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AI tools are blooming. But businesses need transformation, not just tools. Imagine an AI tailored to your needs, integrated seamlessly into your tools and speaking your business's language. It can read your emails and pull data from your systems to bring a magic twist to your workflows. That's what we build for our clients—AI transformation. We create systems that solve YOUR problems, which your team loves to use, and drive exceptional growth. Our Impact: - 2x Faster Time-to-Value. - 3x Growth Rate. - 98% Client retention: our clients love us, and they stay with us. - 250+ Custom AI projects across sectors. Would you like to explore what AI can do for you? Let's start a conversation. Visit our website to see how Cohorte can transform your business with custom AI → cohorte.co

Turning ordinary videos into dynamic 4D worlds? CAT4D is here. Google and Columbia University have developed a groundbreaking method to reconstruct evolving 3D scenes—from nothing but standard videos. What is CAT4D? A multi-view video diffusion model that transforms video footage into fully dynamic 3D scenes (4D), where time becomes an integral dimension. Key innovations: Novel View Synthesis: Generate realistic perspectives from any camera angle and time step. Dynamic 4D Reconstruction: Recreate 3D scenes that evolve and change over time. Sparse Input Handling: Works with minimal video footage, making it efficient for real-world use. State-of-the-Art Benchmarks: CAT4D leads in performance for both novel view synthesis and 4D scene generation. Why CAT4D matters: Robotics: Autonomous systems gain a deeper spatial and temporal understanding of environments. Filmmaking: Revolutionizes visual effects by enabling camera perspectives that didn’t even exist during filming. AR/VR and Gaming: Unlocks new levels of immersion with dynamic and interactive 4D content. Creative AI Applications: Use CAT4D to transform static or AI-generated videos into compelling 4D worlds. Where to explore: Demos and project details: cat-4d.github.io Full paper: arXiv:2411.18613 CAT4D isn't just about seeing the world differently—it's about creating entirely new dimensions of it. #CAT4D #4DReconstruction #DynamicScenes #DiffusionModels #AI _____________ ✔️ Click "Follow" on the Cohorte page for daily AI engineering news. Credits: Rundi Wu, Ruiqi Gao, Ben Poole, Alex Trevithick, Changxi Zheng, Jonathan T. Barron, Aleksander Hołynski

Overfitting Killing Your Model? Ensemble Methods Are the Cure. A brilliant model for training data can fail miserably in the real world. The solution? Ensemble methods like bagging, boosting, and stacking, which bring balance, accuracy, and resilience to your machine learning models. What you’ll uncover in this article: - How bagging tames variance by combining diverse predictions. - Why boosting transforms weak models into strong learners while keeping overfitting at bay. - The magic of stacking, where multiple algorithms unite for robust predictions. - Real-world success stories: from fraud detection to personalized recommendations and medical diagnostics. Don’t let overfitting derail your efforts. Master ensemble methods and build models that thrive in the real world. 👉 Read the Full Article and learn how to outsmart overfitting for good. https://lnkd.in/dZYAXuqW

Tencent Hunyuan Video: A Breakthrough in Large Video Generation Tencent has just unveiled Hunuyan Video, a systematic framework designed specifically for training large-scale video generation models. This marks a significant leap forward in how AI understands and generates video content. Key Highlights Scalable Framework: Tailored for training massive video models, enabling the creation of high-quality, coherent, and dynamic video content. Model: Access the framework here → Hunyuan Video Model. Versatility: Focused on applications in video generation, offering creative solutions across industries like entertainment, education, and more. Why This Matters Large video generation models often require intensive resources and finely tuned training strategies. Hunyuan Video provides a systematic approach, streamlining model development and improving efficiency for developers. As AI continues to push the boundaries of video generation, Tencent’s Hunyuan Video positions itself as a key player in shaping the future of dynamic, AI-driven content creation. Check out the model for an in-depth look! _____________ ✔️ Click "Follow" on the Cohorte page for daily AI engineering news.

Memory in Reinforcement Learning has been a black box—until now. A new framework from AIRI, MIPT, and Mila redefines how we classify and evaluate memory in RL agents, addressing long-standing inconsistencies in the field. Key contributions: Formalizing memory types in RL: Long-term memory (LTM): Retains information across extended timeframes. Short-term memory (STM): Focused on transient, task-specific data. Inspired by cognitive science, the framework also distinguishes between: Declarative memory and knowledge of facts/events. Procedural memory: Skills and action-based knowledge. Memory Decision-Making (Memory DM): Evaluates memory use within a single environment. This contrasts with Meta-RL, which focuses on learning across environments. Memory-Intensive Environments: Designed to rigorously test agents' memory capabilities. Eliminates noise from poorly constructed or mismatched evaluation setups. Why this matters: Consistency in research: Misaligned definitions and testing methods have made it hard to assess true memory capabilities. This framework provides a standardized methodology. Improved experimental design: Ensures memory-related challenges in RL environments are intentional and meaningful. Advancing RL capabilities: Memory is critical for decision-making in real-world scenarios like autonomous systems, robotics, and AI assistants. This research creates a foundation for developing more memory-competent agents. Takeaways for RL researchers: Use robust memory evaluations to avoid misleading conclusions about agent performance. Leverage the framework to explore task-specific memory strategies (e.g., STM for reactive tasks, LTM for planning). Focus on memory-intensive environments for benchmarking and fine-tuning. This work bridges the gap between cognitive science and reinforcement learning, setting the stage for smarter, more adaptive AI. Read more: arXiv:2412.06531 #ReinforcementLearning #MemoryInAI #DeepLearning #RLInnovation _____________ ✔️ Click "Follow" on the Cohorte page for daily AI engineering news. Credits: Egor Cherepanov, Nikita Kachaev, Artem Zholus, Alexey K. Kovalev, Aleksandr I. Panov

Feature Engineering: The Data Scientist’s Superpower Raw data holds potential—but only if you know how to transform it. Feature engineering turns chaotic datasets into actionable insights, unlocking the key to accurate predictions and trend discovery. Here’s what this article reveals: - Why feature engineering is the cornerstone of better model performance. - Step-by-step techniques like scaling, normalization, and time-series transformations. - Real-world applications: from predicting customer churn to enhancing business analytics. Data isn’t the challenge—knowing how to shape it is. Read the Full Article and discover how to supercharge your machine-learning projects. https://lnkd.in/dmKCsG4W

Welcome to the Humanoid Robot Era. Robots are no longer rigid, mechanical tools—they’re becoming human-like in how they move, sense, and function. Meet the Alpha Edition Clone by Clone Robotics A revolutionary bipedal android that replicates the human body with mind-blowing precision: Powered by Myofibers: Synthetic muscles that not only mimic human muscle movement but contract faster and stronger than ours. Human Anatomy, Perfected: A skeleton mirroring all 206 bones, complete with joints, ligaments, and tendons. Soft-bodied design for unparalleled flexibility and movement. Advanced Nervous System: High-precision sensors and cameras for environmental awareness. A neural control system that enables coordinated, lifelike motion. What Makes It Special? This isn’t just a machine—it’s a human-level android, built for both strength and subtlety: Natural Walking: Its gait closely resembles human movement. Human-like Hands: They can manipulate objects with precision and finesse. Learning Ability: Equipped with Clone’s Telekinesis training platform, enabling it to acquire new skills. The Future is Closer Than You Think 279 Alpha Clones will be available for pre-order in 2025. Designed for industries like healthcare, labor, and even personal companionship. Humanoid robots are no longer science fiction—they’re here, and they look eerily human. Clone Robotics is redefining what it means to build a machine. _____________ ✔️ Click "Follow" on the Cohorte page for daily AI engineering news. Credits: Endrit Restelica

Robots are now learning surgery by watching videos—and they’re matching human-level precision. Here’s how this groundbreaking innovation works and why it’s a game-changer for healthcare: How It Works ✅ Video Training → Surgical robots analyze hundreds of real-life surgery videos (e.g., da Vinci System). → This provides diverse examples of techniques and scenarios. ✅ AI-Driven Movement → The robot uses AI architectures similar to ChatGPT but adapted for robot motion. → It translates video data into precise, context-aware movements. ✅ Self-Correction → If errors occur (e.g., dropping a needle), the robot autonomously adjusts and resumes. Why This Matters Faster Training → Robots can master complex procedures in days instead of years. → Speeds up the timeline for autonomous surgical systems. Reduced Errors → Mimics top surgeons for high precision and consistency. → Minimizes risks caused by human fatigue or error. Global Access to Expertise → Over 7,000 da Vinci systems globally could integrate this tech. → Brings advanced surgical capabilities to underserved areas. Potential Impact This isn’t just a step forward—it’s a transformation in surgical innovation: → Healthcare Scalability: More surgeries performed with fewer experts needed on-site. → Skill Gap Bridging: Allows regions lacking skilled surgeons to access top-level care. → Enhanced Outcomes: Higher success rates through ultra-precise robotic performance. As robots learn from human expertise and adapt in real time, the future of surgery looks faster, safer, and more accessible than ever before. Original article: https://lnkd.in/dfjqtuJv _____________ ✔️ Click "Follow" on the Cohorte page for daily AI engineering news. Credits: Robert 지영 Liebhart

Overfitting, Underfitting, and the Magic of Cross-Validation Your machine learning model might look flawless during training—but how does it perform on unseen data? Without cross-validation, you’re guessing, not knowing. In this article, you’ll learn: - How K-fold cross-validation balances training and testing for reliable predictions. - Why overfitting and underfitting are the silent killers of robust models. - Practical strategies like Stratified K-fold for imbalanced datasets and LOOCV for small datasets. Discover how cross-validation ensures your model is ready for the real world, not just the training lab. 👉 Read the Full Article and build models you can trust today! https://lnkd.in/dQ6-9nJg

Open-source multimodal models are catching up to commercial giants. InternVL 2.5 sets a new standard, blending scalability, data quality, and efficiency to rival the performance of leading proprietary models like GPT-4o and Claude-3.5. What’s new in InternVL 2.5? Larger Vision Encoders: Reduces dependency on massive datasets. Achieves better performance with smarter scaling strategies. High-Quality Data Filtering: Eliminates noisy samples, boosting Chain-of-Thought (CoT) reasoning. Improves understanding of complex, multi-step problems. Test-Time Scaling with CoT + Majority Voting: Elevates accuracy on challenging multimodal benchmarks like MMMU. Achievements that matter: First open-source model to exceed 70% on MMMU.Excels in multi-discipline reasoning tasks. Competitive with commercial models.Strong performance across document understanding, multi-image/video tasks, and real-world reasoning. Breakthroughs in long video comprehension. Improved scaling allows the processing of extended video content with higher accuracy. Why it matters: InternVL 2.5 offers a transparent and accessible path for researchers and developers to build state-of-the-art multimodal AI systems. Its open-source nature promotes collaboration while narrowing the gap between open and proprietary AI tools. Code: https://lnkd.in/giYVXpSF Model: https://lnkd.in/dC9EMkes Demo: https://lnkd.in/gJRvFWiQ Paper: https://lnkd.in/ddZfn68N #InternVL #OpenSourceAI #MultimodalModels #DeepLearning _____________ ✔️ Click "Follow" on the Cohorte page for daily AI engineering news. Credits: Zhe Chen, Weiyun Wang, Yue Cao, Yangzhou Liu, Zhangwei Gao, Erfei Cui, Jinguo Zhu, Shenglong Ye, Hao Tian, Zhaoyang Liu, Lixin Gu, Xuehui Wang, Qingyun Li, Yimin Ren, Zixuan Chen, Jiapeng Luo, Jiahao Wang, Tan Jiang, Bo Wang, Conghui He, Botian Shi, Xingcheng Zhang, Han Lv, Yi Wang, Wenqi Shao, Pei Chu, Zhongying Tu, Tong He, Zhiyong Wu, Huipeng Deng, Jiaye Ge, Kai Chen, Min Dou, Lewei Lu, Xizhou Zhu, Tong Lu, Dahua Lin, Yu Qiao, Jifeng Dai, Wenhai Wang