Machine learning (ML) is a promising enabler for the fifth generation (5G) communication systems and beyond. By imbuing intelligence into the network edge, edge nodes can proactively carry out decision-making, and thereby react to local environmental changes and disturbances while experiencing zero communication latency. To achieve this goal, it is essential to cater for high ML inference accuracy at scale under time-varying channel and network dynamics, by continuously exchanging fresh data and ML model updates in a distributed way. Taming this new kind of data traffic boils down to improving the communication efficiency of distributed learning by optimizing communication payload types, transmission techniques, and scheduling, as well as ML architectures, algorithms, and data processing methods. To this end, this article aims to provide a holistic overview of relevant communication and ML principles, and thereby present communication-efficient and distributed learning frameworks with selected use cases.

An IoT-enabled system for transporting dairy products earned its designers the top prize in a competition run by Cisco. The Global Problem Solver Challenge, which is one of Cisco's corporate social responsibility (CSR) initiatives, pays cash awards to entrepreneurial companies using technology to solve the world's biggest challenges. The program also gives out four $25,000 awards and seven $10,000 prizes. This year, I was honored to be invited to help judge the 2020 winners. In full disclosure, I agreed to be a judge but I received no compensation, as I believe we all have to work together to make the world a better place.

Brain-computer interfaces are seeing massive AI breakthroughs including neural bridges being built for learning, treatment of specific diseases and overcoming the electrical-to-biochemical language barrier. These trends are what will optimise the information bandwidth that comes with neuroscience technology. "A monkey has been able to control a computer with its brain." That almost unimaginable yet remarkably accurate observation was made by Elon Musk, author and CEO of Tesla. In his presentation, Musk switched between varying forms of "what is" to "what could be", before announcing the details surrounding Tesla Energy.

Radiant Earth Foundation has released "LandCoverNet," a human-labelled global land cover classification training dataset. This release contains data across Africa, which accounts for 1/5 of the global dataset. Available for download on Radiant MLHub, the open geospatial library, LandCoverNet will enable accurate and regular land cover mapping for timely insights into natural and anthropogenic impacts on the Earth. Global land cover maps derived from Earth observations are not new, but the influx of open-access high spatial resolution Earth observations, such as that from the European Space Agency's Sentinel missions, coupled with improved computer power, encouraged the development of advanced algorithms. Machine learning models applied to high resolution remotely sensed imagery can classify land cover classes more accurately and faster, given the availability of high-quality training data.

One fundamental goal of high-dimensional statistics is to detect or recover structure from noisy data. In many cases, the data can be faithfully modeled by a planted structure (such as a low-rank matrix) perturbed by random noise. But even for these simple models, the computational complexity of estimation is sometimes poorly understood. A growing body of work studies low-degree polynomials as a proxy for computational complexity: it has been demonstrated in various settings that low-degree polynomials of the data can match the statistical performance of the best known polynomial-time algorithms for detection. While prior work has studied the power of low-degree polynomials for the task of detecting the presence of hidden structures, it has failed to address the estimation problem in settings where detection is qualitatively easier than estimation. In this work, we extend the method of low-degree polynomials to address problems of estimation and recovery. For a large class of "signal plus noise" problems, we give a user-friendly lower bound for the best possible mean squared error achievable by any degree-D polynomial. To our knowledge, this is the first instance in which the low-degree polynomial method can establish low-degree hardness of recovery problems where the associated detection problem is easy. As applications, we give a tight characterization of the low-degree minimum mean squared error for the planted submatrix and planted dense subgraph problems, resolving (in the low-degree framework) open problems about the computational complexity of recovery in both cases.

Tensor, also known as multi-dimensional array, arises from many applications in signal processing, manufacturing processes, healthcare, among others. As one of the most popular methods in tensor literature, Robust tensor principal component analysis (RTPCA) is a very effective tool to extract the low rank and sparse components in tensors. In this paper, a new method to analyze RTPCA is proposed based on the recently developed tensor-tensor product and tensor singular value decomposition (t-SVD). Specifically, it aims to solve a convex optimization problem whose objective function is a weighted combination of the tensor nuclear norm and the l1-norm. In most of literature of RTPCA, the exact recovery is built on the tensor incoherence conditions and the assumption of a uniform model on the sparse support. Unlike this conventional way, in this paper, without any assumption of randomness, the exact recovery can be achieved in a completely deterministic fashion by characterizing the tensor rank-sparsity incoherence, which is an uncertainty principle between the low-rank tensor spaces and the pattern of sparse tensor.

The proposed article aims at offering a comprehensive tutorial for the computational aspects of structured matrix and tensor factorization. Unlike existing tutorials that mainly focus on {\it algorithmic procedures} for a small set of problems, e.g., nonnegativity or sparsity-constrained factorization, we take a {\it top-down} approach: we start with general optimization theory (e.g., inexact and accelerated block coordinate descent, stochastic optimization, and Gauss-Newton methods) that covers a wide range of factorization problems with diverse constraints and regularization terms of engineering interest. Then, we go `under the hood' to showcase specific algorithm design under these introduced principles. We pay a particular attention to recent algorithmic developments in structured tensor and matrix factorization (e.g., random sketching and adaptive step size based stochastic optimization and structure-exploiting second-order algorithms), which are the state of the art---yet much less touched upon in the literature compared to {\it block coordinate descent} (BCD)-based methods. We expect that the article to have an educational values in the field of structured factorization and hope to stimulate more research in this important and exciting direction.

In this paper, we develop novel perturbation bounds for the high-order orthogonal iteration (HOOI) [DLDMV00b]. Under mild regularity conditions, we establish blockwise tensor perturbation bounds for HOOI with guarantees for both tensor reconstruction in Hilbert-Schmidt norm $\|\widehat{\bcT} - \bcT \|_{\tHS}$ and mode-$k$ singular subspace estimation in Schatten-$q$ norm $\| \sin \Theta (\widehat{\U}_k, \U_k) \|_q$ for any $q \geq 1$. We show the upper bounds of mode-$k$ singular subspace estimation are unilateral and converge linearly to a quantity characterized by blockwise errors of the perturbation and signal strength. For the tensor reconstruction error bound, we express the bound through a simple quantity $\xi$, which depends only on perturbation and the multilinear rank of the underlying signal. Rate matching deterministic lower bound for tensor reconstruction, which demonstrates the optimality of HOOI, is also provided. Furthermore, we prove that one-step HOOI (i.e., HOOI with only a single iteration) is also optimal in terms of tensor reconstruction and can be used to lower the computational cost. The perturbation results are also extended to the case that only partial modes of $\bcT$ have low-rank structure. We support our theoretical results by extensive numerical studies. Finally, we apply the novel perturbation bounds of HOOI on two applications, tensor denoising and tensor co-clustering, from machine learning and statistics, which demonstrates the superiority of the new perturbation results.

Artificial intelligence is already impacting our lives. And the use of AI for social functioning is on an all-time high. Be it getting riding directions through our smartphone or getting daily reminders by using our health system to extend our workouts; all these are manifestations of how artificial talent is altering the way we function. What is often much less understood is the vast function synthetic brain can play in the social sector. The Artificial Intelligence for social good can probably assist in solving some of the country's most pressing problems. As a count number of facts, it can contribute in some way or every other to tackling and addressing all of the United Nation's Sustainable Development Goals, supporting large sections of the populace in both growing and developed countries. AI is already helping in several real-life situations, from assisting blind humans in navigating and diagnosing cancer to identify sexual harassment victims and helping with catastrophe relief. Let us take a look briefly at integral social domains where AI can be carried out effectively.

Psychological semantics is the study of how people represent the meanings of words and then build sentence meaning out of those representations. People use language dozens of time a day--to have conversations and give instructions, to read and write, to label objects and teach. A theory of psychological semantics must provide the basis for how people do all those things, choosing which words to use and understanding the words they read or hear. In this article we focus on the mental representation of word meaning. Human language is still the gold standard for a communication system, but artificial intelligence (AI) systems have made important progress in language use. Research on Natural Language Processing (NLP) develops systems that understand language to the degree that computers can carry out useful tasks. As described below, such systems use vast text corpora to learn about words, using neural networks and other statistical models. The recent explosion of research in NLP, driven largely by advances in neural networks (also called deep learning), has resulted in continuously improving performance on various benchmarks that require interpreting words and sentences. Systems are now used in interfaces with customers to make sales or solve problems.