Beitrag von PLANET AI

Pulse AI (YC S24) just launched an API for production-grade unstructured document extraction, turning complex information into LLM-ready inputs. No training required. Approximately 75% of enterprise data is unstructured, the majority of this is directly within PDF files. This makes it extremely difficult to build RAG applications with this data, and ingestion is often the bottleneck. The team tested every tool on the market and found they lacked accurate contextual understanding, multi-column PDFs, and multimodal documents. Most current technologies are simply wrappers on Textract or Gemini, which have their own inherent flaws. Pulse trained its own set of Vision Language Models (VLMs) and OCR techniques to bridge this gap. Pulse reached state-of-the-art (SOTA) performance on its vision model for documents and spreadsheets. The API processes all PDF types, including handwritten documents, foreign languages, and more. It seamlessly integrates into new or existing engineering workflows as well. They are also actively working on a novel reasoning tool on spreadsheets using this technology – stay tuned. Pulse's API saw initial success across supply chain teams in the three-way match process and is deployed in companies across hardware, healthcare, manufacturing industries, and more. Sign up at studio.trypulse.ai. Congrats on the launch Sid Manchkanti and Ritvik Pandey! 🚀 https://lnkd.in/gKW37rcH

Low-code prompt engineering is now easier with Power Platform’s AI Builder. Customers can develop generative AI solutions that are grounded in enterprise data, improving accuracy and relevance. Get started: https://msft.it/6048cgW7p #PowerApps

Introducing Teamcenter AI Chat, an AI-powered chatbot that enables you to query text-based data managed in your PLM environment. It utilizes powerful large language models in a secure architecture to provide an enterprise-grade AI-powered question-answering service. ▶ https://sie.ag/3GP922 Key features: 🗣️ Natural language chat 🛡️ Secure ⚙️ Customizable 📈 Scalable 🔁 Continuously updated 🎯 Hallucination-free Discover how Teamcenter AI Chat can help your organization accelerate information searches and unlock insights from your text-based data. #AI #PLM #Siemens #Teamcenter

🚀🧠Interesting Nelson Hall blog on our Eviden Quality Engineering AI Platform. It as well supports German language. Feel free to contact me for more information #eviden #atos #softwaretesting

🤔 How do reasoning models like OpenAi o3 actually work and why are they so expensive? The biggest AI news this week is OpenAI’s O3 model smashing ARC AGI benchmarks. But what makes this model so remarkable? At its core, o3 isn’t just a larger or faster language model—it’s reflective. It reasons. It thinks the way we do when solving problems, especially in complex tasks like coding. If you’ve ever tackled a coding issue with ChatGPT, you know the process: draft a spec, implement (copy and paste) it, test it, debug it, and repeat—sometimes dozens or even hundreds of times until it works. Each iteration (hop) refines the solution, building on what you’ve learned, adding anything missing or fixing things that doesn’t work. That’s exactly what o3 does. It embodies this multi-step, iterative approach natively, rather than relying on us to guide it with repeated prompts or external logic. But this capability comes at a cost. Solving the ARC benchmarks required billions of tokens (a token is word or space) and over a million dollars in compute. Why? Because the model doesn’t cut corners. It exhaustively reasons through problems, internally iterating over potential solutions just as we would, but at a scale that’s orders of magnitude greater. As an example, the autopilot bots I’ve been building for coding operate on a “set it and forget it” model—perfect for running overnight. They generally cost around $100-$200 to produce 30,000 to 40,000 lines of functional code. Completing this process takes several hours and around 3-4 million tokens (the equivalent of 1 million lines of code) before reaching a successful result. They build pretty much anything, the most important part is a decent specification and testing framework to guide it. What o3 really achieves is a baked-in multi-hop reasoning process—what we might call a “private chain of thought.” Instead of multiple independent requests (human or api) to refine a task, the model reasons through the full solution internally, reducing the need for external guidance but demanding massive compute power to pull it off. The o3 model excels by combining deductive, inductive, and abductive reasoning in a unified framework. Deductive reasoning allows it to apply general principles to specific problems, ensuring precision. Inductive reasoning enables it to learn patterns from data, forming broader generalizations. Abductive reasoning fills in gaps, offering plausible explanations when information is incomplete. Combined, these approaches mimic human problem-solving, allowing the model to iterate, adapt, and reason through complex tasks effectively. The result? A model that mirrors human problem-solving but reminds us that reasoning—while transformative—still comes with a hefty price tag.

What’s the deal about event-driven agentic Retrieval-Augmented Generation (RAG) & why is it going to be key to unlock AI-powered agentic workflows and chatbots on an enterprise-level? 🔑🔓   In the rapidly evolving field of GenAI, RAG has gained prominence as a technique that combines the power of Large Language Models (LLMs) with external data sources, thus enhancing the adaptability and contextual accuracy of AI models by dynamically providing relevant information.📄➡️🧠   Event-driven RAG further combines this process with the power of real-time events, providing a robust framework that brings a new level of compliance, scalability, and interactivity to AI applications through these three paramount concepts:   1. Streaming ETL🌊🔄 One of the core benefits of RAG is supplying the LLM with the relevant external knowledge at query time, without the need to expensively retrain the model itself. Most standard systems rely on a static base of knowledge, updated manually or via scheduled jobs. Event-driven architectures enable the concept of Streaming ETL (Extract, Transform, Load), allowing the system to ingest fresh knowledge in real time. This approach ensures that the LLMs are always operating with the most current information, leading to more accurate and contextually relevant outputs. As organizations increasingly rely on real-time data to inform their decisions, Streaming ETL provides the agility and responsiveness necessary to stay ahead.   2. Multi-Agent Collaboration & Orchestration 🤖🤝🤖 The second crucial aspect of event-driven RAG is multi-agent orchestration. In this paradigm, the reasoning is conducted by multiple specialized agents, each responsible for a specific task. These agents operate in a loosely coupled manner, akin to microservices in modern software architecture. This flexibility allows for dynamic collaboration among agents, leading to a more efficient and scalable system. As requirements shift, agents can be adjusted or replaced without disrupting the entire system, fostering innovation and resilience.   3. Event Sourcing📜🔍 Event sourcing involves capturing all changes to an application state as a sequence of events, offering an immutable record of what has happened over time. This level of transparency aligns well with compliance requirements, as it enables clear audit trails and traceability. By integrating event sourcing into RAG, AI developers can create systems that maintain a reliable record of how decisions were made. This not only helps with debugging and troubleshooting, but also acts as a main pillar of compliance and accountability.   In conclusion, event-driven agentic RAG represents a significant advancement in AI systems, enhancing their adaptability and scalability, while ensuring compliance and traceability. 📈✨   By embracing this paradigm, organizations can build AI systems that are robust, responsive, and capable of leveraging real-time information to deliver accurate and insightful results. ⚡🚀