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* At Long Last, Mathematical Proof That Black Holes Are Stable * Who Gets to Work in the Digital Economy? * Mice produce rat sperm with technique that could help conservation.

* Quantum computer can simulate infinitely many chaotic particles * Radar / AI & ML: Scaling False Peaks * Cyber security for the human world | George Loukas | TEDx.

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Advances in the fields of robotics, autonomous driving and computer vision have increased the need for highly performing sensors that can reliably collect data in different environmental conditions. This includes imagers that can operate at near-infrared wavelengths (i.e., 0.7–1.4 µm), thus potentially collecting high resolution images in complex or unfavorable atmospheric conditions, such as in the presence of rain, fog and smoke.

Researchers at Huazhong University of Science and Technology (HUST), HiSilicon Optoelectronics Co. Limited, and Optical Valley Laboratory have recently developed a near-infrared colloidal quantum dot (CQD) imager. This highly efficient imager was presented in a paper published in Nature Electronics.

“Our group was founded at Wuhan National Laboratory for Optoelectronics, HUST in 2012 and continuously conducts research on CQD materials and devices with Associate Prof. Jianbing Zhang,” Liang Gao, one of the researchers involved in the study, told TechXplore.

After capturing the imagination of tech insiders, the autonomous drawing tool is expanding to broader audiences and commercial uses.

Google has described how the researchers have combined machine learning and semantic engines to develop a novel Transformer-based hybrid semantic ML code completion. The increasing complexity of code poses a key challenge to productivity in software engineering. Code completion has been an essential tool that has helped mitigate this complexity in integrated development environments. Intelligent code completion is a context-aware code completion feature in some programming environments that speeds up the process of coding applications by reducing typos and other common mistakes.

Google AI’s latest research explains how they combined machine learning and semantic engine SE to develop a novel transformer-based hybrid semantic ML code completion. A revolutionary Transformer-based hybrid semantic code completion model that is now available to internal Google engineers was created by Google AI researchers by combining ML with SE. The researchers’ method for integrating ML with SEs is defined as re-ranking SE single token proposals with ML, applying single and multi-line completions with ML, and then validating the results with the SE.

A common approach to code completion is to train transformer models, which use a self-attention mechanism for language understanding, to enable code understanding and completion predictions. Additionally, google suggested employing ML of single token semantic suggestions for single and multi-line continuation. Over three months, more than 10,000 Google employees tested the model in eight programming languages.

They claimed that artificial intelligence can actually solve some of the hardest challenges that affect the delivery of dementia treatment to old people, especially those with Alzheimer’s disease.

In 2021, the National Library of Medicine revealed that more than 6.2 million U.S. residents are suffering from Alzheimer’s.

Researchers from TU Delft have constructed the smallest flow-driven motors in the world. Inspired by iconic Dutch windmills and biological motor proteins, they created a self-configuring flow-driven rotor from DNA that converts energy from an electrical or salt gradient into useful mechanical work. The results open new perspectives for engineering active robotics at the nanoscale.

The article is now published in Nature Physics (“Sustained unidirectional rotation of a self-organized DNA rotor on a nanopore”).

Rotary motors have been the powerhouses of human societies for millennia: from the windmills and waterwheels across the Netherlands and the world to today’s most advanced off-shore wind turbines that drive our green-energy future.

Researchers have developed a new chip-based beam steering technology that provides a promising route to small, cost-effective and high-performance lidar (or light detection and ranging) systems. Lidar, which uses laser pulses to acquire 3D information about a scene or object, is used in a wide range of applications such as autonomous driving, free-space optical communications, 3D holography, biomedical sensing and virtual reality.

“Optica l beam steering is a key technology for lidar systems, but conventional mechanical-based beam steering systems are bulky, expensive, sensitive to vibration and limited in speed,” said research team leader Hao Hu from the Technical University of Denmark. “Although devices known as chip-based optical phased arrays (OPAs) can quickly and precisely steer light in a non-mechanical way, so far, these devices have had poor beam quality and a field of view typically below 100 degrees.”

In Optica, Hu and co-author Yong Liu describe their new chip-based OPA that solves many of the problems that have plagued OPAs. They show that the device can eliminate a key optical artifact known as aliasing, achieving beam steering over a large field of view while maintaining high beam quality, a combination that could greatly improve lidar systems.

AI Philosophy

The AI model was trained using answers from Dennett on a range of questions about free will, whether animals feel pain and even favorite bits of other philosophers. The researchers then asked different groups of people to compare the AI’s responses and Dennett’s real answers and see if they could tell them apart. They used responses from 302 random people online who followed a link from Schwitzgebel’s blog, 98 confirmed college graduates from the online research platform Prolific, and 25 noted Dennett experts. Immersion in Dennett’s philosophy and work didn’t prevent anyone from struggling to identify the source of the answers, however.

The research platform participants only managed an average success rate of 1.2 out of 5 questions. The blog readers and experts answered ten questions, with the readers hitting an average score of 4.8 out of 10. That said, not a single Dennett expert was 100% correct, with only one answering nine correctly and an average of 5.1 out of 10, barely higher than the blog readers. Interestingly, the question whose responses most confused the Dennett experts was actually about AI sentience, specifically if people could “ever build a robot that has beliefs?” Despite the impressive performance by the GPT-3 version of Dennett, the point of the experiment wasn’t to demonstrate that the AI is self-aware, only that it can mimic a real person to an increasingly sophisticated degree and that OpenAI and its rivals are continuing to refine the models so that similar quizzes will likely get harder to pass.

A model for autonomous underwater robots could help them navigate previously-unexplored waters.