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Enhancing AI-Powered Computer Vision Through Physics-Awareness

Unite.AI

Concurrently, physics-based research sought to unravel the physical principles underlying many computer vision challenges. However, assimilating the understanding of physics into the realm of neural networks has proved challenging.

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Universal Building Blocks: Research Reveals Neural Networks Learn the Same Patterns When…

NYU Center for Data Science

The research revealed that regardless of whether a neural network is trained to recognize images from popular computer vision datasets like ImageNet or CIFAR, it develops similar internal patterns for processing visual information. The analogy to astrophysics is particularly apt.

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This AI Tool Explains How AI ‘Sees’ Images And Why It Might Mistake An Astronaut For A Shovel

Marktechpost

Thus, there is a growing demand for explainability methods to interpret decisions made by modern machine learning models, particularly neural networks. CRAFT addresses this limitation by harnessing modern machine learning techniques to unravel the complex and multi-dimensional visual representations learned by neural networks.

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Geoffrey Hinton, Godfather of AI Fears for Humanity’s Fate

ODSC - Open Data Science

Geoffrey Hinton is a computer scientist and cognitive psychologist known for his work with neural networks who spent the better part of a decade working with Google. Due to the nature of neural networks they are designed to be similar to human brains. For his part, Hinton responded in two parts. “

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Getting ready for artificial general intelligence with examples

IBM Journey to AI blog

Most importantly, no matter the strength of AI (weak or strong), data scientists, AI engineers, computer scientists and ML specialists are essential for developing and deploying these systems. Connectionist AI (artificial neural networks): This approach is inspired by the structure and function of the human brain.

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Data science vs. machine learning: What’s the difference?

IBM Journey to AI blog

IBM computer scientist Arthur Samuel coined the phrase “machine learning” in 1952. In 1962, a checkers master played against the machine learning program on an IBM 7094 computer, and the computer won. Deep learning teaches computers to process data the way the human brain does.

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Tools for trustworthy AI

IBM Journey to AI blog

The tool uses deep neural network models to spot fake AI audio in videos playing in your browser. Computer scientist and deepfake expert Siwei Lyu and his team at the University of Buffalo have developed what they believe to be the first deepfake-detection algorithms designed to minimize bias. .”

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