Dubai, United Arab Emirates: Alef Education, a leading global education technology provider that empowers 21-st century learning, today announced its participation at the Middle East's premier education event, GESS Dubai 2021, which is taking place November 14-16 at Dubai World Trade Centre. The pandemic underscored the importance of embracing innovative education technologies as the widespread school closures across the globe impacted 1.5 billion students, according to UNESCO. In line with the renewed demand for digital learning, the global education technology market is projected to reach $285.2 billion by 2027, according to business consulting firm Grand View Research. Furthermore, the UAE's education market is expected to touch $7.1 billion by 2023, according to a 2018 report released by the Boston Consulting Group (BCG). Under the theme "Power of AI and Data in transforming education," Alef Education will demonstrate its suite of digital education products.

Satellite imagery of the Sahara desert presents an arid expanse, the endless rolling dunes we know from movies. The thing is, normal satellite images don't show individual trees, but that doesn't necessarily mean they're not there. Researchers from the University of Copenhagen and NASA taught artificial intelligence about trees and had them take another look. It turns out there is lots of vegetation in the Western Sahara: an estimated 1.8 billion trees and shrubs. "We were very surprised to see that quite a few trees actually grow in the Sahara Desert, because up until now, most people thought that virtually none existed," says lead author Martin Brandt of the university's Department of Geosciences and Natural Resource Management.

Automated speech-recognition technology has become more common with the popularity of virtual assistants like Siri, but many of these systems only perform well with the most widely spoken of the world's roughly 7,000 languages. Because these systems largely don't exist for less common languages, the millions of people who speak them are cut off from many technologies that rely on speech, from smart home devices to assistive technologies and translation services. Recent advances have enabled machine learning models that can learn the world's uncommon languages, which lack the large amount of transcribed speech needed to train algorithms. However, these solutions are often too complex and expensive to be applied widely. Researchers at MIT and elsewhere have now tackled this problem by developing a simple technique that reduces the complexity of an advanced speech-learning model, enabling it to run more efficiently and achieve higher performance.

Machine learning models based on temporal point processes are the state of the art in a wide variety of applications involving discrete events in continuous time. However, these models lack the ability to answer counterfactual questions, which are increasingly relevant as these models are being used to inform targeted interventions. In this work, our goal is to fill this gap. To this end, we first develop a causal model of thinning for temporal point processes that builds upon the Gumbel-Max structural causal model. This model satisfies a desirable counterfactual monotonicity condition, which is sufficient to identify counterfactual dynamics in the process of thinning. Then, given an observed realization of a temporal point process with a given intensity function, we develop a sampling algorithm that uses the above causal model of thinning and the superposition theorem to simulate counterfactual realizations of the temporal point process under a given alternative intensity function. Simulation experiments using synthetic and real epidemiological data show that the counterfactual realizations provided by our algorithm may give valuable insights to enhance targeted interventions.

Linear regression is a technique frequently used in statistical and data analysis. The task for standard linear regression is to fit a linear relationship among variables in a data set. Often, the goal is to find the most parsimonious model that can describe the majority of the data. In this work, we consider the situation where only a small portion of the data can be accurately modeled using linear regression. More generally, in many kinds of real-world data, portions of the data of significant size can be predicted significantly more accurately than by the best linear model for the overall data distribution: Rosenfeld et al. (2015) showed that there are attributes that are significant risk factors for gastrointestinal cancer in certain subpopulations, but not in the overall population. Hainline et al. (2019) demonstrated that a variety of standard (real-world) regression benchmarks have portions that are fit significantly better by a different linear model than the best model for the overall data set; Calderon et al. (2020) presented further, similar findings. We will consider cases where linear regression fits well when the data set is conditioned on a simple condition, which is unknown to us. We study the task of finding such a linear model, together with a formula on the data attributes describing the condition, i.e., the portion of the data for which the linear model is accurate. This problem was introduced by Juba (2017), who gave an algorithm for conditional sparse linear regression, using the maximum residual as the objective.

With the rapid development of deep learning techniques, various recent work has tried to apply graph neural networks (GNNs) to solve NP-hard problems such as Boolean Satisfiability (SAT), which shows the potential in bridging the gap between machine learning and symbolic reasoning. However, the quality of solutions predicted by GNNs has not been well investigated in the literature. In this paper, we study the capability of GNNs in learning to solve Maximum Satisfiability (MaxSAT) problem, both from theoretical and practical perspectives. We build two kinds of GNN models to learn the solution of MaxSAT instances from benchmarks, and show that GNNs have attractive potential to solve MaxSAT problem through experimental evaluation. We also present a theoretical explanation of the effect that GNNs can learn to solve MaxSAT problem to some extent for the first time, based on the algorithmic alignment theory.

Supervised learning can learn large representational spaces, which are crucial for handling difficult learning tasks. However, due to the design of the model, classical image classification approaches struggle to generalize to new problems and new situations when dealing with small datasets. In fact, supervised learning can lose the location of image features which leads to supervision collapse in very deep architectures. In this paper, we investigate how self-supervision with strong and sufficient augmentation of unlabeled data can train effectively the first layers of a neural network even better than supervised learning, with no need for millions of labeled data. The main goal is to disconnect pixel data from annotation by getting generic task-agnostic low-level features. Furthermore, we look into Vision Transformers (ViT) and show that the low-level features derived from a self-supervised architecture can improve the robustness and the overall performance of this emergent architecture. We evaluated our method on one of the smallest open-source datasets STL-10 and we obtained a significant boost of performance from 41.66% to 83.25% when inputting low-level features from a self-supervised learning architecture to the ViT instead of the raw images.

Introduction: This report is created for the benefit of strategic planners who seek in-depth study of the Global Machine Learning in Medicine Market . It is compiled for the sake of organizations considering Machine Learning in Medicine industry and those who want to boost their market value from their existing investments. With the advent of globalization of the Machine Learning in Medicine industry, market insights about the continents, countries, regions, as well as cities become the most important criteria while prioritizing markets. The consumption patterns, customer and supplier bargaining power and the structural analysis of the application fields is given in the study. This report covers top 200 countries and other entities operating in the market.

Introduction: This report is created for the benefit of strategic planners who seek in-depth study of the Global Military Artificial Intelligence (AI) Market . It is compiled for the sake of organizations considering Military Artificial Intelligence (AI) industry and those who want to boost their market value from their existing investments. With the advent of globalization of the Military Artificial Intelligence (AI) industry, market insights about the continents, countries, regions, as well as cities become the most important criteria while prioritizing markets. The consumption patterns, customer and supplier bargaining power and the structural analysis of the application fields is given in the study. This report covers top 200 countries and other entities operating in the market.

Application of machine learning in earth sciences is the use of computer systems to classify, cluster, identify and analyze vast and complex data in earth science study, for example, geological mapping, gas leakage detection and geological features identification. Machine learning (ML) is a type of Artificial Intelligence (AI) that allows computer systems to interpret data while eliminating the need for explicit instructions and programming. The Earth system can be subdivided into four major components including the solid earth, atmosphere, hydrosphere and biosphere[3]. A variety of algorithms may be applied depending on the nature of the earth science exploration. Some algorithms may perform significantly better than others for particular objectives. For example, Convolutional Neural Networks (CNN) are good at interpreting images, Artificial Neural Network (ANN) performs well in soil classification[4] but more computationally expensive to train than Support Vector Machine (SVM) learning.