2 years ago

The potential number of applications for machine learning has grown tremendously in the last several years, as AI models become increasingly https://thenewstack.io/openais-gpt-3-makes-big-leap-forward-for-natural-language-processing/. Even more promising is how machine learning models might someday revolutionize https://thenewstack.io/deep-learning-ai-detects-rare-genetic-disorders-by-scanning-faces/, and may even help us grapple with impossibly complex issues like mitigating https://thenewstack.io/machine-learning-shows-how-climate-extremes-change-global-vegetation/. But despite the great potential of machine learning models, they are https://thenewstack.io/hidden-gender-racial-biases-algorithms-can-big-deal/ and can make mistakes — sometimes with https://thenewstack.io/data-ethics-researcher-cautions-against-algorithmic-reordering-of-society/.

Typically, the AI models being used for image recognition are initially trained on a large number of images.

“Adversarial samples are noise-perturbed samples that can fail neural networks for tasks like image classification,” explained the team.