We introduce a new algorithm - Artemis - tackling the problem of learning in a distributed framework with communication constraints. Several workers (randomly sampled) perform the optimization process using a central server to aggregate their computation. To alleviate the communication cost, Artemis compresses the information sent in both directions (from the workers to the server and conversely) combined with a memory mechanism. It improves on existing quantized federated learning algorithms that only consider unidirectional compression (to the server), or use very strong assumptions on the compression operator, and often do not take into account devices partial participation. We provide fast rates of convergence (linear up to a threshold) under weak assumptions on the stochastic gradients (noise's variance bounded only at optimal point) in non-i.i.d. setting, highlight the impact of memory for unidirectional and bidirectional compression, analyze Polyak-Ruppert averaging. We use convergence in distribution to obtain a lower bound of the asymptotic variance that highlights practical limits of compression. And we provide experimental results to demonstrate the validity of our analysis.

The 2nd edition of the Montreal AI Ethics Institute's The State of AI Ethics captures the most relevant developments in the field of AI Ethics since July 2020. This report aims to help anyone, from machine learning experts to human rights activists and policymakers, quickly digest and understand the ever-changing developments in the field. Through research and article summaries, as well as expert commentary, this report distills the research and reporting surrounding various domains related to the ethics of AI, including: AI and society, bias and algorithmic justice, disinformation, humans and AI, labor impacts, privacy, risk, and future of AI ethics. In addition, The State of AI Ethics includes exclusive content written by world-class AI Ethics experts from universities, research institutes, consulting firms, and governments. These experts include: Danit Gal (Tech Advisor, United Nations), Amba Kak (Director of Global Policy and Programs, NYU's AI Now Institute), Rumman Chowdhury (Global Lead for Responsible AI, Accenture), Brent Barron (Director of Strategic Projects and Knowledge Management, CIFAR), Adam Murray (U.S. Diplomat working on tech policy, Chair of the OECD Network on AI), Thomas Kochan (Professor, MIT Sloan School of Management), and Katya Klinova (AI and Economy Program Lead, Partnership on AI). This report should be used not only as a point of reference and insight on the latest thinking in the field of AI Ethics, but should also be used as a tool for introspection as we aim to foster a more nuanced conversation regarding the impacts of AI on the world.

Long before the Covid-19 pandemic, businesses had been on a steady path toward digital transformation to achieve vast improvements in worker productivity, public health and safety, quality of products, services and customer experiences and even to obtain a sustainable planet and a circular economy. The benefits of the next digital era seem almost endless, but the challenges of adopting the technologies that will enable it to transpire -- AI, machine learning and deep learning at the edge (where rapid automation takes place) -- have made businesses pause because it forces great behavioral and structural changes, like new business models, operating procedures, worker skill sets and mindsets. It can even affect cultures. These have been some of the biggest stumbling blocks to reaching the next digital era -- until recently. With our livelihoods at risk, the pandemic has served as a wake-up call to expedite the timeline for digital transformation exponentially.

Atima Lui was in primary school when she first learned that "nude" is not universal. Now 30, she still recalls playing with a white friend's makeup and struggling to find colours that complemented her rich skin tone. "I would try to put [her makeup] on and it would just make me look like a clown," says Lui, who is of Sudanese and African-American descent. "I think back to growing up and how my mother barely wore makeup. Now I know it's because makeup just wasn't made for her."

The Reputed Garner Insights website offers vast reports on different market.They cover all industry and these reports are very precise and reliable. It also offers Artificial Intelligence (AI) in Automotive Market Report 2020 in its research report store. It is the most comprehensive report available on this market. The report study provides information on market trends and development, drivers, capacities, technologies, and on the changing investment structure of the Global Artificial Intelligence (AI) in Automotive Market. The study gives a transparent view on the Global Artificial Intelligence (AI) in Automotive Market and includes a thorough competitive scenario and portfolio of the key players functioning in it.

In this paper, we introduce convolutional proximal neural networks (cPNNs), which are by construction averaged operators. For filters of full length, we propose a stochastic gradient descent algorithm on a submanifold of the Stiefel manifold to train cPNNs. In case of filters with limited length, we design algorithms for minimizing functionals that approximate the orthogonality constraints imposed on the operators by penalizing the least squares distance to the identity operator. Then, we investigate how scaled cPNNs with a prescribed Lipschitz constant can be used for denoising signals and images, where the achieved quality depends on the Lipschitz constant. Finally, we apply cPNN based denoisers within a Plug-and-Play (PnP) framework and provide convergence results for the corresponding PnP forward-backward splitting algorithm based on an oracle construction.

Evaluating the banks' performance has always been of interest due to their crucial role in the economic development of each country. Data envelopment analysis (DEA) has been widely used for measuring the performance of bank branches. In the conventional DEA approach, decision making units (DMUs) are regarded as black boxes that transform sets of inputs into sets of outputs without considering the internal interactions taking place within each DMU. Two-stage DEA models are designed to overcome this shortfall. Thus, this paper presented a new two-stage DEA model based on a modification on Enhanced Russell Model. On the other hand, in many situations, such as in a manufacturing system, a production process or a service system, inputs, intermediates and outputs can be given as a fuzzy variable. The main aim of this paper is to build and present a new fuzzy two-stage DEA model for measuring the efficiency of 15 branches of Melli bank in Hamedan province.

With the vast development and employment of artificial intelligence applications, research into the fairness of these algorithms has been increased. Specifically, in the natural language processing domain, it has been shown that social biases persist in word embeddings and are thus in danger of amplifying these biases when used. As an example of social bias, religious biases are shown to persist in word embeddings and the need for its removal is highlighted. This paper investigates the state-of-the-art multiclass debiasing techniques: Hard debiasing, SoftWEAT debiasing and Conceptor debiasing. It evaluates their performance when removing religious bias on a common basis by quantifying bias removal via the Word Embedding Association Test (WEAT), Mean Average Cosine Similarity (MAC) and the Relative Negative Sentiment Bias (RNSB). By investigating the religious bias removal on three widely used word embeddings, namely: Word2Vec, GloVe, and ConceptNet, it is shown that the preferred method is ConceptorDebiasing. Specifically, this technique manages to decrease the measured religious bias on average by 82,42%, 96,78% and 54,76% for the three word embedding sets respectively.

Performing fact verification based on structured data is important for many real-life applications and is a challenging research problem, particularly when it involves both symbolic operations and informal inference based on language understanding. In this paper, we present a Program-enhanced Verbalization and Graph Attention Network (ProgVGAT) to integrate programs and execution into textual inference models. Specifically, a verbalization with program execution model is proposed to accumulate evidences that are embedded in operations over the tables. Built on that, we construct the graph attention verification networks, which are designed to fuse different sources of evidences from verbalized program execution, program structures, and the original statements and tables, to make the final verification decision. To support the above framework, we propose a program selection module optimized with a new training strategy based on margin loss, to produce more accurate programs, which is shown to be effective in enhancing the final verification results. Experimental results show that the proposed framework achieves the new state-of-the-art performance, a 74.4% accuracy, on the benchmark dataset TABFACT.

With increased speed, enhanced force and lower cost, the application of artificial intelligence (AI) on weapons will define the next generation of warfare, Gary Butler, founder of Starkville-based tech company Camgian Microsystems, told Starkville Rotary Club members Monday afternoon. Butler, a Mississippi native, has been working on advanced technology for roughly 20 years, with a focus on sensor systems and AI-based technologies, according to his LinkedIn page. Over the years, he has worked on system development with the U.S. military and the Defense Advanced Research Projects Agency (DARPA). Camgian, which he founded in 2006, has provided research and development service to several government and financial agencies. With years of experience researching AI technology and its military use, Butler said the application of the technology in weaponry seems the natural step.