Methods & Goals of AI

From intelligent robots to multi-player gaming, from pattern recognition to fraud prevention, from human safety to weather prediction, AI is shaping every industry in one way or the other. Read on to know what is Artificial Intelligence and what are its methods, goals, and application areas.

Artificial Intelligence (AI) has been the talking point of technological advancement for over seven decades. Researchers have managed to develop several features using AI otherwise believed to be impossible. From facial recognition to Chabot, from personal assistants to preference-based ads, AI lies at the centre of several amazing fields today.

As most of the researchers speculate, we have just witnessed the tip of the iceberg, and we are yet to realize the full potential of AI. Leading enterprises are aware of the immense possibilities and opportunities presented by AI for the growth of their business. Therefore, to keep in line with the technology, it makes sense to have a sound knowledge of AI. Moreover, as different industries adopt AI, career prospects in this field looks promising as well. 

Let’s get to know about Artificial Intelligence, its methods, and its goals in detail.

What is Artificial Intelligence (AI)?

As stated by John McCarthy, the father of Artificial Intelligence, “Artificial Intelligence is the science and engineering of making intelligent machines, especially intelligent computer programs.” In simpler terms, AI is the ability of a digital computer or software to take decisions and perform tasks that are associated with humans. AI deals with analysing how a human brain thinks and how it learns, decides, and acts in a situation. These insights can then help in designing algorithms for software and developing intelligent systems. Although AI is a branch of computer science engineering and deals with programming, it depends on several other streams like Psychology, Neuroscience, Philosophy, Statistics, Mathematics, etc. 

Evolution of Artificial Intelligence

By treating human behaviour and properties as objects, Artificial Intelligence aims to find a connection between reasoning and intelligence. The earliest breakthrough in the field of AI dates back to World War II when British cryptanalyst Alan Turning designed the famous Turing Machine. Based on a decade long theoretical work, the Turing machine demonstrated the possibility of machine working on a program and improving its capabilities by learning throughout the process.

To deduct whether a computing machine is exhibiting human intelligence, Turning framed a practical test in 1950. To this date, the Turing Test forms the basis of demonstrating intelligent systems. This simple test involves a computer, an interrogator, and a human being. The interrogator questions the human as well as the machine and tries to distinguish them according to their answers. The interrogator uses a keyboard and a screen for the test. Several people play the roles of interrogator and human beings in this test. If a significant portion of the interrogators fails to differentiate the computer from the human, the machine passes the test and qualifies as an intelligent system.

Methods of Artificial Intelligence

After defining Artificial Intelligence, let’s know about the philosophical methods lying at its core. Every research about AI falls into one of the following two categories:

  1. Symbolic Method: Also known as the “top-down” approach, the symbolic method imitates intelligence without considering the biological structure of the human brain. As the name suggests, this method analyses the thought process of the human brain by processing symbols.
  2. Connectionist Method: The connectionist approach, on the other hand, deals with creating neural networks by imitating the biological structure of the human brain. Also known as the “bottom-up” approach, this method deals with connecting more elemental brain-cells.

Both of these methods are competing approaches to developing AI systems and algorithms. Although they might appear to be similar, they are different in their principle. While the “top-down” approach focuses on symbolic descriptions, the “bottom-up” approach considers neural activities inside the brain. We can highlight the difference between these two approaches with an example. Consider a robot that recognizes the numbers through image processing. 

The Symbolic approach will write an algorithm based on the geometrical pattern of each number. The program will compare and try to match the numeric with the patterns of different numbers stored in its memory.

In the Connectionist approach, the robot will train its artificial neural network by repeated tuning to recognize the numbers. In a way, The Connectionist approach imitates the human brain and its thought process more closely than the Symbolic approach.

Researchers use both these methods of AI implementation while developing algorithms. While the Symbolic approach is famous for simple problems, researchers prefer the Connectionist method for complex, real-world problems. Despite showing immense potential, both these approaches have produced limited results so far.

Apart from these two major classifications, researchers have coined several approaches for implementing AI.

  • Logic-based AI: It uses formal logic to represent knowledge, planning, and learning in the human mind. Instead of simulating human thought, this approach focuses on determining the basis of logical reasoning and abstract thinking.
  • Anti-logic AI: Some researchers argue that it is not possible to capture every aspect of human behaviour using simple generic logic. Rather than using simple logic, the Anti-logic approach deals with ad-hoc solutions for machine learning and vision-processing. 
  • Knowledge-based AI: As computers with significant memories became available around the 1970s, people started adding knowledge to AI applications. As a result, system architecture included facts and rules to illustrate the algorithm in their systems.
  • Statistical Learning: In recent years, researchers across the world have combined advanced mathematical and statistical models like information theory, decision theory, etc. to develop AI algorithms. This approach has resulted in more accuracy and reproducibility in data mining. 

Goals of Artificial Intelligence

Based on the above two methods, AI researchers aim to create systems that exhibit decision making and logical reasoning. To implement human intelligence, one needs to model the processing of thinking, learning, and behaving like humans. Accordingly, AI expects to meet one of the three goals.

  1. Strong AI: Since its inception, Artificial Intelligence has been ambitious to replicate human intelligence. Strong AI deals with designing a machine whose intelligence is unparalleled and indistinguishable from that of humans. Although this goal created great interest in the early days of AI, practical difficulties have weakened the optimism.
  2. Applied AI: Applied AI is a more practical adaptation of Strong AI and works on producing smart systems that are commercially feasible. Applied AI has shown immense success by using advanced information processing. Biometric scanners, personal assistants, medical diagnosis systems, etc. are based on applied AI.
  3. Cognitive Simulation: Another goal of AI is to design and test the theories about the thought process of a human mind. Bioscience disciplines like Neuroscience and Psychology are using Cognitive Simulation as a popular tool to understand how we recall memories or past events and recognize places or people.

Application Areas of Artificial Intelligence

Modern AI-based techniques are relevant in any intelligent task, and the list of its applications continues to grow significantly. Let’s take a quick look at some of them.

  • Healthcare: Artificial Intelligence has proven to be life-saving assistance in the healthcare industry. For example, the AI algorithm in smart-watches monitors vital activity of a person to detect heart problems and even alerts the emergency services. Additionally, AI has helped in increasing the speed and accuracy of medicines.
  • Financial Services: Financial services have benefitted from AI for the last three decades. In banking, AI is useful for book-keeping management, stock-prediction, and even fraud prevention. Large enterprises use AI to process and audit transactions under strict compliance. 
  • Retail Sector: In the retail business, AI algorithms can counter the problems of supply-chain by inventory management. Apart from predicting the future trends in the apparel business, AI can also help in forecasting the demand and enhancing the customer experience through real-time data analysis.
  • Automobile Industry: One of the significant examples of using AI is the automobile industry. An autonomous or self-driven car is the latest research area, and every car manufacturer is investing heavily in it. Apart from improving driver safety, several car manufacturers have already used AI features like voice-control, lane-switch, collision-detection, etc. 

AI as a Rewarding Career Prospect

We hope the brief introduction to Artificial Intelligence in this blog has given you a flavour of its technique and capabilities. As you might have understood by now, AI opens up an ocean of opportunities for your career. By visiting an Indian Career portal, you can learn about different courses and job opportunities for building a successful career in AI.  

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