Deep learning is currently the hottest area of ​​artificial intelligence. Traditional computers, although fast, lack intelligence. These computers cannot learn from past mistakes and must obtain precise instructions when performing a task.
Deep learning techniques involve the development of artificial neural networks that allow computers to simulate how the brain classifies and processes information. "Depth" in deep learning means that the neural network adopts a hierarchical structure. A data processing configuration with this structure is called a deep neural network, and better data processing results can be obtained than other artificial intelligence technologies.
Deep learning is driving innovation in artificial intelligence, and deep learning can be seen in many applications today. As the amount of data continues to grow, the cost of computing power is getting lower and lower, and more industries will be affected. Here, we will look at how Google (microblogging), a pioneer in the industry, applies deep learning technology to products and services.
Why is Google interested in deep learning?
Google is an important driving force in the application of deep learning. At present, deep learning is being widely used and even regarded as synonymous with artificial intelligence. The reason is simple, deep learning technology can bring good results. Using deep learning techniques, data scientists solve problems that have been plagued for many years, such as speech recognition and image recognition, and natural language processing.
The exploration of the possibilities of deep learning began in the 2011 "Google Brain Project." The following year, Google announced the development of neural networks to simulate human cognitive processes. Based on 16,000 computers, this neural network learns to recognize cats in photos after learning about 10 million photos.
In 2014, Google acquired DeepMind, a deep learning startup from the UK.
DeepMind is the first to try to link existing machine learning techniques with cutting-edge research in neuroscience to develop systems that are closer to "real" intelligence, such as the human brain. DeepMind is the developer of AlphaGo. AlphaGo first started learning video games, and then learned Go, which proved how DeepMind's algorithm learns to complete tasks and become a master in a certain field.
What is Google using deep learning technology in its mail service?
The concept of deep learning has been proven in the lab and in the Go game. At the same time, Google is deploying deep learning technologies in many services.
The first practical application of Google Deep Learning is image recognition. Google scanned millions of images compiled by search engines in order to classify them more accurately, giving users more accurate search results. In the use of deep learning for image analysis, Google's latest breakthrough is image enhancement. This involves using existing data to infer, or recovering or filling in missing details in the image with known similar images.
Google’s other platform, Google Cloud Video Intelligence, focuses on providing video analytics to new users. Videos saved on Google servers can be segmented and analyzed, automatically generated, or issued with security alerts when artificial intelligence believes suspicious information.
Language processing is another area of ​​Google's use of deep learning technology. Google Assistant language recognition artificial intelligence uses deep neural networks to learn how to better understand voice commands and problems. The technology developed by the Google Brain team is used in this project. Recently, Google Translate has been included in the responsibility of the Google Brain team. This translation tool has been redeveloped to incorporate everything into a deep learning environment based on "Google Neuro Machine Translation."
The third major way Google’s core services leverage deep learning technology is to provide more useful advice on YouTube. The Google brain team once again provided the technology. This technology monitors and records the user's browsing habits as the user plays the video. The data has shown that recommending to viewers what they want to watch next is the key to retaining users and gaining more advertising revenue. Deep neural networks are used to study and learn the habits and preferences of viewers, pointing out which factors can keep viewers in front of the screen for longer.
What are the applications of Google Deep Learning?
Of course, given the success that has been achieved, there is no doubt that Google hopes to use this technology for more future-oriented projects.
In 2015, Google opened up the TensorFlow machine learning and deep learning software platform. This allows everyone to develop neural network-based solutions using the same technology as Google.
Through the "cloud machine learning engine," Google also provides storage and processing capabilities to third-party developers. This means developers don't have to invest large sums of money in a powerful computing infrastructure.
Alphabet's unmanned subsidiary Waymo is using deep learning algorithms to develop automated driving systems. This can help driverless cars analyze and react to their surroundings more efficiently.
At the same time, DeepMind is currently advancing medical health programs, including identifying early signs of eye damage and judging cancer cell growth.
Where is the future?
Google is an important force in promoting deep learning technology. Google's research and investment can benefit everyone from this technology. We will increasingly use deep learning systems to process our own data. Many people hope that deep learning technology will make a leap forward in the next few years, both for drug development and for space exploration. The basic work done by Google will be an important part of it.
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