Jean-Luc Chatelain

At Decision-Zone we have built autonomous agents that reduce the need for the massive IT infrastructure and human support. The Decentralized Autonomous Decisioning Agent can monitor and control complex concurrent distributed systems by autonomously integrating Processes, rather than working with static data. A DADA X agent has the capability to monitor over 100 million events per second. It can dynamically monitor live events with complex models and autonomously integrate thousands of activity processes— not possible under the current architecture and computing paradigm or with current AI.

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Meta's #Llama3 and Snowflake's open-source #ArcticLLM are reshaping possibilities in tech. What sets Arctic apart? Its commitment to open-source innovation. At AnswerRocket, we're excited to see how these advancements can turbocharge analytics and decision-making. Read on for insights from our CTO Mike Finley on how enterprises should evaluate these new models.💡 #GenAI #OpenSource #Analytics #AI

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Why Low-rank Adaptation(#LoRA) of #LLMs seems more pragmatic for #enterpriseai, some early thoughts (pls share your views) First, how does it work? By freezing the #pretrained model weights and injecting trainable rank decomposition matrices into each layer of the #transformerarchitecture, this reduces the number of trainable parameters for downstream tasks. So, why is this better? Efficiency: Low rank models require fewer parameters and computations compared to their full-rank counterparts ((say for #GPT3, LoRA can reduce trainable params by 10000 times and computation reqs by almost 3 times) . This efficiency leads to faster inference times and reduced computational costs, making them more suitable for large-scale deployments in enterprise settings. Scalability: #Enterpriseapplications often deal with massive datasets and require models that can scale effectively. #Lowrankmodels can handle large volumes of data more efficiently, making them a practical choice for enterprise-scale applications. Memory and storage: With reduced parameter sizes, low rank models consume less memory and storage. This is advantageous for deploying models on resource-constrained devices or when managing a large number of models simultaneously. #Generalization: Low rank models tend to generalize better on unseen data due to their ability to capture the essential patterns in the data while avoiding overfitting. This is crucial in enterprise applications where the model's performance on new data is of paramount importance. #TransferLearning and #Finetuning: Low rank models can serve as a good starting point for transfer learning and fine-tuning on specific enterprise tasks. Their compact representations enable faster adaptation to new domains or tasks with limited labeled data. Privacy and security: In some enterprise settings, there might be concerns about sharing sensitive data with third-party cloud providers. Low rank models can be more easily adapted and deployed locally, reducing the need to send raw data to external servers. Most of all, Cost-effectiveness: Low rank models can offer competitive performance at a fraction of the cost of more complex models. This cost-effectiveness is an attractive feature for enterprises looking to optimize their resources and budgets. These apart from Robustness to Noise, Ease of Interpretability (simpler structure) makes it all the more a good candidate for many firms to experiment for fitment, what do you think? #saxonai #generativeai #languagemodels #qlora #finetuning #llms

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#RAG has fundamental limitations that prevent it from generating accurate answers for diverse queries on complex documents or on datasets at scale. Mehul Shah writes about the need for a new approach: LLM-Powered Unstructured Analytics. We call it LUnA, and this architecture is at the core of #Sycamore. Read about LUnA here: https://lnkd.in/gThndXVf #Sycamore #Aryn #Analytics #UnstructuredData #Search #GenAI

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NEW BLOG POST 🧠 How to create embeddings with Sentence-Transformers in PySpark. #pyspark #sentencetransformers #nlp #generativeai #genai #datascience #datascientist #deeplearning https://lnkd.in/dMvgSDwP

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Snowflake’s Arctic-TILT model, powering our Document Al, beats GPT-4 with just 0.8B parameters, securing a top spot in the standard benchmark for document understanding DocQVA. This efficiency not only reduces operational costs but also enhances the scalability of solutions built on Arctic-TILT. This also means that it does not require large GPU instances or resources to leverage the Arctic-TILT model in Document AI, making it more accessible and adaptable to different customer sizes and needs.

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Yesterday we demonstrated how quickly you can move contextualize and normalized data from Flow to Snowflake during a webinar with our partners at Streamline Control. This strategy makes all of the historical data in your legacy or enterprise data historians and SQL databases immediately available to Snowflake, and it's EASY!

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Another demonstration of ScienceCast.org #AI enhancements of arXiv postings - free, simple interface https://lnkd.in/e_gXsF_9

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Want to learn more about how to get started using Snowflake Cortex LLM Functions? Check out this post from Jon Coulter detailing how to use these tools to quickly classify data, empowering Data Engineers with Data Science tools! https://lnkd.in/gMTFT5xQ

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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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#AIforBusiness —> Right Model for the right use-case running on the right inferencing infrastructure to ensure unit marginal value > unit marginal cost. The probability of successfully moving from POV to scale deployment grows with —> (1) Trust in the data used to train the model… (2) Efficiency and accuracy in model performance and (3) flexability of deployment options. The IBM #Granite model family stands out for it’s performance attributes in these dimensions. See how it stacks up and how this is achieved…. Https://lnkd.in/eMkuX2an

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Navigating the complexities of machine learning (ML) can be challenging, but the rewards are well worth the effort. This article by Serge Masiutin demystifies the process of integrating ML into your business, offering a clear, step-by-step guide to unleashing its potential. From enhancing customer interactions to streamlining operations, join me as we explore how ML can transform not just your workflow, but the very fabric of your business success. https://lnkd.in/eq88K7tg

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From Naive to Advanced ML Pipelines with MLflow In this webinar, Denny Lee (Databricks), Andrew Bauman and Irena Kushner (Accolade, Inc.) will explore how starting with a simple (naive) ML pipeline and progressively introducing complexity can significantly ease the management of machine learning deliverables. Using powerful open source tools like Delta tables and MLflow, you will learn how to enhance your ML workflows from the ground up.

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Sage have created a very nice interactive overview of the median prediction of forecasters on Metaculus and Manifold Markets on various questions related to AI capabilities, harms, and societal impacts. It's well worth checking out if you are interested in this sort of stuff. See: https://lnkd.in/eUgkmzX9 Follow MIT FutureTech if you are interested in technological trends and their social implications. #AI #technology #economics

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#ExplainableAI (XAI) is an emerging subset in #AI that focuses on the readability of #ML models. Want to further understand what #XAI is and how it works? Dive into our blog post: https://hubs.ly/Q02qHJgT0

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EK's Lulit Tesfaye and Jeff Jonas of Senzing teamed up on EK's latest blog, in which they discuss typical problems that would necessitate Entity Resolution, how to unify enterprise data with a Semantic Layer, and the role of entity-resolved knowledge graphs. #semanticlayer #knowledgegraph #entityresolution https://lnkd.in/ePujrbve

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🎙️Today, we’re VERY excited to announce the relaunch of our podcast, now titled "The Data Flowcast: Mastering Airflow for Data Engineering & AI." This podcast is specially designed for the Apache Airflow community and aims to share invaluable insights, useful tips, and engaging discussions about the current and future trends of Airflow. Our first episode features a discussion with Alexander Booth , Director of R&D at Texas Rangers Baseball Club on how they orchestrated a World Series victory with Airflow 🚀 Listen on Spotify, Apple Podcasts, or watch on Youtube! Links in the comments below ⬇️ 🎧⭐️ PSA The best way to support the show is to leave us a 5 star review on your preferred platform— it takes less than 5 minutes but will impact this show for years to come. #ApacheAirflow #DataEngineering #Podcast #TechTalks #WorldSeries

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Follow along John Chun Hong Leung's new tutorial to learn how you can ingest, extract, and visualize time-series data by using PySpark on Databricks (implementation code included).

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