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Meta's massive multilingual translation opus still stumbles on Greek, Armenian, Oromo

ZDNet

"Broadly accessible machine translation systems support around 130 languages; our goal is to bring this number up to 200," the authors write as their mission statement. Meta Properties, owner of Facebook, Instagram and WhatsApp, on Wednesday unveiled its latest effort in machine translation, a 190-page opus describing how it has used deep learning forms of neural nets to double state-of-the-art translation for languages to 202 languages, many of them so-called "low resource" languages such as West Central Oromo, a language of the Oromia state of Ethiopia, Tamasheq, spoken in Algeria and several other parts of Northern Africa, and Waray, the language of the Waray people of the Philippines. The report by a team of researchers at Meta, along with scholars at UC Berkeley and Johns Hopkins, "No Language Left Behind: Scaling Human-Centered Machine Translation," is posted on Facebook's AI research Web site, along with a companion blog post, and both should be required reading for the rich detail on the matter. "Broadly accessible machine translation systems support around 130 languages; our goal is to bring this number up to 200," they write as their mission statement. As Stephanie relates, Meta is open-sourcing its data sets and neural network model code on GitHub, and also offering $200,000 I'm awards to outside uses of the technology.


Financial and banking sector to become biggest AI spender in Mena, Google says

#artificialintelligence

The financial services and banking sector is predicted to become the highest spender on artificial intelligence technology in the Middle East and North Africa, according to Google. The sector will have a share of almost 25 per cent of all AI investments in the region, with the use of the technology in banking alone expected to contribute up to 13.6 per cent to the region's gross domestic product by 2030, the Alphabet-owned company said in the Future of AI in the Mena report. "This would take shape through a range of applications, such as deep learning for algorithmic trading, fraud analysis and investing, as well as smart portfolio management and customer profiling," the report said. The overall potential effect of AI on the region's economic growth is significant, with the Mena region estimated to accrue $320 billion by 2030 from the value added by the technology. This is mostly from costs saved through automating processes, as well as improving products and services across the region's industries, the report said.


Mena

AAAI Conferences

Probabilistic Classifiers Chains (PCC) offers interesting properties to solve multi-label classification tasks due to its ability to estimate the joint probability of the labels. However, PCC presents the major drawback of having a high computational cost in the inference process required to predict new samples. Lately, several approaches have been proposed to overcome this issue, including beam search and an epsilon-Approximate algorithm based on uniform-cost search. Surprisingly, the obvious possibility of using heuristic search has not been considered yet. This paper studies this alternative and proposes an admisible heuristic that, applied in combination with A* algorithm, guarantees, not only optimal predictions in terms of subset 0/1 loss, but also that it always explores less nodes than epsilon-Approximate algorithm. In the experiments reported, the number of nodes explored by our method is less than two times the number of labels for all datasets analyzed. But, the difference in explored nodes must be large enough to compensate the overhead of the heuristic in order to improve prediction time. Thus, our proposal may be a good choice for complex multi-label problems.


How Do AI Represent the Urban?

#artificialintelligence

It's important to remember that these images aren't created from scratch. They're built from "training sets" of images that human researchers feed into the AI to help it learn and recognise patterns. If you're not familiar with how such AI apps work, this old article from 2015 does a pretty good job of explaining this. I suspect that while the process has become more sophisticated over the years, the basic principle of recursively feeding images back into neural nets until the AI "gets it" hasn't changed. Therefore, human biases do exist in the patterns chosen and images generated, which turns these images into AI interpretations of human biases.


Should we care about Philosophy of AI in the Mena region?

#artificialintelligence

The artificial intelligence (AI) race between the global powers has countries everywhere hurriedly rummaging up AI applications. A quick glance at magazine headlines, popular culture, and even peer-reviewed academic literature shows the many grand predictions about AI and the eventual winner of its race. But is that race something to be celebrated or feared? And where does the Middle East and North Africa (Mena) region stand? Today, algorithms, deep learning and AI have emerged as unparalleled forces of power and have made their way into the everyday world.


Why AI is so difficult to apply in finance

#artificialintelligence

The issue of data quality is foremost in the financial sector. In the financial world, abundance of data is not an issue. Data can easily be collected from a wide variety of sources such as instrument prices, news articles, stock fundamentals, social media posts, macroeconomic data, ESG data, credit card transactions, and so on. Some of this data is classified as structured and typically has a numerical quantity and a well-defined structure (e.g. stock prices). Structured data is relatively easy to feed into an ML model whereas unstructured data often requires extra processing to extract meaningful information (e.g.


OPPO Unveils 6G White Paper and Distinctive Next-Generation Communications Vision globally including the MENA region

#artificialintelligence

Global technology company OPPO announced that the OPPO Research Institute has officially released its first 6G white paper - "6G AI-Cube Intelligent Networking". As one of the global and MENA region's telecommunications industry's first in-depth reports on how artificial intelligence (AI) can empower 6G network architecture, the white paper proposes a more detailed vision for the design of next-generation communication networks. OPPO has established a pre-research team to conduct preliminary research on 6G service and technology requirements, key technologies, and system features. The global smartphone leader believes that 6G will reshape the way people interact with AI, as it is utilised to serve the public through a myriad of applications. In June 2021, UAE telecoms provider Etisalat announced plans for 6G – stating that the network is expected to be even faster and support applications such as augmented and virtual reality, as well as AI infrastructure.


ROD: Reception-aware Online Distillation for Sparse Graphs

arXiv.org Artificial Intelligence

Graph neural networks (GNNs) have been widely used in many graph-based tasks such as node classification, link prediction, and node clustering. However, GNNs gain their performance benefits mainly from performing the feature propagation and smoothing across the edges of the graph, thus requiring sufficient connectivity and label information for effective propagation. Unfortunately, many real-world networks are sparse in terms of both edges and labels, leading to sub-optimal performance of GNNs. Recent interest in this sparse problem has focused on the self-training approach, which expands supervised signals with pseudo labels. Nevertheless, the self-training approach inherently cannot realize the full potential of refining the learning performance on sparse graphs due to the unsatisfactory quality and quantity of pseudo labels. In this paper, we propose ROD, a novel reception-aware online knowledge distillation approach for sparse graph learning. We design three supervision signals for ROD: multi-scale reception-aware graph knowledge, task-based supervision, and rich distilled knowledge, allowing online knowledge transfer in a peer-teaching manner. To extract knowledge concealed in the multi-scale reception fields, ROD explicitly requires individual student models to preserve different levels of locality information. For a given task, each student would predict based on its reception-scale knowledge, while simultaneously a strong teacher is established on-the-fly by combining multi-scale knowledge. Our approach has been extensively evaluated on 9 datasets and a variety of graph-based tasks, including node classification, link prediction, and node clustering. The result demonstrates that ROD achieves state-of-art performance and is more robust for the graph sparsity.


IoT-Enabled Social Relationships Meet Artificial Social Intelligence

arXiv.org Artificial Intelligence

With the recent advances of the Internet of Things, and the increasing accessibility of ubiquitous computing resources and mobile devices, the prevalence of rich media contents, and the ensuing social, economic, and cultural changes, computing technology and applications have evolved quickly over the past decade. They now go beyond personal computing, facilitating collaboration and social interactions in general, causing a quick proliferation of social relationships among IoT entities. The increasing number of these relationships and their heterogeneous social features have led to computing and communication bottlenecks that prevent the IoT network from taking advantage of these relationships to improve the offered services and customize the delivered content, known as relationship explosion. On the other hand, the quick advances in artificial intelligence applications in social computing have led to the emerging of a promising research field known as Artificial Social Intelligence (ASI) that has the potential to tackle the social relationship explosion problem. This paper discusses the role of IoT in social relationships detection and management, the problem of social relationships explosion in IoT and reviews the proposed solutions using ASI, including social-oriented machine-learning and deep-learning techniques.