Robert Bates

Expand your knowledge of interpretable-AI approaches by following along Conor O'Sullivan's detailed exploration of LIME, "the OG of XAI methods."

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Anthropic has released Claude 3.5 Sonnet, featuring a 200k context window and advanced reasoning capabilities. This model is generating interest for its potential to compete with leading AI models, including OpenAI. Learn more ⬇️ https://bit.ly/3VFvhu7

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This AI Paper from aiXplain Introduces Bel Esprit: A Multi-Agent Framework for Building Accurate and Adaptive AI Model Pipelines https://lnkd.in/gJPdEiAb Follow, like, and share. Provided by #NathanWildman.

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What's the difference between model explainability, interpretability, and observability? Jason Zhong's debut TDS article outlines the nuanced distinctions you should be aware of.

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Comprehensive Guide to Filtering in Qdrant Filtering is a crucial feature in vector databases like Qdrant, allowing users to refine search results based on specific criteria. This guide will explore how filtering works in Qdrant, its implementation, and best practices for optimal performance. https://lnkd.in/d_ZRB8_7

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What does it take to implement a RAG system effectively? Abhinav Kimothi's deep dive outlines the nitty-gritty details of RAGOps, an emerging discipline among LLM practitioners.

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Was ending the Supply and Confidence Agreement a smart move or a risky gamble by the NDP? Syntax Principal George Soule analyzes the timing of the decision and what it means for both the Liberals and the NDP in his latest op-ed in The Hill Times. Read here: https://lnkd.in/g8Pth5Ns

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A Primer on the Inner Workings of Transformer-based Language Models Ferrando et al.: https://lnkd.in/gSebXJYT #ArtificialIntelligence #DeepLearning #MachineLearning

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For his debut TDS article, Jeremi Nuer presents a thoughtful walkthrough of a recent paper on model interpretability: "We want to have finer-tuned control over LLMs. Being able to detect when certain features are active, and tune how active they are in the middle of generating output, is an amazing tool to have in our toolbox."

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#AzureMachineLearning is comprised of several key components that all come together to facilitate the machine learning process and Alexander Walters explains the processes in our blog article here -> https://lnkd.in/gpDxWyaa

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🎓 From the minds behind NumPy & Anaconda: Travis Oliphant, OpenTeams' President, & Anaconda's Peter Wang share consulting strategies that scale in #OpenSource. Watch the full recording! 👀 https://lnkd.in/d7GXkkWZ #TechEntrepeneurship #OpenSourceBusiness

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Established metrics for assessing the syntactic accuracy of automatic speech recognition models miss a critical aspect of performance: comprehensibility. Learn how we use an #LLM to determine if a transcript accurately captures the intended meaning → https://goo.gle/3zJ8rdu

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GenAI iteration speed is growing exponentially. One outcome is that the context window — the number of tokens a large language model (LLM) can use at one time to generate a response — is also expanding rapidly. https://lnkd.in/dr5njXhM #Database #AI #LargeLanguageModels by Usama Jamil thanks to MyScale

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Elliptic curves are central to many areas of math, including cryptography and number theory. “Rational points” — coordinates on the curve that are rational numbers — pop up in patterns. Knowing one rational point reveals others. https://lnkd.in/epyfbFTJ

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