Facebook, in an attempt to combat climate change, has announced that it is deducting its greenhouse gas emission by 75 per cent and is working on making its operations run on 100 per cent renewable energy by the end of 2020.
According to The Verge, the company began the efforts in 2013 and has signed contracts for more than 3 giga watts of new solar and wind energy ever since. In a blog post released in 2013 the company wrote, "Our long-term focus on efficiency, smart design, and energy sources mean that over time we expect our emission growth to slow, our energy mix to get cleaner, and our carbon intensity to decrease."
According to the social networking website, they have already met their 2015 goal of running 50 per cent of its facilities using renewable energy by 2018. Many other tech companies including Apple, Google and Samsung are also attempting to go green.
Researchers at Facebook have also developed a quicker and more accurate way of translating low-resources languages like Urdu and Burmese using Artificial Intelligence, said a media report. The breakthrough, which will be presented at Empirical Methods in Natural Language Processing or EMNLP, could prove to be important for Facebook, as the social media giant uses automatic language translation to help its users around the world to read posts in their preferred language, the Forbes reported.
The existing machine translation systems can achieve near human-level performance on some languages but they require access to parallel corpus vast quantities of the same sentences in different languages in order to learn, it said. The team from the Facebook AI Research (FAIR) division were able to train a machine translation (MT) system by feeding it large pieces of different text in different languages from publicly available websites like Wikipedia.
The key thing to note is that these pieces of text were independent of one another. When you have different pieces of text in different languages they're referred to as monolingual corpora, it said. "Building a parallel corpus is complicated because you need to find people fluent in two languages to create it. For instance, if you wanted to build a parallel corpus of Portuguese/Nepali, you would need to find people fluent in these two languages, which would be very difficult," Antoine Bordes, a research scientist and the head of FAIR's Paris research lab, was quoted as saying in the report.
With inputs from ANI
