Justin Hamilton

https://lnkd.in/gWNNkjpa Want a crash-course on the technical capabilities Large Language Models when looking at using them at full-scale? This is a great overview from Ben Lorica 罗瑞卡 from Neo4j's conference this week.

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Day 13: Case Study: The Analog Ecosystem Powering Dynamic Neural Networks Imagine a supply chain that organizes itself, aligned to shared goals in real-time. That’s what we accomplished in the Australian fresh produce industry—a system once weighed down by inefficiencies, waste, and unpredictability. It was fragmented, reactive, and out of sync. The challenge was clear: How do we fix it? The solution wasn’t just to tweak the system, but to reimagine it completely. We stopped thinking of it as a traditional supply chain and envisioned it as a living ecosystem. An analog ecosystem powered by collaboration. We introduced ‘One Touch,’ a simple yet revolutionary system that connected growers, manufacturers, wholesalers, and retailers in real-time. They could see exactly what was happening in the supply chain, at every moment. Inventory, demand, availability—all dynamically adjusting, like neurons responding to live data. The impact? Over a billion dollars saved. Waste slashed. Inventory moved faster. What was once a broken system is now self-organizing, adapting, and thriving. This is the power of thinking differently. When you create an ecosystem that evolves with the world, it transforms how we live, work, and trade. Now, we’ve taken that analog ecosystem and digitally twinned it with a Dynamic Neural Network. And this is just the beginning of what’s possible. #SupplyChain #Sustainability #Innovation #ai #machine #NeuralNetworks #BalancedEcosystemScorecard

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Came across this great article on zero-shot CoT (Chain of Thought) prompting mechanism that greatly improves results in most conversational AI scenarios. Thank you Takeshi Kojima, Shixiang Shane Gu, Machel Reid, Yutaka Matsuo and Yusuke Iwasawa for your contribution! Must read for people trying to get the best out of LLMs and conversational AI... https://lnkd.in/gqeaXGt4

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Hi Kanban Singapore folks, There's an upcoming workshop on Systems Thinking—a powerful approach to understanding and managing complex systems. Systems Thinking helps us see the big picture by recognising patterns and interconnections within systems. This is particularly relevant to Kanban, especially with the STATIK (Systems Thinking Approach to Implementing Kanban) method, which we use to optimise our workflows. By applying Systems Thinking, we can better identify bottlenecks, understand feedback loops, and make more informed decisions to improve our processes. The workshop will cover: - Introduction to Systems Thinking - How to apply it in various contexts - Hands-on practice with causal loop diagrams It's a fantastic chance to deepen our knowledge and enhance our Kanban practices. If you're interested in joining, register via Telegram here: https://lnkd.in/gYydvTiS. Cheers, Debbie #KanbanSingapore #KanbanSG #Kanban #KanbanUniversity #YesWeKanban #LeSS #lessworks

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Semantic Chunking or Fixed-Size Chunking: What’s Best for RAG Systems? Our latest blog by Vectara engineers Renyi Qu and Forrest Sheng Bao dives deep into the debate around chunking strategies in Retrieval-Augmented Generation (#RAG) systems. The results? Eye-opening insights into performance, practicality, and computational efficiency. 🔑 What You’ll Learn: ✅ Why fixed-size chunking excels in real-world applications. ✅ The hidden costs of semantic chunking—and when it’s worth it. ✅ How embedding quality can drive greater RAG success. Curious to know more? Read the full blog TODAY! 👉 https://lnkd.in/gP-q3ma5

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TLDR: generative AI, LLM etc is probably asymptotic meaning at some point, more data only results in diminishing returns. https://lnkd.in/gJ4hCqtt

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 ⚡ The wait is over! ⚡ We're excited to release the recording of our recent workshop, a joint effort with Microsoft and unstructured.io, supported by AICamp. This session covered the setup and management of #rag pipelines for diverse data types, specifically focusing on financial data. It combined structured and unstructured data to enhance decision-making.🌀 Learn how to leverage Bytewax for real-time analytics and how Unstructured handles various data types. Hear Microsoft discuss the intricacies of building RAG pipelines, and check out Unstructured’s new API feature for directly reading URLs! 🚀 🤔 Should we host this workshop again? https://lnkd.in/eiUVQ_Vn

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Excited to be speaking about my two favourite things Sustainable Finance and Knowledge Graphs at this upcoming Henry Stewart Events conference in London this June. I'll be speaking about the unique role graphs and semantic technologies play in bringing together complex and disparate datasets such as disclosures, supply chains & spatial to gain a holistic view. #esgdata #knowledgegraph Connected Data World Neural Alpha

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I'll be speaking on a panel discussion tomorrow at the Generative AI Summit on the topic of "How can we gather accurate and relevant data and optimise it for generative AI use?" https://lnkd.in/gU8ZKsdy

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Major news flash: leveraging part of Google’s $1B Digital Futures Initiative we just signed a 1-plus-1-equals-15 deal. Bringing together the two AI and Science powerhouses CSIRO and Google, this partnership will be an inflection point in AI-powered scientific discovery. Press releases with more info here: Google: https://lnkd.in/gDxbsR4b CSIRO: https://lnkd.in/gG2EyqMz Professionally speaking, for my whole career I’ve been passionate about two things more than about anything else- and by far at that: #science and #AI. Can you imagine the enthusiasm I’m feeling at this moment about getting to marry the two so spectacularly…we only got here because of so many others who share that enthusiasm and determination - just calling out a few names on both sides of the aisle: Jon Whittle Scott Riddle Astitva Chopra Heather Tompkins LINDA ALLARD Dr Rebecca Michael Eliza Keck Alison Donnellan Grace Chung Melanie Silva - and thank you to many more whose relentless work has made this possible, you know who you are. To all scientists out there: don’t just watch this space: get in touch and check out the links in the press releases - you do NOT need to be an AI expert or seasoned data scientist to have fun at this party…this is for all scientists and we’re just getting started!! Nicholas Thompson Cade Metz Melissa Heikkilä Mike Cherney https://lnkd.in/giitUt7E

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Semantic chunking vs. fixed-size chunking: Which delivers better results in Retrieval-Augmented Generation (#RAG) systems? 🤔 In our latest blog, Renyi Qu and Forrest Sheng Bao, engineers at Vectara, challenge the hype around semantic chunking. Through systematic evaluation across RAG tasks, they uncover surprising insights into performance, computational costs, and real-world practicality. 🔍 Key Takeaways: ✅ Fixed-size chunking often outperforms semantic chunking in real-world scenarios. ✅ Computational costs of semantic chunking don’t always justify the benefits. ✅ Focus on embedding quality for bigger gains in RAG performance. 👉Check out the full blog here: https://lnkd.in/gZ9M7eYD

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🚨EPISODE 3 ALERT : #Claris Talk #AI Podcast 🚨 In this AUDIO 🎧 episode, Matt and Cris talk about Vector databases, lexical vs. semantic search (and relevance ranking), and upcoming FileMaker conferences in Europe. Matt waxes poetic about JSON and a technique for storing certain types of data in a single, flexible JSON field, rather than having individual fields for everything. Cris recommends a book called 'AI for Good' by Juan M. Lavista Ferres, William B. Weeks, and Brad Smith. https://lnkd.in/gNwFeUVi (Matt checked, and it's not yet available on Audible or Libby) Catch Cris in a Claris Webinar called “Exploring the Potential”: AI Integrations in FileMaker Solutions” In the next episode, Matt and Cris will talk with Ernest Koe and Joris Aarts about: - How did you get started using AI tools? - What AI tools do you use in your personal and professional life? - Which do you think Claris developers should explore as a way to get started with AI? - Looking to the future, how do you see AI effecting the way you run your business? https://lnkd.in/gswRKRbt

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Super interesting intro to GraphRAG today! https://lnkd.in/gFH8TeBn First John Alexander showed how to use Langchain with Neo4J to ask questions about a movie knowledge graph, then Apurva Mody showed how to use Microsoft's graphrag package to answer questions about a book. Get slides & code from https://lnkd.in/gErJw-gz #graphrag #azure #neo4j #langchain

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"How does a vision LLM outperform OCR, and why?" I got this question from our customers a lot and we distill the answers into the blog: https://lnkd.in/guv8waCs Overall, Vision language models (VLM) address key OCR shortcomings by: 1. Accurately interpreting low-quality images and complex layouts 2. Understanding context and semantic meaning in documents 3. Handling multiple languages and scripts seamlessly 4. Reducing post-processing overhead #pdf #word #ocr #llm

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🏗️ Building RAG indexing and retrieval couldn’t be easier. 🥸 Watch George Strong Ph.D's YouTube tutorial to learn how to create a high-quality, production-ready RAG framework in just 30 minutes—simply by writing YAML and dragging and dropping your local files to significantly improve LLM accuracy.

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Anthropic launches tool to connect AI systems directly to datasets. This seems like a really interesting new method for connecting AI to tools, function calling or discovering possible application intents. https://lnkd.in/gUZqgq4Z

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I have been spending (a little too much 😅) time with AI agents powered by technologies like #Microsoft #autogen, #crewAI, #langchain and a few other frameworks. I also got to hear Andrew Nguyen's talk about genetic workflows. (Which is a must watch for anyone interested in AI agents) However, my non-dev friends felt left out. No one should feel like they can’t take advantage of the power of #AI #agents. So inspired by the work Dharmesh Shah is doing with agents.ai, my team and I have been cooking up something at ProvisionAI that will generate an #agentic workflow via a natural language conversation. You won’t worry about which framework to use, how to install python, which tools to use, etc. It’s all done for you.  Launching soon at https://provision.ai - Join our beta for a sneak peek & share your feedback. Can't wait to hear from you! Here is a quick walkthrough of what we have so far 🚀 (PS: it's a bit long video but there is so many interesting things I wanted to share 🫣 ) #ArtificialIntelligence #Innovation #Tech #ProvisionAI

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🚀 Looking to streamline fine-tuning for models like Llama 3, Mistral, and Mixtral? Our latest blog shows how Anyscale simplifies LLMs fine-tuning with Ray: 🔑 Key takeaways: • Scalable fine-tuning with LLM-Forge • Model Deployment with RayLLM Read more: https://lnkd.in/gQwD4A2U

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