The researchers, from the University of Science and Technology of China, hope that the technique – which uses liquid metal to mimic natural muscle movements — could also help to administer drugs inside the body and underwater drones.
Researchers created an artificial muscle using liquid metal that allows it to expand and contract and hope one day to use the technology to help humans.
Researchers at Japan Advanced Institute of Science and Technology and University of Tokyo recently developed AugLimb, a compact robotic limb that could support humans as they complete a variety of tasks. This new limb, presented in a paper pre-published on arXiv, can extend up to 250 mm and grasp different objects in a user’s vicinity.
“We are interested in human augmentation technologies, which aim to enhance human capabilities with information and robotics approaches,” Haoran Xie, one of the researchers who carried out the study, told Tech Xplore. “We particularly focus on the physical augmentation of human bodies.”
Most existing wearable robotic arms are designed to be mounted on a human user’s upper body (e.g., on the upper arm, waist or shoulders). While some of these systems have achieved promising results, they are typically based on bulky hardware and wearing them can be uncomfortable for users.
The U.N. human rights chief is calling for a moratorium on the use of artificial intelligence technology that poses a serious risk to human rights, including face-scanning systems that track people in public spaces.
Michelle Bachelet, the U.N. High Commissioner for Human Rights, also said Wednesday that countries should expressly ban AI applications which don’t comply with international human rights law.
Applications that should be prohibited include government “social scoring” systems that judge people based on their behavior and certain AI-based tools that categorize people into clusters such as by ethnicity or gender.
DSEI 2021: #Rheinmetall experiments with quadripedal UGVs #DSEI2021 #UGVs
Rheinmetall presented a Q-UGV nicknamed Lassie that it is experimenting with at DSEI 2021. (Janes/Nicholas Fiorenza)
Rheinmetall presented a quadripedal unmanned ground vehicle (Q-UGV) that it is experimenting with at the 2021 DSEI defence exhibition being held in London on 14–17 September. The company has been experimenting with three Ghost Robotics Q-UGVs for the last year.
Caption :
MIT economist Daron Acemoglu is co-author of a new study showing that aging populations lead to greater implementation of robots in workplace settings.
A dress worn this week by Democratic Congresswoman Alexandria Ocasio-Cortez (D-NY), which bore the message “tax the rich,” set off a wave of debate over how best to address wealth inequality, as Congress weighs a $3.5 trillion spending bill that includes tax hikes on corporations and high-earning individuals.
The debate coincides with the ongoing pandemic in which billionaires, many of whom are tech company founders, have added $1.8 trillion in wealth while consumers have come to depend increasingly on services like e-commerce and teleconference, according to a report released last month by the Institute for Policy Studies.
In a new interview, artificial intelligence expert Kai Fu-Lee — who worked as an executive at Google (GOOG, GOOGL), Apple (AAPL), and Microsoft (MSFT) — attributed the rise of wealth inequality in part to the tech boom in recent decades, predicting that the trend will worsen in coming years with the continued emergence of AI.
Earlier this year, in June 2,021 the British Ministry of Defence employed Rafael’s DRONE DOME counter-UAV system to protect world leaders during the G7 Summit in Cornwall, England from unmanned aerial threats. Three years ago, Britain’s Defence Ministry purchased several DRONE DOME systems which it has successfully employed in a multitude of operational scenarios, including for protecting both the physical site and participants of this year’s G7 summit. Rafael’s DRONE DOME is an innovative end-to-end, combat-proven counter-Unmanned Aerial System (C-UAS), providing all-weather, 360-degree rapid defence against hostile drones. Fully operational and globally deployed, DRONE DOME offers a modular, robust infrastructure comprised of electronic jammers and sensors and unique artificial intelligence algorithms to effectively secure threatened air space.
Meir Ben Shaya, Rafael EVP for Marketing and Business Development of Air Defence Systems: Rafael today recognizes two new and key trends in the field of counter-UAVs, both of which DRONE DOME can successfully defend against. The first trend is the number of drones employed during an attack, and the operational need to have the ability counter multiple, simultaneous attacks; this is a significant, practical challenge that any successful system must be able to overcome. The second trend is the type of tool being employed. Previously, air defense systems were developed to seek out conventional aircraft, large unmanned aerial vehicles, and missile, but today these defense systems must also tackle smaller, slower, low-flying threats which are becoming more and more autonomous.
Training efficiency has become a significant factor for deep learning as the neural network models, and training data size grows. GPT-3 is an excellent example to show how critical training efficiency factor could be as it takes weeks of training with thousands of GPUs to demonstrate remarkable capabilities in few-shot learning.
To address this problem, the Google AI team introduce two families of neural networks for image recognition. First is EfficientNetV2, consisting of CNN (Convolutional neural networks) with a small-scale dataset for faster training efficiency such as ImageNet1k (with 1.28 million images). Second is a hybrid model called CoAtNet, which combines convolution and self-attention to achieve higher accuracy on large-scale datasets such as ImageNet21 (with 13 million images) and JFT (with billions of images). As per the research report by Google, EfficientNetV2 and CoAtNet both are 4 to 10 times faster while achieving state-of-the-art and 90.88% top-1 accuracy on the well-established ImageNet dataset.
Is Elon right Eric Klien?
It’s been nearly five years since Musk first announced the full self driving feature and very little progress has been made on it.
❤️ Check out the Gradient Dissent podcast by Weights & Biases: http://wandb.me/gd.
📝 The paper “The Relightables: Volumetric Performance Capture of Humans with Realistic Relighting” is available here:
https://augmentedperception.github.io/therelightables/
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