What are the use cases of the neural network in google applications?
Google is providing many services to the user. Services are Google Map, Google mail service, Google Chrome, Google Assistant, YouTube, Google Pay, etc.
What is a neural network ??
Neural networks like a human brain Neurons are connected with each other and sending “messages” from one neuron to another and process the information and provide certain output.
A neural network works with various layers every layer output is input to another layer. Every layer process some data and provide some output.
Google Map using Neural Network
Google map is one of the services provided by Google. Google Maps are providing Real-time traffic updates, find the best route when driving, with real-time updates on traffic jams, accidents, road closures, and speed traps, Real-time updates for public transportation, and more.
Data is collected from various android devices sensors in the android devices we have an option to switch ON and OFF the location. All this information is put into neural networks designed by DeepMind that pick out patterns in the data and use these patterns to predict future traffic.
Gmail application using the neural network
One more feature is added in the Gmail application when the user writes the mail then the machine learning model predicts the next word based on the previous word and helps to frame the sentence. Gmail application goal is to help the user for writing the mail faster and efficiently and helps to focus on work, not on the mail writing.
YouTube using the neural network
YouTube providing video recommendations to users. Users can easily watch the videos according to their choice. YouTube helps the user find videos easily according to the user's wishes.YouTube is one of the largest platforms of videos and users are uploading thousands of videos to the platform every second.YouTube machine learning recommendation model handle this huge data of video and will provide quality recommendations in real-time.
YouTube recommendation system recommends video according to YouTube history, the number of videos watched, user interest and search query tokens, etc.
YouTube is providing meaningful content to its user and helps to save time for users. The user now can easily find the videos according to requirements not have to waste much time searching the videos.
YouTube uses neural network for providing the recommendation to the user. The neural process this huge amount of video data and then providing recommended video as an output.
Google Translator using neural networks
Google translator is one of the applications or a machine used to translate a sentence from one language to another language. Communicating with each other is an essential part of human life. Communication between people makes them connected. Language translation provides a critical cultural and economic bridge between people from different countries and ethnic groups.
Use Cases of translation in communication:
- business: international trade, investment, contracts, finance
- commerce: travel, purchase of foreign goods and services, customer support
- media: accessing information via search, sharing information via social networks, localization of content and advertising
- education: sharing of ideas, collaboration, translation of research papers
- government: foreign relations, negotiation
In the below gif this is a demo of the English language is translating into Japanese and Japanese to the English language. This translation is based on a deep learning neural network. Neural Network taking the English language as an input and giving as Japanese an output.
A neural network designed with different mathematical functions for processing the input language and for convert into output language.
Google Adds using neural network
Consumers are more curious, more demanding, and they expect to get things done faster because of mobile. As a result, users expect ads to be helpful and personalized. Google has introduced responsive search ads. Google Adds combines our creativity with the power of Google’s machine learning to help you deliver relevant, valuable ads to earn the maximum profit by seller and buyer.
How Ads are showing according to their requirement of buyer :
- People watch over 1 billion hours of video on YouTube every day. Before buying any product people take the information about the product, how to to use the product, which one is best, checking product is available or not in nearby shops, etc. For example, car buyer go to YouTube for information before purchasing the car.And people go to youtube for food preparation tips before deciding what ingredients to buy. That means it’s critical video ads show at the right moment to the right audience.
- Whether users start their research on YouTube or Google, people still make the majority of their purchases in physical stores. In fact, mobile searches for “near me” have grown over 3X in the past two years,4 and almost 80 percent of shoppers will go in-store when there’s an item they want immediately. So google ads shows ads according to their search for nearby shops and products.
Brands like GittiGidiyor,eBay company are using Smart Shopping campaigns to simplify how they manage their ads and deliver better results. GittiGidiyor was able to increase return on ad spend by 28 percent and drive 4 percent more sales while saving time managing campaigns.
Google Lens using neural network
Google Lens is an image recognization technology, used for providing relevant information related to objects it identifies using visual analysis based on a neural network.
Google Lens uses Tensor Flow which is Google’s open-source machine learning framework. TensorFlow helps connect images to the words that best describe them.
Not only google using neural networks for its application but also using by almost all companies for various use cases.
The neural network providing the best accurate results used in various use cases such as image classification, recommendation, the medical field for predict test reports, stock market for price estimation, credit card fraud detection , face recognization, etc.
In the future, the neural network will be used by different applications for different usecases. The neural network will always provide the best and accurate result because of its high processing of the input data.
