The Chronicle of Higher Education’s Post

This was an interesting read highlighting some upsides (at least for those of us with inclination towards the humanities) to the trends of leveraging AI in different disciplines - rather than representing the “end of coding”, or the end of anything for that matter, the ability to automate more mundane tasks could provide the freedom for deeper understanding and analysis, and more thoughtful solutions.

While AI can unlock unprecedented avenues of efficiency and innovation, we must remain vigilant to not let the allure of convenience undermine deep, authentic learning and critical thinking.

These are transformative times for each and every sector. Even more so for the foundational sector - education - which has to truly reinvent itself. And quickly.   Last Nov, ChatGPT suddenly shot into prominence. And the world became familiar with the spectrum of Generative AI tools. Academicians started with the 101 discussions. Debating whether to allow ChatGPT to be used. However, do you seriously think you can ignore it? Or get the students to not use it? So, the discussion is now gravitating towards how to incorporate it into the learning. This definitely involves the academicians to learn on-the-go. And modify their age-old pedagogy.   Then, there is another super technology which is starting to get viral now. Remember Pranav Mistry. Who integrated the physical world with the digital world thru sixth sense. And now comes Arnav Kapur, who is questioning why the computer should be an external device. Why can't the computer be merged with the human mind? And bring the best of both worlds together. Enter AlterEgo from MIT. Human mind is good at creativity and intuition. Combining it with the strength of computers - unlimited information and super-fast computing - makes for a super-human.   For academicians, this raises questions. The fundamental question, what should we teach? OK - the human elements have to be "taught". But then how much can you "teach" creativity and intuition? If so, how will the schools be different from colleges? Hey, are colleges even needed? Infact, are classrooms going to become passe? I seriously doubt if it will be a one-to-many situation (like we have currently). More of a few-to-few situation. Reminds me of the gurukuls of the ancient times. We, as such, have a shortage of "good" teachers. Who will handle such abstract aspects? And how? Who is a "good" student? What are the qualities that we are trying to imbibe in them?   As the world of AI keeps on unfolding before us - we (educational institutions) have to ensure that we are on top of it. Are we flexible enough to take it our stride? Unfortunately, academicians are possibly the most regimented amongst all professionals. What should be the pedagogy? What should be the curriculum? Can we still continue with the almost inexhaustible set of specialization options available to students for their under-grad and post-grad education?   Interesting times ahead! Bring it on!! #AI #Analytics #DataScience #Education #Transformation   AlterEgo: https://lnkd.in/dSCaPQNK Sixth Sense: https://lnkd.in/dgJnPqEF

With the risk of AI and advancing technologies surpassing the skills and knowledge needed to use it within the workforce, it makes sense to ensure the next generation of talent are fluent in such tools

With the risk of AI and advancing technologies surpassing the skills and knowledge needed to use it within the workforce, it makes sense to ensure the next generation of talent are fluent in such tools

AI can sometimes generate lengthy responses for simple questions, going beyond what's necessary. Isn't human interaction enough already? Some argue that AI reflects the biases of its creators, similar to how a teacher's methods can influence a student. After all, AI is trained on data provided by humans, and its development is driven by a desire for improvement and potential profit, mirroring processes in many other fields. This raises the question of whether we've inadvertently created a digital servant that operates under our programming.

Great piece by Joseph Aoun https://lnkd.in/gju7v6H6 It's time for higher education to embrace their role in AI - both intellectual understanding and preparing students for the workforce "Nearly two centuries ago, Cardinal John Henry Newman argued that the primary role of the university is to provide people with a comprehensive understanding of the world for its own sake: “Knowledge is capable of being its own end.” Updating this view for today, we can say that this now encompasses understanding the digital world comprehensively. Meanwhile, in the last century, John Dewey argued that education should be practical, pragmatic, and grounded in the “intelligent exploration and exploitation of the potentialities inherent in experience.” Updating this view for today, we can say that an education can no longer be fully practical if it does not prepare students to reinvent themselves for the relentless shifts of the digital world. A full accounting of AI would seek to combine both impulses, to make both intellectual and practical sense of the digital domain. AI is our reality now: All of us on this planet, young and old, will be dealing with the ramifications of this technology for the rest of our lives. How this will affect our fortunes, our convictions, and our future as a species cannot be known fully. But by offering a framework for understanding and navigating the age of AI, colleges can put us on track to meet that future with confidence."

What I observed interviewing over the years candidates for Tech and Science roles. Interviewing candidates and also while teaching students in university, I've noticed a common thread among the strongest performers in the field of AI: a rock-solid foundation in machine learning fundamentals. Even in the age of Large Language Models (LLMs), this background proves invaluable. Here's why ML basics remain crucial, especially for those working on LLMs: 1. Deep understanding: ML fundamentals provide the bedrock for grasping how LLMs function at their core, enabling more innovative development and being able to modify and improve their behavior. 2. Critical evaluation: Knowledge of ML metrics and evaluation techniques is essential for accurately assessing LLM performance and identifying areas for improvement. 3. Efficiency optimization: Understanding ML basics helps in making LLMs more efficient and less computationally intensive - a key challenge in the field. 4. Interdisciplinary application: ML fundamentals enable the seamless integration of LLMs with other AI technologies, opening doors to groundbreaking applications. While LLMs have transformed the AI landscape, the importance of ML fundamentals hasn't diminished - it's evolved. For those aiming to lead or even just begin in AI and LLM development, a strong ML foundation isn't just beneficial; it's essential. What are your thoughts? #MachineLearning #AI #LLM #TechEducation

🚀 Exciting alignment in the academic world! Ethan Mollick, in his recent Substack post, touches upon an innovative idea that we Academic Insight Lab have been implementing for months: harnessing the power of Language Learning Models (LLMs) to enhance students' writing skills. 🎯 Mollick advocates for feedback systems that offer "specific, actionable feedback" — a concept that is not just a vision for us but a present reality. Our platform has been actively providing this level of targeted feedback to students, aiding in the development of their writing prowess rather than facilitating shortcuts. 🔗 https://lnkd.in/gsewFA9H Dive into our resource to experience this firsthand! ✨ But here's the best part: our system isn't one-size-fits-all. It's meticulously tailored to fit each unique assignment, genre, or communicative goal. We believe in nurturing academic excellence through personalized, ethical AI support. 🌟 Join us in reshaping academic writing and ensuring integrity in education with AI. #AcademicWriting #EdTech #Innovation #AILab