How is Google implementing artificial intelligence
The Amazing Ways Google Uses Deep Learning AI
Deep learning is the area of artificial intelligence where the real magic is happening right now. Traditionally computers, while being very fast, have not been very smart – they have no ability to learn from their mistakes and have to be given precise instructions in order to carry out any task.
Deep learning involves building artificial neural networks which attempt to mimic the way organic(living) brains sort and process information. The “deep” in deep learning signifies the use of many layers of neural networks all stacked on top of each other. This data processing configuration is known as a deep neural network, and its complexity means it is able to process data to a more thorough and refined degree than other AI technologies which have come before it.
Deep learning is already driving innovation at the cutting edge of artificial intelligence and it can be seen in many applications today. However, as data volumes continue to increase and processing technology becomes more affordable, many more sectors of society are likely to be impacted. Here’s a look at how one of the pioneers - Google - is already using it across many of its products and services.
Why is Google interested in deep learning?
Google has been a powerful force in championing the use of deep learning – a technology now so prevalent in cutting edge applications that its name is pretty much synonymous with artificial intelligence. There’s a simple reason for this – it works. Putting deep learning to work has enabled data scientists to crack a number of difficult cases which had proved challenging for decades, such as speech and image recognition, and natural language generation.
It’s first publicly-discussed explorations of the possibilities of deep learning began with the Google Brain project in 2011. The following year, Google announced that it had built a neural network, designed to simulate human cognitive processes, running on 16,000 computers and which was capable, after studying around 10 million images, of identifying cats.