What is Artificial Intelligence?
The term Artificial Intelligence comprises of two words ‘Artificial’ and ‘Intelligence’, where, Artificial means ‘copy of something natural’ and ‘Intelligence’ means ‘able to think.’
So, Artificial Intelligence can be defined as a copy of a human brain with thinking ability.
According to John McCarthy, who is known as the father of AI,
“AI is the science and engineering of making intelligent machines, especially computer programs.”
The objective of AI is to explore the ways onto a machine that can reason like a human, think like a human and act like a human. Its approach is to train a machine (i.e., a computer or a robot) with the same capabilities as of a human brain. In the future, AI will prove itself as an excellent helping hand.
Need for Artificial Intelligence
Consider a scenario where a human brain may fail to take an intelligent decision and need someone who can make a wise and intelligent decision for him. In such a situation, we can understand the need for AI.
Therefore, AI has groomed the world with its exploring intelligent power, and the following points will make us understand the need for AI more effectively:
- AI can be used to make an intelligent decision as compared to human beings.
- AI can be used to analyze data more deeply.
- To maintain security and privacy of data more effectively and efficiently.
- To create expert systems having the capability to explain and give advice to users.
- AI can also be used to speed up the work performance.
How Artificial Intelligence came into existence?
Earlier Greeks used to discuss Artificial Intelligence in rumors or stories. As a result, In the 1940s and 50s, a group of classical philosophers and mathematicians decided to convert the myth of Artificial Intelligence into reality.
Turing Machine and Turing Test
1936: Alan Turing created a Turing machine which formalized the concept of algorithm and computation. Turing machine was highly influential in the development of theoretical computer science.
1950: Alan Turing published a seminal paper on “Computing Machinery Intelligence” in which he described the “Turing Test” to determine whether a machine is intelligent or not.
The term AI was coined
1956: Several scientists attended the Dartmouth Summer Conference at New Hampshire. During the conference, it was claimed that “every aspect of learning or any other feature of intelligence can be so precisely described that a machine can be made to simulate it” and finally it was named “A.I.”
1966: Joseph Weizenbaum, a German-American computer scientist, invented ‘ELIZA’, which is a computer program that communicates with humans.
AI in the medical field.
1972: Ted Shortliffe developed an expert system named ‘MYCIN’ which is used for the treatment of illnesses.
Voice of the Computer: NETtalk
1986: Terrence J. Sejnowski and Charles Rosenberg developed an artificial neural network,’ NETtalk.’ It was able to read words and pronounce them correctly and could apply what it learned for understanding more new words.
Victory over champions
1997: Deep Blue from IBM became the first computerized chess-playing system to defeat the world chess champion, Garry Kasparov.
2005: A robot from Stanford University won the DARPA Challenge. It drove autonomously for 131 miles across an unrehearsed desert trail.
2007: A team from CMU won the DARPA Urban Challenge by autonomously navigating 55 miles in an urban environment by following all traffic laws.
2011: IBM’s question answering system, Watson, defeated the two greatest Jeopardy Champions, Brad Rutter and Ken Jennings in a Jeopardy! Quiz exhibition match.
Nowadays, faster computers and advanced machine learning techniques have been introduced to access a large amount of data. It has resolved many economical and financial problems. Currently, experts are working on Deep learning, Big Data, Machine learning, and several other techniques and taking the world to a highly advanced level.
Artificial Intelligence: The Superset.
AI provides ways to make machines intelligent. AI uses algorithms and expert systems to make the artificial brain. ML is the subset of artificial intelligence because ML makes AI algorithms more advance so that machines may automatically improve through experiences without manual intervention. Thus, Machine learning is an application/component of Artificial Intelligence.
Components of Artificial Intelligence
- Reasoning, problem-solving: Researchers had developed machines with algorithms that enable machines to solve puzzles or quiz similar to humans. AI can also deal with uncertain or incomplete information through advanced algorithms.
- Knowledge Representation: It is the representation of all the knowledge which is stored by an agent to make an expert system. Knowledge can be a set of objects, relations, concepts, or properties.
- Planning: Intelligent agents should be able to set goals and make plans to achieve those goals. They should be able to visualize the future and make predictions about their actions taken for achieving the goal.
- Learning: It is the study of the computer algorithms which improve automatically through experiences. This concept is known as Machine Learning.
- Natural Language Processing: This processing enables a machine to read and understand human language by processing the human language into machine language.
- Perception: An ability of the machine to use input from sensors, microphones, wireless signals, etc. for understanding different aspects of the world.
Types of Artificial Intelligence
Classification of AI can be done in several ways:
- Weak AI: It is also known as narrow AI, which is designed to perform a specific task. It acts like it can ‘think’.
- Strong AI: It is also known as artificial general intelligence which has generalized human cognitive abilities. It is intelligent enough to find a solution.
Arend Hintze, an assistant professor of integrative biology and computer science, classified Artificial Intelligence into four types:
- Reactive Machines: These machines are designed for small purposes, but it has no memory and cannot use past experience for future decision. An example of a reactive machine is Deep Blue from IBM.
- Limited Memory: This system uses past experience for future decisions, for example, autonomous vehicles.
- Theory of Mind: This is a psychological term which refers to the understanding that “Every mindset is different, so is the decision.”At present, this type does not exist.
- Self-awareness: In this type, machines have self-awareness ability to understand their current state and can predict what others feel. Currently, this type of AI does not exist.
Recent Tools and Technologies
- Speech Recognition: It recognizes the human voice and transforms into the format, which can be understood by different computer applications.
- Natural Language Generation: It is a tool which produces text from the computer data.
- Virtual Agent: The agent serves as an online customer service representative. It behaves intelligently with the customer and responses well.
- Machine learning: ML provides a platform to develop algorithms and APIs for the improvement of the machine and to make machines self-supervised.
- Biometrics: It is used for identifying access management and access control. It is also used to identify the person under surveillance.
Applications of Artificial Intelligence
AI in Business
- AI has become a supporting tool for the growth of the business.
- AI helps in determining the consequences of each action for decision making.
- AI can also make decisions on its own and can act in situations not foreseen by the person.
- Machine learning algorithms are integrated with CRM (Customer Relationship Management) to provide better services to customers.
- Chatbots used by e-companies provide immediate responses to the customers.
AI in Healthcare
- Hospitals use ML algorithms for better and fast diagnosis than humans. IBM’s Watson (a question-answering system) used to form a hypothesis from the patient’s data.
- AI can assist both the patients and doctors well.
- Autonomous robots help surgeons in surgery.
- It helps doctors for the right treatments of Cancer.
- It provides a way to try and monitor multiple high risks patients by interrogating them.
- It provides a laboratory for examination and representation of medical information.
AI in Education
- It provides a platform for the students to learn and grab things quickly.
- It automates grading systems that help staff to monitor marks easily.
- AI saves much time of students and teachers.
AI for Robotics
With the help of AI, it becomes easy to take care of the aging population and can see a drastic reduction in the death rate of people.
AI in Autonomous Vehicles
- AI has automated the systems of cars and other vehicles.
- AI has provided sensors to understand the world around them and learn from the environment
AI in Agriculture
- AI showed improvements in gaining yield and increased research and development of growing crops.
- Crop and soil monitoring has become easy.
- AI has made farming easier for farmers to know when the fruit or vegetable be ready to ripe.
Advantages of Artificial Intelligence
- Chances of the error have approximately become negligible and achieved higher accuracy.
- Intelligent robots have explored the world.
- AI has become a helping hand for humans in laborious work.
- AI also helps in making the best decision.
- AI has made fraud detection on smart card possible.
- Provide in-depth analyses of data.
Disadvantages of Artificial Intelligence
- It can cost lot of money to build, rebuild or repair machines.
- Robots can replace humans and take off their jobs.
- AI can cause unemployment.
- If given in wrong hands, machines may lead to destruction.
- AI will make human dependent on it which lead to rust on their brains.
Artificial Intelligence Topics
Artificial Intelligence Introduction
- Uninformed Search
- Informed Search
- Heuristic Functions
- Local Search Algorithms and Optimization Problems
- Hill Climbing search
- Differences in Artificial Intelligence
- Adversarial Search in Artificial Intelligence
- Minimax Strategy
- Alpha-beta Pruning
- Constraint Satisfaction Problems in Artificial Intelligence
- Cryptarithmetic Problem in Artificial Intelligence
Knowledge, Reasoning and Planning
- Knowledge based agents in AI
- Knowledge Representation in AI
- The Wumpus world
- Propositional Logic
- Inference Rules in Propositional Logic
- Theory of First Order Logic
- Inference in First Order Logic
- Resolution method in AI
- Forward Chaining
- Backward Chaining
- Classical Planning
Uncertain Knowledge and Reasoning
- Quantifying Uncertainity
- Probabilistic Reasoning
- Hidden Markov Models
- Dynamic Bayesian Networks
- Utility Functions in Artificial Intelligence