LLMs RAG chunk size - is it too small and lacks relevant data or is it too large and includes unnecessary information? 🤯 This is where context enrichment comes into a play! 🌟 Instead of worrying about optimizing the chunk size, read Episode 3 of Pixion AI blog series and let our Franjo Mindek show you how to combine the benefits of both chunk sizes in a single solution. ➡️ https://bit.ly/4au7Z04 #PixionAiBlogSeries #LLM #RAG
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Join us at Ai4 to discuss the communication and burnout challenges of hybrid work and explore how AI can provide solutions. I look forward to seeing you there #ai4 #belt #unifiedcalendar
Last chance to attend! Passes are quickly selling out to Ai4 2024 – North America’s largest AI industry event – taking place on August 12-14 in Las Vegas. Today, we’re excited to welcome Sheetal Jain, Co-Founder & CTO at Belt Software to the stage! Join the discussion around “Combatting Communication Overload in Hybrid Work with AI.” Register now: https://lnkd.in/e6dTnzj9 Tag a friend who’d be interested in this talk!
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This month has been insane regarding model releases, so I don't blame you if you haven't been able to catch up. To name a few: - LLaMA3 by Meta, the new open-weights king at 8B and 70B params 👑 - Mistral 8x22B by Mistral AI, a permissively licensed MoE model 🔀 - Phi3 by Microsoft, trained with knowledge distillation to achieve parameter efficiency ⚗️ - Snowflake Arctic by Snowflake, with a keen focus on enterprise uses 💼 - Reka Core by Reka AI, competing with big names despite the small team behind it 💪 - WizardLM2 by the WizardLM team, which mysteriously disappeared hours after its release 👀 - OpenELM by Apple, their first contribution to truly open-source models 🍎 - PileT5 by EleutherAI, updating T5's knowledge & a modern tokenizer 📚 - Idefics2 by Hugging Face, offering a strong open VLM at 10B parameters 🤗 To learn about these in more detail, along with more AI R&D news and the top 10 trending research papers of the month, join us this Friday at 4 PM CEST for the next installment of the Trends in AI webinar!
Gearing up for our May 2024 edition of Trends in AI ... with focus on LLMs, and all things RAG and Vector Search. How good is LLaMA 3 really? Plus all the latest news and model releases of the month, like Phi-3, Reka Core & Snowflake Arctic, the new Atlas by Boston Dynamics, and developments in self-driving cars from Wayve and Mobileye. Are the famous Chinchilla scaling laws burnt toast? As usual, we’ll dissect this month’s trending AI R&D papers, including LLM2Vec, LongEmbed, Infini-attention, Megalodon, and much much more, plus a short ICLR 2024 conference preview as a bonus. Join us this Friday, May 3rd, at 4 PM CEST, In-person at Lab42 (room L3.35) with drinks and snacks or virtually via Zoom with Bring-Your-Own. Sign up here! https://lnkd.in/ef-kP5PA
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Thrilled to have joined this panel discussing AI's potential in the alternative data realm. Honored to share the stage with Hope Skibitsky, RJ Jones and Aris Tentes. A big thank you to Neudata for orchestrating such an insightful event! #ai #alternativedata #datascience
I had so much fun moderating a panel on unleashing the potential of AI in the alternative data space with panelists who are are at the top of their game. Adam D. Nahari RJ Jones Aris Tentes Thanks for a great event, Neudata!
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💫 Ready to experience the NEW Cymulate AI Copilot and Dynamic Attack Planner? 💫 We've dropped this new video to give you a peek behind the curtain of how our new AI-powered solution works. Check it out here: https://lnkd.in/eqUBuesh 👋 At Black Hat? Swing by Cymulate booth 1560 to experience our AI Copilot first-hand and learn why it provides the perfect assist to SecOps teams. 🤝
Introducing Cymulate's AI Attack Planner
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It was such a pleasure discussing the Gradient founder journey. If you want to hear about: - Enterprise AI Automation - The hard things about hard things - Long Context Agents This is the episode to tune into. As always Amanda Robson and Timothy Chen were the best hosts!
Are enterprises ready for AI agents? Mark Huang & the Gradient team think so 💪 They're leading the charge for cos in finance & healthcare as they embark on their LLM journeys - and pushing the limits on what's possible with their 4M context window Llama-3 finetune 🤯 Full episode 👉 https://lnkd.in/gMkBDH3w
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AI Beyond Imagination: Contemplating the Events Landscape 50 Years from Now Join this Eventsforce article as it delves into the scenarios and experiences that could shape the landscape of the event sector in the years to come. https://lnkd.in/g46Yc8F5
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𝗟𝗶𝘃𝗲 𝗼𝗻 𝘀𝘁𝗮𝗴𝗲! 📹 Yoann Hinard, Witbe's COO, was featured yesterday at the BEIT Conference 🎤 "𝗔𝗜 𝗜𝗻𝗻𝗼𝘃𝗮𝘁𝗶𝗼𝗻𝘀 𝗶𝗻 𝗧𝗲𝘀𝘁𝗶𝗻𝗴 𝗮𝗻𝗱 𝗠𝗼𝗻𝗶𝘁𝗼𝗿𝗶𝗻𝗴: How Machine Learning is Transforming Testing and Monitoring on Real Devices" 🤖 If you missed it, come by our 📍 𝗕𝗼𝗼𝘁𝗵 𝗪𝟮𝟬𝟱𝟯 to get a tech demo of our AI integrations! #AdMonitoring #QualityOfExperience #RemoteEyeController #witbe #NABShow #videotesting #admonitoring #videomonitoring #FAST #ABRStream NAB Show
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Ever wondered how LLMs turn data chaos into clarity? Catch up on Episode 3 of our Data and AI video series featuring Saad Hashmi to understand the full potential of LLMs. Episode 3: https://hubs.li/Q02CYx0y0 Stay tuned for more episodes to keep up with the AI buzz! #EngineeringIntelligentandScalableDigitalSolutions
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🔥 Fine-Tuning Mistral AI Models and RAG Using LlamaIndex 🚀 Mistral AI has recently launched a fine-tuning API that enables users to efficiently enhance their models. 👉 Our new cookbook illustrates how to fine-tune the open-mistral-7b model using the MistralAIFinetuneEngine to achieve response quality comparable to the mistral-large model: 1️⃣ Create synthetic training and evaluation datasets. 2️⃣ Fine-tune the open-mistral-7b model to match the performance of the mistral-large. 3️⃣ Evaluate the RAG pipeline using the fine-tuned model with RAGAS, and monitor using Weights & Biases. 📈 We’ve observed a significant increase in the faithfulness score with just about 40 samples for fine-tuning. Check out the full guide here: https://lnkd.in/gUd3auMw
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Co-Founder of Altrosyn and DIrector at CDTECH | Inventor | Manufacturer
5moThe debate over the optimal chunk size in LLMs' RAG framework highlights the delicate balance between relevance and efficiency. Context enrichment indeed presents a promising solution, offering the flexibility to tailor chunk sizes dynamically based on the specific task and dataset. However, how do we address potential challenges such as maintaining coherence across varied chunk sizes and ensuring smooth transitions between them? If we envision a scenario where LLMs are deployed in real-time conversational systems, how would you propose implementing context enrichment techniques to adapt chunk sizes dynamically based on evolving dialogue contexts?