Francesca Lazzeri, Ph.D.

Ohio State University's AI Fluency initiative (https://lnkd.in/etB8EyMB) sets an ambitious goal: every undergraduate will graduate with practical generative AI skills by 2029. Let's unpack this: ✅ Strong Points: ▪️Bold, clearly articulated institutional vision. ▪️Multi-layered integration (Gen Ed courses, discipline-specific modules, innovation opportunities). ▪️Dedicated resources and infrastructure (faculty support via Drake Institute). ▪️Commitment to hands-on innovation (hackathons, prototyping workshops). ⚠️ Areas of Concern: ▪️Faculty workload: Realistic expectations for widespread course redesign and adoption? ▪️Standardization & Assessment: What does "AI fluency" mean in practice across diverse fields? ▪️Curriculum Overload: Risk of student pushback if mandatory modules aren't streamlined or clearly valuable. ▪️Equity and Faculty Incentives: What's the motivation for faculty, especially those skeptical or hesitant about AI integration? What if they're not on board with using AI in the classroom, or it doesn't make sense for their course? ▪️Soft Skills and Ethical Frameworks: How thoroughly are ethics, interpretability, and responsible AI practices embedded? OSU's vision is exciting, but effective execution will be key. I'm curious: What does successful "AI fluency" look like in your discipline? And how should institutions balance ambition with realistic implementation? Let's talk!

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Fine-tuning LLMs with evolution strategies (an alternative to gradient descent) has turned out to be a productive research direction! Four new papers from Cognizant AI Lab evaluate it in several new tasks including Math and ARC-AGI, extend it to fine-tuning quantized models, demonstrate how LLMs can be trained to know what they know, and show why high dimensionality of models is a blessing (not a curse) in fine-tuning. Overview blog: https://cgnz.at/6005QZNMb Papers: https://cgnz.at/6007QZN13 (tasks)     https://cgnz.at/6003QZNHN (quantized)     https://cgnz.at/6002QZNGG (metacognition)     https://cgnz.at/6000QZNyI (blessing) Animated illustration: https://lnkd.in/gUUaYVAT

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Research Paper Highlights: "When Retrieval Succeeds and Fails: Rethinking Retrieval-Augmented Generation for LLMs", By Yongjie Wang Large Language Models (LLMs) have transformed language understanding and generation, enabling numerous real-world applications. Yet because they are trained on static datasets, LLMs struggle with rapidly changing information and niche domain-specific tasks. Retrieval-Augmented Generation (RAG) emerged to bridge this gap by supplying LLMs with fresh, relevant external knowledge. As LLMs grow more capable, this paper reevaluates the importance, relevance, and future direction of RAG systems. Challenges in RAG: - Contextually irrelevant retrieval can mislead LLMs and undermine answer quality. - Dependency on external retrieval systems adds architectural complexity and latency. - Difficulty in aligning retrieved content with LLM reasoning, leading to hallucinations or mismatched answers. - Retrieval bottlenecks, especially when queries are ambiguous or poorly formed. Key Takeaways: - RAG remains highly valuable in scenarios where LLMs alone fail, especially in domains requiring up-to-date, factual, or specialized knowledge. - The field needs re-examination and innovation, as conventional RAG designs may not keep pace with rapidly evolving LLM capabilities. - A deeper understanding of RAG objectives, architecture, and limitations is essential for building the next generation of knowledge-augmented systems. - Effective RAG can significantly boost reliability, factual grounding, and domain expertise when combined with powerful LLMs. This work highlights the need to rethink the role of RAG in an era where LLMs are rapidly advancing. It stresses that while LLMs are improving, RAG remains crucial for dynamic, specialized, and fact-sensitive tasks. Further reading-https://lnkd.in/da5_QtP6

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Based on years of experience building LLM applications, Shuai Guo shares detailed insights for practitioners interested in optimizing their process before, during, and after their project launch.

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