Humanitarian groups have been calling for a ban on autonomous weapons. Autonomous weapon systems – commonly known as killer robots – may have killed human beings for the first time ever last year, according to a recent United Nations Security Council report on the Libyan civil war. History could well identify this as the starting point of the next major arms race, one that has the potential to be humanity's final one. The United Nations Convention on Certain Conventional Weapons debated the question of banning autonomous weapons at its once-every-five-years review meeting in Geneva Dec. 13-17, 2021, but didn't reach consensus on a ban. Established in 1983, the convention has been updated regularly to restrict some of the world's cruelest conventional weapons, including land mines, booby traps and incendiary weapons.

A top technology adviser to the judiciary has proposed the creation of a new institute of legal innovation that would spot gaps in the law thrown up by technologies such as crypto assets and AI, and promote the greater use of English law in global business contracts. Professor Richard Susskind, technology adviser to the Lord Chief Justice and a director of think-tank LegalUK, believes an independent body, along the lines of the Alan Turing Institute, which pioneers research into artificial intelligence, would highlight areas of law that had failed to keep up with innovation. The institute would also promote English law to global companies as the law of choice to govern transactions in new areas such as blockchain. The proposal comes as some lawyers are concerned that England's legal sector, which employs 365,000 people, could lose out to rival centres such as Singapore and Dubai if its legislation fails to keep pace with advancing tech.

Keywords are no science but an art. There is no such thing as'the right keyword,' as we're talking about a core concept incorporated into a piece of content in the broadest form. Texts don't necessarily need to contain an exact keyword. For example, if the term'European Union' is used several times, 'European Commission' may be a suitable keyword even though the writer never uses the term. Despite this fluid definition, keywords should be understandable to those who try to find the right ones.

Andrew Yan-Tak Ng, a computer scientist and technology entrepreneur who focuses on machine learning and Artificial Intelligence (AI), said'AI is the new electricity.' In a world where we are all becoming increasingly dependent on technology, it would be hard to think of any industry that is untouched by AI. Just as electricity transformed almost every single aspect of our lives a 100 years ago with its incredible capabilities and convenience, in a similar manner AI is now being embedded in our daily experiences. From unlocking your phone using facial recognition software to making recommendations for restaurants that will be close by based off what you like to eat! Even when you chat with a customer care chatbot, you are literally talking with AI.

Wearable devices that integrate multiple sensors, processors, and communication technologies have the potential to transform mobile health for remote monitoring of health parameters. However, the small form factor of the wearable devices limits the battery size and operating lifetime. As a result, the devices require frequent recharging, which has limited their widespread adoption. Energy harvesting has emerged as an effective method towards sustainable operation of wearable devices. Unfortunately, energy harvesting alone is not sufficient to fulfill the energy requirements of wearable devices. This paper studies the novel problem of adaptive energy management towards the goal of self-sustainable wearables by using harvested energy to supplement the battery energy and to reduce manual recharging by users. To solve this problem, we propose a principled algorithm referred as AdaEM. There are two key ideas behind AdaEM. First, it uses machine learning (ML) methods to learn predictive models of user activity and energy usage patterns. These models allow us to estimate the potential of energy harvesting in a day as a function of the user activities. Second, it reasons about the uncertainty in predictions and estimations from the ML models to optimize the energy management decisions using a dynamic robust optimization (DyRO) formulation. We propose a light-weight solution for DyRO to meet the practical needs of deployment. We validate the AdaEM approach on a wearable device prototype consisting of solar and motion energy harvesting using real-world data of user activities. Experiments show that AdaEM achieves solutions that are within 5% of the optimal with less than 0.005% execution time and energy overhead.

Modern aircraft are designed with redundant control effectors to cater for fault tolerance and maneuverability requirements. This leads to an over-actuated aircraft which requires a control allocation scheme to distribute the control commands among effectors. Traditionally, optimization based control allocation schemes are used; however, for nonlinear allocation problems these methods require large computational resources. In this work, a novel ANN based nonlinear control allocation scheme is proposed. To start, a general nonlinear control allocation problem is posed in a different perspective to seek a function which maps desired moments to control effectors. Few important results on stability and performance of nonlinear allocation schemes in general and this ANN based allocation scheme, in particular, are presented. To demonstrate the efficacy of the proposed scheme, it is compared with standard quadratic programming based method for control allocation.

Most researchers have tried to enhance the robustness of deep neural networks (DNNs) by revealing and repairing the vulnerability of DNNs with specialized adversarial examples. Parts of the attack examples have imperceptible perturbations restricted by Lp norm. However, due to their high-frequency property, the adversarial examples usually have poor transferability and can be defensed by denoising methods. To avoid the defects, some works make the perturbations unrestricted to gain better robustness and transferability. However, these examples usually look unnatural and alert the guards. To generate unrestricted adversarial examples with high image quality and good transferability, in this paper, we propose Adversarial Lightness Attack (ALA), a white-box unrestricted adversarial attack that focuses on modifying the lightness of the images. The shape and color of the samples, which are crucial to human perception, are barely influenced. To obtain adversarial examples with high image quality, we craft a naturalness-aware regularization. To achieve stronger transferability, we propose random initialization and non-stop attack strategy in the attack procedure. We verify the effectiveness of ALA on two popular datasets for different tasks (i.e., ImageNet for image classification and Places-365 for scene recognition). The experiments show that the generated adversarial examples have both strong transferability and high image quality. Besides, the adversarial examples can also help to improve the standard trained ResNet50 on defending lightness corruption.

NASA's Perseverance Mars rover has run into another snag on its mission to find evidence of ancient microbial life. 

New technologies and data sources have tremendous potential to improve statistical production. They offer a way to generate statistics in a more timely, accurate and cost-efficient manner. Yet, keeping up with the pace of change is challenging, especially for National Statistical Organisations (NSOs) that must innovate with care to maintain a "gold standard" in their outputs. International cooperation between NSOs and other official statistical bodies is one way to help accelerate change in a responsible way. In 2021, the Office for National Statistics (ONS) and the United Nations Economic Commission for Europe (UNECE) Machine Learning Group (ML 2021) demonstrated the benefits of international cooperation for technological advance.

As multiple industries shift more into the world of cloud computing, talks of Artificial Intelligence (AI) integration in order to enhance cloud performance has continued at a dramatic pace. Combining both AI and cloud technology together, is beneficial to varying degrees, nevertheless, there is still some further progress to be made across the substantial challenges that technical developers are facing for a more cohesive integration. Cloud computing alone allows companies to be more flexible whilst simultaneously providing economic value when hosting data and applications on the cloud. AI-powered analytical data insights plays an essential role in its enhanced capabilities in data management However, it begs the question, can AI and cloud unification streamline data efficiently and what other benefits can arise from this integration? Due to the financial and personal sensitivity in which organizations carry, thoughts also turn to the important question of integration effectiveness and more specifically how well it can protect privacy whilst companies are continually at risk of a potentially serious cybersecurity breach, especially because an increased rate of workforces are now working from home remotely.