While many news stories focus on the pitfalls of technology, Wired did something a little different and put together a cover story of 25 people and groups who the magazine says are "racing to save us." From climate change to the growing power of big tech behemoths, the world is facing any number of challenges, in some cases existential ones. The innovations that facilitate our lives are frequently double edged swords. The power plants that quench our thirst for electricity are spewing planet warming emissions into the atmosphere. The facial recognition algorithms that can help organize smartphone photo collections also have inherent biases against women and minorities.

People around the world marched for climate change on September 20, 2019, with protests taking place across 4,500 locations in 150 countries, all inspired by Swedish climate activist Greta Thunberg. It is obvious the call for a healthier planet is being demanded by more and more people internationally. But what is the answer? Millions of people across the globe marched on September 20, 2019 to demand urgent action on climate change. One of the questions being posed: Can Artificial Intelligence (AI) and tech companies help address climate change and save the planet?

Airbus backed startup UP42 offers satellite imagery and geospatial analytics from a wide range of sources, allowing the users to explore different datasets and run their own algorithms. Last month, UP-42, a subsidiary of Airbus Defence and Space, launched its commercial data and analytics platform and marketplace. Founded in 2019 and headquartered in Berlin, UP42 offers access to geodata and processing tools that enable observation and analysis of portions of the planet at scale, facilitating customers to build new geospatial products. UP42 has ready-to-use algorithms for vegetation indexing and moisture detection, object detection, change detection, and pre-processing tools. It provides access to data from a range of sources, including both commercial and open-source high-resolution satellite/drone imagery and IoT data.

If you make a purchase by clicking one of our links, we may earn a small share of the revenue. However, our picks and opinions are independent from USA TODAY's newsroom and any business incentives. Whether you just like the idea of letting a robot handle cleaning up your floors or you just don't like to vacuum, a robot vacuum cleaner can be a real help. But with so many companies making robot vacuums, how do you know if any of them are actually worth the money? Luckily, we've done the hard work for you. We have a specially built obstacle course in our labs that tests how well robot vacuums pick up dirt, navigate around ytour furniture, and deal with floor types from hardwood floors to low- and high-pile carpets.

Dimensionality reduction is a technique for reducing the number of variables of data samples and has been successfully applied in many fields to make machine learning algorithms faster and more accurate, including the pathological diagnoses of gene expression data [26], the analysis of chemical sensor data [16], the community detection in social networks [27], the analyses of neural spike sorting [1], and others [22]. Due to their dependence on label information, dimensionality reduction methods can be divided into supervised and unsupervised methods. Typical unsupervised dimensionality reduction methods are the principal component analysis (PCA) [12, 15], the classical multidimensional scaling (MDS) [4], the locality preserving projections (LPP) [11], and the t-distributed stochastic neighbor embedding (t-SNE) [28]. To make use of prior knowledge on the labels, we focus on supervised dimensionality reduction methods. Supervised dimensionality reduction methods map data samples into an optimal low-dimensional space for satisfactory classification while incorporating the label information. One of the most popular supervised dimensionality reduction methods is the linear discriminant analysis (LDA) [3], which maximizes the between-class scatter and reduces the within-class scatter in a low-dimensional space.

In this paper, we propose a gamification approach as a novel framework for smart building infrastructure with the goal of motivating human occupants to reconsider personal energy usage and to have positive effects on their environment. Human interaction in the context of cyber-physical systems is a core component and consideration in the implementation of any smart building technology. Research has shown that the adoption of human-centric building services and amenities leads to improvements in the operational efficiency of these cyber-physical systems directed towards controlling building energy usage. We introduce a strategy in form of a game-theoretic framework that incorporates humans-in-the-loop modeling by creating an interface to allow building managers to interact with occupants and potentially incentivize energy efficient behavior. Prior works on game theoretic analysis typically rely on the assumption that the utility function of each individual agent is known a priori. Instead, we propose novel utility learning framework for benchmarking that employs robust estimations of occupant actions towards energy efficiency. To improve forecasting performance, we extend the utility learning scheme by leveraging deep bi-directional recurrent neural networks. Using the proposed methods on data gathered from occupant actions for resources such as room lighting, we forecast patterns of energy resource usage to demonstrate the prediction performance of the methods. The results of our study show that we can achieve a highly accurate representation of the ground truth for occupant energy resource usage. We also demonstrate the explainable nature on human decision making towards energy usage inherent in the dataset using graphical lasso and granger causality algorithms. Finally, we open source the de-identified, high-dimensional data pertaining to the energy game-theoretic framework.

In recent years, probabilistic forecasting is an emerging topic, which is why there is a growing need of suitable methods for the evaluation of multivariate predictions. We analyze the sensitivity of the most common scoring rules, especially regarding quality of the forecasted dependency structures. Additionally, we propose scoring rules based on the copula, which uniquely describes the dependency structure for every probability distribution with continuous marginal distributions. Efficient estimation of the considered scoring rules and evaluation methods such as the Diebold-Mariano test are discussed. In detailed simulation studies, we compare the performance of the renowned scoring rules and the ones we propose. Besides extended synthetic studies based on recently published results we also consider a real data example. We find that the energy score, which is probably the most widely used multivariate scoring rule, performs comparably well in detecting forecast errors, also regarding dependencies. This contradicts other studies. The results also show that a proposed copula score provides very strong distinction between models with correct and incorrect dependency structure. We close with a comprehensive discussion on the proposed methodology.

During the Gilded Age and the second industrial revolution, the world saw rapid adoption of life-altering technologies -- electricity, rail transport, the automobile, telegraph communications and then the telephone. Thanks to these innovations, companies were able to create and sell products they could not before to people they had not previously been able to reach, in ways they never could have envisioned. That young United States saw unprecedented growth, with total national wealth increasing from $16 billion in 1860 to $88 billion by 1900. Today's evolution looks set to be just as transformative. Disruptive technologies like augmented reality, artificial intelligence (AI) and the internet of things (IoT) are already having an impact.

A spherical, cargo robot that carries up to 40 pounds while trailing behind its owner can be yours for just $3,250. Vespa scooter maker has unveiled a redesign of its personal robot called Gita, which aims to free its human's hands so they can engage with others and enjoy activities. The ball-like machine stands about 26 inches and uses vision sensors to follow you -- and it will be available to the public next month. A spherical, cargo robot that carries up to 40 pounds while trailing behind its owner can be yours for just $3,250. Vespa scooter maker has unveiled a redesign of its personal robot called Gita, which aims to free its human's hands so they can engage with others and enjoy activities Instead of deciding to use an automobile or truck to transport to lug packages and other goods, Piaggio Fast Forward, the creating firm, wants to help people walk, run, pedal and skate through life with the assistance of a family of vehicles like Gita.

Imagine a scene, set in the future, where a child in Burning Man–style punk clothing is standing in front of a yurt powered by solar panels. There weren't many books with scenes like that in 2014, when Sarena Ulibarri, an editor, first grew interested in a genre of science fiction that imagines a renewable and sustainable future. Welcome to solarpunk, a new genre within science fiction that is a reaction against the perceived pessimism of present-day sci-fi and hopes to bring optimistic stories about the future with the aim of encouraging people to change the present. The first book that explicitly identified as solarpunk was Solarpunk: Histórias ecológicas e fantásticas em um mundo sustentável (Solarpunk: Ecological and Fantastic Stories in a Sustainable World), a Brazilian book published in 2012. In 2014, author Adam Flynn wrote Solarpunk: Notes Toward a Manifesto.