Artificial Intelligence (AI) and Robotics

Artificial intelligence (AI) is the branch of computer science which concerned with designing intelligent Computer System. AI is the ability of a digital computer or computer-controlled robot to perform tasks commonly associated with intelligent beings. It is the simulation of human intelligence processes by machines, especially computer systems. Specific applications of AI include expert systems, natural language processing, speech recognition and machine vision.

Component of Artificial Intelligence:

  1. Learning: Learning is a very essential part of AI and it happens in a number of different forms. The simplest form of learning is by trial and error. In this form, the program remembers the section that has given the desired output and discards the other trial actions and learns by itself.
  2. Reasoning: Reasoning is also called as logic or generating judgments from the given set of facts. The reasoning is carried out based on a strict rule of validity to perform a specified task.
  3. Perception: To work in the environment, intelligent agents need to scan the environment and the various objects in it by means of different sense-organs, real or artificial. Agent scans the environment using sense organs like camera, temperature sensor, etc. This is called perception.
  4. Philosophy: It introduces the concept of logic and methods of reasoning and studying the mind as a physical system. It creates the foundation for learning, language, and judgement.
  5. Psychology: It introduces the concept of the brain as information processing device and phenomenon of perception and sensory-motor control.
  6. Computer science and engineering: This component introduces the concept of hardware, software, and operating system. Apart from this, it also discussed the programming language and tools used in AI.

Can a computer really think with the aid of AI?
Use/ Applications of AI

The concept of whether computers can truly “think” in the same way humans do is a complex and debated topic. The field of artificial intelligence (AI) has made important steps in representing certain aspects of human thought, but it’s key to understand that AI doesn’t possess consciousness or self-awareness.

However, AI can perform complex tasks that were traditionally associated with human intelligence. Here are some aspects of AI that might be considered as a form of “thinking”:

  1. Pattern Recognition: AI algorithms can analyze huge amounts of data, identify patterns, and make predictions or decisions based on those patterns.
  2. Learning: Machine learning algorithms allow AI systems to learn from data and experiences. This can involve recognizing patterns, adapting to new information, and improving performance over time.
  3. Problem-Solving: AI systems can solve specific problems or optimize processes by processing information and making decisions based on predefined rules or learned patterns.
  4. Natural Language Processing: AI can understand and generate human language to some level. This includes tasks like language translation, sentiment analysis, and chatbot interactions.
  5. Decision Making: AI systems, especially support learning, can make decisions and take actions based on feedback and rewards.
  6. Speech Reconciliation: In speech reconciliation, the input is given to the computer in the form of vibrations produced by the sound. This is done with the help of an analog to digital converter that converts the vibrations produced by the sound into digital format. Examples of speech Reconciliation are: Google Voice typing, Siri, Google Assistant etc.

While AI can work in specific reasoning tasks, it lacks the general and subjective nature of human thought. Human thought involves consciousness, self-awareness, emotions, creativity, and ethical reasoning, elements that current AI systems do not possess. AI operates based on algorithms, patterns, and data, and it lacks the essential understanding and awareness that characterize human thinking.

How Does AI works?

The functioning of an AI application can vary based on its specific purpose, but in general, AI applications operate by leveraging algorithms and data to perform tasks that usually required human intelligence. Here’s a broad overview of how AI applications work:

  1. Data Collection: AI applications often require large amounts of data to train their models. This data can be text, images, audio, video, or any other type of information relevant to the task the AI is designed for.
  2. Algorithms: AI applications use algorithms to process and analyze data. These algorithms can be rule-based or learned through machine learning. They form the logic and decision-making components of the AI system.
  3. Feature Extraction: In machine learning, features are the characteristics or attributes of the data that the algorithm uses for learning. Feature extraction involves selecting and transforming relevant aspects of the data to represent it in a way that the model can understand.
  4. Optimization and Iteration: AI applications often suffer continuous optimization and repetition. This may involve fine-tuning the model, updating algorithms, or combining new data to improve performance and adjust to changing conditions.
  5. Deployment and Integration: Once a model is trained and estimated effective, it is deployed in real-world applications. This may involve integrating the AI system into existing software, hardware, or processes.

It’s important to note that the particulars can vary widely based on the type of AI application, whether it’s a natural language processing system, computer vision application, recommendation system, or any other specialized task. Additionally, developments in AI research may lead to new approaches and techniques over time.


Robotics is the branch of technology that deals with the design, construction, operation, and application of robots. It is discipline overlapping artificial intelligence and mechanical engineering. Robotics research is used in artificial intelligent as a framework for exploring key problem and technology through a well-defined application.

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