Tecton’s Post

This Hybrid AI approach to improving decision-making tasks is super interesting! Combining the power of LLMs with formal logic and constraint optimization, Elemental Cognition has created a platform that uses real-time data streaming via Apache Kafka to ensure reliable and accurate AI-driven solutions. #AI #GenAI #DataStreaming #Confluent #Kafka #TechInnovation #ElementalCognition

🔍 Dive into the nuanced world of LLMOps with a sneak peek into the transformative role of prompt management. "Prompt Management: The Keystone of LLMOps" unveils the vital interplay shaping the success of AI applications in business. 👉 Explore this introduction to tactical AI integration, where every instruction goes beyond code to clarity and relevance. #LLMOps #AI #PromptManagement #BusinessAI #TechInnovation #promptengineering #prompt #prompts

🔒 🧠What are open-source large language models (LLMs) and their Impact on businesses? With the introduction of Generative AI, these machine learning models have become a driving force behind this disruptive technology. Discover what these open-source LLMs are, how they differ from closed models, the various types of open-source models available, and the three main benefits for businesses.

🌐✨ Welcome to the reality of LLMOps, where AI isn't just code but a strategic ally. In "The LLMOps Reality: Developing Scalable AI Features Beyond the Prototype," we introduce the challenges of deploying AI at scale. Learn why prompt management is not just technical—it's the gateway to user-oriented solutions that redefine value in the digital space. 💡📈 🔗 https://lnkd.in/eedqSNp4 #LLMOps #AI #MachineLearning #DataScience #PromptManagement #BusinessStrategy #TechInsight #ArtificialIntelligence 

Most of the commentary about GPT-4o is missing the main point. Yes its faster, cheaper and feels like the movie Her. But there are much greater implications to consider. Earlier this year, Yann LeCun, the head of AI at Meta, pointed out that biggest LLMs are trained on text. But text is an extremely low bandwidth way to learn how the world works compared to video. In fact, a 4 year old child will have seen 50x more data than our largest LLMs. Up until a couple of weeks ago, even our most sophisticated models were built around text. For example, GPT-4 handled audio by transcribing it first into text, then applying reasoning to that text. GPT-4o, however, was designed to understand video and audio natively. That has implications for how much more data future versions can be trained on. Consider that a 4 year old will have experienced the equivalent of 16K hours of video. Compare that to YouTube, which alone contains over 150 million hours of video. How much smarter can AI get? With a natively multi-modal architecture, I suspect the answer is much, much better. Edit: Wow, this post really blew up. I want to acknowledge that we don't know with certainty how GPT-4o is architected (or Gemini 1.5 for that matter), what OpenAI means when it says "natively" multi-modal, and that the model can be further trained on video. It's an underlying assumption in my commentary above, but a reasonable one I think.

Setting New Standards: Databricks' DBRX LLM in the Realm of Open Models #AI #AItechnology #artificialintelligence #Databricks #DatabricksMarketplace #DBRX #HuggingFace #llm #machinelearning #open

Checkout my latest blog post "Retrieval Augmented Generation on Notion Docs via LangChain"! Learn how to: - Load and Vectorize Notion pages and databases efficiently. - Set up and leverage Supabase for indexing and retrieving information effectively. - Implement Retrieval Augmented Generation (RAG) using LangChain for seamless integration of external knowledge into AI models. Read the full post here: https://lnkd.in/gFVvb3xp Don't forget to like, share, and leave your thoughts in the comments section. #AI #Automation #LangChain #Notion #Supabase #OpenAI #RAG

🚀 Exciting Breakthrough in Efficient LLM Inference! 🚀 LazyLLM: A Game-Changer for Dynamic Token Pruning in Long Context Large Language Models (LLMs) 𝐖𝐡𝐚𝐭 𝐢𝐬 𝐭𝐡𝐞 𝐜𝐮𝐫𝐫𝐞𝐧𝐭 𝐢𝐬𝐬𝐮𝐞? LLMs inference process is divided into two stages: 𝐏𝐫𝐞𝐟𝐢𝐥𝐥𝐢𝐧𝐠: Computing the Key-Value (KV) cache of prompts and generating the first token. 𝐃𝐞𝐜𝐨𝐝𝐢𝐧𝐠: Generating subsequent tokens. For long prompts, computing the KV cache during the prefilling stage can significantly slow down the time-to-first-token (TTFT), making it a bottleneck. 𝐖𝐡𝐚𝐭 𝐢𝐬 𝐭𝐡𝐞 𝐬𝐨𝐥𝐮𝐭𝐢𝐨𝐧𝐬 𝐩𝐫𝐨𝐩𝐨𝐬𝐞𝐝? To address this, researchers from Apple and AI at Meta have developed LazyLLM, a novel method that dynamically selects and prunes tokens during both the prefilling and decoding stages. Unlike static pruning approaches, LazyLLM allows LLMs to dynamically choose different subsets of tokens at each generation step, optimizing efficiency without compromising performance. 𝐊𝐞𝐲 𝐁𝐞𝐧𝐞𝐟𝐢𝐭𝐬: 𝐔𝐧𝐢𝐯𝐞𝐫𝐬𝐚𝐥 𝐈𝐧𝐭𝐞𝐠𝐫𝐚𝐭𝐢𝐨𝐧: Seamlessly integrates with any existing transformer-based LLM to improve inference speed. 𝐓𝐫𝐚𝐢𝐧𝐢𝐧𝐠-𝐅𝐫𝐞𝐞: No need for fine-tuning or parameter modifications. 𝐇𝐢𝐠𝐡𝐥𝐲 𝐄𝐟𝐟𝐞𝐜𝐭𝐢𝐯𝐞: Demonstrates significant speed improvements across various tasks with negligible accuracy loss. 𝐑𝐞𝐬𝐮𝐥𝐭𝐬: LazyLLM has been rigorously tested and shows remarkable improvements: 2.34𝐱 𝐒𝐩𝐞𝐞𝐝𝐮𝐩: in the prefilling stage for multi-document question-answering tasks using the LLaMA 2 7B model, while maintaining high accuracy. 𝐑𝐞𝐝𝐮𝐜𝐞𝐝 𝐂𝐨𝐦𝐩𝐮𝐭𝐚𝐭𝐢𝐨𝐧𝐚𝐥 𝐋𝐨𝐚𝐝: Dynamically prunes unimportant tokens, reducing overall computation time and accelerating the entire generation process. Looking forward to the relevant code and examples on github. Paper link in the comment. _________________________________ ✅ Follow me for regular updates and insights on Generative AI and my journey as a CTO in AI product development

To RAG or Not to RAG The RAG (Retrieve-Augment-Generate) system is revolutionizing AI by enhancing contextual relevance and accuracy through real-time data retrieval and augmentation. However, its higher computational demands and integration challenges raise important questions about its overall viability. While RAG systems can significantly improve applications like financial analysis, traditional models and hybrid approaches also offer valuable benefits. As AI continues to evolve, it's crucial to evaluate whether RAG is the best solution for your specific needs, considering factors like scalability and data privacy. For more insights, check out this article on Technology Watch. #RAGSystems #DataScience

Learn how KDB.AI and LangChain are transforming enterprise AI capabilities in our recent blog. KDB.AI empowers developers with seamless scalability and efficient compression algorithms, making it a go-to solution for handling large datasets. LangChain is an open-source framework that simplifies the creation of natural language applications. Combined, KDB.AI and LangChain address limitations such as low accuracy, high latency, poor scalability, and lack of explainability. Visit our blog to explore the integration with LangChain here: https://bit.ly/3vD3H7L #KDBAI #LangChain #Innovation #KX #DataScience