NEWSLINKER’s Post

Explore how realistic object placement transforms object detection models. Our research showcases substantial performance enhancements in real-world scenarios, setting new standards for machine learning accuracy using Inverted AI's INITIALIZE model. #ObjectDetection #MachineLearning #InvertedAI https://lnkd.in/gsyHAgFf

"Thrilled to unveil my latest project: an AI Calculator designed with OpenCV and MediaPipe! 🚀 This intuitive tool simplifies basic arithmetic equations through hand gesture recognition in real-time. Just point, gesture, and calculate! Join me in exploring the seamless integration of computer vision and machine learning in simplifying everyday tasks. Let's connect to discuss its potential impact! #AI #OpenCV #MediaPipe #Innovation #ComputerVision #MachineLearning"

Secure your seats today! Leveraging AI for Condition Monitoring and Predictive Maintenance May 9, 2024, 5:30 PM (GMT+8) Predictive maintenance has emerged as one of the most important applications driving digital transformation. However, many organizations are struggling to develop predictive maintenance services. According to many studies, this is most often due to a lack of required skills or concerns about data quality. Join this webinar, learn how to use machine/deep learning techniques in MATLAB to tackle various challenges related to predictive maintenance and anomaly detection. #AI #PredictiveMaintenance #MATLAB Register now: https://hubs.ly/Q02w7gHR0

Secure your seats today! Leveraging AI for Condition Monitoring and Predictive Maintenance May 9, 2024, 5:30 PM (GMT+8) Predictive maintenance has emerged as one of the most important applications driving digital transformation. However, many organizations are struggling to develop predictive maintenance services. According to many studies, this is most often due to a lack of required skills or concerns about data quality. Join this webinar, learn how to use machine/deep learning techniques in MATLAB to tackle various challenges related to predictive maintenance and anomaly detection. #AI #PredictiveMaintenance #MATLAB Register now: https://hubs.ly/Q02w7klb0

Object Detection vs. Image Segmentation: What's the Difference? If you're venturing into the realm of computer vision or deep learning, you've probably come across terms like Object Detection and Image Segmentation. But what exactly do they mean, and how are they different? Let me break it down for you in a way that makes sense: - Object Detection is about finding objects in a scene and marking them with bounding boxes. It's like giving each object a "name tag" and saying, "I see you over there." This is the go-to method for applications like self-driving cars, security cameras, and retail analytics. - Image Segmentation, however, takes it to the next level. It doesn't just find objects; it defines their exact boundaries, segmenting the image into distinct areas. Think of it as coloring in the lines to create a more detailed picture. This precision is key for tasks like medical imaging and geographic information systems (GIS). If Object Detection is like pointing out items in a shop window, Image Segmentation is like drawing their exact shapes on a sketchpad. Whether you're building AI models or just curious about the technology, understanding these concepts is crucial. Which one do you think has more impact? Let me know in the comments! #ComputerVision #DeepLearning #AI #ObjectDetection #ImageSegmentation

Understanding the generative AI development process Developing generative AI applications is very different from developing traditional machine learning applications. In the new paradigm for generative AI, the development process is very different from how it used to be. The overall idea is that you initially pick your generative AI model or models. Then you fiddle with your prompts (sometimes called “prompt engineering,” which is an insult to actual engineers) and adjust its hyperparameters to get the model to behave the way you want. Steps in the generative AI development process : Model selection Prompt engineering Hyperparameter tuning Retrieval-augmented generation (RAG) Agents Model fine-tuning Continued model pre-training To know more, register for this online video session event. https://lnkd.in/g2muVN32

Ever wondered how the frontier of AI and machine learning is pushing boundaries? Here are some groundbreaking insights from the most recent papers: 1. Scaling Diffusion Transformers to 16 Billion Parameters (DiT-MoE): This sparse version of the diffusion Transformer seamlessly scales with dense networks, exhibiting highly optimized inference through shared expert routing and expert-level balance loss. The result? Captured common knowledge and reduced redundancy. 🚀 2. GraphFM: A Scalable Framework for Multi-Graph Pretraining: Imagine a generalist model trained on 152 datasets with 7.4M nodes and 189M edges, performing competitively across diverse tasks. GraphFM tackles the burdens of the current graph training paradigm, creating a scalable, efficient solution. 🌐 3. Reliable and Efficient Concept Erasure of Text-to-Image Diffusion Models (RECE): In just 3 seconds, RECE erases inappropriate content without extra fine-tuning, using a closed-form solution to derive new target embeddings. Reliable and efficient indeed. 🚫🖼️ 4. Fully Non-Linear Neuromorphic Computing with Linear Wave Scattering: This novel neural network implementation via optical systems promises sustainability for ML by achieving high expressivity through linear wave scattering - an intriguing paradox. 🌀 5. Prover-Verifier Games Improve Legibility of LLM Outputs: Training strong models to produce text verifiable by weaker models results in outputs that are clearer for humans too. This emphasizes the trifecta of correctness, clarity, and ease of verification. 📝🤖 6. Robotic Control via Embodied Chain-of-Thought Reasoning (ECoT): Empowering robot control systems with ECoT enhances their decision-making capabilities through task and environment reasoning. Think of it as a smarter, more intuitive robotic assistant. 🤖🔄 What are your thoughts on these innovations? Which one excites you the most? Share your insights in the comments. #AI #MachineLearning #GraphFM #NeuromorphicComputing #DiffusionModels #RoboticControl #TechInnovation #DataScience

Day 5 - 🚀 Training Generative Models: A Step-by-Step Guide 🚀 Generative AI models, like GANs and VAEs, require a thorough process from data gathering to model deployment. Here’s a crisp walkthrough: 1. Define the Goal: Determine the objective: image generation, text synthesis, video creation, or music composition. Tailor the process to support this goal with relevant content. 2. Data Collection: Gather a large, high-quality dataset related to the objective. Preprocess and clean the data to ensure consistency. 3. Select Architecture: Choose the appropriate model architecture (GANs, VAEs, Transformers). Consider the strengths and limitations of each for your dataset. 4. Model Implementation: Use frameworks like TensorFlow or PyTorch to build the neural network. Define the network layers and establish connections. 5. Training the Model: Present training data iteratively, adjusting parameters to minimize errors. Use substantial computational resources, monitoring progress, and adjusting training parameters. 6. Evaluation and Optimization: Assess the model's performance with new data. Optimize by adjusting architecture, training parameters, or dataset entries if results are unsatisfactory. 7. Iterate and Improve: Continuously refine the model based on user feedback and new training data. Focus on iteration and improvement for a high-quality generative AI model. Developing a generative AI model is an iterative process that requires careful planning and constant refinement to achieve optimal results. #GenerativeAI #MachineLearning #AI #TechInnovation #ModelTraining #DataScience #GANs #VAEs

https://lnkd.in/g7hb5CHx Vision now the battlefield in multi-modal perception of the real world.High quality data this is all pointing to augmented reality as the future no one will walk around with there mobile pointed at the world every Ai device failed except for one rayban glasses. My work is for this future I've always believed the Ai war ends with augmented reality.It enables always on sensors pointed at the world and a direct beam of information to the optic nerve and spatial audio to your auditory nerve its a fully symbiotic relationship between artificial intelligence and humans without surgery like neuralink.

🚀 Experience the Future of Machine Vision with pylon AI!🚀 Curious about how AI can elevate your vision applications? Book a demo with our experts today to find out more! 🔍 Why pylon AI? Advanced Image Analysis: Utilize deep learning algorithms for object detection, classification, and segmentation. Performance Benchmarking: Determine the best processing hardware for your applications. User-Friendly: No programming required—create image analysis functions with drag-and-drop ease. 📅 Book Your Demo Now: https://lnkd.in/g29ihFaQ Don’t miss out—schedule your demo today and see how easy AI-driven solutions can be! 💡 #AI #MachineVision