If Facebook has an unofficial slogan, an equivalent to Google's "Don't Be Evil" or Apple's "Think Different," it is "Move Fast and Break Things." It means, at least in theory, that one should iterate to try news things and not be afraid of the possibility of failure. In 2021, however, with social media currently being blamed for a plethora of societal ills, the phrase should, perhaps, be modified to: "Move Fast and Fix Things." One of the many areas social media, not just Facebook, has been pilloried for is its spreading of certain images online. It's a challenging problem by any stretch of the imagination: Some 4,000 photo uploads are made to Facebook every single second.
Facebook's researchers have unveiled a new AI model that can learn from any random group of unlabeled images on the internet. Facebook's researchers have unveiled a new AI model that can learn from any random group of unlabeled images on the internet, in a breakthrough that, although still in its early stages, the team expects to generate a "revolution" in computer vision. Dubbed SEER (SElf-SupERvised), the model was fed one billion publicly available Instagram images, which had not previously been manually curated. But even without the labels and annotations that typically go into algorithm training, SEER was able to autonomously work its way through the dataset, learning as it was going, and eventually achieving top levels of accuracy on tasks such as object detection. The method, aptly named self-supervised learning, is already well-established in the field of AI: it consists of creating systems that can learn directly from the information they are given, without having to rely on carefully labeled datasets to teach them how to perform a task such as recognizing an object in a photo or translating a block of text.
The news: Facebook revealed a self-supervised artificial intelligence model it claims can accurately learn to categorize Instagram images with less human assistance than before. Here's how it works: Researchers at Facebook fed the AI, called SEER, over 1 billion unlabeled images extracted from public IG accounts. Using self-supervised learning--a method where a machine learns to train itself without human data labeling--SEER achieved a classification accuracy score of 84.2%, outperforming "the most advanced, state-of-the-art self-supervised systems," per Facebook. What's next?: While SEER is still in its early stages, Facebook believes it can bring about real-world benefits. Here are some of SEER's possible use cases: The bigger picture: Ever-increasing data sharing by users will likely lead to rapid AI advancement.
Our mission to make business better is fueled by readers like you. To enjoy unlimited access to our journalism, subscribe today. Facebook has created an artificial intelligence system that may make it much more efficient for companies to train such software for a range of computer vision tasks, from facial recognition to functions needed for self-driving cars. The company unveiled the new system in a series of blog posts Thursday. Today, training machine-learning systems for such tasks often requires hundreds of thousands or even millions of labeled data sets.
Recently, self-supervised learning methods like MoCo, SimCLR, BYOL and SwAV have reduced the gap with supervised methods. These results have been achieved in a control environment, that is the highly curated ImageNet dataset. However, the premise of self-supervised learning is that it can learn from any random image and from any unbounded dataset. In this work, we explore if self-supervision lives to its expectation by training large models on random, uncurated images with no supervision. Our final SElf-supERvised (SEER) model, a RegNetY with 1.3B parameters trained on 1B random images with 512 GPUs achieves 84.2% top-1 accuracy, surpassing the best self-supervised pretrained model by 1% and confirming that self-supervised learning works in a real world setting. Interestingly, we also observe that self-supervised models are good few-shot learners achieving 77.9% top-1 with access to only 10% of ImageNet. Code: https://github.com/facebookresearch/vissl