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Explore Meta AI’s self-supervised learning demo for images
Article's Summary:
Today, we are releasing the first-ever external demo based on Meta AI's self-supervised learning work. Computer vision powered by self-supervised learning is an important part of helping Meta AI researchers deliver AI systems that are more robust and less domain-centric in nature. Using self-supervised learning to advance computer visionWhile we previously released the DINO code, this demo allows researchers and engineers to explore how the model understands images, to test its robustness, and to try it on their own images. Through image retrieval, a person could select a picture and discover similar images from a third-party data set of five million images. We'll need AI that can learn from everything it sees and hears, and that's only possible with self-supervised learning.
Article's Keywords: 'meta', 'demo', 'learning', 'ais', 'data', 'selfsupervised', 'ai', 'similar', 'future', 'image', 'explore', 'dino', 'images'
Using AI to deliver more inclusive biographical content on Wikipedia
Article's Summary:
Together, we built an AI system that can research and write first drafts of Wikipedia-style biographical entries. Today, we are open-sourcing an end-to-end AI model that automatically creates high-quality biographical articles about important real-world public figures. This starts with the web content used to create Wikipedia entries, which may be flawed or reflect cultural biases. Beyond this, Wikipedia articles must be written based on factual evidence, often sourced from the internet. When a Wikipedia editor or our AI model writes a biography, information is pulled from around the internet and cited.
Article's Keywords: 'biographical', 'biographies', 'wikipedia', 'used', 'deliver', 'women', 'model', 'information', 'using', 'inclusive', 'groups', 'ai', 'text', 'articles', 'work', 'content'
How AI is helping address the climate crisis
Article's Summary:
As a force multiplier for scientific research, AI is helping accelerate the rate of progress across many domains, including those most important to solving the climate crisis. One promising attempt at this is the Open Catalyst Project, run by a partnership between Meta AI and Carnegie Mellon University’s Department of Chemical Engineering. We believe this work will enable AI systems to grow sustainably and with lower infrastructure needs. In agriculture, AI systems are helping optimize water and fertilizer usage and increase the productivity of farm equipment and systems. We’re incredibly optimistic about the impact AI is going to have on climate and sustainability, and the role that our researchers and engineers can play in helping build it.
Article's Keywords: ''carbon', 'meta', 'energy', 'researchers', 'systems', 'data', 'efficiency', 'climate', 'helping', 'ai', 'crisis', 'models', 'needed', 'address'
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