Michael Pruitt

New: Kyvos Insights' Dharmendra Singh Chouhan offers commentary on #SemanticIntelligence and how it is the missing layer in scalable #AI and #BISystems: This article originally appeared in InsightJam.com, an enterprise IT community that enables human conversation on AI. https://lnkd.in/ei2stbxx

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AI is not a singular technology, nor is the compute that powers it. Silicon diversity is not only key for supply chain resiliency but also efficiency. To be profitable, AI must be delivered at scale for the least amount of energy consumed. Large-scale GPU clusters are not always the best answer. CPUs have always been the workhorses of enterprise IT, and now they are also the engines of hyper-efficient AI inference. AMD CPUs are not a commodity. They are the global computing gold standard, ranking #1 in 19 out of 20 categories on Amazon. Read more on TechSpot: https://lnkd.in/gEJ__CWj #Ryzen Dan McNamara Priya Vasudevan Curt Waltman Robert Hoffman Sam Naffziger Andrej Zdravkovic Kumaran Siva Suresh Andani

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Large language models process emotion words as semantic patterns, not emotional experiences. New research shows that when an LLM outputs "I am frustrated," it's activating the same computational pathways as "The sky is blue." Both are pattern completions based on training data. No actual frustration exists. This matters more than most realize. We anthropomorphize AI responses because emotion concepts feel authentic when rendered in natural language. But these models lack the neurochemical substrate that generates actual emotions in humans. The practical impact? LLMs can simulate emotional intelligence without experiencing it. They predict appropriate emotional responses by pattern matching, not feeling. #AI #LLM #EmotionalAI

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📄 AI for documents just got smarter and leaner. dots.ocr is a lightweight yet powerful model that can parse contracts, reports, slides, and multilingual documents with industry-leading accuracy. ✅ Outperforms large AI models at a fraction of the size ✅ Faster, cheaper deployment for enterprise-scale OCR ✅ Works across 100+ languages with strong benchmarking results Have to tip the hat to the Chinese Labs and continued open source excellence. 🔗 Check it out: https://lnkd.in/gZyNAYgc

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The Titans architecture and the MIRAS framework represent a significant step in AI development by addressing the long-standing challenge of long-term memory and context handling in large language models. Here are five key points highlighting their importance: Enables Extreme Long-Context Processing: Titans allows models to effectively scale to context window sizes larger than 2 million tokens, surpassing the limitations of traditional Transformers, whose computational cost is too high for such massive inputs (like full documents or genomic data). Introduces Adaptable, Selective Long-Term Memory: Titans features a novel neural memory module that learns to memorize at test time (as data streams in). It uses a "surprise metric" (a measure based on the input gradient) to selectively prioritize and store only the most unexpected and important information, ensuring efficiency and high retention of critical context. Achieves High Accuracy with Linear Speed: By combining a deep neural network memory with a fast update mechanism, the architectures achieve accuracy and reasoning performance comparable to or better than large Transformer models (like GPT-4) on long-context tasks, while maintaining the fast, linear inference speed characteristic of recurrent models. MIRAS Unifies Sequence Modeling Theory: MIRAS (Memory, Attentional bias, Retention, and Algorithm for Sequence modeling) is a general theoretical framework that unifies diverse architectures (Transformers, RNNs, Titans) by viewing them as different configurations of an associative memory module. This unification provides a blueprint for systematically designing the next generation of efficient models. Moves Beyond Standard Optimization Paradigms: MIRAS allows researchers to explore objectives beyond the common mean squared error ($l_{2}$ regression) and dot-product similarity used in most existing models. This shift opens the door to creating new, more robust architectures with customized attentional biases and retention mechanisms (forgetting gates) that are less sensitive to data outliers https://lnkd.in/gKaSHmCB

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