Brendan Burns

In the spirit of sharing more code, I have posted an example of computer generated hologram rendering using C++. This computation expensive process sees the RGB light/wavefront contribution of every point in a scene on every pixel to construct holographic interference patterns. This example loads a point cloud as a source model and generates rows of binary float images. I have not yet verified the results with a true holographic projection system. The github repo is https://lnkd.in/gPkjZZy6.

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LLMs and Optimization: An Unbeatable Combo!

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When *open* isn't *open source* 🙋🏼‍♂️👀 Reflecting on the open-source landscape for Large Language Models, I explore the essentials: permissive licensing, transparency in training data and model architecture & more #LLM #LLMs #AI #GenerativeAI #opensource https://lnkd.in/gMxbN4Nb

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Test time training on LLMs to improve the accuracy of answers: https://lnkd.in/gWYeDFBu

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I had a talk at LDBC's TUC meeting last weekend, the main topic is around our recent studies around GPU vs. CPU on graph algorithm performance, system cost, and energy consumption... there are some "disturbing" findings that CPU-centric architecture can be 10X faster than GPU-centric arch... meanwhile, being 70-75% cheaper and greener... #Ultipa #XAI #GraphAnalytics

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Earlier this week, Armand Ruiz kicked off a brilliant thread on small language models. Today, Microsoft announced Phi-3-vision (a 4.3B parameter multimodal model) and Phi-Silica (3.3B parameters). Phi-Silica will ship with every new Copilot+ PC and is optimized for the NPUs with 650 token/s processing. Learn more here - https://lnkd.in/eUp6fAS5

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Do LLMs really have "Emergent Properties"? That one single feature that is used to market and over-hype LLMs is actually a big debate topic! In this length medium article “A Sanity Check on Emergent Properties in Large Language Models” the author, Anna Roger, critically examines the often-cited phenomenon of emergent properties in LLMs. She highlights the lack of clarity surrounding what constitutes “emergence” and presents empirical data that challenges its validity. One of the most striking findings is that many NLP researchers are skeptical of emergent properties themselves—only 39% believe they exist, yet 70% think that most of their peers believe in them, reflecting a sociological misconception in the field. Furthermore, the article argues that what is often called emergence could be an artifact of in-context learning or data contamination, suggesting that these properties are not as “unexpected” as believed. Perosnally, I think that while LLMs undoubtedly show impressive capabilities, we should remain cautious in ascribing phenomena like “emergence” to them without more rigorous definitions and investigations, especially with what this promise brings to business stakeholders and how it sets the expectations of what LLMs can do.. if overdone, it may put us again into another AI winter. Article Link : https://lnkd.in/eKQCdcN2

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Google brought Hugging Face Hub to its Vertex AI Model Garden! 🔥🔥 You read that right 😆 In this short video, learn how to 1-click deploy over 4,000 open models in Vertex AI and Google Kubernetes Engine, and how to bring your own model from Hugging Face to this experience. Learn more here: https://lnkd.in/e8yiBuzQ

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I recently stumbled upon this incredible YouTube playlist that dives deep into the world of databases, and let me tell you, it's an absolute gem! 💎 From the fundamentals to advanced concepts, this playlist covers it all with crystal-clear explanations and real-world examples. https://lnkd.in/grjnqSKz

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Watch my new tutorial on: Kube-green🌱☸️is a Cloud Native Computing Foundation (CNCF) Landscape project contributing by increasing #sustainability in our clusters & reducing CO2 footprints in workloads. - It helps by making your pods go to sleep or suspends them when no one is using them, thus scale down your clusters to save energy and reduce CO2 emissions. Shoutout to Mia-Platform for supporting this awesome project by Davide Bianchi 🔥& CNCF TAG Environmental Sustainability for supporting sustainability🌱 projects by spreading awareness amongst people. @The Linux Foundation https://lnkd.in/gRzkvTWp

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Innovation never stops… our journey towards even faster and larger AI superclusters continues with GB200 :) . Coming really really soon to an OCI AI Supercluster near you ;).

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New use cases for LLMs are emerging every day, and a recent paper from MIT highlights an exciting application in anomaly detection. The researchers demonstrated how LLM models can effectively detect anomalies in time series data, eliminating the need for traditional deep learning and extensive ML training. This approach can be particularly valuable in the security space, where we can apply similar techniques to analyze time series data to enhanced security monitoring.

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Using generative AI, NVIDIA operations built an AI Planner agent, developed on NVIDIA Inference Microservices (NIM). The agent leverages LLM, NeMo Retriever and CuOpt NIM to reduce re-planning time from hours to just seconds. https://nvda.ws/4bIfS2T

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AWS announces the integration of Amazon Redshift with Amazon Bedrock, a fully managed service offering high-performing foundation models (FMs) making it simpler, and faster for you to build generative AI applications. This integration enables you to leverage large language models (LLMs) from simple SQL commands alongside your data in Amazon Redshift.

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With NVIDIA AI Foundry, enterprises and nations can supercharge #generativeAI with a service spanning curation, synthetic data generation, fine-tuning, retrieval, guardrails, and evaluation to deploy custom AI at Meta Llama 3.1 NVIDIA NIM Microservices.

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Exciting news for customers: Fine-tuning for Anthropic’s Claude 3 Haiku model is now available in Amazon Bedrock—currently the only managed service to offer this capability. This means our customers can now customize Claude 3 Haiku with their own data to build GenAI applications tailored to their specific needs, while keeping their data secure. Since we launched the preview a few months ago, we've already seen some great use cases. One of our customers, Robin AI, a legal AI company, is using this feature to optimize legal workflows and help lawyers speed up contract processes. With a fine-tuned Claude 3 Haiku in Bedrock, they are helping lawyers finish contract review processes 90% faster. Will be great to see how other customers use this new capability. https://lnkd.in/g2jU8Kfv

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