Susanne Rafelski and her colleagues had a deceptively simple goal. "We wanted to be able to label many different structures in the cell, but do live imaging," says the quantitative cell biologist and deputy director of the Allen Institute for Cell Science in Seattle, Washington. "And we wanted to do it in 3D." That kind of goal normally relies on fluorescence microscopy -- problematic in this case because, with only a handful of colours to use, the scientists would run out of labels well before they ran out of structures. Also problematic is that these reagents are pricey and laborious to use.

Google is adding more information to Search and Maps about businesses that offer options for grocery delivery and pickup. The information is getting added to search automatically, which means there's no work needed on the part of businesses, but it's an update worth being aware of. This addition to Google Search and Maps is rolling out as part of a larger update which includes a number of other useful features. We'll look at the other features at the end of this article – let's first go over the enhancements to Google My Business profiles. Google is bringing shopping information to stores' business profiles to assist people with finding convenient grocery delivery and pickup options.

Every day, militaristic and violent metaphors are used by journalists and political actors alike to communicate and mobilize action. These word choices may seem effective yet, these metaphors, imbued with violent imagery, can be dangerous. From a policy standpoint, they are also ineffective (and potentially harmful). One example is how the global "war on drugs" terminology victimized, stigmatized, and misplaced blame. As noted by others, as with any war, there are always civil rights abuses.

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.