Challengers Are Coming for Nvidia's Crown (14 minute read)
Nvidia's dominance in AI chips has propelled it to immense market value, largely thanks to its GPU capabilities and CUDA software ecosystem. However, competitors like AMD, Intel, Cerebras, and SambaNova are developing innovative solutions to challenge Nvidia's supremacy in AI hardware. While Nvidia's lead remains secure for now, the landscape is dynamic, with multiple players striving to carve out their own niches in the AI market.
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Jina Embeddings v3 (Hugging Face Hub)
The Jina series of embeddings are a high quality and powerful suite of models that can be used for embedding and retrieval. Its development team has released the next version of their model with improved performance and training.
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Trustworthiness of RAG Systems (30 minute read)
This work introduces a framework to evaluate the trustworthiness of Retrieval-Augmented Generation (RAG) systems across six key areas: factuality, robustness, fairness, transparency, accountability, and privacy.
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AI Comic Understanding (GitHub Repo)
The last frontier of Visual Language Models is the ability to understand and reason about comics. This project is a survey and a call for research.
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Word Llama (GitHub Repo)
A lightweight toolkit for fuzzy deduplication, reranking, and other NLP-based tasks. Optimized to run on the CPU.
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Syllable Segmentation in Speech Models (GitHub Repo)
This project enhances speech representation learning by separating syllabic structures from speaker information in self-supervised models. By fine-tuning the HuBERT model with speaker perturbation techniques, researchers improved syllable segmentation, leading to better syllabic unit organization.
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Data Pipelines are the new AI secret sauce (16 minute read)
With models being somewhat commoditized, much of the advantage in AI comes from the data. It also, by extension, comes from the pipeline that ingests and creates the data. This post discusses the challenges and opportunities associated with data pipelines in the modern age.
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Why Copilot is Making Programmers Worse at Programming (5 minute read)
AI tools like GitHub Copilot enhance programming productivity but risk eroding essential coding skills. Over-reliance on AI-generated code can lead to quality, security, and maintainability issues and reduce learning opportunities. These tools may also limit creative problem-solving and foster a false sense of expertise among developers.
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Andrew Tan & Andrew Carr
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