Aoun Lutfi

Benchmarking Arabic Semantic Similarity Models Following the release of a few Arabic semantic similarity models (based on Sentence Transformers and ColBERT: Omartificial-Intelligence-Space/Arabert-all-nli-triplet-Matryoshka, AbderrahmanSkiredj1/Arabic_text_embedding_for_sts and akhooli/Arabic-ColBERT-100k), it is great to have robust benchmarking to compare performance. This involves selecting the right datasets (no leakage from training to evaluation) and running the scripts (and maybe publishing a leaderboard like the English MTEB for pure Arabic). This is probably a good idea for a researcher and a paper (I am not into either) but will indicate a relevant repo and some datasets to filter/clean/edit. I noticed that triplets used in the 100K ColBERT are poorly translated and focus on Western content, so there is room to work on better training and evaluation data for Arabic, but for a starter, even a decent ColBERT model is showing promising results (see previous post for code).

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🚀 Just wrapped up an insightful day at the Google Cloud Summit Saudi Arabia’s Innovators Hive! The hands-on labs and real-time challenges were exactly the kind of experience that keeps me motivated and engaged. From exploring GCP’s functionality to competing in labs on multimodal AI and BigQuery, each session sharpened my skills and expanded my problem-solving toolkit. It’s refreshing to see a summit that prioritizes real, actionable knowledge and empowers a whole community of tech enthusiasts. The thrill of diving into new tools, collaborating, and finding solutions on the spot is exactly why I love these events. A big thank you to Google Cloud for putting together a truly value-driven experience. Looking forward to more opportunities to learn, connect, and contribute! 🌐 #GoogleCloudSummit #InnovatorsHive #GCP #AI #BigQuery

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➡️ Saudi Arabia's $71B AI revolution begins with a Google-powered leap into the future ⬅️ Saudi Arabia’s Public Investment Fund (PIF) has partnered with Google Cloud to create an advanced AI hub, projected to generate thousands of jobs and contribute $71 billion to the country's GDP over the next eight years. This agreement, signed at the Future Investment Initiative in Riyadh, aligns with Saudi Arabia's goal of becoming a regional tech hub by 2030. The partnership will focus on providing AI training for millions of Saudi students and professionals, aiming to expand the information and communication technology sector by 50%. It includes joint research on Arabic language models and AI applications specific to Saudi Arabia. Key benefits of this collaboration include enhanced access to AI applications across various industries such as healthcare, retail, and financial services. Additionally, PIF and Google Cloud plan to improve the Arabic-language capabilities of Google's Gemini AI model by integrating more Arabic datasets, facilitating the development of AI solutions tailored for Arabic-speaking users. https://lnkd.in/ghPQudJn #SaudiAI #GoogleCloud #TechInnovation #SaudiVision2030 #MiddleEastTech #AIinvestment #DigitalTransformation #ArabicAI #PIF #TechHub #SaudiTech #MENA #ArtificialIntelligence #SaudiEconomy

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🤖 The Next Edition of the World Summit AI in Doha, Qatar! Curious to hear from Global giants as well as some international thought leaders. Sarah Porter founder and CEO of WSAI setted the tone in her introduction: 🧓 Human-Centered AI: AI reflects human choices and cannot solve all problems; the focus must remain on humanity and inclusivity. 👍 Potential for Good: AI has the potential to make a positive impact, especially in areas like medicine and education, but must benefit everyone. 🤝 Collaborative Efforts: The summit in Qatar brings together leaders, policymakers, and technologists to achieve tangible results and ensure the responsible use of AI. Looking forward to the next 2 days! #WSAIQatar #FutureOfAI #GenerativeAI

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Balancing Generative AI and Machine Learning for Business Success: In the past few years, data science and machine learning were prominent topics of discussion. However, the current trend has shifted, and now there is a growing focus on buzzwords such as LLM and Generative AI. It is worth noting that while GenerativeAI is a subset of machine learning, the mention of machine learning itself seems to have become less prominent in these discussions. These terms are being widely mentioned by leaders, organizations in various media outlets, social media platforms, and AI summits. Question is why? There could be multiple reasons for this shift, but one possible explanation is that individuals are increasingly inclined to associate themselves with terms like LLM and GenerativeAI. They may believe that these buzzwords are necessary to gain recognition and value in their respective fields. If there is a bias towards generativAI and businesses neglect other aspects of ML, there can be several consequences: 1.    While generative AI has its own unique applications, neglecting other aspects of machine learning can result in missed opportunities to apply machine learning techniques to solve practical business problems such as predictive analytics, anomaly detection, customer segmentation, and recommendation systems. 2.    ML techniques have different strengths and are suited for different types of problems. By solely focusing on generativeAI, businesses may limit their ability to address a diverse range of business challenges. This can hinder their ability to innovate, optimize processes, and make data-driven decisions. 3.    Disproportionate investment in generativeAI without considering other aspects of ML can lead to inefficient resource allocation and skill set imbalances within the organization. 4.    Neglecting other aspects of ML can put businesses at a competitive disadvantage. If competitors are leveraging a broader range of machine learning techniques to gain insights, automate processes, and improve decision-making, they may outperform businesses that are solely focused on generative AI. This can impact market share, customer satisfaction, and overall business performance. To address these issues, businesses and leaders should adopt a balanced approach, recognizing the complementary nature of generative AI and ML. For example, in e-commerce, machine learning is vital for product recommendations, forecasting sales, while generative AI shines in tasks like generating product descriptions from images and converting user feedback to ratings. By considering their organization's specific needs and goals, they can explore the complete range of ML and generative AI techniques to leverage the most suitable tools for their business challenges.

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Completed in-depth learning on Fine-Tuning Large Language Models from DeepLearning.AI. This experience has sharpened my skills in customizing LLMs for specific tasks, enhancing their performance and relevance. 👍 #GenerativeAI #LLMs #DataScience #AI

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LLMs are game changers. Very early on, before the LLM hype, I realized that language models would fundamentally change human-computer interaction. That’s why we developed an AI assistant at OrganAi.se before the LLM hype and were ready once the technology got available. We realized our vision of building an affordable personal assistant! Of course, the product developed much more than we originally planned in every single way. One thing is for sure, you can’t build an AI product that users want, if you don’t build a product that people want even without AI 🤖.

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🌟 Embracing the Future of AI and Generative AI 🌟 Over the past few years, building chatbots with API keys has become standard practice. But the real challenge—and the exciting opportunity—lies in integrating AI and Generative AI seamlessly into existing systems. It’s not just about building tools; it’s about tackling challenges like memory management, system compatibility, and scalability to truly unlock their potential. While LLMs (Large Language Models) have been shaping the AI landscape for some time now, the emergence of LAMs (Large Action Models) is pushing boundaries even further. These models aim to enable AI systems to handle complex, multi-step tasks autonomously, opening up new possibilities. It’s incredible to see how fast the field is evolving and how every day brings something new to learn. I’m deeply grateful to educators and thought leaders like Andrew Ng, Krish Naik, Aishwarya Naresh Reganti, and so many others who continue to share their knowledge. Their contributions make it easier for people like me to stay updated and grow in this ever-changing space. 💼 Actively Exploring Opportunities 💼 I’m currently looking for opportunities in AI/ML, Generative AI, and Data Science. I’m excited to contribute my skills, collaborate with forward-thinking teams, and work on projects that drive real impact. Let’s connect, share ideas, and build something amazing together! 🚀 #AI #GenerativeAI #MachineLearning #DataScience #CareerOpportunities #Innovation #OpentoWork

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🚀 Embracing the Future with Generative AI and Prompt Engineering 🚀 In my role, I have worked on developing AI-powered solutions using generative AI and prompt engineering that have provided me with deep insights into how AI can be leveraged to meet business objectives and exceed expectations. 🌐💡 With hands-on experience in developing AI-powered solutions, I’ve seen firsthand how generative AI and prompt engineering can revolutionize the way we tackle complex challenges and drive innovation across various industries. My work involves building advanced chatbots, designing sophisticated machine learning models, and developing AI tools that streamline operations and enhance decision-making processes. 🤖🛠️ ✨ Creativity and Personalization: Generative AI allows us to push the boundaries of creativity by generating unique artworks, music, and designs. In marketing, it helps craft personalized content that resonates with individual preferences, enhancing customer engagement and satisfaction. ⏱️ Efficiency and Automation: By automating tasks that require creativity and decision-making—such as drafting emails, writing reports, and even coding—generative AI saves time and reduces human error, allowing us to focus on more strategic activities. 🎮 Realistic Simulations and Immersive Experiences: From virtual reality to video games, generative AI enhances the realism and immersion of simulations, creating more engaging and interactive experiences. 🧠 Enhanced Decision-Making with Prompt Engineering: Effective prompt engineering is crucial for optimizing the performance of AI models. By crafting precise and context-aware prompts, we can guide AI to produce accurate and relevant outputs. Techniques like iterative refinement, contextual embedding, and prompt tuning are essential for achieving high-quality results in applications like natural language processing and image generation. As we continue to explore and harness the potential of generative AI and prompt engineering, the possibilities for innovation and improvement are truly endless. These technologies are paving the way for a future where AI-driven solutions not only meet but exceed our expectations. 🚀🌟 Let's connect and discuss how we can leverage generative AI and prompt engineering to drive innovation and achieve our goals. Feel free to share your thoughts and experiences! 💬 #GenerativeAI #MachineLearning #DataScience #PromptEngineering #Innovation #AI #TechTrends #openai

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🌍 Building a Common Language in AI Governance 🌐 As AI weaves into every aspect of life, understanding core terms like accountability, fairness, and safety is essential. A shared lexicon can make or break responsible development. It’s not just about setting policies—it’s about fostering dialogue among developers, business leaders, and regulators. For those interested in diving deeper, I invite you to explore the attached document. It provides a comprehensive glossary of key terms that are vital for ensuring transparency and trustworthiness in AI. #AIGovernance #EthicsInAI #Transparency #TrustworthyAI

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Big news from OpenAI 📣 ChatGPT can now access the web and provide links to relevant web sources. Relevant release article: https://lnkd.in/dXTEx9_P #AI #OpenAI #ChatGPT #LLM #ML

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See you next week in Dubai at the Data Innovation Summit MEA where I will be discussing the impact of #OpenSource Generative AI on Innovation! #OpenInnovation

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The rapid advancements in generative AI have been largely dominated by the English language, leaving a significant gap in the development of AI for non-English languages, including Indic and Arabic languages. This disparity has far-reaching implications for linguistic diversity, cultural representation, and equal access to the benefits of AI technology. In my latest article, I explore the challenges in developing generative AI for non-English languages, such as data scarcity, cultural nuance, and scalability. The article proposes approaches to improve it, including curating diverse datasets, developing multilingual models, and prioritizing underrepresented languages. Moreover, I tried emphasize the importance of ethical considerations, mitigating biases, and promoting cultural authenticity in the development of generative AI for non-English languages. Which equally applies to english as well. As we continue to push the boundaries of generative AI, it is crucial to recognize the importance of linguistic diversity and cultural representation in this transformative technology. By bridging the language gap and creating truly inclusive AI systems, we can unlock the full potential of generative AI to empower communities, foster innovation, and promote global understanding. https://lnkd.in/e_gaDHTK

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👉🏼 Investigating influencing factors of learning satisfaction in AI ChatGPT for research: University students perspective 🤓 Mohammed Abdullatif Almulla 👇🏻 https://lnkd.in/eYu7c93c 🔍 Focus on data insights: - Interaction learning and collaborative learning are pivotal in fostering ChatGPT adoption. - Information quality substantially influences researchers' decisions to continue using ChatGPT. - Perceived ease of use and perceived usefulness play intermediary roles in linking ChatGPT engagement to learning satisfaction. 💡 Main outcomes and implications: - User-friendly interfaces and perceived utility are crucial factors affecting overall satisfaction levels. - ChatGPT positively impacts learning motivation, enhancing student engagement and interest in learning. 📚 Field significance: - Educational practitioners can improve the implementation of AI technologies in university students by focusing on user-friendly design and collaborative learning. - Continued research is needed to comprehensively understand the interplay between AI-powered tools, learning objectives, and motivation. 🗄️: [#ChatGPT #learning satisfaction #AI adoption #interaction learning #collaborative learning]

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Sam Altman and his team have introduced "Pro Mode" for ChatGPT, a premium offering priced at $200 per month that includes access to the enhanced o1 Pro model. This launch underscores a significant shift—intelligence is now a scalable and accessible resource. While the total number of ChatGPT users exceeds 250 million, only about 5% currently pay for the premium subscription. From this group of 12–13 million, select users will now have access to an upgraded version of o1, debuting alongside the standard model today. The new version boasts several key enhancements: - Improved overall performance beyond the standard o1 model. - Dedicated processing capacity for solving specific tasks with greater efficiency. - Multimodal capabilities, supporting inputs like images, voice, code, and documents. - Adaptive reasoning, delivering quick answers for straightforward queries while investing more time in complex problem-solving. For more details, you can explore the o1 system card here: https://lnkd.in/erTDvFcC.

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From King Abdullah University of Science and Technology (KAUST),At my country Saudi Arabia! Introducing SkinGPT-4, an AI-powered model designed to diagnose skin diseases through image analysis. SkinGPT-4 leverages computer vision algorithms and large language models to provide preliminary diagnoses for a wide range of skin conditions, from common issues like acne to serious diseases such as melanoma. Our goal with SkinGPT-4 is to enhance access to dermatological diagnostics, especially in underserved regions lacking qualified dermatologists. It also supports busy clinics by offering initial assessments, allowing doctors to focus on complex cases and provide deeper consultations. The system's learning capabilities continuously improve through performance analysis and user feedback, integrating data to enhance diagnostic accuracy over time. Importantly, SkinGPT-4 is not a replacement for physicians but a complementary tool that enhances healthcare efficiency and empowers patients with initial informative insights about their health. #AIinHealthcare #Dermatology #MedicalInnovation #SkinGPT Details: https://lnkd.in/eUsBr5CR

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Muhammad Irfan, ranked #2 globally in AI & Machine Learning and #7 Top LinkedIn Creator in UAE by Favikon, is the visionary Founder & CEO of Xeven Solutions. With over 8 years of leadership, he transformed a three-member team into a 200+ strong enterprise delivering AI-driven solutions in healthcare. As CTO at My Florida Green and Chief Technical Advisor for The Doc App, he has pioneered innovative tech advancements. Passionate about community growth, Irfan has trained 10,000+ individuals in AI and mentored startups to success, exemplifying his commitment to technology and empowerment. hashtag #AI hashtag #MachineLearning hashtag #Leadership hashtag #HealthcareInnovation hashtag #TechEntrepreneurship #datascience #deeplearning

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