Yusuf Khan’s Post

Fall is back-to-school time for many students in the Northern Hemisphere (including my own kids!). With students back in class, here’s a look at how innovative #GenerativeAI tools are supporting their learning journey. From elementary school to higher education, AI is not just a tool — it’s a partner in learning and offers personalized support to students. Students are using AI-enabled learning accelerators like Reading Coach to practice reading, speaking, and language skills. AI-enabled educational bots built on Microsoft #Azure #OpenAI Service are also gaining popularity in classrooms and campuses around the world. Check out the blog below to learn more about how students and educators say AI is transforming the learning experience and making a difference in global education. https://lnkd.in/gPE7EwdK #msftadvocate #microsofteducation #artificialintelligence #AI Microsoft Education

I recently came across a fascinating blog on Microsoft's news platform that highlights how students are harnessing the power of AI to redefine their learning experiences. The post, "Smart Ways Students Are Using AI," is a compelling showcase of innovation at the intersection of education and technology. Key takeaway? AI isn't just about the future—it's actively reshaping the present. Students are using AI-driven tools to personalize learning, streamline research, and even co-create content, demonstrating a remarkable blend of creativity and technical acumen. For instance, AI is being utilized to generate study resources tailored to individual learning styles, making education more accessible and effective. But what truly stands out is the empowerment this technology offers. By automating repetitive tasks and providing deeper insights into complex subjects, AI allows students to focus on critical thinking and problem-solving—skills that are invaluable in today's fast-paced world. It's a perfect example of technology enhancing human potential, not replacing it. From my perspective as an Data Scientist, this evolution is more than just impressive; it’s necessary. As AI continues to advance, it’s crucial that we support and encourage its ethical and effective use in education. The next generation isn’t just learning from AI; they’re shaping it—and that’s something worth celebrating. 🎉 Kudos to the students leading the way and to the educators fostering this environment of innovation. The future of learning is here, and it’s AI-powered! https://lnkd.in/gJ8Ujb-u #AI #Education #Innovation #FutureofLearning #EdTech

🌟 Imagine a classroom where a "swarm" of intelligent agents adapts to each student's unique learning path. 🌟 In the world of #AI and education, a "Swarm Educational #Multi-Agent System" could change everything. Think of it as a digital hive, where each agent—a virtual tutor or coach—works collaboratively, providing real-time guidance, personalized feedback, and adapting to each student's pace. 🐝📚 Imagine the possibilities: ➡️ Adaptive Learning: With OpenAI-powered agents, students get explanations that fit their learning style and depth of understanding. If a topic needs more work, multiple agents could "swarm in," offering alternative explanations and extra support. ➡️ Collaborative Problem Solving: Tackling complex challenges becomes an interactive experience, with agents helping break down problems and offering strategic solutions. It's like having a group of expert peers in your pocket! ➡️ Dynamic, Personalized Content: The swarm doesn't follow a one-size-fits-all approach. It continuously analyzes progress, adjusts to students' evolving needs, and ensures that learning stays challenging and engaging. A future where AI drives individualized, interactive learning experiences may not be far off. 🚀 Could a swarm of AI tutors be the key to transforming education? OpenAI unofficial still in testing phase to make it live we at Data Automation are experimenting with it here is the link to Play around -https://lnkd.in/euKuZmnK #AI #EdTech #FutureOfLearning #PersonalizedEducation #OpenAI

Achievement Unlocked: Microsoft Learn Badges! 🌟 I'm thrilled to share that I recently completed multiple learning pathways under the Code; Without Barriers initiative by Microsoft Learn: ~Security Learning Pathways (Level 1) ~Copilot Learning Pathways (Levels 2 & 3) ~AI Learning Pathways (Levels 1, 2, & 3) These courses have deepened my understanding of security, AI, and how to harness the power of tools like Copilot to create impactful solutions. It’s incredible to see how learning platforms like Microsoft Learn empower developers and professionals to upskill effectively.♾️✨ These hands-on labs and courses have been an incredible opportunity to deepen my understanding of AI, security, and leveraging tools like Copilot effectively. A big thank you to Microsoft Learn for creating such inclusive and engaging learning experiences. #MicrosoftLearn #CodeWithoutBarriers #AI #Copilot #Security

I was part of the team at Microsoft that helped transition students to online learning during the pandemic. 🎒 If there’s one thing I learned from that is there are some serious vulnerabilities in the current education system. If students could learn and go to school online, why couldn’t they use Generative AI (and other digital tools for that matter) to enhance their education? Some fear that students will use AI to “cheat” The truth is some probably will. But that doesn’t mean we need to be afraid of AI. Students can use these tools to deepen their understanding of complex topics and enhance their problem solving skills. Yes — AI can come up with some really cool and unique ideas. But it’s up to the student to understand it, refine it, and further use the tool to expand these ideas. Just like a hammer is a really great tool to build a house. 🔨 It still needs the hand of a skilled carpenter to use it. 

We invite you to consider submitting your contribution to the 8th International Workshop & Tutorial on Interactive Adaptive Learning (IAL 2024) Co-Located With ECML-PKDD 2024 Monday, 09 September 2024 #research #interactive #machinelearning #activelearning #ECML https://lnkd.in/eJqDaNUP Interactive adaptive learning comprises methods that improve the overall life-cycle of machine learning models, including interactions with human supervisors, interactions with other processing systems, and adaptations to different forms of data that become available at different points in time.

Don't want AI to replace you? Here are 20+ FREE courses with certifications to help you upskill 👇 [Link in the comments] 1. AI for everyone by DeepLearning.AI 2. AI fundamentals in 10 hours by IBM [multiple courses] 3. GenAI and ML learning paths by Google [multiple courses] 4. AI for students by IBM [multiple courses] 5. 12 days no cost GenAI training by Google [multiple courses] 6. 18 lessons on building GenAI apps by Microsoft [multiple courses] ---- < Experiment: Used a meme to make learning more entertaining! > I hope it helped you! Help people in your network by liking and reposting this post ❤️ It inspires me to do more for the community. Follow Bhavishya Pandit to stay updated on GenAI! 🔥

🌟 Smart Ways Students Are Using AI 🌟 AI is revolutionizing education! From personalized tutoring to innovative study guides, students are leveraging AI to enhance their learning experiences. Students are using Microsoft Copilot to simplify complex texts, making studying more efficient. Students are also benefiting from AI-powered tools boosting their confidence and academic performance. "Using AI is a basic life skill now, and if you’re not using it, you’ll be at a competitive disadvantage." 📚 Discover how AI is transforming education and helping students succeed! 🚀 #AI #Education #Learning #Microsoft #Copilot

My student's AI model thought he was happy. He was actually making a disgusted face. This weekend project taught him more than a month of tutorials... The problem wasn't his code. The model had barely seen 'disgusted' faces in its training data. Here's how he learnt this through action: First 2 hours:  - Read CNN architecture basics - Watched key tutorials on emotion detection - Set up the development environment Next 2 hours: - Built a simple classifier - Tested with basic expressions - Found the 'happy vs disgusted' confusion Final hour: - Documented the class imbalance issue - Learnt about data distribution - Listed improvements needed The flaw became his best teacher. He learnt more about data bias through this fun failure than from any textbook. This is my 1:1:1 learning framework. Every 'mistake' teaches me something new. Why this approach works: - Real problems beat perfect theory - Mistakes become discoveries - Learning has a clear structure Student's message later: "Sir, fixing that 'happy' vs. 'disgusted' confusion taught me more about real-world AI challenges than all my theory classes." You don't need to feel ready. You need to start making interesting mistakes. What technical skill have you been avoiding? Share below - let's find a practical way to begin. _________ Building bridges between theory and practice in tech education. Follow Shankar J. for weekly insights on making complex tech simple. Join 1.3K+ tech learners - transforming tech education with AI & digital tools.

Interested in learning about a new 'Big' paradigm for vision foundation models?We are excited to host the 1st edition of our tutorial "Time is Precious: Self-Supervised Learning Beyond Images" with Mohammadreza Salehi and Yuki Asano on Monday 30th of September at #ECCV2024 from 09:00 to 13:00 at Amber 7+8. More information: https://lnkd.in/dq9BdkM2 We also have an exciting line of speakers - Ishan Misra, João Carreira and Emin Orhan, who will share their wonderful insights into leveraging video pretraining for different applications. Quick Summary of our tutorial: The primary goal of this tutorial is to introduce to the computer vision community, the concept of learning robust representations by leveraging the rich information in video frames. While, image-based pretraining has gained recent popularity with SimCLR, the practice of pretraining models from videos dates back much earlier. This tutorial will recapitulate both early and recent works, which have pretrained image encoders using videos for different pretext tasks such as egomotion prediction, active recognition, dense prediction etc. We also discuss practical implementation details relevant for practitioners and highlight connections to other, existing works such as VITO, TimeTuning, DoRA, V-JEPA etc. We also discuss recent works aimed to mimic human visual systems such learning from one continuous video stream and by learning from longitudinal audio-visual headcam recordings from young children, thereby putting this concept into a broader context. Key questions we aim to tackle in this tutorial include: - Can we learn strong image encoders from good quality videos (i.e. with limited data)? - Do we need synthetic augmentations? How useful are the natural augmentations in videos? - Can we learn from a continuous stream similar to humans?