency. It is about 24 centimetres long, and its legs and feet were programmed to mimic the gait of climbing lizards.

Artificial Intelligence is one of the fastest growing technologies of the 21st century and accompanies us in our daily lives when interacting with technical applications. However, reliance on such technical systems is crucial for their widespread applicability and acceptance. The societal tools to express reliance are usually formalized by lawful regulations, i.e., standards, norms, accreditations, and certificates. Therefore, the T\"UV AUSTRIA Group in cooperation with the Institute for Machine Learning at the Johannes Kepler University Linz, proposes a certification process and an audit catalog for Machine Learning applications. We are convinced that our approach can serve as the foundation for the certification of applications that use Machine Learning and Deep Learning, the techniques that drive the current revolution in Artificial Intelligence. While certain high-risk areas, such as fully autonomous robots in workspaces shared with humans, are still some time away from certification, we aim to cover low-risk applications with our certification procedure. Our holistic approach attempts to analyze Machine Learning applications from multiple perspectives to evaluate and verify the aspects of secure software development, functional requirements, data quality, data protection, and ethics. Inspired by existing work, we introduce four criticality levels to map the criticality of a Machine Learning application regarding the impact of its decisions on people, environment, and organizations. Currently, the audit catalog can be applied to low-risk applications within the scope of supervised learning as commonly encountered in industry. Guided by field experience, scientific developments, and market demands, the audit catalog will be extended and modified accordingly.

A data-driven computational heuristic is proposed to control MIMO systems without prior knowledge of their dynamics. The heuristic is illustrated on a two-input two-output balance system. It integrates a self-adjusting nonlinear threshold accepting heuristic with a neural network to compromise between the desired transient and steady state characteristics of the system while optimizing a dynamic cost function. The heuristic decides on the control gains of multiple interacting PID control loops. The neural network is trained upon optimizing a weighted-derivative like objective cost function. The performance of the developed mechanism is compared with another controller that employs a combined PID-Riccati approach. One of the salient features of the proposed control schemes is that they do not require prior knowledge of the system dynamics. However, they depend on a known region of stability for the control gains to be used as a search space by the optimization algorithm. The control mechanism is validated using different optimization criteria which address different design requirements.

Training quantum neural networks (QNNs) using gradient-based or gradient-free classical optimisation approaches is severely impacted by the presence of barren plateaus in the cost landscapes. In this paper, we devise a framework for leveraging quantum optimisation algorithms to find optimal parameters of QNNs for certain tasks. To achieve this, we coherently encode the cost function of QNNs onto relative phases of a superposition state in the Hilbert space of the network parameters. The parameters are tuned with an iterative quantum optimisation structure using adaptively selected Hamiltonians. The quantum mechanism of this framework exploits hidden structure in the QNN optimisation problem and hence is expected to provide beyond-Grover speed up, mitigating the barren plateau issue.

Google Maps is no longer an outdoor navigation system – the tech giant is rolling out an upgrade that provides directions inside different facilities. Users can now use Live View on smartphones to maneuver around malls, airports and train stations. The Indoor Live View Feature uses AR cues to provide turn-by-turn directions over a live shot of the area. The 2021 upgrade also includes a new weather layer that shows the current and forecasted predictions, along with eco-friendly routes as many nations aim for a carbon free world. Google Maps is no longer an outdoor navigation system – the tech giant is rolling out an upgrade that provides directions inside different facilities.

Consumer fashion may be among the most unpredictable markets on the planet, but one startup in India has created an AI-based demand sensing platform that combines the brilliance of data scientists with seasoned industry experts to ferret out trends with uncanny accuracy. The idea is to close the gap between supply and demand. Omni-channel retailers are using AI to synch design and merchandising decisions with breaking consumer demand trends for sustainable growth. "We help companies create demand-driven fashion forecasts from consumer data across a holistic value chain," Ganesh Subramanian, founder and CEO at Stylumia. "Our demand sensing engine collects and analyzes publicly available global data to rank product trends, providing fashion designers, retail buyers, and merchandisers with a much deeper understanding of real-time consumer demand signals."

With 30 years' experience in print and digital media, Helen has written articles for clients ranging from The Sydney Morning Herald, The Australian Financial Review and GQ Australia, to CommBank, Swissotels and Bunnings. Her father was a civil engineer and she remains fascinated by the use of scientific principles to design and build a better world.

Varroa destructor is a deadly stowaway that port authorities are determined to keep away from the bee population in the southeast Australian state of Victoria. Artificially intelligent beehives are being installed at Victorian ports to detect pests as they arrive at ships rapidly. "The Varroa mite is extremely destructive; it kills bees very rapidly," said Mary-Anne Thomas, the Victorian agriculture minister. "I would look forward to a project like the Purple Hive rolling out across the country. Purple Hive was launched on March 29 at the Port of Melbourne -- a solar-powered device that detects Varroa destructor, a mite that feeds on honey bees. Using artificial intelligence and cameras, Purple Hive provides alerts in real-time and has been trialed in New Zealand, where the mite is established. The technology scans each honey bee entering the Purple Hive to determine if Varroa mite is present. The hive is colored purple because it attracts bees. Thomas tweeted a picture of a hive being installed. "At #BegaCheese, we're absolutely buzzing with excitement to announce that B honey's Purple Hive has officially found its first home at the Port of Melbourne, as we join forces with @VicGovAg to help protect honey bee populations from Varroa destructor," read the tweet of Jimmy Coleman, marketing manager of digital and communications, Bega Cheese. "Varroa destructor is the world's most devastating pest of Western honey bees, Apis mellifera Linnaeus," as per the website of the University of Florida. "Accurate estimates of the effect of Varroa on the apiculture industry are hard to find, but it is safe to assume that the mites have killed hundreds of thousands of colonies worldwide, resulting in billions of dollars of economic loss." The adult female mites are reddish-brown to dark brown and oval. Adult males are yellowish with light tan legs and have a spherical body shape. Varroa destructor, the most significant single driver of the global honey bee health decline, was detected on a ship that entered the Port of Melbourne in 2018, but authorities stopped it from becoming an outbreak. "Australia is the only populated country in the world that the Varroa destructor hasn't impacted.