Since the invention of computers or machines, they have experienced exponential growth in their ability to perform a wide variety of tasks. ...
Since the invention of computers or machines, they have experienced exponential growth in their ability to perform a wide variety of tasks. Humans have developed more and more power in diverse areas of work with computer systems, and computers have increased in speed and shrunk in size over time.
A branch of computer science called artificial intelligence pursues the creation of computers or machines that are as smart (intelligent) as humans.
Basic Concepts of Artificial Intelligence (AI)
According to John McCarthy, the father of artificial intelligence, it is "the science and engineering of making intelligent machines, especially intelligent computer programs".
Artificial intelligence is a way of thinking intelligently in a similar way to the human mind with computers, computer-controlled robots or software. Artificial intelligence is accomplished by studying how the human brain thinks and how humans learn, decide and work while solving problems, and then using the results of this research as the basis for developing intelligent software and systems.
In harnessing the power of computer systems, human's also get curious: "Can machines think and behave like humans?"
Thus, the development of artificial intelligence began in the creation of those intelligences that we find and think in humans, can also be similar in machines.
The need to learn AI
As we know. AI seeks to create machines that are as smart as humans. There are many reasons why we learn AI. Some of the main reasons are as follows -
- AI can learn through data
In our daily lives, there is a lot of data to process and the human brain cannot keep track of so much data. We need to automate these things. In order to automate, we need to learn AI because it can learn from data and can perform repetitive tasks with accuracy.
- AI can learn itself
A system should be able to learn itself because the data itself is constantly changing and the knowledge that stems from that data must be constantly updated. We can use AI for this purpose, because AI-enabled systems can learn themselves.
- AI can respond in real time
Artificial intelligence can analyze data in greater depth with the help of neural networks. Thanks to this ability, AI can think and respond to situations based on real-time situations.
- AI achieves a high level of accuracy
With the help of deep neural networks, AI can achieve extremely high accuracy. AI helps the medical field to diagnose diseases such as cancer from a patient's MRI.
- Artificial intelligence can organize data to maximize its use
Data is the intellectual property of systems that use self-learning algorithms. We need AI to index and organize data in a way that always provides the best results.
- Understanding Intelligence
With artificial intelligence, intelligent systems can be built. We need to understand the concept of intelligence so that our brains can build another intelligent system like ourselves.
What is Intelligence?
A system that can compute, reason, perceive relationships and analogies, learn from experience, store and retrieve information from memory, solve problems, understand complex ideas, use natural language fluently, classify, generalize, and adapt to new situations.
Types of Intelligence
As described by Howard Gardner, an American developmental psychologist, intelligence comes from many sources, as shown in the following table -
No |
Intelligence |
Description |
Example |
|---|---|---|---|
1 |
Language |
Able to say, identify and use phonological (phonetics), syntactic (grammar) and semantic (meaning) mechanisms. |
Narrator, Speaker |
2 |
Music |
The ability to create, communicate and understand the meaning made by the understanding of sounds, tones and rhythms. |
Musician, singer, composer |
3 |
Logic Math |
The ability to use and understand relationships in the absence of operations or objects. This is also the ability to understand complex and abstract ideas. |
Mathematician, Scientist |
4 |
Space |
The ability to perceive visual or spatial information, change it, and recreate visual images without reference to objects, build 3D images as well as move and rotate them. |
Map reader, astronaut, physicist |
5 |
Body-Motion |
Ability to use complete or partial bodies to solve problems or fashion products, control fine and gross motor skills, and manipulate objects. |
Ballplayer, dancer |
6 |
Intra-personal |
The ability to distinguish between one's feelings, intentions and motivations. |
Buddha |
7 |
Interpersonal |
Ability to identify and differentiate between the feelings, beliefs and intentions of others. |
Mass communicator, interviewer |
It can be said that a machine or a system is artificially intelligent when it is equipped with at least one or all intelligences.
What are the components of intelligence?
Intelligence is intangible. It consists of -
- Reasoning
- Learning
- Problem solving
- Perception
- Linguistic Intelligence
All components are briefly described below -
Reasoning
It is a set of procedures that enable us to provide the basis for judgment, making decisions and predictions. There are roughly two types -
| Inductive reasoning | Deductive reasoning |
|---|---|
| It makes specific observations to make broad general statements. | It starts with a general statement and examines the possibilities to reach a specific, logical conclusion. |
| Even though all the premises are true in the statement, inductive reasoning allows the conclusion to be false. | In general, if one class of things is true, then so are all members of that class. |
| Example - "Nita is the teacher, and Nita is very good at learning, so the teachers are very good at learning." | Example - "All women over 60 are grandmothers, and Shalini is 65, so Shalini is a grandmother." |
Learning - i
The ability to learn is possessed by humans, specific species of animals, and AI-enabled systems. Learning is classified as follows -
Auditory learning
It learns by hearing and listening. For example, students who listen to recorded lectures.
Episodic learning learns by remembering a series of events that people witness or experience. It is linear and sequential.
Motor Learning
It learns through the precise movement of muscles. Examples include selecting objects, writing, etc.
Observation learning
It learns by watching and imitating others. For example, a child tries to learn by imitating her parents.
Perceptual learning
It is learning to recognize that a stimulus has been seen before. For example, identifying and classifying objects and situations.
Relational learning
It involves learning to distinguish between various stimuli on the basis of relational properties rather than absolute properties. For example, adding a "pinch of salt" when cooking the last salty potato, when because then adding a tablespoon of salt.
- Spatial learning - Learning through visual stimuli such as images, colors, maps, etc. For example, a person can create a road map in his or her mind before actually following the road.
- Stimulus response learning - Learning to perform a specific behavior when a certain stimulus is present. For example, a dog raises its ears when it hears a doorbell.
Problem solving
People perceive and try to reach the desired solution from the present situation by walking a path blocked by known or unknown obstacles. Problem solving also involves decision making, the process of selecting the most appropriate alternative from among multiple options to achieve the desired goal.
Perception
This is the process of acquiring, interpreting, selecting and organizing sensory information.
Perception assumes perception. In humans, perception is aided by the sensory organs. In the field of artificial intelligence, perceptual mechanisms put together the data acquired by sensors in a meaningful way.
Linguistic Intelligence
This is the ability to use, understand, speak and write verbal and written language. This is important in interpersonal communication.
What is Artificial Intelligence about?
Artificial intelligence is a vast field of research. This field of research helps in finding solutions to real-world problems.
Let's take a look at the different research areas of AI research -
Machine Learning
It is one of the most popular areas of AI. The basic concept of this field is to let the machine learn from the data as human can learn from his/her experience. It contains learning models based on which predictions can be made on unknown data.
Logic
Mathematical logic is used to execute computer programs is another important area of research. It contains rules and facts for performing pattern matching, semantic analysis, etc.
Search
This research area is basically used for games like chess, Go, etc. The search algorithm gives the optimal solution after searching the entire search space.
Artificial Neural Network
This is an efficient network of computational systems whose central theme is borrowed from the analogy of biological neural networks. ANNs can be used for robotics, speech recognition, speech processing, etc.
Genetic Algorithm
Genetic algorithms help to solve problems with the help of multiple programs. The results will be based on the selection of the fittest.
Knowledge representation
This is the area of research where we can represent facts by machines that can understand them. Representation of more effective knowledge; more systems will be intelligent.
Applications of AI
In this section, we will see the different areas supported by AI -
Board games
Artificial intelligence plays a crucial role in strategic games such as chess, poker, tic-tac-toe, etc. Machines can consider a large number of possible ways to play based on heuristic knowledge.
Natural Language Processing
It is possible to interact with computers that understand natural human language.
Expert systems
There are applications that integrate machines, software and special information to deliver reasoning and advice. They provide explanations and suggestions to the user.
Vision Systems
These systems understand, interpret and comprehend visual input on a computer. For example
- Spy planes take photographs that are used to compute spatial information or maps of areas.
- Doctors use clinical expert systems to diagnose patients.
- Police use computer software that can identify the face of a criminal from a stored portrait by a forensic artist.
Speech Recognition
Some intelligent systems are able to hear and understand sentences of language and their meaning while people are conversing. It can handle different accents, slang, noises in the background, variations in human noise due to cold, etc.
Handwriting Recognition
Handwriting recognition software reads text written on paper with a stylus on the pen or on the screen. It recognizes the shape of letters and converts them into editable text.
Intelligent robots
Robots are able to perform tasks given by humans. They have sensors to detect real world physical data such as light, heat, temperature, motion, sound, collision and pressure. They have efficient processors, multiple sensors and huge memory to show intelligence. In addition, they are able to learn from their mistakes and adapt to new environments.
Cognitive Modeling: Simulating Human Thought Processes
Cognitive modeling is basically a field of computer science research that involves studying and simulating human thought processes. The main task of AI is to make machines think like humans. The most important feature of the human thought process is problem solving. That is why more or less cognitive modeling tries to understand how humans solve problems. The model can be used in various AI applications such as machine learning, robotics, natural language processing, etc.
Agents and Environments
In this section, we will focus on agents and environments and how these can help in artificial intelligence.
Proxies
An agent is anything that can perceive its environment through a sensor and act on that environment through an effector.
Human agents have sensory organs such as eyes, ears, nose, tongue, and skin in parallel with sensors and other organs such as hands, legs, and mouth for effectors.
The robotic agent replaces the camera and infrared rangefinder of the sensor, and the motors and actuators of the various effectors.
Software agents have encoded bit strings into their programs and operations.
Environment
Some programs run in a completely artificial environment limited to keyboard input, databases, computer file systems, and on-screen character output.
In contrast, some software agents (software robots or softbots) exist in a rich infinite soft domain. Simulators have a very detailed, complex environment. Software agents need to choose from multiple behaviors in real time. Softbot is designed to scan customers' online preferences and show them interesting objects working in real as well as artificial environments.
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