The lack of dedicated CS datasets limits ASR performance, as most models rely on monolingual or mixed-language corpora that fail to reflect real-world CS patterns. This issue is critical in multilingual societies where CS occurs in informal and formal settings. A key example is Catalan-Spanish CS, widely used in media and parliamentary speeches. In this work, we improve ASR for Catalan-Spanish CS by exploring three strategies: (1) generating synthetic CS data, (2) concatenating monolingual audio, and (3) leveraging real CS data with language tokens. We extract CS data from Catalan speech corpora and fine-tune OpenAI's Whisper models, making them available on Hugging Face. Results show that combining a modest amount of synthetic CS data with the dominant language token yields the best transcription performance.

As part of ongoing efforts to diversify their economies and build a platform for sustainable future growth, MENA nations are increasingly turning towards artificial intelligence (AI). A slew of recent investment and initiatives – primarily in academia and the government, but also in the private sector – has reinvigorated interest from industry leaders around the globe in the potential for AI to strengthen the efficiency and sustainability of MENA economies. According to a report from the Economist Impact Unit (EIU) and Google published earlier this year, AI could bring about an additional $320bn in economic growth in the MENA region by 2030. Many long-term economic strategies in the region target high-value sectors with the potential to benefit from the Fourth Industrial Revolution – a raft of technological advancements in AI, data and cloud computing that merge the physical, digital and biological worlds. In recent years the UAE, Saudi Arabia, Qatar and Egypt have published ambitious, government-driven strategies to develop AI.

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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.

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.

The talk that followed, entitled How to Land a Remote AI Job and presented by Mr. Marc Banoub, Founder and CEO of LyRise, addressed the previously raised concerns regarding the skill gap between graduates and industry. The beginning of the discussion revealed statistical data on different aspects of working from home, such as its impact on employee productivity and long-term connections. It also highlighted the growing demand for AI-related careers in such an environment. Mr. Banoub then proceeded to engage attendees with thought-provoking questions that aim to help them self-diagnose where they are in their career, where they want to get and how they can get there. His advice covered subjects like internships, mentoring and personal projects that showcase added value.

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.

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.

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.

Welcome to the fourth edition of the AI Index Report. This year we significantly expanded the amount of data available in the report, worked with a broader set of external organizations to calibrate our data, and deepened our connections with the Stanford Institute for Human-Centered Artificial Intelligence (HAI). The AI Index Report tracks, collates, distills, and visualizes data related to artificial intelligence. Its mission is to provide unbiased, rigorously vetted, and globally sourced data for policymakers, researchers, executives, journalists, and the general public to develop intuitions about the complex field of AI. The report aims to be the most credible and authoritative source for data and insights about AI in the world.