Kili Technology

🚀 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

🚀 France is on a mission to become a global AI powerhouse. And Kili Technology is mentioned as an AI pioneer. ✨ With groundbreaking research, cutting-edge startups, and a thriving R&D ecosystem, we are rapidly scaling our AI capabilities. Backed by strong government initiatives, major investments in infrastructure, and a growing pool of world-class talent, France is shaping the future of AI across industries. ✌️ The AI Summit 2025 outlines France’s strategic vision—covering advancements in AI research, public and private sector collaborations, and the push for AI sovereignty in Europe. 😉 📥 Check out the full report to explore how France is positioning itself as a leader in AI innovation, security, and sustainability. #AI #Innovation #FranceAI #TechLeadership

🌈 Unleash the Power of Multi-Spectral Imagery 🔍 See beyond the visible spectrum and unlock deeper insights with Kili Technology: 📊 Multi-Spectral Layering: Effortlessly toggle between spectral bands to analyze plant health, moisture content, or urban heat islands. 🎯 Enhanced Annotation Precision: Annotate features using the most informative spectral band, improving model accuracy and decision-making. From agriculture to environmental monitoring, multi-spectral imaging transforms raw data into actionable insights. 🔗 Start exploring: https://lnkd.in/eVPxunZc #SatelliteImagery #MultiSpectral #DataAnnotation

🌟 Satellite imagery annotations often require high geospatial accuracy. That’s why Kili Technology handles GeoTIFF files easily: 🗺️ 𝗚𝗣𝗦 𝗖𝗼𝗼𝗿𝗱𝗶𝗻𝗮𝘁𝗲 𝗣𝗿𝗲𝗰𝗶𝘀𝗶𝗼𝗻: With a simple click, copy exact GPS coordinates for annotations. 📏 𝗠𝗲𝗮𝘀𝘂𝗿𝗲𝗺𝗲𝗻𝘁 𝗧𝗼𝗼𝗹𝘀: Measure distances directly on images in meters or feet for detailed environmental and infrastructure analysis. ✨ 𝗣𝗿𝗲𝘀𝗲𝗿𝘃𝗶𝗻𝗴 𝗚𝗲𝗼𝘀𝗽𝗮𝘁𝗶𝗮𝗹 𝗖𝗼𝗻𝘁𝗲𝘅𝘁: Export annotations with embedded longitude and latitude, keeping your data GIS-ready. Ready to simplify geospatial labeling? 😉✌️ 🔗 Learn more: https://lnkd.in/eVPxunZc #geoint #remotesensing #geospatial

Kili Technology is featured in the 2025 mapping of French AI startups conducted by France Digitale with the support of Sopra Steria Ventures! A mapping that shines a spotlight on 751 startups putting AI at the heart of their business to change the world with their French touch 🇫🇷 Click here to explore the complete mapping (in French) 👉 https://lnkd.in/gBvSJkhK #MappingIA #AIFrance #2025

Boosting LLM Logical Reasoning: Addressing Current Limitations and Future Directions 🛸✌️ 😬 Current Large Language Models (LLMs) face significant challenges in logical reasoning. Their reliance on pattern matching and probability often falls short when confronted with complex or novel situations. However, advancements are being made to overcome these limitations. 1. Chain-of-Thought (CoT) prompting: Guides the LLM through intermediate reasoning steps, improving accuracy. 2. Enhanced RLHF: Refined Reinforcement Learning from Human Feedback leads to more nuanced and logical outputs. 3. Code integration during training: Incorporating code improves problem-solving capabilities and logic handling. ✨ These techniques aim to bridge the gap between human-like reasoning and current LLM capabilities. 🔥 Explore these advancements and future challenges in logical reasoning for LLMs by visiting The Ultimate Guide to LLM Reasoning (2025) 🔗 #article #LLM #reasoning

😮💨 Handling massive geospatial images can be daunting, with performance bottlenecks and inefficiencies slowing workflows. That’s where Kili Technology and Meta's SAM2 step in: 🛠️ Image Tiling: Automatically divides images larger than 30MB into smaller, manageable tiles, ensuring smoother navigation and faster processing. ⚡ Lazy Loading: Efficiently handles over 1,000 annotations by loading only what’s visible on-screen, delivering a seamless experience. 🤖 SAM2 Visual Segmentation: Leverage cutting-edge segmentation with fewer interactions, enhanced precision, and optimized performance. Choose between the Rapid Model for speed or the High-Res Model for unparalleled detail. Whether you’re mapping cities or monitoring deforestation, Kili Technology + SAM2 make annotation faster, smoother, and more accurate. 😉 🔗 Explore more with our blog article (link below) #geoint #remotesensing #geospatial

We’re super excited by this partnership with Kyutai! 🔥 As two prominent French innovators, we take pride in joining forces to advance the frontiers of AI and data excellence. 🚀 💭 Here’s what Kyutai’s CEO (Patrick Pérez) thinks about this partnership: ‘’At Kyutai, our mission is to push the frontiers of artificial intelligence through open science. Teaming up with Kili Technology enables us to accelerate progress by integrating unparalleled data expertise into our research efforts.’’ 🤝 Stay connected on LinkedIn as we deliver groundbreaking innovations to the AI community! 😉 #AI #LLM #partnership

LLMs: Still Struggling with Logic? ❌ 💭 🤔 Current Large Language Models (LLMs) face significant hurdles regarding logical reasoning. They heavily rely on pattern matching and probability, often falling short in complex or unfamiliar scenarios. 💥 Let's explore some key limitations: 1. Over-reliance on Pattern Matching: LLMs excel at identifying patterns in vast datasets, but this can lead to inaccurate conclusions when dealing with situations beyond their training data. Read more on how this impacts performance in our blog! 2. Probability-Based Predictions: Their probabilistic nature means LLMs can sometimes generate plausible-sounding but factually incorrect or illogical answers. Our blog delves into the nuances of this limitation. 3. Difficulty with Novel Situations: LLMs struggle with tasks requiring out-of-the-box thinking or reasoning in scenarios not encountered during training. Our blog mentions how this affects real-world applications. Want to learn more about these limitations and potential solutions? Check out our detailed blog post in the first comment 👇 #LLM #Reasoning #Logic