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Artificial Intelligence its 6 Most Powerful Fields Driving the Future

What is Human Intelligence? 

Humans are considered the most intelligent of all species. The human brain can process vast amounts of information, discern underline meanings, recognize patterns. We possess statistical, mathematical, and computational abilities to understand, analyze and evaluate the world around us. Humans innate capacity to learn, reason, solve problems, perceive the surroundings with their sensory organs, and exploit linguistic capabilities is generally termed human intelligence.

What is Artificial Intelligence? 

Humans have made significant advancements in science and technology since the advent of the industrial revolution. The thirst for knowledge and curiosity to create computers and robots and program them to carry out diverse activities dates back to Alan Turing, who in his paper ‘Computing and Machine Intelligence’ 1950 ask a fundamental question ‘Can Machines Think?’ This particular question opened up new vistas. We can call this a desire of everyone interested in AI to produce machines that can think like humans.

The fundamental goal and vision of professors, researchers, scientists, and organizations desiring to make strides in the AI field is to create machines that act intelligently like humans. As discussed earlier, the essential traits of human intelligence, namely learning, reasoning, perception, and others. As we know it today, AI encompasses diverse fields that try to replicate the activities of the human brain. Driverless cars, virtual assistants like Apple’s Siri, Amazon Alexa, speech and facial recognition systems are a few examples of artificial intelligence. 

Fields of Artificial Intelligence

six fields of Artificial Intelligence.

Machine Learning. 

Machine learning focuses on training machines with vast amounts of data to utilize their databases for solving real-life problems. These AI machines are not explicitly programmed to carry out certain specific tasks. Instead, they learn from their experiences and apply the available data to different situations they will come across in the future. Machine learning is of three ways. 

Supervised learning. 

Supervised learning uses a tremendous amount of data using label data sets. For example, captioned photos, labeled images, and written sentences with footnotes indicate a particular word, such as ‘bass’ in fish or a musical instrument.

Unsupervised learning. 

Unsupervised learning uses pattern recognition, where machines use algorithms to identify similarities among distinct data sets and group the items having identical features into clusters applying a classifier. 

Reinforced learning. 

It is a trial and error method. The machines in this type of learning propose a particular solution based on their intellect. If the proposed solution is acceptable, it is awarded a positive point or otherwise penalized. This kind of learning helps these machines to train themselves based on gained rewards. Its predictive ability increases over time with new data.

Expert Systems. 

Expert systems are really talented. These complex AI models have a knowledge base that is a repository of the knowledge pertaining to a particular field. Based on this knowledge, expert AI systems produce inferences. The models utilize ‘if-then’ rules. AI has made rapid advancements in the development of expert systems. The cognitive requisites have to perform in a narrow field, and their performance is more outstanding in many cases than a human expert. In credit analysis, scrutiny of insurance claims, loan disbursements, financial management, these expert systems have played a pivotal role in deciphering rules and pointing out frauds and lacunas which may go unnoticed by a human. 

Neural Networks. 

The architecture of artificial neural networks replicates the neurons of the human brain. Several hidden layers of artificial neurons called perceptrons process the data to obtain the desired output. The output of the previous layer serves as the input for the next layer. The deep learning of neural networks enables AI machines to handle billions of complex and intangible data sets, enhancing the predictive ability of the neural network with the progress of time.

image of artificial neural networks.
Source data-flair

There has been continuous development in the neural network, focusing on designing more deep and complex neural networks called ‘long short-term memory’ (LSTM). Speech recognition, language processing, image recognition, and computer vision increasingly apply neural networks. The deep learning of the AI system portrays a scenario where AI machines will learn and replicate the human brain’s complex psychological processes. 

Natural Language Processing.

Researchers in the AI field are endeavoring to empower machines to communicate like humans. However, learning a language is intricate and complex and encompasses four primary reading, writing, listening, and speaking elements. The mastery of all these four elements depends on understanding the meaning, analyzing the sentiments, deciphering the context of the written (typed) or spoken word, and applying the embedded knowledge to reach a specific conclusion.

Scientists are laboriously training machines to talk to humans. For example, chatbots, Google’s Voice Assistant is a good example of natural language processing (NLP). It understands your voice and performs as per your instructions. Translation systems translating text from one language to another also use NLP. Though a great deal of accuracy has been achieved in empowering machines to understand human voice or communicate within specific domains still AI machines, lack far behind the ability of the human brain to pick out the minute variations in voice, choice of words, pitch, and the tone. In short, NLP still stands on the borders of understanding and capacity utilization of language compared to the human brain. 


Robotics involves many disciplines such as mechanical and electrical engineering, computer science, programming, and others. The employment of AI technology in robotics focuses on equipping robots to move and navigate the real world automatically.

Humans have made prolific strides in developing robots whose functional abilities are pretty striking. Driverless cars and delivery robots are good examples. In automobiles and other heavy industries, robots are successfully employed to carry out repetitive laborious tasks, thus easing the human workforce.

image of robots working in amazon warehouse.
Source Reuters

Amazon has used several robots in its warehouses, replacing human labor. It, however, raises the question of unemployment and the adverse effects of AI on human beings. In aerospace and aviation engineering, robots have been of great assistance, especially in moving large objects in space and assisting astronauts in various ways. 

Fuzzy logic. 

Fuzzy logic differs from standard logic because standard logic applies absolute true or false values to conditions and situations. However, it is impossible to strictly compartmentalize every situation and phenomenon in these two fundamental domains in the real world. There is an intermediate plane between (0.0 absolute false) and (1.0 absolute true) described as fuzzy logic. For example, the day may be freezing according to one person, while others may describe it as pleasantly cold based on the individual perception.

image showing concepts of fuzzy logic.
Source Brighterion

Fuzzy l.ogic provides a very flexible reasoning system to express the kind of uncertainty and inaccuracy. Neither variable is true or false. It is the intellectual capacities that assign varying values based on circumstances, events, and other factors. AI aims to devise such systems which can manifest such kind of fuzzy logic. For example, Amazon’s AI systems throw messages ‘you may like’ for different products based on your search. The flexibility of the fuzzy logic enables the AI system to throw such messages without being sure that the searcher may or may not like the proposed products. 

The essential traits of human intelligence provides a yardstick for measuring the success of AI. The AI systems have worked hard to develop strategies and machines that can replicate a particular aspect of human intelligence. AI has achieved remarkable progress in diverse fields, but it is still far from the goal of creating machines that can think independently. 


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