New version of Facebook's algorithm was trained by analysing one billion Instagram photos.

Article Edited by | Jhon N |


Research from Facebook has been successful in computerised learning with image recognition.

Facebook's AI can learn from a group of unknown images, which will change the face of internet.

Dubbed Self-Supervised (SEER), machine learning models were fed one billion Instagram images, which had not been manually curated. The AI system was shown to be able to work by itself because it did not need help from a human.

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 relying on carefully labelled datasets to train their ability to recognise objects or translate text.

This research has gain a lot of attention because it seems to be a way to completely automate machine learning tasks. Self-supervised methods can serve diverse datasets without supervision.

Some natural language processing algorithms have enabled breakthroughs in application areas including question answering, machine translation, natural language inference, and so on.

Computer vision has yet to totally leap onto the supervised learning trend. Priya Gopal, software engineer at Facebook AI Research, says the SEER is a unique system. "SEER is the first fully self-supervised computer vision model that's been trained on the random internet images, and other computer vision model that was trained on the highly curated ImageNet dataset."

ImageNet provides millions of labelled pictures to computer vision communities.

SEER was one of the research project in Facebook and was used as a benchmark to measure how good self-supervised learning algorithms is.

"SEER" succeeds in learning from just random training images. This indicates that we do not need such highly curated datasets for image recognition like ImageNet for computer vision and self-training on random images produces very high-quality models.

Given the complexity and difficulty of supervised learning methods, the researchers' work was not without challenge. With the images, AI has to decide the properties of a pixel from which to determine a concept.

The researchers needed tonnes of data to derive the ideal visual representation.

She employed AI and machine learning to solve the environmental problems, which AI has been improving since Facebook introduced it. Research scientists also created a neural network, a deep-learning algorithm that models the brain's synaptic connectivity patterns to highlight different objects in a picture.

The size of the dataset was large, to say the least. FACEBOOK has many GPUs and 32GB of RAM to process the photo. Goyal explains that the current system will have to be expanded to meet new demands.

"We need to reduce training time on more GPUs as the model grows more complex. Such a challenge would best be addressed by developing efficient software and techniques "He says.

There are still some issues to be resolved, that's why the developers give potential for the platform. "SEER train large models on large abundance of random internet images to speed up modelling process," she says.

"This could be the start of a major leap forward in artificial intelligence systems that learn just like humans do."


Facebook has called for additional research and development work to push the SEER into its next stage of development. The research team developed the VISSL library, which is a PyTorch project open-sourced for everyone to contribute with it.