Since the introduction of computers and machines, the potential of these devices to perform a variety of tasks has significantly increased. Thanks to the development of computing, computers have become capable of performing complicated tasks more quickly and efficiently. Additionally, the size of computers has reduced drastically over time.
A branch of Computer Science dedicated to the creation of intelligent machines, known as Artificial Intelligence, is dedicated to making computers that can think and act as humans do. AI technology has enabled computers to understand, reason, and learn from data much like humans do. This has enabled computers to solve complex tasks with greater speed and accuracy than ever before.
Basic Concept of Artificial Intelligence (AI)
According to the father of Artificial Intelligence, John McCarthy, it is the study and engineering of building machines, particularly computer programs, that possess the capacity to think and act as humans do. Artificial Intelligence is the process of making a computer, robot, or software act intelligently by studying how humans think, learn, make decisions, and work in order to solve problems. By taking advantage of the power of computer systems, humans have wondered if machines could think and behave in the same way as they do. This curiosity has sparked the development of Artificial Intelligence, with the aim of creating a similar level of intelligence as that found in humans.
The Necessity of Learning AI
In today’s world, where vast amounts of data are generated, it is difficult for humans to keep track of all of it. To automate processes and make life easier, we need to study Artificial Intelligence, as it can learn from data and help us do repetitive tasks with accuracy and without fatigue.
AI can respond in real time
By using neural networks, AI can analyse data more deeply, allowing it to think and respond to situations based on conditions in real time.
AI can learn through data
In today’s world, where vast amounts of data are generated, it is difficult for humans to keep track of all of it. To automate processes and make life easier, we need to study Artificial Intelligence, as it can learn from data and help us do repetitive tasks with accuracy and without fatigue.
AI can teach itself
Given that data is constantly changing, the knowledge gained from it must also constantly be updated. AI can be used to achieve this goal, as an AI-enabled system can teach itself.
AI can respond in real time
By using neural networks, AI can analyse data more deeply, allowing it to think and respond to situations based on conditions in real time.
AI achieves accuracy
Thanks to deep neural networks, AI can achieve incredible accuracy. AI can be used in medicine to detect diseases such as cancer simply from patient MRIs.
AI can organize data to get most out of it
Data is an intellectual asset for any system using self-learning algorithms. AI can be used to index and organize data in such a way that it always produces the best results.
Understanding Intelligence
Complex, smart systems can be built using AI. To construct an intelligence system like our own brains, we need to understand the concept of intelligence.
What is Intelligence?
Intelligence is the capacity to think, reason, perceive, and learn in order to solve problems, comprehend complex ideas, and use language fluently. It involves the ability to calculate, perceive relationships and analogies, store and retrieve information from memory, learn from experience, and adapt to new situations. Intelligence enables systems to reason abstractly, solve problems, and use language effectively.
1. Logical-Mathematical Intelligence: This is the ability to think logically, reason abstractly, and calculate using numbers. It also involves the ability to think scientifically and critically about problems. 2. Linguistic Intelligence: This is the ability to use language to express oneself and to understand what others are saying. It involves the ability to use words fluently and to understand the nuances of language. 3. Spatial Intelligence: This is the ability to think in three dimensions. It involves the capacity to visualize objects in space and to mentally manipulate them. 4. Bodily-Kinesthetic Intelligence: This is the ability to use one’s body to express ideas and feelings and to manipulate objects. It is the capacity to coordinate physical movement and use one’s body to solve problems. 5. Musical Intelligence: This is the ability to understand, create, and appreciate music. It involves the ability to recognize patterns, create and reproduce sounds, and to understand the structure and elements of music. 6. Interpersonal Intelligence: This is the ability to relate to and understand other people. It involves the capacity to recognize, appreciate, and respond to the feelings and motivations of others. 7. Intrapersonal Intelligence: This is the ability to understand one’s own emotions, motivations, and goals. It involves an understanding of oneself and the capacity to use this understanding to manage oneself and one’s relationships.
| Linguistic Intelligence | Musical Intelligence | Logical-mathematical Intelligence | Spatial Intelligence | Bodily-Kinesthetic Intelligence | Intra-personal Intelligence | Interpersonal Intelligence |
|---|---|---|---|---|---|---|
| Narrators, Orators | Musicians, Singers, Composers | Mathematicians, Scientists | Map readers, Astronauts, Physicists | Players, Dancers | Gautam Buddhha | Mass Communicators, Interviewers |
What is Intelligence Composed Of?
Intelligence is composed of a variety of mental abilities including problem-solving, memory, creativity, planning, abstract reasoning, and the ability to learn from experience. It also includes the ability to recognize and understand one’s own emotions and the emotions of others.
- Reasoning
- Learning
- Problem Solving
- Perception
- Linguistic Intelligence
Reasoning
Reasoning in AI is the process of using logic and computing algorithms to draw conclusions from data. AI reasoning involves the use of logical rules and constraints to process problem-solving, decision-making, and the formation of new knowledge. AI reasoning helps machines to make decisions, solve problems, and understand language. AI reasoning systems can be used to create autonomous agents that can interact with the environment to make decisions and take actions.
Inductive Reasoning and Deductive Reasoning comaparison
Inductive reasoning is the process of making generalizations based on observations or limited sets of data. It involves making conclusions about the world based on limited evidence and the likelihood that similar outcomes occur in the future. Deductive reasoning is the process of applying logical principles to arrive at specific conclusions. It involves making conclusions based on accepted premises or facts. In AI, inductive reasoning is used to identify patterns in data and can be used to develop models that can make predictions and decisions. Deductive reasoning is used to create logical rules and constraints to process knowledge and make decisions.
Learning − l
Auditory Learning: Auditory learning is the process of learning through listening and hearing, such as a student listening to recorded audio lectures. Episodic Learning: Episodic learning involves the process of remembering sequences of events that one has witnessed or experienced. It is a linear and orderly approach to learning. Motor Learning: Motor learning refers to the process of learning by precise movement of muscles, such as picking up objects and writing. Observational Learning: Observational learning is the ability to learn by watching and imitating others. For example, a child trying to learn by mimicking their parent. Perceptual Learning: Perceptual learning is the process of recognizing stimuli that one has seen before, such as identifying and classifying objects and situations. Relational Learning: Relational learning involves learning to differentiate among various stimuli on the basis of relational properties, rather than absolute properties. For example, adding a ‘little less’ salt at the time of cooking potatoes that came up salty last time, when cooked with adding say a tablespoon of salt. Spatial Learning: Spatial learning is learning through visual stimuli such as images, colors, maps, etc. For example, a person can create a roadmap in their mind before actually following the physical road. Stimulus-Response Learning: Stimulus-response learning is the process of performing a particular behavior when a certain stimulus is present. For example, a dog raising its ears on hearing a doorbell.
Problem Solving
Problem solving is the process of taking a present situation and utilizing a range of strategies and tactics to arrive at a desired solution. It involves recognizing and overcoming obstacles that may be known or unknown to reach the goal. Problem solving also includes decision making, which is the process of choosing the best option from a set of alternatives.
Perception
Perception is the process of acquiring, interpreting, selecting and organizing sensory information. In humans, sensory organs help to perceive the world around them. In the field of AI, perception mechanisms are utilized to put together data from sensors in a meaningful way.
Linguistic Intelligence
Linguistic intelligence is the ability to use, comprehend, speak and write verbal and written language. This type of intelligence is important in interpersonal communication, as it allows people to effectively communicate with one another.
What’s Involved in AI
Let us now see the different fields of study within AI
Machine Learning Machine learning is a field of artificial intelligence (AI) which enables machines to learn from data and make predictions. It involves building models from data that can be used to make predictions on unknown data. Machine learning is used in a variety of applications such as robotics, speech recognition, and speech processing. Logic Logic is a field of AI which uses mathematical logic to create computer programs. It uses rules and facts to perform pattern matching and semantic analysis. It is used in games like chess and tic-tac-toe. Searching Searching is a field of AI which involves finding optimal solutions by searching through a search space. It is used in games like chess and tic-tac-toe to select the best move. Artificial Neural Networks Artificial neural networks are networks of computing systems that are inspired by the architecture of biological neural networks. Artificial neural networks can be used for robotics, speech recognition, and speech processing. Genetic Algorithm A genetic algorithm is a type of AI that uses multiple programs to solve problems. It works by selecting the fittest solutions from the search space. Knowledge Representation Knowledge representation is the field of AI that enables machines to understand facts. Representing knowledge in a way that is understandable to machines is essential for creating intelligent systems.
Application of AI
Gaming
AI plays a critical role in strategic games such as chess, poker, and tic-tac-toe, where machines can think of a large number of possible positions based on their heuristic knowledge. This allows the AI to develop more effective strategies, making the games more challenging and interesting to play.
Natural Language Processing
Natural Language Processing (NLP) is a type of AI that enables computers to understand and interact with humans using natural language. NLP systems are able to understand the context, syntax, and semantics of spoken language, allowing them to parse and interpret the meaning of human speech.
Expert Systems
Expert systems are AI applications that integrate software, machine learning, and special information to provide explanations and advice to users. They are able to simulate the decision-making process of a human expert, allowing for more efficient and accurate problem solving.
Vision Systems
Vision systems are AI applications that are able to interpret and comprehend visual input from a computer. Examples include the use of high-resolution cameras on spying aircraft to map out terrain, or clinical expert systems used by doctors to diagnose patients. Police forces also use computer software to recognize criminal faces with stored portraits made by forensic artists.
Speech Recognition
Speech recognition is an intelligent system capable of understanding and interpreting spoken language. It is able to distinguish between different accents, slang words, and background noise while a human is speaking and can adjust itself accordingly.
Handwriting Recognition
Handwriting recognition is an AI application designed to read text written on paper or a screen by a pen or stylus. The AI is able to interpret the shapes of the letters and convert them into editable text.
Intelligent Robots
Robots are AI-enabled machines capable of performing tasks on command. They are equipped with sensors to detect physical data from the environment, such as light, heat, temperature, movement, sound, bumps, and pressure. They also have efficient processors, multiple sensors, and large memory capacities, allowing them to exhibit intelligent behavior. Additionally, robots can learn from their mistakes and adapt to new environments.
Cognitive Modeling: Simulating Human Thinking Procedure
Cognitive modeling is a field of study within computer science that focuses on understanding and simulating the thought processes of human beings. AI attempts to enable machines to think like humans, and the most essential feature of human thought is problem solving. Cognitive modeling strives to identify the methods that humans use to solve problems, so that this knowledge can be applied to a range of AI applications, such as machine learning, robotics, and natural language processing.
Agent & Environment
An agent is any entity that can perceive its environment through sensors and act on that environment through effectors. A human agent has sensory organs such as eyes, ears, nose, tongue and skin, and effectors such as hands, legs and mouth. A robotic agent may use cameras and infrared range finders as sensors, and motors and actuators as effectors. A software agent has encoded bit strings as its programs and actions.
The environment in which programs operate can range from entirely artificial, with keyboard input, databases, computer file systems, and character output on a screen, to rich, unlimited domains with detailed, complex environments. Software agents may need to choose from a wide array of actions in real time, such as softbots which scan online customer preferences and show interesting items to the customer. These agents operate in both artificial and real environments.