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Diving Into The Top Artificial Intelligence Techniques


By NIIT Editorial

Published on 21/03/2021

9 minutes

Technology has gradually seeped its feet into our lives and revolutionized the way we work, think, and live in our day-to-day life. From autonomous vehicles to voice-operated digital assistants like Google’s Siri, Artificial Intelligence boasts its powerful capabilities to perform in diverse working domains. These AI-powered computers or machines outperform human beings in terms of capacity, speed, efficiency, and accuracy.

In this post, we will go through the top most AI techniques: Heuristics, Natural Language Processing, Artificial Neural Networks, Machine Learning, Support Vector Machines, and Markov Decision Process.

But before jumping to this main thread, let’s have a brief understanding of what exactly is AI?

What is Artificial Intelligence?

Alan Turing defines Artificial Intelligence as “the science of making machines do things that would require intelligence if done by man.

Integrating complex software and algorithms, Artificial Intelligence is an immensely advanced branch of computer science that can emulate human cognition. In simple terms, AI refers to the designing of machines to make them work like humans.

Being the most powerful and evolving technology in the market nowadays, AI has extended its grip to various spectrums, such as marketing, healthcare, education, etc. From problem-solving and learning to medical diagnosis and speech recognition, AI-powered machines have the potential to perform tasks that would otherwise need human intelligence.

What Does AI Comprise of?

Having known about artificial intelligence, let’s go ahead with the main elements of AI that primarily contribute to the functioning of various intelligent systems.

Feature Engineering -

When a meaningful set of features or attributes are chosen from the provided dataset, the process is known as feature extraction. Feature engineering helps in the mitigation of the degree of disorder and also ensures no correlation among the features.

Artificial Neural Networks -

Artificial Neural Networks, or simply Neural Networks, are created by replicating a web of model neurons in a computer. Neural Networks have been used in spheres ranging from gene predictions, speech recognition, to the classification of cancers.

Deep Learning -

In comparison to Artificial Neural Networks, Deep Learning has more covert layers between the input and the output. It automates various processes, such as feature extraction and classification learning.

AI Techniques

1. Heuristics

One of the most prominent search algorithms used in Artificial Intelligence systems is Heuristics. In this technique, a heuristic is employed for making moves and providing one of the best options. Having based on the principle of ‘trial and error’, this technique is considered as one of the best options to solve highly complex or difficult problems faster.

For example, you are an ambulance driver and it becomes your topmost responsibility to take the patient to the hospital as soon as possible. Of course, you will start finding the shortest route to your destination. Following the heuristic technique, the best way to do it is to identify all the possible routes, and then to choose the shortest one to avoid any wastage of time.

For solving problems in the world of AI technology, the Heuristic technique serves as a perfect assistant in finding out the best possible solutions for optimally meeting the immediate goals.

2. Natural Language Processing

In basic terms, NLP or Natural Language Processing is software that can understand spoken or written language.

For example, the voice assistant software captures the audio of the human talk. Then the audio is converted into text for processing the information, and then the relevant text is converted into the audio. This audio is then used by the AI machine to respond to human beings. Another example of the application of NLP can be seen in the popular Microsoft Word, which checks the grammar as well as the accuracy of the text.

Let’s understand a few of the important techniques in NLP!

Techniques of NLP

Ø  Part-Of-Speech Tagging: POS tagging or Part-of-Speech tagging is the very first step involved in NLP. This technique analyzes the lexical structure of a sentence and tags each one of its words with the corresponding words having similar grammatical characteristics.

Let’s say the sentence is: “Marc goes to the school.”

So, the results of the tagging will be ‘Marc’ as a noun (N), ‘goes’ as a verb (V), ‘to’ as a preposition (P), ‘the’ as a determiner (D), and ‘school’ as a noun (N).

POS tagging technique can also be used for tagging the paragraphs as well as for processing unknown texts.

Ø  Named Entity Recognition (NER): NER tags words with the associated kind of entity that they represent, such as companies, places, people, plants, animals, etc.

Ø  Parsing: In this technique, the grammar of the text, as well as the manner in which words are arranged in a sentence, are analyzed syntactically. The machine uses the POS tagging for the lexical analysis and then arranges the words into a phrase to reach the desired goal.

3. Aritificial Nueral Networks (ANN)

All living organisms have neural networks in their brains, which help them to transmit as well as process information. Millions and billions of neurons help an organism to learn new things and make day-to-day life decisions.

Inspired by the design of these natural neural networks, Artificial Neural Networks work in the same manner in a machine. While the natural neural system consists of neurons, ANN includes nodes. These nodes help the machines powered by AI technology to identify the patterns from the specific data set and then learn from it.

Based on the type and complexity of the problem, different network patterns are available in ANN. For example, image recognition software typically uses ‘Convolutional Networks’, whereas, ‘Recurrent Networks’ are used for speech recognition.

4. Machine Learning

AI and Machine Learning are closely correlated to each other. Machine Learning is one of the applications of AI, which possesses the capability to learn from experiences.ML primarily deals with the thinking part and analyzing data to deliver relevant outputs. Rarer than explicitly programmed to perform a certain task, the ML technique helps machines in learning as well as improving from their experiences.

Various techniques used for learning in ML are:

Ø  Unsupervised Learning: The machine does not use any specific information to act.

Ø  Supervised Learning: The machine learns under the guidance of training data.

Ø  Reinforcement Learning: The machines are made to learn how to take appropriate actions to earn rewards and find the best possibilities.

5. Support Vector Machines (SVM)

You probably create different folders on your smartphone for different files, right? Broadly speaking, you identify the class of each file and then put them in the ‘Images’, Music’, or ‘Videos’ folders accordingly. But what about classifying a text message as ‘spam’ or ‘not spam?

For solving such complex classification problems, there is one of the most powerful Artificial Intelligence techniques called Support Vector Machines (SVM).

For example, you might have seen your emails neatly classified as ‘Primary’, ‘Social’, and ‘Promotions’. The SVM used by your email system categorizes your emails as per their respective categories. Whether or not an email is a spam, is also decided by SVM. This technique is also widely used for text recognition, image recognition, speech recognition, and face recognition systems.

6. Markov Decision Process

Markov Decision Process (MDP) is basically the modeling of a decision-making process for AI technologies. In simple terms, what specific action will be taken by the machine, at what state, and at what time – are all indicated by MDP.

To understand the basic parts of the MDP model, let’s take an example of a quiz game!

In a quiz game, there are 6 levels. If the answers given by the player are correct, then he/she is rewarded accordingly. At the end of each level, the player is asked whether to collect the reward and quit OR play further. In case the player fails in any one round, he/she will lose all the rewards earned so far. Thus, the MDP model of this quiz game will be as follows:

 

Ø  S (Finite Set ofStates): Level 1, Level 2, Level 3, Level 4, Level 5, and Level 6 

Ø  A (Finite Set ofActions): ‘Play Further’ OR ‘Quit’

Ø  P (State of Transition Probability):The transition probabilities are:

Win: Transition to the next level + The reward amount of the completed level

Lose: Transition to the end of the game + No reward

Quit: Ends the game + The reward earned so far.

Ø  R (Rewards)

Level 1: $20

Level 2: $30                             

Level 6: $70

The Bottom Line

Undoubtedly, Artificial Intelligence has impacted our lives on a great scale and is continuing to do so. The above-mentioned techniques are simply the advanced versions of sophisticated models, which are cleverly designed to compute operations that would otherwise require human cognition. These techniques have assisted a plethora of organizations in understanding the data patterns, performing mundane tasks, and working for maximum productivity.

While professionals acquire expertise through on-the-job training, learners need a more synthesized route towards building AI foundations. 

The following programs by the acclaimed StackRoute flagship of NIIT contribute towards that end goal: 

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