At a time when many versions of AI rely on pre-established data sets for image recognition, Facebook has developed SEER (Self-supERvised) – a deep learning solution able to register images on the Internet independent of curated and labeled data sets. With major advances already underway in natural language processing (NLP) including machine translation, natural language interference and question answering, SEER uses an innovative billion-parameter, self-supervised computer vision model able to learn from any online image. Thus far, the Facebook AI team has tested SEER on one billion uncurated and unlabeled public Instagram images. The new program performed better than the most advanced self-supervised systems as well as self-supervised models on downstream tasks such as low-shot, object detection, image detection and segmentation. In fact, exposure to only 10 percent of the ImageNet data set still resulted in a 77.9 percent recognition rate by SEER.
The extent to which AI systems have made life easy in possibly every field needs no special mention. Healthcare, defence, transportation, or any sector for that matter – you name it and you know how positive the impact has been. Be it assisting the doctors while surgery is performed, controlling the traffic, assisting you at restaurants, teaching online or even getting done with your daily chores, AI has got you covered. However, a point here to note is that no matter how much AI promises to automate, human involvement cannot be eliminated totally. Simply put, AI has no meaning unless humans are involved.
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.
AI systems already display vision, language and controlled motor skills, but researchers are looking to answer the question: 'When will we have robots that can do housework, communicate in natural language conversations and defend themselves against discrimination?' At the global artificial intelligence conference IJCAI-ECAI 2018 held in Stockholm, Sweden, AI experts and research students from top universities around the world came together to discuss the state of AI as it stands today, and where we are headed in the not-so-distant future. "There won't be a Big AI Bang where complete AI systems suddenly surround us in the next year," says Christian Guttmann, Executive Director of the Nordic Artificial Intelligence Institute, "Instead, we will see more and more AI features being included in our products and services." Most researchers are in agreement – artificial intelligence will not become ubiquitous in a day. "The truth is, that despite tremendous advances in AI technologies, we are still far from having robot maids," according to Joyce Chai, Director of the Language and Interaction Research Group at Michigan State University.
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.