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Visual AI Takes Flight at Canada’s Largest, Busiest Airport

NVIDIA

A member of the NVIDIA Metropolis vision AI partner ecosystem, Zensors helped the Toronto Pearson operations team significantly reduce wait times in customs lines, decreasing the average time it took passengers to go through the arrivals process from an estimated 30 minutes during peak periods in 2022 to just under six minutes last summer.

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Applying Visual AI to Legacy Security Systems

DataRobot Blog

Artificial intelligence (AI) can accelerate inspections by automating some reviews and prioritizing others, and unlike humans at the end of a long shift, an AI’s performance does not degrade over time. The training dataset used to train the AI model contains approximately 5,000 X-ray security images. AI CLOUD FOR PUBLIC SECTOR.

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The Plagiarism Problem: How Generative AI Models Reproduce Copyrighted Content

Unite.AI

The rapid advances in generative AI have sparked excitement about the technology's creative potential. How Neural Networks Absorb Training Data Modern AI systems like GPT-3 are trained through a process called transfer learning. are more prone to regenerating verbatim text passages compared to smaller models.

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AI Emotion Recognition Using Computer Vision

Heartbeat

It gives the computer the ability to observe and learn from visual data just like humans. In this process, the computer derives meaningful information from digital images, videos etc. and applies this learning tosolving problems. Summary Facial expression recognition is a crucial component of human-computer interaction.

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YOLOv7: The Most Powerful Object Detection Algorithm (2023 Guide)

Viso.ai

It requires several times cheaper hardware than other neural networks and can be trained much faster on small datasets without any pre-trained weights. Most algorithms use a convolutional neural network (CNN) to extract features from the image to predict the probability of learned classes.

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Generative AI: The Idea Behind CHATGPT, Dall-E, Midjourney and More

Unite.AI

The Technologies Behind Generative Models Generative models owe their existence to deep neural networks, sophisticated structures designed to mimic the human brain's functionality. By capturing and processing multifaceted variations in data, these networks serve as the backbone of numerous generative models.