6 Major Branches of Artificial Intelligence (AI)

“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)

Artificial intelligence is the practice of computer recognition, reasoning, and action. It is all about bestowing machines the power of simulating human behavior, 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 such as AI is firmly used in the banking and media industry.

 

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, and 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.   

 

Take a look at our section “Artificial Intelligence” to explore more about AI and its trending appliances.

 

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, robotics, planning, natural language processing, decision science, etc. Let us acquire information about some of the major subfields of AI in deep;

 

1. Machine learning

 

In terms of advanced technology, one of the most demanded 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 science that enables machines to translate, execute and investigate data for solving real-world problems.

 

• ML algorithms are created by complex mathematical skills that are coded in a machine language in order to make a complete ML system.

 

• ML enables individuals to execute tasks to categorize, decipher and estimate data from a given dataset.

 

  (You can have a look at our previous blog to learn more about machine learning and its applications)

 

 

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). 

 

• Replicating 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. 

 

• Neural network and machine learning combinedly solve many complex tasks with ease while many of these tasks can be automated.

 

Visit here if you want to know the notorious relationship between neuroscience and artificial intelligence research.  

 

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.

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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. (Source)

 

• 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. 

 

• 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.

 

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.

 

• 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.

 

• 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.

 

• Natural language processing depicts the developing methods that assist in communicating with machines using human languages such as English.

 

• NLP is the processing of the human language by computer programs, examples include; spam detection by looking at the subject of a line or text of an email and checking if it is junk.

 

• NLP tasks are text translation, sentiment analysis, and speech recognition. NLP is used by Twitter to percolate terroristic language from their tweets, by amazon to interpret user reviews and enhance user 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.

 

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. 

 

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