Facebook is playing a huge role in artificial intelligence research. It’s not only a core part of the Facebook platform, it’s central to how the organization works. The company launched its AI research lab – FAIR – back in 2013.
Today, led by some of the best minds in the field, it’s not only helping Facebook to leverage artificial intelligence, it’s also making it more accessible to researchers and engineers around the world.
Let’s take a look at some of the tools built by Facebook that are doing just that.
PyTorch: Facebook’s leading artificial intelligence tool
PyTorch is a hugely popular deep learning framework (rivalling Google’s TensorFlow) that, by combining flexiblity and dynamism with stability, bridges the gap between research and production. Using a tape-based auto-differentiation system, PyTorch can be modified and changed by engineers without losing speed. That’s good news for everyone.
Although PyTorch steals the headlines, there are a range of supporting tools that are making artificial intelligence and deep learning more accessible and achievable for other engineers.
Read next: Is PyTorch better than Google’s TensorFlow?
Find PyTorch eBooks and videos on the Packt website.
Facebook’s computer vision tools
Another field that Facebook has revolutionized is computer vision and image processing.
Detectron, Facebook’s state-of-the-art object detection software system, has powered many research projects including Mask R-CNN – a simple and flexible way of developing Convolution Neural Networks for image processing. Mask R-CNN has also helped to power DensePose, a tool that map all human pixels of an RGB image to a 3D surface-based representation of the human body.
Facebook has also heavily contributed to research in detecting and recognizing Human-Object interactions as well. Their contribution to the field of generative modeling is equally very important, with tasks such as minimizing variations in the quality of images, JPEG compression as well as image quantization now becoming easier and more accessible.
Facebook, language and artificial intelligence
We share updates, we send messages – language is a cornerstone of Facebook. This is why it’s such an important area for Facebook’s AI researchers.
There are a whole host of libraries and tools that are built for language problems. FastText is a library for text representation and classification, while ParlAI is a platform pushing the boundaries of dialog research.
The platform is focused on tackling 5 key AI tasks: question answering, sentence completion, goal-oriented dialog, chit-chat dialog, and visual dialog. The ultimate aim for ParlAI is to develop a general dialog AI.
There are also a few more language tools in Facebook’s AI toolkit – Fairseq and Translate are helping with translation and text generation, while Wav2Letter is an Automatic Speech Recognition system that can be used for transcription tasks.
Rational artificial intelligence for gaming and smart decision making
Although Facebook isn’t known for gaming, its interest in developing artificial intelligence that can reason could have an impact on the way games are built in the future.
ELF is a tool developed by Facebook that allows game developers to train and test AI algorithms in a gaming environment. ELF was used by Facebook researchers to recreate DeepMind’s AlphaGo Zero, the AI bot that has defeated Go champions. Running on a single GPU, the ELF OpenGo bot defeated four professional Go players 14-0. Impressive, right?
There are other tools built by Facebook that aim to build AI into game reasoning. Torchcraft is probably the most notable example – its a library that’s making AI research on Starcraft – a strategy game – accessible to game developers and AI specialists alike.
Facebook is defining the future of artificial intelligence
As you can see, Facebook is doing a lot to push the boundaries of artificial intelligence. However, it’s not just keeping these tools for itself – all these tools are open source, which means they can be used by anyone.
