In September 2019, four researchers wrote to the publisher Wiley to "respectfully ask" that it immediately retract a scientific paper. The study, published in 2018, had trained algorithms to distinguish faces of Uyghur people, a predominantly Muslim minority ethnic group in China, from those of Korean and Tibetan ethnicity1. China had already been internationally condemned for its heavy surveillance and mass detentions of Uyghurs in camps in the northwestern province of Xinjiang -- which the government says are re-education centres aimed at quelling a terrorist movement. According to media reports, authorities in Xinjiang have used surveillance cameras equipped with software attuned to Uyghur faces. As a result, many researchers found it disturbing that academics had tried to build such algorithms -- and that a US journal had published a research paper on the topic. And the 2018 study wasn't the only one: journals from publishers including Springer Nature, Elsevier and the Institute of Electrical and Electronics Engineers (IEEE) had also published peer-reviewed papers that describe using facial recognition to identify Uyghurs and members of other Chinese minority groups. The complaint, which launched an ongoing investigation, was one foray in a growing push by some scientists and human-rights activists to get the scientific community to take a firmer stance against unethical facial-recognition research.

The ongoing global pandemic has created an urgent need for rapid tests that can diagnose the presence of the SARS-CoV-2 virus, the pathogen that causes COVID-19, and distinguish it from other respiratory viruses. Now, common respiratory from Japan have demonstrated a new system for single-virion identification of common respiratory pathogens using a machine learning algorithm trained on changes in current across silicon nanopores. This work may lead to fast and accurate screening tests for diseases like COVID-19 and influenza. In a study published this month in ACS Sensors scientists at Osaka University have introduced a new system using silicon nanopores sensitive enough to detect even a single virus particle when coupled with a machine learning algorithm. In this method, a silicon nitride layer just 50 nm thick suspended on a silicon wafer has tiny nanopores added, which are themselves only 300 nm in diameter.

IBM Corp. said today it's hoping to provide a standardized solution for developers to create and deploy machine learning models in production and make them portable to any cloud platform. To do so, it said it's open-sourcing the Kubeflow machine learning platform on Tekton, a continuous integration/continuous development platform developed by Google LLC. It's popular with developers who use Kubernetes to manage containerized applications, which can run unchanged across many computing environments. IBM said it created Kubeflow Pipelines on Tekton in response to the need for a more reliable solution for deploying, monitoring and governing machine learning models in production on any cloud platform. That's important, IBM says, because hybrid cloud models are rapidly becoming the norm for many enterprises that want to take advantage of the benefits of running their most critical business applications across distributed computing environments.

Autonomous vehicle startup Cruise has partnered with Walmart to deliver orders from a Scottsdale, AZ, Walmart store to local customers' homes, starting early next year. General Motors-backed autonomous vehicle startup Cruise has announced a partnership with Walmart to deliver orders from a Scottsdale, AZ, Walmart store to local customers' homes, starting early next year. Customers will be able to place orders to the store and have them delivered in one of Cruise's electric self-driving Chevy Bolts. If the pilot goes well, a Cruise spokesperson said, the company will mull launching on-demand delivery with other retailers in the future. Walmart has forged driverless vehicle delivery partnerships with other automakers and startups.

Artificial Intelligence and the Future of Work (2021) - Welcome to the new Artificial Intelligence (AI) Technology Revolution in 2021 impacting workplaces worldwide. Created by Srinidhi Ranganathan Students also bought Artificial Intelligence A-Z: Learn How To Build An AI Deep Learning and Computer Vision A-Z: OpenCV, SSD & GANs Artificial Intelligence: Reinforcement Learning in Python The Beginner's Guide to Artificial Intelligence in Unity.Preview this course Udemy GET COUPON CODE A great technological shift is on the verge of occurring very soon. Disruptive Artificial Intelligence technologies are going to change the world and human labor will be replaced by robot workers and the shift has in-fact started. This mind-blowing course introduces you to the concept of Artificial Intelligence usage in the workplace along with providing you practical examples of the different platforms that deploy the same for automation. You will learn about the numerous Human Resources tools and usage of these in Artificial Intelligence, along with sales-based AI tools that can help you close the deal.

Imagination Technologies has launched a scalable neural network accelerator IP core optimised for automotive and autonomous systems but also aimed at industrial designs. The Series4 Neural Network Accelerator (NNA) core has been optimised for the YOLOv3 neural network framework, for processing large, rectangular images, rather than a general purpose execution unit. It is aimed at developer of system-on-chip devices for sensor fusion in high performance autonomous vehicles such as robotaxis, last mile delivery and automated street sweepers. The NNA core achieves 12.5TOPS of performance through 4096 multiply accumulate (MAC) units in 1mm2 on a 5nm process technology, all connected by a 256 network on chip (NOC). This that is over 20x faster than an embedded GPU and 1000x faster than an embedded CPU for AI inference says the company.

The brain's thalamus has historically been thought of as a relay centre that transmits sensory and motor inputs to the cortex for processing, or that transmits information from one part of the cortex to another. In 2017, three groups made the unexpected discovery that the thalamus also has a key role in short-term memory -- specifically, in maintaining the recurrent patterns of cortical activity that underlie memory1–3. However, the genetic basis of this role for the thalamus remained unexplored. Writing in Cell, Hsiao et al.4 reveal that the gene Gpr12 is key to thalamic maintenance of short-term memory. Their findings will have relevance for many fields, from cognitive therapeutics to artificial intelligence.

This story originally appeared in the December issue of Discover magazine as "Scientist in Toyland." It's easy to pin labels on Chuck Hoberman, but hard to stick with just one. He's a toymaker -- the brains behind the colorful, expanding Hoberman sphere, which you and your kids have been playing with since the early 1990s (and which earned a place in the Museum of Modern Art's permanent collection). Physically, he works sometimes from an airy room on the second floor of a house-turned-office-suite near Harvard Square in Cambridge, Massachusetts. The Cambridge office is tidy, with white walls and plenty of light. The surfaces are usually cleared, but today they're cluttered with the material expressions of his geometric dreams: Models made of two-dimensional pieces, hinged together to form 3D structures that deform, bend or otherwise fold in prescribed ways.

There's plenty of competition: VirtualAPT, based in Brooklyn, has robots that glide through homes and provide immersive virtual reality tours; REX, a brokerage in Woodland Hills, Calif., has an AI-trained robot to answer potential buyers' questions at open houses; RealFriend and OjoLabs have AI-powered chatbots that mimic human conversation while providing deeply personalized home listings and buying advice. In Zenny's case, the robot is powered remotely by the real estate broker or property manager who is handling the showing from afar. It is also equipped with sensors to keep it from running into walls or people. In addition to Zenny, Zenplace's platform includes a full suite of rental management solutions, including tenant screening, electronic lockboxes for on-demand property viewings, and a secure online portal for rent payment. The company charges a $599 flat fee for some properties, and $99 a month for others. VirtualAPT's robots, which roll through homes capturing 360-degree videos in 4K resolution, provide ultra-crisp, high-quality images.

BBC Click’s Omar Mehtab looks at some of the best technology news stories.