NEWS IN BRIEF: AI/ML FRESH UPDATES

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Unlocking the Secrets of Time Series for LLMs

Foundation models, like Large Language Models (LLMs), are being adapted for time series modeling through Large Time Series Foundation Models (LTSM). By leveraging sequential data similarities, LTSM aims to learn from diverse time series data for tasks like outlier detection and classification, building on the success of LLMs in computational linguistic...

Cutting-Edge Innovations in Computer Vision

TDS celebrates milestone with engaging articles on cutting-edge computer vision and object detection techniques. Highlights include object counting in videos, AI player tracking in ice hockey, and a crash course on autonomous driving...

Unraveling Language Models' Visual Intelligence

MIT researchers found that large language models can understand the visual world and generate complex scenes. By querying LLMs to self-correct code for images, they improved simple drawings and trained a vision system without using visual...

Shadow Modeling Unveils Hidden Objects in 3D Scenes

MIT and Meta researchers develop PlatoNeRF, a computer vision technique using shadows and machine learning to create accurate 3D models of scenes, improving autonomous vehicles and AR/VR efficiency. Combining lidar and AI, PlatoNeRF offers new opportunities for reconstructions and will be presented at the Conference on Computer Vision and Pattern...

Boosting ML Efficiency with Sprinklr on AWS Graviton3

Sprinklr utilizes AI to enhance customer experience, achieving 20% throughput improvement with AWS Graviton3 for cost-effective ML inference. Thousands of servers fine-tune and serve over 750 AI models across 60+ verticals, processing 10 billion predictions...

Divergent AI Applications

Choosing the right AI use case is crucial for success. AI can be valuable even with moderate performance, offering unique solutions. Examples include Sensor Fusion and Generative AI in everyday...

AI-powered Video Action Finder

Scientists at MIT and the MIT-IBM Watson AI Lab have developed a new approach to teach computers to pinpoint actions in videos using only transcripts. This method, called spatio-temporal grounding, improves accuracy in identifying actions in longer videos and could have applications in online learning and...

Revolutionizing Computer Vision: Navigating the AI Landscape

Recent advancements in AI, including GenAI and LLMs, are revolutionizing industries with enhanced productivity and capabilities. Vision transformer architectures like ViTs are reshaping computer vision, offering superior performance and scalability compared to traditional...

Enhancing AI's Peripheral Vision

MIT researchers developed a dataset to simulate peripheral vision in AI models, improving object detection. Understanding peripheral vision in machines could enhance driver safety and predict human behavior, bridging the gap between AI and human...

ML Deployment: From Model to Cloud in Python

Article highlights deploying ML models in the cloud, combining CS and DS fields, and overcoming memory limitations in model deployment. Key technologies include Detectron2, Django, Docker, Celery, Heroku, and AWS...

Transforming Food Images into Recipes: The Power of AI and FIRE

AI technology has the ability to transform food images into recipes, allowing for personalized food recommendations, cultural customization, and automated cooking execution. This innovative method combines computer vision and natural language processing to generate comprehensive recipes from food images, bridging the gap between visual depictions of dishes and symbolic...

The Reign of ResNet: A New Era with Vision Transformers

Computer vision has evolved from small pixelated images to generating high-resolution images from descriptions, with smaller models improving performance in areas like smartphone photography and autonomous vehicles. The ResNet model has dominated computer vision for nearly eight years, but challengers like Vision Transformer (ViT) are emerging, showing state-of-the-art performance in computer...

Revolutionizing Music AI: 3 Breakthroughs to Expect in 2024

2024 could be the tipping point for Music AI, with breakthroughs in text-to-music generation, music search, and chatbots. However, the field still lags behind Speech AI, and advancements in flexible and natural source separation are needed to revolutionize music interaction through...

The Power of Gaussian Splatting: Revolutionizing 3D Representations

Gaussian splatting is a fast and interpretable method for representing 3D scenes without neural networks, gaining popularity in a world obsessed with AI models. It uses 3D points with unique parameters to closely match renders to known dataset images, offering a refreshing alternative to complex and opaque methods like...

Revolutionizing Mining Equipment Monitoring with AWS Prototyping and Computer Vision

ICL, a multinational manufacturing and mining corporation, developed in-house capabilities using machine learning and computer vision to automatically monitor their mining equipment. With support from the AWS Prototyping program, they were able to build a framework on AWS using Amazon SageMaker to extract vision from 30 cameras, with the potential to scale to...