How Google is benefited using AI and ML in Google Photos

In this article, I am going to discuss how MNCs are getting benefited from using AI/ML. Before going into the case study first I want to discuss something on AI and ML.

What is artificial intelligence (AI)?

AI refers to the ability of a computer or a computer-enabled robotic system to process information and produce outcomes in a manner similar to the thought process of humans in learning, decision making, and solving problems. So that it can function without the intervene of humans into the system.

What is Machine Learning (ML)?

Machine learning is a part of AI, that deals in the training part of the system or machine.

Machine learning enables computer systems to learn from and interpret data without human input, refining the process through iterations to produce programs tailored to specific purposes.

Machine learning can also be used to run simulations, using predictive data models to discover patterns based on a variety of inputs.

Now we have defined ML, lets see what can we do using ML. There are different algorithms we can train our system, they are

Supervised learning:

Let's take an example, Suppose your school teacher shows you some fruits and explains to you taking each fruit one at a time, Taking one fruit, and explains that “ This is an apple ”, taking another fruit and says “ This is mango” and so on...

Now that you know some fruits when we some fruits by the side of the road we can say which fruit is that. because we have an idea about it before we can say what fruit is it. This is called Supervised Learning.

A common example of this is image classification. Often, we want to build systems that will be able to describe a picture. To do this, we normally show a program thousands of examples of pictures, with labels that describe them. During this process, the program adjusts its internal parameters. Then, when we show it a new example of a photo with an unknown description, it should be able to produce a reasonable description of the photo.

Unsupervised Learning:

Unsupervised learning is the training of a machine using information that is neither classified nor labeled and allowing the algorithm to act on that information without guidance. Here the task of the machine is to group unsorted information according to similarities, patterns, and differences without any prior training of data.

Here we don't have any teacher to teach the machine, the machine is restricted to find the hidden structure in unlabeled data by our-self.

Unsupervised learning studies how systems can infer a function to describe a hidden structure from unlabeled data. The system doesn’t figure out the right output, but it explores the data and can draw inferences from datasets to describe hidden structures from unlabeled data.

An example of this includes clustering to create segments in a business’s user population. In this case, an unsupervised learning algorithm would probably create groups (or clusters) based on parameters that a human may not even consider.

Semi-Supervised Learning:

It is in between supervised and unsupervised learning since they use both labeled and unlabeled data for training — typically a small amount of labeled data and a large amount of unlabeled data. The systems that use this method are able to considerably improve learning accuracy.

Reinforcement learning:

It is a learning method that interacts with its environment by producing actions and discovers errors or rewards. Trial and error search and delayed reward are the most relevant characteristics of reinforcement learning. This method allows machines and software agents to automatically determine the ideal behavior within a specific context in order to maximize its performance. Simple reward feedback is required for the agent to learn which action is best; this is known as the reinforcement signal.

Google Photos

• The App will recognize the face on the photos based on the past user’s data, which is possible with the latest technology.
• Once you snap a shot of someone, a known person whom you photographed earlier also, then the app will recognize his or her face and suggest sharing the photos with him or her.
• In the case of grouped photos, the app will suggest the list of persons with whom you will share the photos based on the faces it recognized.
• The Google App will also help you to delete the photos which are not good such as blurry photos. You can delete them easily on its suggestion.
• Once you have provided the necessary permission to the app, the app will keep sending the photos automatically to the related person whose face it recognizes in the photos also share on connected social media platforms.

This has given a user-friendly experience to the users. The user can easily access his photos. He can also search for others by tapping on the faces. Google uses image processing in this app to determine the faces and sort them accordingly.

Coming to google search it has been ruling the market of the search for a long time. In July 2020, online search engine Bing accounted for 6.43percent of the global search market, while market leader Google had a market share of 86.86 percent. Chinese search engine Baidu’s market share was 0.68 percent.

Ever since the introduction of Google Search in 1997. Google has dominated the search engine market, maintaining an 86.86 percent market share as of July 2020. The majority of Google revenues are generated through advertising. The company has also expanded its services to mail, productivity tools, enterprise products, mobile devices, and other ventures. As a result, Google earned one of the highest tech company revenues in 2019 with roughly 160.74 billion U.S. dollars.

Each algorithm used has AI and ML in them so as to make the search results effective.

Google handles over 40,000 queries per second. That is 3.5 billion searches per day.

Google has been updating its algorithms in google search engines in such that the user gets his/her query resolved fast and efficiently.