Multilingual Large Language Models (LLMs) are increasingly used worldwide, making it essential to ensure they are free from gender bias to prevent representational harm. While prior studies have examined such biases in high-resource languages, low-resource languages remain understudied. In this paper, we propose a template-based probing methodology, validated against real-world data, to uncover gender stereotypes in LLMs. As part of this framework, we introduce the Domain-Specific Gender Skew Index (DS-GSI), a metric that quantifies deviations from gender parity. We evaluate four prominent models, GPT-4o mini, DeepSeek R1, Gemini 2.0 Flash, and Qwen QwQ 32B, across four semantic domains, focusing on Persian, a low-resource language with distinct linguistic features. Our results show that all models exhibit gender stereotypes, with greater disparities in Persian than in English across all domains. Among these, sports reflect the most rigid gender biases. This study underscores the need for inclusive NLP practices and provides a framework for assessing bias in other low-resource languages.

Machine learning is a rapidly expanding field and it can be difficult to find beginner resources to get introduced to the topics you are interested in. When I first started getting into machine learning is was hard to find resources that both covered in detail the topic in which I was interested and were also written for someone with a beginner's knowledge of machine learning. After about four years of researching a variety of topics related to machine learning, these are the resources that I have found to be the most helpful for someone getting started in the field. Machine learning is a field that is evolving extremely rapidly, and as such printed material cannot keep up with all of the new innovations. Because of this, some of the best platforms to get updated information in machine learning are blogs and websites. Medium is an online publishing platform that hosts several blogs that are dedicated to machine learning and data science.

Fully appreciating the overarching scope of CS requires weaving more than ethics into the reigning curricula.

The University of Texas at San Antonio is participating in a pioneering program to introduce artificial intelligence principles to students in all academic disciplines. UTSA is working with MITRE, a not-for-profit corporation dedicated to research and development in the public interest, to help faculty develop lesson modules incorporating AI, big data analytics and data visualization in classrooms across campus this academic year. The project, codenamed "Generation AI Nexus" or "Gen AI," refers to anyone born in 1995 and later. The goal is to help all students, regardless of their major, understand AI and how to use it as an effective tool. "As an organization of system thinkers and problem solvers, MITRE recognizes the need for novel partnerships with universities to develop talent for the 21st century workforce," said Bobby Blount, department head for cyber ops and C2 effects at MITRE.

The University of Texas at San Antonio is participating in a pioneering program to introduce artificial intelligence (AI) principles to students in all academic disciplines. UTSA is working with MITRE, a not-for-profit corporation dedicated to research and development in the public interest, to help faculty develop lesson modules incorporating AI, big data analytics and data visualization in classrooms across campus this academic year. The project, codenamed "Generation AI Nexus" or "Gen AI," refers to anyone born in 1995 and later. The goal is to help all students, regardless of their major, understand AI and how to use it as an effective tool. "As an organization of system thinkers and problem solvers, MITRE recognizes the need for novel partnerships with universities to develop talent for the 21st century workforce," said Bobby Blount, department head for cyber ops and C2 effects at MITRE.

We explore decision intelligence, an emerging discipline that is a must-have an asset for HR leaders in the AI era. From people analytics and automation to predictive analysis and digital assistants, HR is finally on par with other business functions in the implementation of AI and data analytics. And, while HR is no stranger to these technologies, decision-making HR leaders still rely on human emotional judgment when it comes to business decision-making. A study by MIT Sloan Management Review and The Boston Consulting Group suggests that although 85% of CEO's believe that AI has the potential to drive exponential value to their businesses, only a few admit on incorporating AI capabilities in their decision-making processes and operations. AI applications such as predictive analysis provide in-depth insights to leaders via algorithms.

What if physiologists were the only people who study human behavior at all scales: from how the human body functions, to how social norms emerge, to how the stock market functions, to how we create, share, and consume culture? What if neuroscientists were the only people tasked with studying criminal behavior, designing educational curricula, and devising policies to fight tax evasion? Despite their growing influence on our lives, our study of AI agents is conducted this way--by a very specific group of people. Those scientists who create AI agents--namely, computer scientists and roboticists--are almost exclusively the same scientists who study the behavior of AI agents. We cannot certify that an AI agent is ethical by looking at its source code, any more than we can certify that humans are good by scanning their brains.

Data science is first and foremost a talent-based discipline and capability. Platforms, tools and IT infrastructure play an important but secondary role. Nevertheless, software and technology companies around the globe spend significant amounts of money talking business managers into buying or licensing their products which often times results in unsatisfying outcomes that do not come close to realizing the full potential of data science. Talent is key - but unfortunately very rare and hard to identify. If you are trying to hire a data scientist these days you are facing the serious risk of recruiting someone with the wrong or an insufficient skill set. On top of things, talent is even more crucial for small or medium-sized companies whose data science teams are likely to stay relatively small. Wasting one or two head counts on wrong profiles might render an entire team inefficient.