The benchmark spans five core categories: grammar, morphology, spelling, reading comprehension, and syntax, through 150 expert-designed multiple choice questions that directly assess structural language understanding. Evaluating 35 Arabic and bilingual LLMs reveals that current models demonstrate strong surface level proficiency but struggle with deeper grammatical and syntactic reasoning. AraLingBench highlights a persistent gap between high scores on knowledge-based benchmarks and true linguistic mastery, showing that many models succeed through memorization or pattern recognition rather than authentic comprehension. By isolating and measuring fundamental linguistic skills, AraLingBench provides a diagnostic framework for developing Arabic LLMs. The full evaluation code is publicly available on GitHub.

MS in AI courses at American universities are very popular globally. This is why many aspiring AI experts go to the US for completing their training. Besides this, the country houses numerous AI startups which means there is no shortage of jobs for AI professionals in America. In short, it's worthwhile to study artificial intelligence in the country. With this in mind today we are sharing with you a guide regarding studying MS in Artificial Intelligence in USA. Here are all the things you must know if you plan to study Artificial Intelligence in USA.

The rapid expansion of data worldwide invites the need for more distributed solutions in order to apply machine learning on a much wider scale. The resultant distributed learning systems can have various degrees of centralization. In this work, we demonstrate our solution FLoBC for building a generic decentralized federated learning system using blockchain technology, accommodating any machine learning model that is compatible with gradient descent optimization. We present our system design comprising the two decentralized actors: trainer and validator, alongside our methodology for ensuring reliable and efficient operation of said system. Finally, we utilize FLoBC as an experimental sandbox to compare and contrast the effects of trainer-to-validator ratio, reward-penalty policy, and model synchronization schemes on the overall system performance, ultimately showing by example that a decentralized federated learning system is indeed a feasible alternative to more centralized architectures.

An American University math professor and his team created a statistical model that can be used to detect misinformation in social posts. The model also avoids the problem of black boxes that occur in machine learning. With the use of algorithms and computer models, machine learning is increasingly playing a role in helping to stop the spread of misinformation, but a main challenge for scientists is the black box of unknowability, where researchers don't understand how the machine arrives at the same decision as human trainers. Using a Twitter dataset with misinformation tweets about COVID-19, Zois Boukouvalas, assistant professor in AU's Department of Mathematics and Statistics in the College of Arts and Sciences, shows how statistical models can detect misinformation in social media during events like a pandemic or a natural disaster. In newly published research, Boukouvalas and his colleagues, including AU student Caitlin Moroney and Computer Science Prof. Nathalie Japkowicz, also show how the model's decisions align with those made by humans.

Over the last few years, the conversation around emerging technologies like AI and machine learning has increased massively. However, this conversation is limited only to the research and developers' community. The general public, which is at the receiving end, is largely left out of such conversations. This is mainly because there has been very little effort to give cultural and linguistic context to such technologies. To give an example, most of us might be unaware of what AI is called in our local tongue or worse; there might not be any local term to refer to AI to begin with.

The Arab region consists of many teaching-intensive universities that are intrinsically committed to holistic educational excellence. According to a recent UNESCO report,5 the higher education sector in the Arab region is undergoing a need for massive expansion given exponentially growing populations, record-breaking youth cohorts, coupled with a strong recognition of the economic and social value of higher education. Such an enormous need for growth poses a significant challenge for publicly funded universities yet offers many opportunities for private universities to meet the ever-increasing demands of advanced education.2 As is the case with many similar universities worldwide, not being dedicated research institutions often results in limited availability of research funds, resources, and hence innovation throughput. The examples given in this paper are those of universities in the region that were initially focused on consolidating their teaching, except for one which started first as research-intensive. However, it was not long before a shift in policy included research excellence in undergraduate education by harnessing the most valuable resource of any university: the aspiring students themselves.

The Arab region, composed of 22 countries spanning Asia and Africa, opens ample room for communications and networking innovations and services and contributes to the critical mass of the global networking innovation. While the Arab world is considered an emerging market for communications and networking services, the rate of adoption is outpacing the global average. In fact, as of 2019, the mobile Internet penetration stands at 67.2% in the Arab world, as opposed to a global average of 56.5%.12 Furthermore, multiple countries in the region are either building new infrastructure or developing existing infrastructure at an unprecedented pace. Examples include, Neom city in Saudi Arabia, the new administrative capital in Egypt, as well as the Smart Dubai 2021 project in the United Arab Emirates (UAE), among others. This provides a unique opportunity to fuse multiple advanced networking technologies as an integral part of the infrastructure design phase and not just as an afterthought.

Law enforcement in America is facing a day of reckoning over its systemic, institutionalized racism and ongoing brutality against the people it was designed to protect. Virtually every aspect of the system is now under scrutiny, from budgeting and staffing levels to the data-driven prevention tools it deploys. A handful of local governments have already placed moratoriums on facial recognition systems in recent months and on Wednesday, Santa Cruz, California became the first city in the nation to outright ban the use of predictive policing algorithms. While it's easy to see the privacy risks that facial recognition poses, predictive policing programs have the potential to quietly erode our constitutional rights and exacerbate existing racial and economic biases in the law enforcement community. Simply put, predictive policing technology uses algorithms to pore over massive amounts of data to predict when and where future crimes will occur.

Tens of thousands of students at universities across the country are using Otter.ai to stay on top of classes, reading lists, group assignments, research interviews, and exam preparation. A dozen students will be profiled on Otter's blog over the coming months, and their insights have been compiled into a helpful article by college student Sydney Kuntz of American University. "Otter is more than speech recognition. It is a new medium which captures what is said in a form that can be reviewed, freeing students to ask questions, develop ideas and participate in discussions," said Sydney Kuntz, Otter user and journalism student at American University. "Using Otter, I can record directly or upload audio/video files to transcribe. I can also add photos of whiteboards or slides from class presentations, during or after recording. And with features like keywords and highlighting, even hour-long lectures are very easy to navigate and search."

This week's furor over FaceApp has largely centered on concerns that its Russian developers might be compelled to share the app's data with the Russian government, much as the Snowden disclosures illustrated the myriad ways in which American companies were compelled to disclose their private user data to the US government. Yet the reality is that this represents a mistaken understanding of just how the modern data trade works today and the simple fact that American universities and companies routinely make their data available to companies all across the world, including in Russia and China. In today's globalized world, data is just as globalized, with national borders no longer restricting the flow of our personal information - trend made worse by the data-hungry world of deep learning. Data brokers have long bought and sold our personal data in a shadowy world of international trade involving our most intimate and private information. The digital era has upended this explicit trade through the interlocking world of passive exchange through analytics services.