Artificial Intelligence Tutorial | AI Tutorial
Today, Artificial Intelligence (AI) has become the norm of how humans communicate with and understand technology. Even though you've been hearing this buzzword now and then, do you know what it actually is? Welcome to this AI tutorial, where you'll be discovering the technical side of things–both for starters and experts looking to glance and revise quickly.
What is Artificial Intelligence?
The term Artificial Intelligence comprises two words: ‘Artificial’ and ‘Intelligence’. Artificial means 'a mimicry of something natural', and 'Intelligence' means 'the ability to think'. Overall, the term Artificial Intelligence translates to any machine attempting to replicate and synthesise human-like intelligent behaviour.
According to John McCarthy, 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 for 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 a human brain. In the future, AI will prove itself as an excellent helping hand.
Need for Artificial Intelligence
Consider situations where humans struggle to make an intelligent decision and need some validating machine that can either validate the existing idea or formulate a completely new and optimal solution. This is where the need for something like AI becomes obvious.
Hence, AI machines could be intelligent decision-makers, assisting humans in tasks of varying complexity levels. Tasks like data analysis, decision making, data security, expert system creation, and many more can be possible through AI machines.
History of Artificial Intelligence
Earlier Greeks used to discuss Artificial Intelligence in rumours 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 formalised 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 in 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 "AI."
First Chatbot
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 to understand more new words.
Victory over champions
1997: Deep Blue from IBM became the first computerised 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.
AI Today
Nowadays, faster computers and advanced machine learning techniques have been introduced to process large amounts of data in parallel, resolving huge global problems. Currently, experts are working on Deep Learning, Big Data, Machine Learning, and several other cutting-edge techniques, making today’s technology more advanced.
Components of Artificial Intelligence
- Reasoning: Researchers have developed machines with algorithms that enable machines to solve puzzles or quizzes 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 that 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 visualise the future and make predictions about the actions taken to achieve the goal.
- Learning: It is the study of computer algorithms that 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.
Recent Technologies in Artificial Intelligence
- Speech Recognition: It recognises human voice and translates it into a format understood by different computer applications.
- Natural Language Generation: It is a tool that produces human-like text from computer data.
- Virtual Agents: The agent serves as an online customer service representative. It behaves intelligently with the customer and responds well.
- Machine learning: ML provides a platform to develop algorithms and APIs for the improvement of the machine and to make machines self-supervised.
Some Applications of Artificial Intelligence
AI in Business
- AI helps in determining the consequences of each action for decision making through predictive analysis.
- Machine learning algorithms are integrated with CRM (Customer Relationship Management) to provide better services to customers.
- Chatbots used by eCommerce platforms provide quick information to potential customers.
AI in Healthcare
- Hospitals use AI algorithms that accurately and quickly diagnose patients. For example, IBM's Watson (a question-answering system) used to form a hypothesis from the patient's data.
- Autonomous robots help surgeons in performing surgery of varying degrees of complexity.
- It provides a laboratory for examination and representation of medical information.
AI in Education
- AI tools are used for designing educational content based on each student’s specific learning rate and pattern.
- It automates grading systems for teachers.
- AI saves much time for students and teachers.
Challenges observed in Artificial Intelligence
- The initial costs of building AI machines are high and time-consuming.
- AI machines may end up replacing low-level jobs that previously required extensive manual intervention.
- AI can only be moderated to an extent, giving dangerous levels of control that could potentially overpower humans.
- AI would certainly increase human dependency on machines, leading to a decreasing trend of thinking capacities across future generations.
Artificial Intelligence Topics
Search Algorithms
- Problem-solving
- 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 Uncertainty
- Probabilistic Reasoning
- Hidden Markov Models
- Dynamic Bayesian Networks
- Utility Functions in Artificial Intelligence