Steve Dubois

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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We just released a blog on whether the use of multimodal feature transfer can detect deception (phenomena like sarcasm, irony, and condescension) in conversation. The short answer... Yes. This particular write-up profiles a subset of our work at Symbl.ai on multimodal large language models, and is research led by one of our summer interns, Benjamin R. (from Prof. Larry Heck's AVA Lab at Georgia Institute of Technology). We have a paper currently under submission on this work, and we can't wait to share the full details with all of you soon. Blog Link: https://lnkd.in/g6m_wFDi #multimodal #featuretransfer #LLMs #deception #conversation #sarcasm #irony #language #AI #ML #NLP #LargeLanguageModels #SummerInternship #ResearchPapers #ACL #Llama2 #SymblDotAI

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In his new blog, Kyle Garcia details the use of Large Language Models (LLMs) vs. Traditional Machine Learning for text summarization. His blog delivers a side-by-side comparison, defining some of the common terms in the space and offerings recommendations on the right approach for your use cases. https://lnkd.in/e8HQbc6K #artificialintelligence #semanticlayer #machinelearning

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The idea of agents has been around for a while. What’s new here? In his talk, Andrew Ng explains agentic systems that use design patterns such as reflection, tool use, planning, and multi-agent collaboration to outperform the results of zero-shot prompting. #llms #aiagent #ml #rlhf #mlops #datascience #llmops https://lnkd.in/d2Q8Yj7X

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🚀 Claude 3.5 Sonnet: Better than GPT-4o? Exciting news in AI! Anthropic just launched Claude 3.5 Sonnet. Here's what you need to know: 🧠 Smarter: Outperforms competitors in graduate-level reasoning, undergrad knowledge, and coding ⚡ Faster: 2x the speed of Claude 3 Opus 👨‍💻 Coding Pro: Solved 64% of problems in internal tests (vs. 38% for Claude 3 Opus) 👁️ Vision Expert: Excels at interpreting charts, graphs, and imperfect images 💼 Accessible: Free on Claude.ai and iOS app; available via API for businesses 🛡️ Safety-Focused: Rigorously tested with external expert input What could Claude 3.5 Sonnet do for your industry? Let's discuss! #technology #ai #artificialintelligence #claude https://lnkd.in/dNmB6pWt

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Anthropic's new methods for contextual retrieval are reshaping how we interact with documents. By parsing complex data structures and using advanced ranking mechanisms, they’re improving the quality of information retrieved. Learn more from Umair Ali Khan now. #GenAI #LLM

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Webinar alert - April 30, 1PM EDT. George Anadiotis from LinkedDataOrchestration and George Demarest of #Aerospike will guide you through selecting the right graph database and essential selection criteria tailored to your organizational needs. https://lnkd.in/eDV-QQuB #GraphDatabase #DataManagement

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Can you use #llms for every machine learning task? Members of MIT Data-to-AI Lab Kalyan Veeramachaneni and Sarah Alnegheimish tried #llms for a well known task of unsupervised time series anomaly detection in their paper. In an article in VentureBeat they explore the affordances that #llms provide and how to make most of them. Article: https://lnkd.in/emvEDDkg Research paper: https://lnkd.in/ezNzHkBj

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LongRoPE is making it possible to extend language model context windows, including for the Microsoft Phi-3 family of SLMs, while maintaining performance. Learn about the work, featured at #ICML2024, with podcast guest and Senior Researcher Li Lyna Zhang. https://msft.it/6043lbpeH

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In this podcast, we speak with Mosaic ML's Hagay Lupesko about Mixture of Experts and their new DBRX LLM. #DataScience #AI #ArtificialIntelligence https://hubs.li/Q02y7pD00

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In this podcast, we speak with Mosaic ML's Hagay Lupesko about Mixture of Experts and their new DBRX LLM. #DataScience #AI #ArtificialIntelligence https://hubs.li/Q02ywxGY0

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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/gSJuK_J4

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🚀 Tackling #MachineLearning challenges! Learn how Phillies are using Airflow to deploy, retrain, and monitor ML models, empowering teams to focus on baseball research. ⚾ Join Mike Hirsch & Sophie Keith at #AirflowSummit Register at: https://airflowsummit.org/ Apache Airflow #AirflowSummit #Airflow #ApacheAirflow #AirflowSummit2024 #dataorchestration #batchprocessing #dataprocessing #datapipelines #data #datasience #opensource

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Introducing YOCO: A Breakthrough in Language Model Architecture 🚀 Excited to share insights from the recent paper, "You Only Cache Once: Decoder-Decoder Architectures for Language Models." YOCO is set to revolutionize how we use GPUs by significantly reducing memory demands without sacrificing global attention capabilities. Key Highlights: - Efficient Memory Use: Encodes global key-value caches that drastically cut down GPU memory usage. - Enhanced Performance: Improves inference memory, prefill latency, and throughput dramatically, regardless of context length or model size. This architecture could be a game-changer for developers and researchers looking to optimize performance while managing resource constraints. Curious to learn more? Dive into the full details here: https://lnkd.in/gPjvvTx7 #AI #MachineLearning #LanguageModels #TechInnovation

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JHFE Board Member, Ann Kowal Smith, has revelations about Generative AI that clarifies an unwieldy topic into a practical way of thinking about this new tool. Her main advice -- "to start thinking of generative AI as an eager colleague, one with a vast array of information at their fingertips, but who needs your help - your human expertise and insight - to turn that information into knowledge... Success requires you to craft good questions, evaluate its responses with a critical eye and remain the human in the loop." @annkowalsmith Read her latest well-crafted piece published in Forbes to learn more. https://lnkd.in/gKsCQ36Y #JHFEBoardMember #annkowalsmith #GenerativeAI #newtool #humanintheloop

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A Survey of RAG and RAU: Advancing Natural Language Processing with Retrieval-Augmented Language Models Quick read: https://lnkd.in/gnGqk44W Paper: https://lnkd.in/g8-VftbX #llms #largelanguagemodels #ai

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Join Gihan Liyanage TODAY as he explores the intersection of Retrieval-Augmented Generation (RAG) and knowledge graphs, and how combining them with agentic approaches enhances the capabilities of Large Language Models (LLMs). We’ll dive into how knowledge graphs provide structured context to LLMs, improving reasoning, accuracy, and interpretability. This session will cover practical applications, architecture insights, and innovative use cases to demonstrate the transformative potential of agent-driven LLMs in real-world scenarios. Register now using this link (https://shorturl.at/cFS0m) or the QR code below and join us at 4:30pm #today to discover how these cutting-edge techniques are reshaping the future of AI! #regenerativeAI #RAG #knowledgegraphs #FIKA #knowledgeshare #lifeatascentic #AI #largelanguagemodels

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