AI year in review
At Facebook, we think that artificial intelligence that learns in new, more efficient ways – much like humans do – can play an important role in bringing people together. That core belief helps drive our AI strategy, focusing our investments in long-term research related to systems that learn using real-world data, inspiring our engineers to share cutting-edge tools and platforms with the wider AI community, and ultimately demonstrating new ways to use the technology to benefit the world. In 2018, we made important progress in all these areas.
We presented new research highlighting the long-term feasibility and immediate benefits of working with less supervised data, in projects that ranged from improved image recognition to expanding the number of languages that our services can understand and translate. We released a number of platforms and tools to help others transition their AI research into production applications, including updating our popular open source PyTorch deep learning framework with a new, more versatile 1.0 version that includes additional support and entry points for newcomers. And in addition to publishing a wide range of public research papers and related models and data sets, we showed that AI has the potential to improve lives, by assisting with MRI scans, disaster relief efforts and tools to help prevent suicides.
Here are some highlights of our efforts in AI throughout the year.