Gunasekar et al. (2017) observed We study the implicit regularization effects of that for matrix factorization when there are no constraints on deep learning in tensor factorization. While implicit the rank, the solution of the optimization problem via gradient regularization in deep matrix and'shallow' descent turns out to be a low-rank matrix. Furthermore, tensor factorization via linear and certain type of they conjectured that, with small enough learning rate and non-linear neural networks promotes low-rank solutions initialization, gradient descent on full-dimensional matrix with at most quadratic growth, we show factorization converges to the solution with minimal nuclear that its effect in deep tensor factorization grows norm. Arora et al. (2019) and Razin & Cohen (2020) extended polynomially with the depth of the network. This the analysis to deep matrix factorization and showed provides a remarkably faithful description of the in this case that implicit regularization of gradient descent observed experimental behaviour. Using numerical cannot be formulated as a norm-minimization problem. By experiments, we demonstrate the benefits of studying the dynamics of gradient descent, they found theoretically this implicit regularization in yielding a more accurate and experimentally that it instead promotes sparsity estimation and better convergence properties. of the singular values of the learned matrix, indicating that implicit regularization in deep learning has to be studied from a dynamical point of view. Moreover, Razin et al. (2021) studied implicit regularization in'shallow' tensor

Artificial intelligence has seen a slew of investments throughout the Middle East, with many nations seeing the tech as playing a key role in creating a more sustainable future

Francesco Petruccione was born in 1961 in Genova (Italy). He studied Physics at the University of Freiburg i. Br. and received his PhD in 1988. He was conferred the "Habilitation" degree (Dr. In 2004 he was appointed Professor of Theoretical Physics at the University of KwaZulu-Natal (UKZN), in Durban (South Africa). In 2005 he was awarded an Innovation Fund grant to set up a Centre for Quantum Technology.

Disclaimer: this text expresses the opinions of a student, researcher, and engineer who studies and works in the field of Artificial Intelligence in the Netherlands. I think the contents are not as nuanced as they could be, but the text is informed -- in a way, it is just my opinion. Allow me then to begin by iterating Wittgensteins' de facto sentence with which he ends his first treaty in philosophy, Tractatus Logico-Philosophicus: "Whereof one cannot speak thereof one must remain silent"[7]. The problem with Ethics of AI, put succinctly, is the demand for morally-based changes to an empirical scientific field -- the field of AI or Computer Science. These changes have been easily justified in AI due to its engineering counterpart -- one of the fastest growing and most productive technological fields at the moment whose range of possible reforms threatens every social dimension. Most of these changes, for better and for worst, have been demanded by the political class and for the most part only in the West. The aim of this article is not to take any part in the political discussion, although this might be impossible by definition -- after all, everything is political. It is still important to attempt to disentangle the views expressed here-in from those barked in the political sphere. The very root of the problem is linked to the over-politicization, indeed, perhaps even radicalization of systems that are not political by nature, like Science. The problem, that a scientific field has been mixed-up with its applications in industry -- is a prominent one.

While artificial intelligence (AI) has emerged as the next major wave of innovation, its power dynamics are not evenly distributed. This is according to Mozilla's Internet Health Report 2022, which examines how humanity and the internet intersect, scrutinising the nature of an AI-driven world. AI in this case includes a wide range of automation and algorithmic processes, including machine learning, computer vision, natural language processing, and more, states the report. Mozilla found that the growing power disparity between who benefits from AI and who is harmed by AI is the top challenge facing the health of the internet. Solana Larsen, Mozilla's internet health report editor, explains: "The centralisation of influence and control over AI doesn't work to the advantage of the majority of people. We need to strengthen technology ecosystems beyond the realm of big tech and venture capital start-ups if we want to unlock the full potential of trustworthy AI." Research and advisory firm IDC forecasts that worldwide revenue for the AI market will grow by 19.6% year-over-year in 2022, to $432.8 billion, with the market expected to break the $500 billion mark in 2023.

Powered by cloud, AI and machine learning, digital transformation at Africa's largest broadcaster, MultiChoice, is embedded in everything it does.

This overview paper describes the first shared task on fake news detection in Urdu language. The task was posed as a binary classification task, in which the goal is to differentiate between real and fake news. We provided a dataset divided into 900 annotated news articles for training and 400 news articles for testing. The dataset contained news in five domains: (i) Health, (ii) Sports, (iii) Showbiz, (iv) Technology, and (v) Business. 42 teams from 6 different countries (India, China, Egypt, Germany, Pakistan, and the UK) registered for the task. 9 teams submitted their experimental results. The participants used various machine learning methods ranging from feature-based traditional machine learning to neural networks techniques. The best performing system achieved an F-score value of 0.90, showing that the BERT-based approach outperforms other machine learning techniques

This paper gives the overview of the first shared task at FIRE 2020 on fake news detection in the Urdu language. This is a binary classification task in which the goal is to identify fake news using a dataset composed of 900 annotated news articles for training and 400 news articles for testing. The dataset contains news in five domains: (i) Health, (ii) Sports, (iii) Showbiz, (iv) Technology, and (v) Business. 42 teams from 6 different countries (India, China, Egypt, Germany, Pakistan, and the UK) registered for the task. 9 teams submitted their experimental results. The participants used various machine learning methods ranging from feature-based traditional machine learning to neural network techniques. The best performing system achieved an F-score value of 0.90, showing that the BERT-based approach outperforms other machine learning classifiers.

Pushing back the frontiers of collaborative robots in industrial environments, we propose a new Separable-Sparse Graph Convolutional Network (SeS-GCN) for pose forecasting. For the first time, SeS-GCN bottlenecks the interaction of the spatial, temporal and channel-wise dimensions in GCNs, and it learns sparse adjacency matrices by a teacher-student framework. Compared to the state-of-the-art, it only uses 1.72% of the parameters and it is ~4 times faster, while still performing comparably in forecasting accuracy on Human3.6M at 1 second in the future, which enables cobots to be aware of human operators. As a second contribution, we present a new benchmark of Cobots and Humans in Industrial COllaboration (CHICO). CHICO includes multi-view videos, 3D poses and trajectories of 20 human operators and cobots, engaging in 7 realistic industrial actions. Additionally, it reports 226 genuine collisions, taking place during the human-cobot interaction. We test SeS-GCN on CHICO for two important perception tasks in robotics: human pose forecasting, where it reaches an average error of 85.3 mm (MPJPE) at 1 sec in the future with a run time of 2.3 msec, and collision detection, by comparing the forecasted human motion with the known cobot motion, obtaining an F1-score of 0.64.

Hyperdimensional Computing (HDC) has obtained abundant attention as an emerging non von Neumann computing paradigm. Inspired by the way human brain functions, HDC leverages high dimensional patterns to perform learning tasks. Compared to neural networks, HDC has shown advantages such as energy efficiency and smaller model size, but sub-par learning capabilities in sophisticated applications. Recently, researchers have observed when combined with neural network components, HDC can achieve better performance than conventional HDC models. This motivates us to explore the deeper insights behind theoretical foundations of HDC, particularly the connection and differences with neural networks. In this paper, we make a comparative study between HDC and neural network to provide a different angle where HDC can be derived from an extremely compact neural network trained upfront. Experimental results show such neural network-derived HDC model can achieve up to 21% and 5% accuracy increase from conventional and learning-based HDC models respectively. This paper aims to provide more insights and shed lights on future directions for researches on this popular emerging learning scheme.