Short Bytes: Facebook AI hardware was recently opened up for public, following the steps of other tech giants like Google, IBM, Microsoft etc. Know how such hardware work and what they are capable of.
Facebook AI hardware, called ‘Big Sur’, was designed as a powerful learning tool. The main purpose of these tools is to study and make predictions to enhance the user interactions based on the user behavioral patterns. Big Sur used roughly simulated neurons to learn Artificial intelligence almost simulating the human brains. These servers were powered by NVidia powerful GPUs which have the capacity of parallel processing.
Recently, Facebook released the design of its powerful computer servers. It was designed to accelerate artificial intelligence to address AI problems like voice-recognition and Image search. These servers were as fast as twice when compared to the previous servers used by Facebook. These opening up of free software is seen as a way to speed up their growths in a broader way, spur their reputation as a technology company and also making key hiring at the end of the day.
These recent developments have been seen in the wake of a lot of giveaways by big companies. In November, Tensor by Google was opened up for the public. Tensor empowers Google’s speech recognition and Image search. Similarly, Microsoft released a machine learning software across various platforms just after the announcement was made by Google.
Following the trend, IBM also announced SystemML software which was powering the Banking sector. These software basically use machine learning. Machine learning is a subject of computer science where a machine/ software trains itself on data. With more training on data, the machine/software starts recognizing the patterns and later, is also used to make the predictions based on the patterns observed.
Looking at these recent developments by these companies, the days would not be far when people can make their own social networks or some powerful intelligent system on a small scale.
Also read: A Neural Network in 11 Lines of Python