What are the 4 techniques based on artificial intelligence and machine learning?

“Artificial Intelligence (AI) is the part of computer science concerned with designing intelligent computer systems, that is, systems that exhibit characteristics we associate with intelligence in human behavior – understanding language, learning, reasoning, solving problems, and so on.”  - (Barr & Feigenbaum, 1981)

Índice Show

• Prefacing Artificial intelligence
• Branches of Artificial Intelligence As AI Capabilities
• 1. Machine learning
• 2. Neural Network
• 3. Robotics
• 4. Expert Systems
• 5. Fuzzy Logic
• 6. Natural Language Processing  
• Conclusion 

Artificial intelligence is the practice of computer recognition, reasoning, and action. It is all about bestowing machines the power of simulating human behaviour, notably cognitive capacity. However, Artificial intelligence, Machine learning, and Data Science are all related to each other. 

In the commencement of this blog, we will gain expertise in Artificial Intelligence and its major six branches. 

Prefacing Artificial intelligence

In terms of easy definition, Artificial Intelligence is the capability of a machine or computer device to emulate human intelligence (cognitive process), acquire from experiences, adapt to the latest information and operate humans-like-activities.

Artificial Intelligence executes tasks intelligently that yield in generating huge accuracy, adaptability, and productivity for the entire system. Tech decision-makers are seeking many ways to adequately implement artificial intelligence technologies into their businesses to draw interference and add values to them.

For example, AI, in the media industry, is used at large scales, such as in social media, in automated journalism, etc. Another example can be seen at AI in banking applications like chatbots, mobile banking, fraud detection, customer engagement, etc. 

Apart from this, AI has various fundamental application incorporating NLP, healthcare, automotive, gaming, speech recognition, finance, vision system, etc. and required for;

• To design expert systems equipped with the knowledgeable practice that is proficient to acquire, manifest, decipher and justify to its users.

• Stimulating devices to identify results for complicated issues like humans do and implement them in the mode of algorithms in computers.   

Branches of Artificial Intelligence As AI Capabilities

There is a broad set of techniques that come in the domain of artificial intelligence such as linguistics, bias, vision, planning, robotic process automation, natural language processing, decision science, etc. Let us acquire information about some of the major subfields of AI in deep;

In the video, six major branches of artificial intelligence are explained in a quick way.

1. Machine learning

In terms of advanced technology, one of the most demanding fields is Machine Learning, it is making buzz every day whenever a new product is introduced by any company that deploys ML techniques and algorithms for delivering the consumer in a highly creative manner. 

Machine Learning is the technique that gives computers the potential to learn without being programmed, it is actively being used in daily life, machine learning applications in daily life, even without knowing that. Fundamentally, it is the science that enables machines to translate, execute and investigate data for solving real-world problems.

With the deployment of complex mathematical expertise, programmers design machine learning algorithms that are coded in a machine language in order to make a complete ML system. By this way, ML enables us to perform tasks to categorize, decipher and estimate data from a given dataset.

In the last few years, it has given us self-driving cars, image and speech recognition, demand forecasting models, useful web search and various extensive applications. It basically converges on the applications that adapt from experience and advance their decision-making potential or predictive accuracy over a period of time.

Moreover, depending on the types of data available, data professionalists select types of machine learning (algorithms) for what they want to predict from data, 

1. Supervised Learning: In this type of learning, data experts feed labelled training data to algorithms and define variables to algorithms for accessing and finding correlations. Both the input and output of the algorithm are particularized/defined.

2. Unsupervised Learning: This type of learning include algorithms that train on unlabelled data, an algorithm analyzes datasets to draw meaningful correlations or inferences. For example, one method is cluster analysis that uses exploratory data analysis to obtain hidden or grouping patterns or groups in datasets.

3. Reinforcement Learning: For teaching a computer machine to fulfil a multi-step process for which there are clearly defined rules, reinforcement learning is practised. Here, programmers design an algorithm to perform a task and give it positive and negative signal to act as algorithm execute to complete the task. Sometimes, the algorithm even determines on its own what action to take to go ahead. 

2. Neural Network

Incorporating cognitive science and machines to perform tasks, the neural network is a branch of artificial intelligence that makes use of neurology ( a part of biology that concerns the nerve and nervous system of the human brain). Neural network replicates the human brain where the human brain comprises an infinite number of neurons and to code brain-neurons into a system or a machine is what the neural network functions. 

In simple terms, a neural network is a set of algorithms that are used to find the elemental relationships across the bunches of data via the process that imitates the human brain operating process. 

So, a neural network refers to a system of neurons that are original or artificial in nature, where artificial neurons are known as perceptrons, know from here, the complete perceptron model in the neural network.

A neuron in a neural network is a mathematical function (such as activation functions) whose work is to gather and classify information according to a particular structure, the network strongly implements various statistical techniques, such as regression analysis, to accomplish tasks.

From forecasting to market research, they are extensively used for fraud detection, risk analysis, stock-exchange prediction, sales prediction and many more. 

3. Robotics

This has emerged as a very sizzling field of artificial intelligence. An interesting field of research and development mainly focuses on designing and constructing robots. 

• Robotics is an interdisciplinary field of science and engineering incorporated with mechanical engineering, electrical engineering, computer science, and many others. 
• Robotics determines the designing, producing, operating, and usage of robots. It deals with computer systems for their control, intelligent outcomes, and information transformation. 

Robots are deployed often for conducting tasks that might be laborious for humans to perform steadily. Major of robotics tasks involved- assembly line for automobile manufacturing, for moving large objects in space by NASA. AI researchers are also developing robots using machine learning to set interaction at social levels.

Six major branches of Artificial Intelligence

4. Expert Systems

Expert systems were considered amid the first successful model of AI software. For the first time, they were designed in the 1970s and after that escalated in the 1980s.

Under the umbrella of an AI technology, an expert system refers to a computer system that mimics the decision-making intelligence of a human expert. It conducts this by deriving knowledge from its knowledge base by implementing reasoning and insights rules in terms with the user queries.

The effectiveness of the expert system completely relies on the expert’s knowledge accumulated in a knowledge base.  The more the information collected in it, the more the system enhances its efficiency. For example, the expert system provides suggestions for spelling and errors in Google Search Engine. 

Expert systems are built to deal with complex problems via reasoning through the bodies of proficiency, expressed especially in particular of “if-then” rules instead of traditional agenda to code. The key features of expert systems include extremely responsive, reliable, understandable and high execution. 

5. Fuzzy Logic

In the real world, sometimes we face a condition where it is difficult to recognize whether the condition is true or not, their fuzzy logic gives relevant flexibility for reasoning that leads to inaccuracies and uncertainties of any condition. 

In simpler terms, Fuzzy logic is a technique that represents and modifies uncertain information by measuring the degree to which the hypothesis is correct. Fuzzy logic is also used for reasoning about naturally uncertain concepts. Fuzzy logic is convenient and flexible to implement machine learning techniques and assist in imitating human thought logically.

It is simply the generalization of the standard logic where a concept exhibits a degree of truth between 0.0 to 1.0.  If the concept is completely true, standard logic is 1.0 and 0.0 for the completely false concept. But in fuzzy logic, there is also an intermediate value too which is partially true and partially false.

6. Natural Language Processing  

It is hard from the standpoint of the child, who must spend many years acquiring a language … It is hard for the adult language learner, it is hard for the scientist who attempts to model the relevant phenomena, and it is hard for the engineer who attempts to build systems that deal with natural language input or output. These tasks are so hard that Turing could rightly make fluent conversation in natural language the centerpiece of his test for intelligence. — Page 248, Mathematical Linguistics, 2010.

In layman words, NLP is the part of computer science and AI that can help in communicating between computer and human by natural language. It is a technique of computational processing of human languages. It enables a computer to read and understand data by mimicking human natural language. 

(Recommend blog: Types of machine translation)

NLP is a method that deals in searching, analyzing, understanding and deriving information from the text form of data. In order to teach computers how to extract meaningful information from the text data, NLP libraries are used by programmers. A common example of NLP is spam detection, computer algorithms can check whether an email is a junk or not by looking at the subject of a line, or text of an email.

Implementing NLP gives various benefits such as;

• It improves the accuracy and efficiency of documents.
• It has the ability to make automated readable summary text.
• It is very advantageous for personal assistants such as Alexa,
• It enables organizations to opt chatbots for customer support.
• It makes sentiment analysis easier.

Some of the NLP applications are text translation, sentiment analysis, and speech recognition. For example, Twitter uses NLP technique to filter terroristic language from various tweets, Amazon implements NLP for interpreting customer reviews and enhancing their experience.

Conclusion 

Artificial intelligence systems turn to extend more capable by augmenting in size and complexity. AI analysts are continuously attempting to build up software systems for diverse applications like automatic learning, knowledge, natural language, and speech recognition. 

(Read also: What is Knowledge Graph?)

Depending on the functioning of AI systems, we have studied six branches under the umbrella of the Artificial Intelligence field. The six fields are now the buzz word in the industries and organizations. Numerous corporations are promoting it to make use of it and serve people in a much better approach.