Artificial Intelligence has been around since a long time, and has been in the buzz due to its extraordinary applications. The term AI was first proposed by John McCarthy in 1956, at the time when the IT industry hadn’t taken the revolution as it is in the 21st century, so the concepts were only limited to the theories. Today, we find the AI being used in almost every successful product which is directly or indirectly communicating with the user.
AI is strongly used in almost every industries some of them are as follows :
- Social Networks
- Health Care
- Digital Media
Any leading product in above industries uses AI services. AI powered products have given the users the ability to use the products with ease and give the friendliness in the service. In today’s competitive market users have many options for products in the same industry, things are getting changed rapidly due to increase of data and users, no user will waste time in manually setting the app’s requirements, the product itself has to adapt to the user’s perspective. And like this all sort of requirements are fulfilled by AI.
What does AI actually mean?
The primary goal of any product would be to serve the user with the best output, for example, if user needs to reach any particular destination then one would need Google Maps. User will provide the source and destination and will expect the best route, so in this case the Google Map will take many factors into consideration like the traffic in different routes, shortest possible route etc, and based on these factors decides the final destination route. So considering multiple factors and using the available data and generating the appropriate output is what AI actually means. Today many successful products like Facebook, Twitter, Netflix, Self Driving cars, Gmail (Autocomplete feature) uses AI driven programs in one or the other way. There have been many concepts explaining the mechanism of Artificial Intelligence. The definition stated for AI is artificial intelligence (AI) is the ability of a machine or a computer program to think and learn. The concept of AI is based on the idea of building machines capable of thinking, acting, and learning like humans.
People are often confusing with the two terms AI and Machine Learning as interchangeable words. But actually Machine Learning falls under the area of AI, so if somebody refers to Machine Learning then they are referring to the applications of AI. So basically Machine Learning is a subset of AI.
Branches of AI
To understand the AI in deep, we need to go through the sub levels or the branches of AI. As we have already gone through the meaning of AI, let us go through the branches of AI .
The science of making the software learn by itself from the provided data is known as Machine Learning. ML is the branch or can be said as application of AI. It is a type of application in which it provides the system to improve and learn from the data provided. With every iteration the system is smarter from the previous iteration. Knowledge is the base for every ML application because the system keeps on improving from the knowledge provided. The more the knowledge the more accurate the result will be.
It is next to impossible to ignore the Machine Learning applications in today’s tech generation, some of the applications which are widely used in our day to day life are as follows.
- Virtual Assistants : In the world of Information Technology, all the people are connected to smartphone and every smartphone no matter the manufacturer, are having the services of Virtual Assistants, Android users have Google Assistant, iPhone users have Siri, Amazon also provides the Virtual assistant named Alexa. All these Virtual Assistants are the result of Machine Learning. Now the role of Machine Learning in this Virtual Assistant is to gather the information asked by the user based on his/her previous queries and information. Virtual assistant also triggers any app from the device, for example if any command given to Assistant like “Make a Phone call to John”, then the virtual assistant will open the voice call app and will dial to John.
- Social Media applications : Social Media users are increasing day by day, with increase in the users, there is an increase in the data. Managing every user manually is quite impossible so many features are powered by Machine Learning which helps the users to use the applications and get the best result. Some of the Social Media examples are as follows :
- Facebook has one of the most popular features which is Friends Suggestion. The user will get the suggestion of the friends based on the profile visits, mutual friends and some common city or work place shared by users. This feature takes the huge advantage of the Machine Learning.
- One of the widely used social networking services is Twitter, it is hard to ignore the influence of this platform in today’s generation. Twitter shows the suggestion of people to follow to the users based on some similar following.
- Face recognition is expanding in almost every photo sharing application, one of the prime services given by Facebook is to detect the person and the things get visible in the picture, is again the example of Machine Learning. For this the software of face recognition should have the base knowledge of the person visible in the picture, based on this knowledge the software will identify the user.
- World’s most successful video sharing platform YouTube makes takes the most advantage of Machine Learning concepts, the recommended videos shown below the video is always related to the current playing video.
Natural Language Processing
Natural Language Processing is again one of the most important areas of Artificial Intelligence. The main goal if NLP is to process the human language and transform it into the computer understandable language. NLP becomes the bridge between human and the computer. The data which is used or processed by the computer is in linear form, and the data exchanged between Humans is in non linear or unstructured form, which is not understandable by computers. Current tech generation gives users the ability to talk with the computers, this feature is only possible because of NLP. Let us go through the step by step mechanism of NLP :
- Sentence Segmentation : The process of breaking down the paragraph into the sentence and sentence into the meaningful words. These words will have some meaning which will create the links between the words.
- Words Tokenization : In the first step the paragraph is broken down into the sentences and now in this step the sentences are further broken down into the words. These words will have separate meanings.
- Deriving the meaning : When the words and tokens are separated from the sentence, now it’s time to analyze each word separately and know whether it’s a noun, verb, adjective or any other parts of speech. This step will help us in understanding the meaning of the words and will also make the sentence more sensible.
- Text Lemmatization : The process of finding the similar concept from two different sentences is known as Text Lemmatization. Let us go through the below example :
- India is democratic country.
- Bharat is a democratic country.
In the above sentences both are referring to one country, but computer identifies this as two separate words and has different meanings, but making computer understand that both words (India & Bharat) are the name of on single country, we need to follow this:
- Checking Stop words : The words which creates lots of noise in between the sentence are often referred as the stop words. The words like ‘and’, ‘the’, ‘a’ are used in between the sentences by the humans. These words are sometimes not useful for the meaning of the sentence. This stop words are removed by checking the static list of stop words.
- Dependency Parsing : This step will decide the relation between the words and will form one link between the words.This relation will be forming the overall meaning of the sentence. Basically the tree like structure is created using the keywords, this structure will help in determining the relationship between the words.
- Grouping : The words which have similar meaning are grouped together. The words which represent the single idea as a meaning. For grouping of the words which has the similar meaning, the dependency parsing tree is used.
- Named Entity Recognition : This step will detect the nouns from the sentences and will map with the real world concepts that they represent. This step does not simply maps the meaning but instead find the context of the word.
- Conference Resolution : The last and the most important step is the conference resolution because this step actually decides the core context of the sentence. This step will let the machine know the current context of the sentence.
So all of these steps will make a sentence which is meaningful to the computer. One can hate AI or support AI but can’t ignore from the real world applications, because this is what gives power to any product. Managing every bit of data and users individually is next to impossible, so the only best option to service the users is by giving them the extraordinary features of AI.