Scientists in Hong Kong have developed artificial skin that bruises like the real thing. The material, called I-skin, could be used on artificial limbs to alert users they have damaged their prosthetics. It's embedded with a gel that turns from yellow to welt-like purple when subjected to physical stress. Volunteers wearing strips of I-skin on their fingers, hands and knees repeatedly banged the appendage against a wall, proving the'bruise' would appear if enough force was used. Scientists in Hong Kong have developed an artificial skin that will mimic the discoloration of a bruise if hit hard enough.

Data Bridge Market Research published a new report, titled, "Artificial intelligence in medical imaging Market". The report offers an extensive analysis of key growth strategies, drivers, opportunities, key segments, and competitive landscape. This study is a helpful source of information for market players, investors, VPs, stakeholders, and new entrants to gain a thorough understanding of the industry and determine steps to be taken to gain a competitive advantage. Businesses can bring about an absolute knowhow of general market conditions and tendencies with the information and data covered in the large scale Artificial intelligence in medical imaging market survey report. To get knowledge of all the above things, this market report is made transparent, wide-ranging and supreme in quality.

Fighting climate change is both an urgent global imperative and a massive business opportunity. Climate change is the most pressing threat that the human species faces today. Artificial intelligence is the most powerful tool that humanity has at its disposal in the twenty-first century. Can we deploy the second to combat the first? A group of promising startups has emerged to do just that. Both climate change and artificial intelligence are sprawling, cross-disciplinary fields. Both will transform literally every sector of the economy in the years ahead. There is therefore no single "silver bullet" application of AI to climate change. Instead, a wide range of machine learning use cases can help in the race to decarbonize our world. Nearly every major activity that humanity engages in today contributes to our carbon footprint to some extent: building things, moving things, powering things, eating things, computing things.

Welcome back to our "Life in 2050" series! In previous installments, we looked at how accelerating change and environmental issues will affect the future of warfare, economy, education, everyday living, and space exploration (in two installments). Today, we look at how people will get from A to B by mid-century, whether it's across town, from one city to the next, or one continent to the next. Transportation is another sector that is expected to undergo a major revolution in the coming decades. In several respects, this revolution is already underway thanks to the introduction of autonomous vehicles, the wide-scale adoption of electric vehicles, the growth of renewable energy, and the advent of commercial spaceflight. Between now and 2050, these technologies and trends will accelerate and lead to the creation of new transportation infrastructure, radically different from what we know today. Of course, the infrastructure of tomorrow will be built on existing transportation networks.

UCLA Researchers have developed a method to change the apparent race of faces in datasets that are used to train medical machine learning systems, in an attempt to redress the racial bias that many common datasets suffer from. The new technique is capable of producing photorealistic and physiologically accurate synthetic video at an average rate of 0.005 seconds per frame, and is hoped to aid the development of new diagnostics systems for remote healthcare diagnosis and monitoring – a field that has expanded greatly under COVID restrictions. The system is intended to improve the applicability of remote photoplethysmography (rPPG), a computer vision technique that evaluates facial video content to detect volumetric changes in blood supply in a non-invasive manner. Though the work, which utilizes convolutional neural networks (CNNs), incorporates previous research code published by the UK's Durham University in 2020, the new application is intended to preserve pulsatile signals in the original test data, rather than just visually changing the apparent race of the data, as the 2020 research does. The first part of the encoder-decoder system uses the Durham race transfer model, pre-trained on VGGFace2, to generate proxy target frames with the prior Caucasian-to-African component of the Durham research.

Every day across Australia, truckies are driving thousands of kilometres to get fresh produce from farms to markets. But what if the truck could do this job, without a driver? The NASDAQ-listed company TuSimple is celebrating a milestone, after transporting watermelons from Arizona to Oklahoma City using an autonomous truck. There were two humans in the truck during the trial -- and they did take control of the vehicle at the front and back end of the journey -- but for more than 1,500 kilometres, the truck was driving itself. "Our business case is to take the human driver out," TuSimple's Jim Mullen said.

In September 2020, Facebook open-sourced a new NLP model called Retrieval Augmented Generation (RAG) on the Hugging Face Transformer library. RAG is capable to use a set of support documents from an external knowledge base as a latent variable to generate the final output. The RAG model consists of an Input Encoder, a Neural Retriever, and an Output Generator. All three components are initialized with pre-trained transformers. However, the original Hugging Face implementation only allowed fine-tuning the Input Encoder and the Output Generator in an end-toend manner, while the Neural Retriever needs to be trained seperately. To the best of our knowledge, an end-to-end RAG implementation that trains all three components does not exist.

Place Recognition is a crucial capability for mobile robot localization and navigation. Image-based or Visual Place Recognition (VPR) is a challenging problem as scene appearance and camera viewpoint can change significantly when places are revisited. Recent VPR methods based on ``sequential representations'' have shown promising results as compared to traditional sequence score aggregation or single image based techniques. In parallel to these endeavors, 3D point clouds based place recognition is also being explored following the advances in deep learning based point cloud processing. However, a key question remains: is an explicit 3D structure based place representation always superior to an implicit ``spatial'' representation based on sequence of RGB images which can inherently learn scene structure. In this extended abstract, we attempt to compare these two types of methods by considering a similar ``metric span'' to represent places. We compare a 3D point cloud based method (PointNetVLAD) with image sequence based methods (SeqNet and others) and showcase that image sequence based techniques approach, and can even surpass, the performance achieved by point cloud based methods for a given metric span. These performance variations can be attributed to differences in data richness of input sensors as well as data accumulation strategies for a mobile robot. While a perfect apple-to-apple comparison may not be feasible for these two different modalities, the presented comparison takes a step in the direction of answering deeper questions regarding spatial representations, relevant to several applications like Autonomous Driving and Augmented/Virtual Reality. Source code available publicly https://github.com/oravus/seqNet.

We propose a curriculum-driven learning strategy for solving difficult multi-agent coordination tasks. Our method is inspired by a study of animal communication, which shows that two straightforward design features (mutual reward and decentralization) support a vast spectrum of communication protocols in nature. We highlight the importance of similarly interpreting emergent communication as a spectrum. We introduce a toroidal, continuous-space pursuit-evasion environment and show that naive decentralized learning does not perform well. We then propose a novel curriculum-driven strategy for multi-agent learning. Experiments with pursuit-evasion show that our approach enables decentralized pursuers to learn to coordinate and capture a superior evader, significantly outperforming sophisticated analytical policies. We argue through additional quantitative analysis -- including influence-based measures such as Instantaneous Coordination -- that emergent implicit communication plays a large role in enabling superior levels of coordination.

Hardware architectures can range from the use of GPUs and other hardware accelerators, through multi-core multi-threaded architectures, to shared-nothing cloud computing. Causes for failure to exploit these architectures include lack of expertise in the architectural features, lack of manpower more generally, and difficulty in updating legacy systems. Such problems can be ameliorated by mapping a logic to logic programming as an intermediate language. This is a common strategy in the implementation of defeasible logics. The first implementation of a defeasible logic, d-Prolog, was implemented as a Prolog meta-interpreter (Covington et al. 1997). Courteous Logic Programs (Grosof 1997) and its successors LPDA (Wan et al. 2009), Rulelog (Grosof and Kifer 2013), Flora2 (Kifer et al. 2018), are implemented in XSB (Swift and Warren 2012).