According to research by mobile security firm Certo Software and confirmed by MIT Technology Review, Google Search queries related to tracking partners such as a wife or girlfriend commonly return ads for software and services that explicitly offer to spy on other individuals. Stalkerware, also referred to as spyware, is software designed to secretly monitor another person, tracking their location, phone calls, private messages, web searches, and keystrokes. Although Google banned ads promoting stalkerware in August 2020, stalkerware companies are still able to buy ads containing phrases including "app to see spouse's text messages," "see who your girlfriend is texting," and "it's like having their device" against search results such as "read wife's texts app." "We understand that this is not a war between Ukraine and Russia. This is a war of the pure and the light that exists on this earth, and darkness." The problem is that no one can agree how to save it.

Researchers have demonstrated that artificial intelligence may be performed using small nanomagnets that interact like neurons in the brain. Researchers have shown it is possible to perform artificial intelligence using tiny nanomagnets that interact like neurons in the brain. The new technology, developed by a team led by Imperial College London researchers, could significantly reduce the energy cost of artificial intelligence (AI), which is currently doubling globally every 3.5 months. In a paper published today (May 5, 2022) in the journal Nature Nanotechnology, the international team has produced the first proof that networks of nanomagnets can be used to perform AI-like processing. The researchers showed nanomagnets can be used for'time-series prediction' tasks, such as predicting and regulating insulin levels in diabetic patients.

A small island in the South Pacific Ocean recently shot to fame by becoming the first territory on our planet to derive its energy needs from the Sun. Covering a small area of 10 square kilometers, Tokelau is a part of New Zealand and lies to the North of Samoan islands . Funded by the government of New Zealand, Tokelau spent about $7 million to put in place three solar grids that will now enable its 1500 residents to harness and utilize solar energy for their daily needs. Why spend $7 million for a power plant in the middle of nowhere you might ask! While the small island generates a small sum of $ 500,000 every year by selling agricultural produce, it spends over $2.8 million, most of which is spent of food and fuel.

The Distributed Constraint Optimization Problem (DCOP) formulation is a powerful tool for modeling multi-agent coordination problems. To solve DCOPs in a dynamic environment, Dynamic DCOPs (D-DCOPs) have been proposed to model the inherent dynamism present in many coordination problems. D-DCOPs solve a sequence of static problems by reacting to changes in the environment as the agents observe them. Such reactive approaches ignore knowledge about future changes of the problem. To overcome this limitation, we introduce Proactive Dynamic DCOPs (PD-DCOPs), a novel formalism to model D-DCOPs in the presence of exogenous uncertainty. In contrast to reactive approaches, PD-DCOPs are able to explicitly model possible changes of the problem and take such information into account when solving the dynamically changing problem in a proactive manner. The additional expressivity of this formalism allows it to model a wider variety of distributed optimization problems. Our work presents both theoretical and practical contributions that advance current dynamic DCOP models: (i) We introduce Proactive Dynamic DCOPs (PD-DCOPs), which explicitly model how the DCOP will change over time; (ii) We develop exact and heuristic algorithms to solve PD-DCOPs in a proactive manner; (iii) We provide theoretical results about the complexity of this new class of DCOPs; and (iv) We empirically evaluate both proactive and reactive algorithms to determine the trade-offs between the two classes. The final contribution is important as our results are the first that identify the characteristics of the problems that the two classes of algorithms excel in.

The fourth industrial revolution (Industry 4.0) is already happening, and it's transforming the way manufacturing operations are carried out. Industry 4.0 is a product of the digital era as automation and data exchange in manufacturing technologies shift the central industrial control system to a smart setup that bridges the physical and digital world, addressed via the Internet of Things (IoT). Industry 4.0 is creating cyber-physical systems that can network a production process enabling value creation and real-time optimisation. The main factor driving the revolution is the advances in artificial intelligence (AI) and machine learning. The complex algorithms involved in AI use the data collected from cyber-physical systems, resulting in "smart manufacturing".

Climate change has been a problem for many years. Climate change influences our health, cultivation, dwellings, security and employment. CO2 stands for carbon dioxide, which is found in the atmosphere and comes from natural sources and burning fossil fuels. They are followed by some solutions that researchers and developers can implement instantly to transform the future. AI has been the driving force for numerous sound transformations to the environment.

We analyze the regression accuracy of convolutional neural networks assembled from encoders, decoders and skip connections and trained with multifidelity data. Besides requiring significantly less trainable parameters than equivalent fully connected networks, encoder, decoder, encoder-decoder or decoder-encoder architectures can learn the mapping between inputs to outputs of arbitrary dimensionality. We demonstrate their accuracy when trained on a few high-fidelity and many low-fidelity data generated from models ranging from one-dimensional functions to Poisson equation solvers in two-dimensions. We finally discuss a number of implementation choices that improve the reliability of the uncertainty estimates generated by Monte Carlo DropBlocks, and compare uncertainty estimates among low-, high- and multifidelity approaches.

Arun Subramaniyan joined Intel to lead the Cloud & AI Strategy team. Arun joined Intel from AWS, where he led the global solutions team for Machine Learning, Quantum Computing, High Performance Computing (HPC), Autonomous Vehicles, and Autonomous Computing at AWS. His team was responsible for developing solutions across all areas of HPC, quantum computing, and large-scale machine learning applications, spanning $1.5B portfolio. Arun founded and grew the global teams for Autonomous Computing and Quantum Computing Go-to-market and solutions at AWS and grew the businesses 2-3x. Arun's primary areas of research focus are Bayesian methods, global optimization, probabilistic deep learning for large scale applications, and distributed computing.

Unmanned aerial vehicles (UAVs) are widely used platforms to carry data capturing sensors for various applications. The reason for this success can be found in many aspects: the high maneuverability of the UAVs, the capability of performing autonomous data acquisition, flying at different heights, and the possibility to reach almost any vantage point. The selection of appropriate viewpoints and planning the optimum trajectories of UAVs is an emerging topic that aims at increasing the automation, efficiency and reliability of the data capturing process to achieve a dataset with desired quality. On the other hand, 3D reconstruction using the data captured by UAVs is also attracting attention in research and industry. This review paper investigates a wide range of model-free and model-based algorithms for viewpoint and path planning for 3D reconstruction of large-scale objects. The analyzed approaches are limited to those that employ a single-UAV as a data capturing platform for outdoor 3D reconstruction purposes. In addition to discussing the evaluation strategies, this paper also highlights the innovations and limitations of the investigated approaches. It concludes with a critical analysis of the existing challenges and future research perspectives.

This thesis tackles the subject of spatio-temporal forecasting with deep learning. The motivating application at Electricity de France (EDF) is short-term solar energy forecasting with fisheye images. We explore two main research directions for improving deep forecasting methods by injecting external physical knowledge. The first direction concerns the role of the training loss function. We show that differentiable shape and temporal criteria can be leveraged to improve the performances of existing models. We address both the deterministic context with the proposed DILATE loss function and the probabilistic context with the STRIPE model. Our second direction is to augment incomplete physical models with deep data-driven networks for accurate forecasting. For video prediction, we introduce the PhyDNet model that disentangles physical dynamics from residual information necessary for prediction, such as texture or details. We further propose a learning framework (APHYNITY) that ensures a principled and unique linear decomposition between physical and data-driven components under mild assumptions, leading to better forecasting performances and parameter identification.