Despite GSU's (and Turnitin's) warning not to rely on it, he was given a zero on the assignment, and the two hours he spent were wasted. Then he got a note from his professor that he got a 30% AI score on Turnitin. He literally wrote a paper in my dining room, asking me to help edit on the way, doing everything by hand because of his professor's very restrictive #AI policy (he isn't even allowed to use Grammarly or grammar check on Microsoft). This scenario, according to my students, happens often. I don't know why the universities would expose themselves to this kind of liability! I am waiting for the first wave of student lawsuits against Turnitin and the universities who allow it. The Problem With AI Policies in Higher Education Alternatively, you will be operating in permanent catch-up mode. The task of understanding generative AI cannot be delegated. I argue it is key to accelerate both fundamental, personal understanding and policy development before further integration. It's essential that leaders directly encounter the "What on earth is happening here?" moments that reveal the technology's potential and pitfalls. However, senior leadership making strategic decisions about these technologies rarely have direct experience using them.īut without hands-on experience, how can institutional leadership effectively steer institutions towards a meaningful educational future with AI? Professors, students, and staff are turning to AI for everything from writing assignments to answering questions in class. The capabilities of generative AI to create content are already making profound changes in teaching, learning, and operations. If not, perhaps you should ask them to read this piece. If you're into this field or just curious, I'd love to get your feedback.Is your top leadership team on board with your institutional AI agenda? While it's still a work in progress and not exactly the Vox style I envisioned, I genuinely hope this approach of storytelling simplifies Deep Learning for many. The result? Not only better image analysis but also a more efficient use of computing resources.Ĭoming back to the video. This ensures tailored analysis for different object sizes and details, veering away from the "one size fits all" concept. Think of it as using different magnifying glasses for various parts of a picture. In essence, DiT introduces a fascinating approach that tailors how each section of an image is processed, based on its content. The video dives into a recent paper I read - "DiT: Efficient Vision Transformers with Dynamic Token Routing." And yes, this idea had been living rent-free in my mind for way too long! □ So, I finally took the plunge and tried my hand at it. You know how Vox-style videos simplify complex topics so brilliantly? I've always dreamt of having something similar for Deep Learning topics. I have never seen the ML Community of Kolkata so vibrant, punctual, and eager.Įastern India’s largest ML Conference and rightfully so!Īlso if anyone is interested, here are the slides which me and Aritra Roy Gosthipaty presented: #keras #kerascommunitydays #gde #googledevelopergroups #tensorflowusergroup #kolkata #machinelearning Rishiraj Acharya has outdone himself, along with his co-organizer Anubhav Singh, and a team of excellent and adept volunteers who have all done a fantastic job. Through the day and the weeks that preceded one person's dedication and drive to bring this fantastic event to fruition has been clearly visible! The event not only boasts a fantastic lineup of speakers but also is one of the most thoughtfully organized and well-managed events I have been to. Keras Community Days Kolkata organised by TensorFlow User Group (TFUG) Kolkata probably ticks all these boxes and does more. What makes a tech community successful? The number of schwags at their events? The quality of food served? The quality of the talks and the technical prowess of the speakers? The curiosity and the eagerness of the audience to attend and soak the sessions?
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