Hajar Mousannif

Live from Harvard University on Thursday, April 25, 2024 at 1:30 PM – 3:30 PM EDT, a CS50 workshop on artificial intelligence (AI) via Zoom and YouTube. Open to all. Register at https://cs50.ly/register. A workshop in two parts for CS50x students, teachers, and alumni. First, a look at how CS50 has incorporated artificial intelligence (AI), including its new-and-improved rubber duck debugger, and how it has impacted the course already. 🦆 Then, a hands-on introduction to implementing your own AI-based chatbot using OpenAI's APIs, maybe a duck or even a cat! "In Summer 2023, we developed and integrated a suite of AI-based software tools into CS50 at Harvard University. These tools were initially available to approximately 70 summer students, then to thousands of students online, and finally to several hundred on campus during Fall 2023. Per the course's own policy, we encouraged students to use these course-specific tools and limited the use of commercial AI software such as ChatGPT, GitHub Copilot, and the new Bing. Our goal was to approximate a 1:1 teacher-to-student ratio through software, thereby equipping students with a pedagogically-minded subject-matter expert by their side at all times, designed to guide students toward solutions rather than offer them outright. The tools were received positively by students, who noted that they felt like they had 'a personal tutor.' Our findings suggest that integrating AI thoughtfully into educational settings enhances the learning experience by providing continuous, customized support and enabling human educators to address more complex pedagogical issues. In this paper, we detail how AI tools have augmented teaching and learning in CS50, specifically in explaining code snippets, improving code style, and accurately responding to curricular and administrative queries on the course's discussion forum. Additionally, we present our methodological approach, implementation details, and guidance for those considering using these tools or AI generally in education." Recording will be available on demand afterward. #education #community #ai #duck

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𝐌𝐮𝐬𝐭-𝐫𝐞𝐚𝐝, 𝐟𝐫𝐞𝐞 𝐛𝐨𝐨𝐤 𝐭𝐡𝐚𝐭 𝐩𝐫𝐨𝐯𝐢𝐝𝐞𝐬 𝐚 𝐩𝐚𝐭𝐡𝐰𝐚𝐲 𝐟𝐫𝐨𝐦 𝐆𝐀𝐈 𝐭𝐨 𝐀𝐆𝐈! ​ Yann LeCun has expressed skepticism about the potential of LLMs to achieve Artificial General Intelligence (AGI), citing limitations in memory, planning, and grounding in real-world understanding. However, we argue that 𝗟𝗟𝗠 𝗖𝗼𝗹𝗹𝗮𝗯𝗼𝗿𝗮𝘁𝗶𝘃𝗲 𝗜𝗻𝘁𝗲𝗹𝗹𝗶𝗴𝗲𝗻𝗰𝗲 (𝗟𝗖𝗜)—particularly with multiple multimodal LLMs working together—offers a promising architecture to address these challenges and progress toward AGI. ​ Two key architectural innovations underlie LCI: ​ 𝐒𝐞𝐩𝐚𝐫𝐚𝐭𝐢𝐨𝐧 𝐨𝐟 𝐔𝐧𝐜𝐨𝐧𝐬𝐜𝐢𝐨𝐮𝐬 𝐏𝐫𝐨𝐜𝐞𝐬𝐬𝐢𝐧𝐠 𝐚𝐧𝐝 𝐂𝐨𝐧𝐬𝐜𝐢𝐨𝐮𝐬 𝐑𝐞𝐚𝐬𝐨𝐧𝐢𝐧𝐠 𝐚𝐧𝐝 𝐏𝐥𝐚𝐧𝐧𝐢𝐧𝐠: Inspired by the human mind’s dual-layer structure, LCI divides its architecture into foundational and adaptive layers. The foundation layer, akin to unconscious processing, leverages extensive datasets to build robust pattern recognition, encoding essential responses much like human instincts. The adaptive layer, mirroring conscious thought, enables rapid adaptation and contextual reasoning. This dual-layer approach allows LLMs to perform complex tasks with foundational knowledge while adjusting to new information on the fly, much as a child learns new concepts within a developed cognitive framework. ​ 𝐃𝐢𝐬𝐭𝐢𝐧𝐜𝐭 𝐑𝐨𝐥𝐞𝐬 𝐟𝐨𝐫 𝐄𝐚𝐜𝐡 𝐋𝐋𝐌-𝐀𝐠𝐞𝐧𝐭: Each LLM-agent is assigned a specific role, such as executive (knowledge processing), legislative (behavior guardrails), and judicial (adapting to cultural norms). ​ Additionally, our validation of the LCI framework has been thoroughly tested through real-world deployment in applications across various domains, including healthcare, sales planning, investment, and debiasing news. These empirical deployments highlight the practical viability and adaptability of the LCI framework, showcasing its ability to enhance reasoning and decision-making through collaborative intelligence. ​ The eleven aphorisms presented in this free book illustrate the philosophical foundation of the LCI framework, backed by empirical studies. Through LCI, we envision a clear pathway to AGI, rooted in collaborative intelligence and flexible, context-aware adaptation. https://lnkd.in/gn74DkNj (free) https://lnkd.in/gszSvfqP ($6.98 printing fee)

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🚀 Dive into the Exciting World of Reinforcement Learning and supercharge your Research Journey! 🤖 Join us for the Season 4 Premiere Tech Talk organized by the Data Science Student Community at SLIIT. 🌐 Topic : Getting Started with Reinforcement Learning Discover the power of RL at the intersection of AI and decision-making, and unlock your golden ticket towards research success! Whether you're new to RL or seeking to refresh your knowledge, this session is your ultimate launchpad! Don't miss out – stay tuned! ✨

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📚 Book Review - 𝐌𝐀𝐒𝐓𝐄𝐑𝐈𝐍𝐆 𝐍𝐋𝐏 𝐅𝐑𝐎𝐌 𝐅𝐎𝐔𝐍𝐃𝐀𝐓𝐈𝐎𝐍𝐒 𝐓𝐎 𝐋𝐋𝐌𝐒 by Lior Gazit and Meysam Ghaffari, Ph.D. I recently read this book and found it to be an excellent resource for anyone interested in Natural Language Processing (NLP) and machine learning. 𝐊𝐞𝐲 𝐇𝐢𝐠𝐡𝐥𝐢𝐠𝐡𝐭𝐬: - 𝐂𝐨𝐦𝐩𝐫𝐞𝐡𝐞𝐧𝐬𝐢𝐯𝐞 𝐂𝐨𝐧𝐭𝐞𝐧𝐭: Covers everything from basic NLP concepts to advanced Large Language Models (LLMs). - 𝐒𝐭𝐫𝐨𝐧𝐠 𝐅𝐨𝐮𝐧𝐝𝐚𝐭𝐢𝐨𝐧𝐬: Clearly explains important math concepts like linear algebra, probability, and statistics. - 𝐏𝐫𝐚𝐜𝐭𝐢𝐜𝐚𝐥 𝐄𝐱𝐚𝐦𝐩𝐥𝐞𝐬: Includes Python code to help readers apply what they learn. - 𝐑𝐞𝐚𝐥-𝐖𝐨𝐫𝐥𝐝 𝐀𝐩𝐩𝐥𝐢𝐜𝐚𝐭𝐢𝐨𝐧𝐬: Shows how to solve real business problems using NLP. - 𝐄𝐱𝐩𝐞𝐫𝐭 𝐎𝐩𝐢𝐧𝐢𝐨𝐧𝐬: Features insights from leading industry experts. Accessible for Beginners: Great for those with basic Python and machine learning knowledge. Whether you're new to NLP or looking to deepen your understanding, this book is a must-read. Kudos to authors for this outstanding contribution! #NLP #LLM #DeepLearning #MachineLearning

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𝐀 𝐭𝐞𝐚𝐜𝐡𝐞𝐫 𝐦𝐮𝐬𝐭 𝐡𝐞𝐥𝐩 𝐚 𝐬𝐭𝐮𝐝𝐞𝐧𝐭 𝐫𝐞𝐚𝐜𝐡 𝐭𝐡𝐞 𝐞𝐬𝐜𝐚𝐩𝐞 𝐯𝐞𝐥𝐨𝐜𝐢𝐭𝐲.  Then, the students have the skills to explore themselves, freely. This ensures that the seed of creativity, and curiosity is sown well. This is my personal opinion. Do share in the comments, what you think. #teaching #statistics #machinelearning #datascience #deeplearning

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A new paper "Leveraging Auto-Distillation and Generative Self-Supervised Learning in Residual Graph Transformers for Enhanced Recommender Systems," has been accepted at the 13th International Conference on Computational Data and Social Networks (CSoNet 2024)! CESI #LINEACT #AI #SelfSupervisedLearning #RecommenderSystems #GraphTransformers #Research #CSoNet2024

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In this video, I dive deep into using loops and conditional statements (if statements) to streamline your workflow and add dynamic content to your LaTeX documents. Whether you're a seasoned LaTeX user or just getting started, this video will equip you with the skills to: • Automatically generate numbered lists and figures • Include figures and captions based on conditions • Create dynamic graphics using loops and TikZ Check out the video here: https://lnkd.in/gc-u8TFp #latex #loops #conditionals #automation #productivity #scientificwriting #research #phdlife #engineering #mathematics

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Mistakes in math models aren't failures but opportunities for deeper learning. Mfon Akpan, CGMA shows how slight discrepancies prompt critical thinking and double-checking, enriching the educational process. Students must analyze, cross-check, and question their results, leading to a more thorough understanding. Ever found a minor error that made you rethink everything? How did it impact your approach? Let's discuss! #AI #gamification #GameInspired #GameThinking #seriousgames #gbl #games4ed #education #ProfessorGame #podcast

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CNN Surgery : exploring and experience to build CNN from scratch and trying to see what is happening behind the neurons, with gradients. #ai #artificial_intelligence #deep_learning #machine_learning #cnn #convolution_neural_networks

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I’m pleased to share my Algerian Darija Text Dataset. This dataset contains more then 2k rows of raw text collected from YouTube comments and other online sources. It’s designed for text generation tasks and serves as a starting point for projects involving Algerian Darija. 🧹 Cleaned Data: Initial cleaning steps included removing duplicate characters, URLs, emails, and phone numbers. 🌐 Sources: Existing Hugging Face Data sets , web scraping, and YouTube (note: some transcripts are imperfect due to speech-to-text limitations with Algerian Darija). ⚠️ Note: While this dataset is a great starting point, it still requires further cleaning to improve its quality for more advanced NLP tasks. Looking ahead, I’ll be releasing a series of LLMs for Algerian Darija soon. Stay tuned for more updates! Feel free to explore the dataset for your NLP projects and research! https://lnkd.in/eskaHJRp #AI #NLP #DeepLearning #llms #genai #nlp #lora #RNN #LSTM #moe #moa #lvms #lmms

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🌌 Exploring the Geometric Trinity of Gravity: A Critical Analysis 🌌 In the latest academic discourse, Alexey Golovnev from the Centre for Theoretical Physics at The British University in Egypt presents a thought-provoking paper titled "Is there any Trinity of Gravity, to start with?" This work delves into the intricate relationship between gravity's geometric representations—curvature, torsion, and nonmetricity—challenging prevalent assumptions in modern theoretical physics. 🔍 Key Insights: - The paper critiques the notion that alternative geometric frameworks for gravity merely offer different languages to describe a singular physical reality. Golovnev argues that while torsion and nonmetricity can introduce new mathematical structures, they do not alter fundamental physical predictions. - He emphasizes that General Relativity (GR) fundamentally relies on a metric structure. Any deviation from this standard framework introduces complexities without providing observable benefits. - Golovnev's analysis extends to teleparallel models of gravity, which propose new connections on spacetime manifolds. He argues these models often complicate rather than clarify our understanding of gravitational phenomena. 📚 Contributions to Ongoing Discussions: This paper is part of an ongoing dialogue in theoretical physics regarding how best to conceptualize and model gravitational interactions. By scrutinizing alternative geometrical frameworks against GR's established principles, it invites physicists and mathematicians alike to rethink their approaches. 💡 Conclusion: Golovnev concludes that while exploring different geometrical interpretations is valuable for theoretical exploration, it is crucial not to lose sight of observable phenomena dictated by GR’s pseudo-Riemannian geometry. His work serves as a reminder that simplicity often holds greater explanatory power than complexity laden with unobservable constructs. 🔗 Join the conversation about this pivotal topic in gravitational theory! #AI #Algorithms #ArtificialIntelligence #DL #DS #DataScience #DeepLearning #GeneralRelativity #Geometry #Gravity #ML #MachineLearning #PhysicsCommunity #ResearchInsights #Tech #Technology #TheoreticalPhysics Source: https://lnkd.in/eRk6qKuZ

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Physics-Based Deep Learning: Insights into Physics-Informed Neural Networks (PINNs) https://lnkd.in/gxZSTmvS

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Tiny-ViT: Revolutionizing Computer Vision I'm pleased to introduce our newest endeavor, Tiny-ViT, transformers in computer vision! in other words the end of CNN's era!? I had the honor to lead this project: Pests and diseases in MDS - Moroccan Data Scientists where we have implemented this model from scratch for pests and diseases detection. Special thanks to our outstanding collaborators who have contributed immensely to this achievement: Asmae El-Ghezzaz IDRISS EL HOUARI Farheen Akhter Aicha Dahrouch Here is the link to the repository, feel free to check it out and provide us with your feedback: https://lnkd.in/eTrhGs_Q #TinyViT #ComputerVision #AIResearch #ArtificialIntelligence #DeepLearning #MachineLearning #ImageRecognition

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Relase: 𝐒𝐢𝐧𝐚𝐓𝐨𝐨𝐥𝐬 - 𝐎𝐩𝐞𝐧 𝐒𝐨𝐮𝐫𝐜𝐞 𝐓𝐨𝐨𝐥𝐤𝐢𝐭 𝐟𝐨𝐫 𝐀𝐫𝐚𝐛𝐢𝐜 𝐍𝐋𝐏 𝐚𝐧𝐝 𝐍𝐋𝐔 يسعدنا إطلاق مجموعة أدوات ذكاء اصطناعي مفتوحة المصدر لفهم ومعالجة اللغة العربية Python APIs, command lines, online demos, and many datasets and language models. SinaTools outperforms all related tools in speed and accuracy, in all tasks: ▸ 𝗠𝗼𝗿𝗽𝗵𝗼𝗹𝗼𝗴𝘆 𝗧𝗮𝗴𝗴𝗲𝗿: Lemmatizer, POS tagger, root tagger. ▸ 𝗪𝗦𝗗 𝗧𝗮𝗴𝗴𝗲𝗿: Pipeline of semantic taggers: single-word WSD, multi-word WSD, and NER ▸ 𝗦𝘆𝗻𝗼𝗻𝘆𝗺𝘀 𝗚𝗲𝗻𝗲𝗿𝗮𝘁𝗼𝗿: Extends a set of synonyms with more synonyms. ▸ 𝗦𝗲𝗺𝗮𝗻𝘁𝗶𝗰 𝗥𝗲𝗹𝗮𝘁𝗲𝗱𝗻𝗲𝘀𝘀: Association between two sentences across various dimensions, meaning, underlying concepts, domain-specificity, etc. ▸ 𝗡𝗮𝗺𝗲𝗱 𝗘𝗻𝘁𝗶𝘁𝘆 𝗥𝗲𝗰𝗼𝗴𝗻𝗶𝘁𝗶𝗼𝗻: Nested and flat NER, 21 entity types. ▸ 𝗥𝗲𝗹𝗮𝘁𝗶𝗼𝗻 𝗘𝘅𝘁𝗿𝗮𝗰𝘁𝗶𝗼𝗻: Extract events and their arguments (agents, locations, and dates). ▸ 𝗗𝗶𝗮𝗰𝗿𝗶𝘁𝗶𝗰-𝗕𝗮𝘀𝗲𝗱 𝗠𝗮𝘁𝗰𝗵𝗶𝗻𝗴: Decides whether two Arabic words are the same taking into account diacratization compatibility. ▸ 𝗨𝘁𝗶𝗹𝗶𝘁𝗶𝗲𝘀: A set of useful NLP methods for sentence splitting, duplicate word removal, Arabic Jaccard similarity metrics, transliteration, and others. Try and Download: https://lnkd.in/dznzKb5b Birzeit University Birzeit University Alumni #arabic #arabicnlp #NLProc #arabicontology #LinguisticSearchIngine #lexical #Lexical_recourse #Sinalab #birzeit #Birzeit University | جامعة بيرزيت # #ArtificialIntelligence #ArtificialIntelligence #machinelearning #datascience

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M. Claude MADOUMA (Meilltic) et M. Hamid Zemirline. 1. #ArtificialIntelligence 2. #AI 3. #MachineLearning 4. #DeepLearning 5. #DataScience 6. #BigData 7. #AIResearch 8. #AIDevelopment 9. #NLP (Natural Language Processing) 10. #ComputerVision 11. #Robotics 12. #Automation 13. #AIethics 14. #AIinHealthcare 15. #AIinFinance 16. #AIinMarketing 17. #AITrends 18. #AItechnology 19. #AIapplications 20. #AIinnovation 21. #AIstartups 22. #AITech 23. #AIforGood 24. #AIengineer 25. #AICommunity 26. #AIprojects 27. #AIstrategy 28. #AItools 29. #AIjobs 30. #AIecosystem

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Introduction To R Programming Language https://lnkd.in/gQ3cwmP3

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Molmo is a family of open vision-language models developed by the Allen Institute for AI. Molmo 7B-D is based on the Qwen2-7B model and uses the OpenAI CLIP vision backbone. It performs well on both academic benchmarks and human evaluation, falling between GPT-4V and GPT-4o in capability. Molmo models are trained on PixMo, a dataset of 1 million, highly-curated image-text pairs. It has state-of-the-art performance among multimodal models with a similar size while being fully open-source. We’ve just published a step-by-step guide on deploying Molmo 7B-D-0924 in the NodeShift GPU Virtual Machine! ✔Read more here: [https://lnkd.in/gRZ9XXpT] #Molmo #Aimodels #Opensource #GPUs #Cloud

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