Kili Technology

🔍 Revolutionizing Object Segmentation in Geospatial Data with Calculating Multispectral Indices + SAM2 Here's a preview of what we're coming up with for our geospatial tool: 📊 Index Calculation: Enhancing multi-spectral imagery to clearly identify specific objects across diverse landscapes 🤖 SAM2 Segmentation: Deploying AI to create pixel-perfect annotations of your target features 🎯 Streamlined Workflow: Building high-quality training datasets for geospatial AI models in minutes, not hours This powerful combination delivers unmatched accuracy for detecting and segmenting any objects of interest in your geospatial assets - from water bodies to infrastructure, vegetation zones to urban features. 📍 Meet us at the Geospatial World Forum (GWF) 2025! We'll be showcasing our latest tools and discussing our projects in the geospatial field. Stop by booth H21, and discover how we're reshaping geospatial data annotation. #GeospatialAI #RemoteSensing #DataAnnotation #GWF2025 #NDWI #SAM2

🔊 Exciting Announcement! 🌱 Join us at the Geospatial World Forum as our AI Solutions expert Maxime Michel takes the stage to discuss "Precision Agriculture with Collaborative Geospatial Intelligence"! ⏰ April 24, 2025 | 9:30 - 11:30 📍 Agricultural Summit, Geospatial World Forum 🌍 Madrid, Spain 🌾 Discover how collaborative geospatial intelligence is revolutionizing the agriculture industry and beyond through collaborative data structuring that ensures high-quality data at scale. 🔍 Want to learn more? Visit us at booth H21, where our expert Guillaume A. will be ready to discuss our cutting-edge geospatial annotation solutions. 🚀 Don't miss this opportunity to connect with industry leaders and see firsthand how Kili Technology is transforming the geospatial landscape with a secure and robust platform built for enterprise-level high-resolution imagery analysis. See you in Madrid! 👋 #GeospatialWorldForum #GWF2025 #GeospatialTech #Agritech

We're excited to announce that we'll be attending the Geospatial World Forum (GWF)! Join us as we explore the latest innovations in geospatial technology, connect with industry experts, and share our vision for a smarter, more connected world. Visit us at booth no. H21 to learn more about our groundbreaking solutions and see firsthand how our technology is transforming the geospatial landscape. And if you're interested in diving deeper into enterprise geospatial data structuring solutions, connect with our experts Guillaume A., and Maxime Michel early to start the conversation. Looking forward to connecting with fellow professionals and enthusiasts—let’s shape the future of geospatial together! #GeospatialWorldForum #GWF2025 #GeospatialTech #GIS #GeospatialData

↗️ When we set out to evaluate LLM reasoning capabilities, we knew traditional one-size-fits-all benchmarks wouldn't capture the nuanced reality of how these models think. So we built something different: a multi-dimensional framework examining 8 leading models across three critical domains: 📊 Mathematics: We watched O3 Mini achieve an impressive 90.32% success rate, showing remarkable evolution in numerical reasoning 🔤 Language: O1 demonstrated exceptional linguistic understanding at 91.67%, revealing specialized strengths 🧩 Reasoning: Claude 3.7 excelled with 87.50% success in logical deduction tasks 🔬 Our journey revealed fascinating evolutionary patterns: - Models are developing specialized strengths (O1 in language, O3 Mini in mathematics) - The Claude 3.5 → 3.7 leap represents a 33.33 percentage point improvement across all domains - Reinforcement learning approaches (like DeepseekR1) are creating alternative pathways for reasoning development What truly surprised us was seeing how differently models handle increasing complexity. While some struggled significantly with advanced problems, Claude 3.7 maintained consistent performance across all difficulty levels. Our benchmark tells the story of an AI field in rapid evolution, with specialized strengths emerging alongside breakthrough general reasoning capabilities. Read about our full journey: https://lnkd.in/dUF88qr3

🧠 NEW BENCHMARK REPORT: Comparing LLM Reasoning Capabilities Across 8 Top Models Our team at Kili Technology is excited to share our first comprehensive LLM Reasoning Benchmark, systematically evaluating the reasoning capabilities of today's leading models: OpenAI's O1 & O3 Mini, Anthropic's Claude 3.5 & 3.7, Mistral AI's Mistral-Large-2411, Qwen2.5, DeepSeek AI R1, and Qwen's QwQ-32B. 📊 Key findings across three critical domains: MATH: Top models achieve 90% accuracy in mathematical reasoning tasks, while others struggle at 35% LANGUAGE: Dramatic 70-point gap in language understanding capabilities between leading and trailing models REASONING: Complex multi-step logical reasoning reveals the widest performance variations The results show Claude 3.7 and O3 Mini leading with 84% overall accuracy, but no single model dominates across all domains. This highlights the importance of strategic model selection based on your specific use case. 🔍 The full report presents our methodologically rigorous evaluation across five complexity levels from basic to post-graduate, revealing how performance degrades as tasks become more challenging. Want to make data-driven decisions about which LLM is right for your business needs? Download the full report today! ⬇️ #LLMBenchmark #AIResearch #MachineLearning #DeepLearning #NLP #Claude #GPT #Mistral #DeepseekAI

🌍 Finding the Right Geospatial Annotation Tool in 2025 🛰️ Quality geospatial annotation directly impacts the success of your ML models and computer vision applications. But with so many options available, how do you choose? 🔍 Our latest article compares: → Open-source tools like QGIS and CVAT with their limitations → Enterprise solutions that overcome scalability challenges → Advanced features necessary for high-precision annotation ⚡ Key capabilities that make the difference: 🔹 Multi-spectral layering for beyond-RGB analysis 🔹 Precise geo-referencing with coordinate precision 🔹 Intelligent memory management for gigabyte-sized imagery 🔹 Human-in-the-loop verification for mission-critical accuracy 💼 Real-world impact across industries: Defense Intelligence: Sub-meter annotation precision for tactical analysis Environmental Conservation: Detailed ecosystem mapping from coral reefs to forests Urban Planning: Building footprint extraction and infrastructure mapping Precision Agriculture: Crop health assessment and field boundary delineation 🌟 Discover which tools can handle your enterprise needs in our comprehensive guide! Check out the full article to elevate your geospatial annotation workflows 👇 #GeospatialData #DataAnnotation #RemoteSensing #GEOINT

📘 We’ve just updated The Ultimate Guide to LLM Reasoning with insights from DeepSeek AI’s latest research—comparing Proximal Policy Optimization (PPO) and Group Relative Policy Optimization (GRPO) for large-scale language model training! 🔥 What’s New? 👀 ✅ PPO Explained – A widely used RL method that uses a critic model and clipped objectives to stabilize policy updates. ✅ GRPO Unveiled – An alternative that doesn’t require a critic, assigning rewards by comparing responses to each other—cutting down on computational overhead. ✅ Why DeepSeek-R1 Chose GRPO – By using group-based relative rankings, DeepSeek-R1 streamlined its RL pipeline, reducing costs while boosting its ability to refine reasoning. ✅ Two Stages of GRPO – DeepSeek-R1-Zero (pure RL) emerged with advanced reasoning but some readability issues. Then DeepSeek-R1 added a small supervised “cold start,” followed by GRPO again for more robust, clear outputs. Why It Matters? 👀 While PPO has been a go-to strategy for LLMs, but DeepSeek AI’s work on GRPO is showing immense promise for large-scale LLMs—helping models like DeepSeek-R1 achieve strong reasoning with lower overhead. These insights offer a new frontier in RL-based LLM optimization. 🚀 😉 We’ve woven these state-of-the-art findings into our guide so you can stay on top of the evolving LLM reasoning landscape. 📖 Check out the updated guide now: https://bit.ly/4khOLjM #DeepSeek #ReinforcementLearning #GRPO

🐋 DeepSeek AI R1 is an LLM designed to push the boundaries of structured reasoning using a reinforcement learning-only approach. It refines problem-solving and logical consistency by focusing on self-correction, multi-step reasoning, and self-verification. ✨ Impressively, DeepSeek AI R1 delivers performance on par with leading closed-source models and demonstrates a robust training process without major setbacks. 📘😉 We have detailed everything in our latest article that dives into its multi-stage reinforcement learning approach, detailing how it balances accuracy, coherence, and efficiency. Don’t miss out on the full story behind DeepSeek R1’s breakthroughs—check out the article now! 👇 #AI #LLM #ReinforcementLearning

🚀 Big Update: The Ultimate Guide to LLM Reasoning Just Got Smarter! 🔥 We’ve just updated The Ultimate Guide to LLM Reasoning with groundbreaking insights from DeepSeek’s latest research! Their new DeepSeek AI-R1 model leverages pure reinforcement learning (RL) to incentivize reasoning capabilities—without relying on traditional supervised fine-tuning. What’s New? DeepSeek’s team tackled some of the biggest challenges in reasoning for Large Language Models (LLMs), including: ✅ Training without Supervised Fine-Tuning (SFT) – DeepSeek-R1-Zero learned reasoning entirely from RL, unlocking self-evolution and self-verification abilities. ✅ Cold-Start Data for Enhanced Readability & Performance – Overcoming RL-generated language mixing issues, their multi-stage training pipeline boosts accuracy and clarity. ✅ Distillation to Smaller Models – They successfully transferred powerful reasoning abilities from large models to smaller, more efficient ones without performance loss. ✅ Solving Traditional Bottlenecks – Their approach outperforms OpenAI’s o1-mini in several key reasoning benchmarks, proving that RL can drive general reasoning improvements in LLMs. Why It Matters? LLMs have historically relied on pattern-matching over true logical reasoning—but DeepSeek-R1 represents a breakthrough in making AI models genuinely reason through complex problems. This has huge implications for math, coding, logical reasoning, and beyond! 🚀 We’ve incorporated these state-of-the-art findings into our guide to help you stay ahead in the evolving landscape of LLM reasoning strategies. 📖 Check out the updated guide now: Link in the comments Your opinion: What’s the next big leap for reasoning in LLMs? 👇 #LLM #DeepSeek #ReinforcementLearning

🚀 Smarter Geospatial Data thanks to smarter tools 🌍 From defense intelligence to environmental conservation, high-quality geospatial annotation is a game-changer. But handling massive datasets and complex imagery requires precision, scalability, and the right tools. That’s where Kili Technology comes in. 🗾 Our geospatial annotation platform is built for: → High-resolution satellite imagery 🌎🎯 → Complex object detection with bounding boxes & polygons 📍 → AI-powered labeling with human-in-the-loop verification 👨💻 🔍 Real-world impact: 🔹 Defense Intelligence: Defense companies use Kili Technology’s platform for ultra-precise geospatial labeling, ensuring mission-critical accuracy. 🔹 Climate Protection: Climate protection organizations rely on Kili Technology to map marine and terrestrial ecosystems with unprecedented detail. 🌟 AI-driven. Scalable. Accurate. We are redefining geospatial annotation. Read more about out case studies here below. 🤔👇 #geoint #remotesensing #geospatial