The robotic artist known as Ai-Da was scheduled to display her artwork alongside the great pyramids of Egypt on Thursday, though the show was nearly called off after both the robot and her human sculptor, Aidan Meller, were detained by Egyptian authorities for a week and a half until they could confirm that the artist was actually a spy. The incident began when border guards objected over Ai-da's camera eyes, which it uses in its creative process, and its on-board modem. "I can ditch the modems, but I can't really gouge her eyes out," Meller told The Guardian. The robot artist, which was built in 2019, typically travels via specialized cargo case and was held at the border until clearing customs on Wednesday evening, hours before the exhibit was scheduled to begin. "The British ambassador has been working through the night to get Ai-Da released, but we're right up to the wire now," Meller said, just before Ai-Da was sprung from robo-jail.

Following the successful use of online apps, tracking devices and telemedicine to control a pandemic, while enabling remote care, doctors in the Middle East have started using medtech gadgets for non-invasive as well as accurate procedures. Robots have been pitched as cops, office assistants and guides for the future, but the need for contactless healthcare has led to their adoption as support staff in hospitals, to monitor patients in quarantine. Robotic pharmacies have also become a feature at AI-backed facilities in the UAE, while droids have even conducted operations, one of which involved removal of a tumour. Considering the precision that smart machines bring to the table, robo-arms are now being used for removal and transfer of kidneys from donors to patients, at a hospital in Dubai. The surgeries that benefited two people were carried out using the state-of-the-art Da Vinci tool, which is a pair of robotic arms, that can imitate the movement of human hands.

Lily Xu is a PhD student at Harvard University, applying machine learning and game theory to wildlife conservation. She is particularly focused on the prevention of illegal wildlife poaching, and she told us about this interesting, and critically important, area of research. Green security is the challenge of environmental conservation under some unknown threat. The three domains that we focus on are illegal wildlife poaching, illegal logging and illegal fishing. Across all of these settings we have an environmental challenge, which is to preserve our natural ecosystems.

The Old Testament's'Tower of Babel' story is an origin myth that tries to explain why humanity doesn't speak a single, universal language. According to the Bible, a united human race that speaks the same language arrived in the land of Shinar and decided to build a tower tall enough to reach heaven. Annoyed -- once again, it can probably be said -- by humanity's growing arrogance and budding hubris, God confounded humanity's speech, dividing its people into separate linguistic groups that couldn't understand one another. Just to ensure they don't start comparing and contrasting their languages to reach some form of translating breakthrough, God dispersed humankind to all corners of the earth and set the stage for what is today a world of 6,500 languages. For God, a job well done and the situation remained static for centuries, that was until tribes starting trading with each other, armies started fighting one another, and diplomats initiated conflict resolution measures to try to end the wars that were often started due to misunderstandings of one kind or another.

After years of complaints that there were no rules to determine what constitutes a "sustainable" investment, investors are now fretting that there will soon be too many to navigate easily. More than 30 taxonomies outlining what is and isn't a green investment are being compiled by governments across Asia, Europe and Latin America, each one reflecting national economic idiosyncrasies that can jar with a global capital market that has seen trillions pour into sustainable funds. The European Union will introduce its green investment taxonomy, or common framework, in January to help asset managers inside the bloc and make green activities more visible and attractive to investors. The rules also aim to stamp out "green washing," whereby organizations overstate their environmental credentials. The U.K., which hosts the COP26 climate change conference from Oct. 31, is set to finalize its own taxonomy next year but has already signaled it will not just replicate what is drawn up across the channel.

Artificial intelligence (AI) is on the cusp of becoming democratic, inclusive, and useful to people living in under-served places in some very exciting ways. We believe an approach we call Mindful AI can help AI realize its potential to be more inclusive and human-centered, too. McKinsey's James Manyika interviewed Kevin to discuss concepts related to Kevin's recently published book, Reprogramming the American Dream: From Rural America to Silicon Valley – Making AI Serve Us All. The book draws on Kevin's personal experiences to show how AI can become more inclusive by helping people who live in under-served areas ranging from rural towns to working-class communities. For instance, as reported in The Wall Street Journal, Microsoft's FarmBeats program uses AI to improve farming.

Gaussian processes (GPs), implemented through multivariate Gaussian distributions for a finite collection of data, are the most popular approach in small-area spatiotemporal statistical modelling. In this context they are used to encode correlation structures over space and time and can generalise well in interpolation tasks. Despite their flexibility, off-the-shelf GPs present serious computational challenges which limit their scalability and practical usefulness in applied settings. Here, we propose a novel, deep generative modelling approach to tackle this challenge: for a particular spatiotemporal setting, we approximate a class of GP priors through prior sampling and subsequent fitting of a variational autoencoder (VAE). Given a trained VAE, the resultant decoder allows spatiotemporal inference to become incredibly efficient due to the low dimensional, independently distributed latent Gaussian space representation of the VAE. Once trained, inference using the VAE decoder replaces the GP within a Bayesian sampling framework. This approach provides tractable and easy-to-implement means of approximately encoding spatiotemporal priors and facilitates efficient statistical inference. We demonstrate the utility of our VAE two stage approach on Bayesian, small-area estimation tasks.

A combination of high-resolution satellite imaging and'deep learning' has identified more than 1.8 billion trees across the West African Sahara, Sahel and sub-humid zone – significantly more trees than were previously thought to exist in the region. The collaboration between NASA and several geoscience departments across the world used 11,128 satellite images from four satellites to count individual trees across 1.3 million square kilometres. The deep-learning approach has, for the first time, allowed researchers to identify individual trees across the dryland expanse. Because of the absence of closed canopies, many parts of the Sahara and the Sahel have previously been mapped with zero per cent tree cover. 'You need high-resolution satellite images to be able to detect individual trees and not just to make estimations based on identified areas of canopy cover,' says Martin Brandt from the University of Copenhagen.

Abstract--In this article, we will present an NLP application in text classifying process using the content-based router. The ultimate goal throughout this article is to predict the event described by a legal ad from the plain text of the ad. This problem is purely a supervised problem that will involve the use of NLP techniques and conventional modeling methodologies through the use of the Multi-label Residual Convolutional Neural Network for text classification. We will explain the approach put in place to solve the problem of classified ads, the difficulties encountered and the experimental results. The company in question is revolutionizing its acquisition process of daily articles and newspapers by applying state-ofthe-art NLP models in order to automate the whole process.

In this paper, we consider the singular values and singular vectors of low rank perturbations of large rectangular random matrices, in the regime the matrix is "long": we allow the number of rows (columns) to grow polynomially in the number of columns (rows). We prove there exists a critical signal-to-noise ratio (depending on the dimensions of the matrix), and the extreme singular values and singular vectors exhibit a BBP type phase transition. As a main application, we investigate the tensor unfolding algorithm for the asymmetric rank-one spiked tensor model, and obtain an exact threshold, which is independent of the procedure of tensor unfolding. If the signal-to-noise ratio is above the threshold, tensor unfolding detects the signals; otherwise, it fails to capture the signals.