The History of Artificial Intelligence may be centuries ago, but its research application areas are rapidly evolving and making AI the next generations powerful technology. Let’s have detailed knowledge on Artificial Intelligence, its origin, level and branches.
Intelligence is the best gift given to a human being in the form of the power to reason, the power to think or analyze. In this universe, human beings are the only creature which has boomed with this intelligence- the power to distinguish between wrong or right, the power to take right decisions and act upon the decision. Some animals like Cow, Shark and Dog have also been provided with a glimpse of intelligence. But human beings are always curious and innovative in nature. They always look for a certain way to explore their intelligence in some or other way through research and invention. Similarly, there arises the doctrine of Artificial intelligence from the ancient time where humans expected and explored the thought of intelligent machines which has been powered by the philosophy “Artificial Intelligence.”
Philosophy of AI:
While exploiting the power of the computer systems, the curiosity of human, lead him to wonder, “Can a machine think and behave like humans do?” Thus, the development of AI started with the intention of creating similar intelligence in machines that we find and regard high in humans.
Ancient History of Artificial Intelligence:
The intellectual roots of AI, and the concept of intelligent machines, may be found in Greek mythology. Intelligent artifacts appear in literature since then, with real (and fraudulent) mechanical devices actually demonstrated to behave with some degree of intelligence. Some of these conceptual achievements are listed below under "Ancient History of AI.”
Ø Talos
In Greek Mythology, Talos was a giant constructed of bronze who acted as guardian for the island of Crete. He would throw boulders at the ships of invaders, and would complete 3 circuits around the islands perimeter daily.
Ø Golem
It has been recorded that ancient Greek philosophers discussed automatons or machines with inherent intelligence. In 1517, the Prague Golem was created. The Golem is made of clay, but according to Jewish folklore, it could be animated to carry out various acts of vengeance and retribution to parties responsible for anti-Semitic acts.
Ø Turk
A more fanciful AI experiment example—or more appropriately stated, a hoax—is an automated chess player that made the rounds in Europe in the late 18th to mid-19th centuries. It was known as The Turk.
Modern History of Artificial Intelligence:
After modern computers became available, following World War II, it has become possible to create programs that perform difficult intellectual tasks. From these programs, general tools are constructed which have applications in a wide variety of everyday problems. Some of these computational milestones are listed below under "Modern History of AI."
Ø Turing's test:
In 1950 Alan Turing published a landmark paper “Computing Machinery & Intelligence”, in which he speculated about the possibility of creating machines that think. A Turing Test is a method of inquiry in artificial intelligence (AI) for determining whether or not a computer is capable of thinking like a human being. The test is named after Alan Turing, the founder of the Turing Test and an English computer scientist, cryptanalyst, mathematician and theoretical biologist.
Ø Game AI:
In 1951, using the Ferranti Mark 1 machine of the University of Manchester, Christopher Strachey wrote a checkers program and Dietrich Prinz wrote one for chess. Game AI would continue to be used as a measure of progress in AI throughout its history.
The Birth of Artificial Intelligence:
But the field of AI wasn't formally founded until 1956. At a conference at Dartmouth College, in Hanover, New Hampshire, where the term "artificial intelligence" was coined by John McCarthy in 1956.
McCarthy stated this now classic definition of AI, remains the “gold standard” that most people use when asked to define AI: “It is the science and engineering of making intelligent machines, especially intelligent computer programs. It is related to the similar task of using computers to understand human intelligence, but AI does not have to confine itself to methods that are biologically observable.”
John McCarthy (September 4, 1927 – October 24, 2011) was an American computer scientist and cognitive scientist. McCarthy was one of the founders of the discipline of artificial intelligence. He developed the Lisp programming language family, significantly influenced the design of the ALGOL programming language, popularized time-sharing, invented garbage collection, and was very influential in the early development of AI.
Levels of Artificial Intelligence:
1. Narrow AI:
Narrow AI is a term used to describe artificial intelligence systems that are specified to handle a singular or limited task. The Narrow AI is sometimes referred to also as weak AI. This kind of artificial intelligence operates within a limited context and is a simulation of human intelligence. Some examples of Narrow AI include: Google search, Image recognition software, Siri, Alexa and other personal assistants, Self-driving cars and IBM's Watson.
2. General AI:
An artificial intelligence reaches the general state when it can perform any intellectual task with the same accuracy level as a human would.
3. Strong AI: An AI is strong when it can beat humans in many tasks.
[Note: The current research area of AI is still in Narrow AI stage.]
Research Areas of Artificial Intelligence:
1. Machine Learning:
Machine learning is a buzzword for today's technology. We are using machine learning in our daily life.
Machine learning (ML) is a type of artificial intelligence (AI) that allows software applications to become more accurate at predicting outcomes without being explicitly programmed to do so. Machine learning algorithms use historical data as input to predict new output values.
Below are some most trending real-world applications of Machine Learning:
Image Recognition: Image recognition is one of the most common applications of machine learning.. The popular use case of image recognition and face detection is Automatic friend tagging suggestion. Facebook provides us a feature of auto friend tagging suggestion.
Speech Recognition: Speech recognition is a process of converting voice instructions into text, and it is also known as "Speech to text", or "Computer speech recognition." Google assistant, Siri, Cortana, and Alexa are using speech recognition technology to follow the voice instructions.
Product recommendations: Machine learning is widely used by various e-commerce and entertainment companies such as Amazon, Netflix, etc., for product recommendation to the user.
Self-driving cars: One of the most exciting applications of machine learning is self-driving cars. Machine learning plays a significant role in self-driving cars. Tesla, the most popular car manufacturing company is working on self-driving car.
2. Deep Learning:
Deep learning is a subset of machine learning in artificial intelligence that has networks capable of learning unsupervised from data that is unstructured or unlabeled. Also known as deep neural learning or deep neural network.
Deep learning is an AI function that mimics the workings of the human brain in processing data for use in detecting objects, recognizing speech, translating languages, and making decisions. It can be used to help detect fraud or money laundering, among other functions.
Deep learning is also an important element of data science, which includes statistics and predictive modeling. It is extremely beneficial to data scientists who are tasked with collecting, analyzing and interpreting large amounts of data; deep learning makes this process faster and easier.
3. Artificial Neural Network(ANN):
ANN are an attempt to emulate the basic functions of the human brain to perform complex functions that everyday computer systems are incapable of doing. Computers can be operated in nanoseconds, and work without error. But it can’t do walking, talking and reasoning like human being.
The concept of a neural network appears to have first been proposed by Alan Turing in his 1948 paper "Intelligent Machinery". An ANN involves a network of simple processing elements (artificial neurons) which can exhibit complex global behavior.
Application areas of ANN also include
The system identification and control (vehicle control, process control)
Function approximation or regression analysis
Time series prediction
Modeling game- playing
Sequential decision making (chess, racing)
Pattern recognition (radar systems, face identification, object recognition, etc.)
Sequence recognition (gesture, speech, handwritten text recognition)
Data processing (including filtering, clustering)
Knowledge discovery in databases (KDD)
Visualization and e-mail spam filtering.
4. Natural Language Processing:
Natural Language Processing, usually shortened as NLP, is a branch of artificial intelligence that deals with the interaction between computers and humans using the natural language. The ultimate objective of NLP is to read, decipher, understand, and make sense of the human languages in a manner that is valuable.
Uses of NLP:
Language translation applications such as Google Translate.
Interactive Voice Response (IVR) applications used in call centers to respond to certain users’ requests.
Personal assistant applications such as OK Google, Siri, Cortana, and Alexa.
Word Processors such as Microsoft Word and Grammarly that employ NLP to check grammatical accuracy of texts.
5. Expert System:
Expert systems (ES) are one of the prominent research domains of AI. An expert system is a machine system in which useful human knowledge is added in machine memory in order to give intelligent advice and offer explanations and justifications of its decisions or demand.
Expert system relies on a large database of well defined specialized knowledge about a particular area. Construction of such programs is referred to as Knowledge Engineering. These programs contain the knowledge used by human experts, in contrast to knowledge gathered from textbooks.
Because of this expert systems are like human experts e.g. doctors, engineers, analysts, teachers, geologists etc which encapsulate the skills of an expert and to dispense advice to less knowledgeable users.
There are many expert systems exists which have been designed for giving expertise training, designing and trouble-shooting etc. like MYCIN, TURNX, PROSPECTOR. The expert systems are still in their infancy.
6. Robotics:
Robotics is a domain in artificial intelligence that deals with the study of creating intelligent and efficient robots. AI has become an increasingly common presence in robotic solutions, introducing flexibility and learning capabilities in previously rigid robotics applications.
Virtual Assistants & Chatbots: Virtual assistants are a manifestation of AI and machine learning through the simulation of conversation with humans. Virtual assistants and chatbots are designed to obey automated rules using capabilities called Natural Language Processing (NLP).
Autonomous Flying: Autonomous flying uses computer vision technology for hovering in the air while avoiding obstacles and moving in a straight path. The application of computer vision in autonomous flying includes obstacle detection, collision avoidance, self-navigation, and object tracking.
Agriculture & Farming: AI and automation are believed to provide relief from the effects of an aging agricultural workforce. With the likes of autonomous drones, self-driving agricultural machines, etc., farmers can spend more time focusing on creating sustainable harvests and less time watching the path in front of them.
7. Fuzzy Logic
Fuzzy Logic (FL) is a method of reasoning that resembles human reasoning. The approach of FL imitates the way of decision making in humans that involves all intermediate possibilities between digital values YES and NO.
The conventional logic block that a computer can understand takes precise input and produces a definite output as TRUE or FALSE, which is equivalent to human’s YES or NO. The inventor of Fuzzy Logic, Lotfizadeh observed that unlike computers, the human decision making includes a range of possibilities between Yes and No, such as CERTAINLY YES, POSSIBLY YES, CANNOT SAY,POSSIBLY NO and CERTAINLY NO.
Fuzzy Logic are used as a profitable tool to control the subway systems, complex industrial processes, entertainment electronics, diagnosis systems and household appliances e.g. in washing machines fuzzy logic sense load size and detergent concentration and adjust their wash cycles automatically. Fuzzy logic is very useful in manufacturing processes as it can handle situations that cannot be adequately handled by traditional true/false logic.
EndPoint:
In day to day life, we come across many Artificial Intelligence applications about which we are unaware. Artificial Intelligence Applications have been hugely used by Google, Facebook, Amazon, Netflix for providing the customer better suggestions and recommendations. Google is doing much research on Artificial Intelligence applications for social good. The Flood Alert, Cyclone Alerts and Corona Alert are the essential application of Artificial Intelligence by Google. Similarly Amazon uses Amazon Web Service for providing better customer experience. Facebook also spent a large million dollars on Artificial Intelligence for more customer engagement and rich experience.It's Artificial Intelligence which is making our life easier in many unaware ways. We all must be aware of Artificial intelligence and its application in the real world.
Please share it, so that maximum people can be aware.
Yes,AI will be part of our almost all aspects of life very soon.
Artificial Intelligence is the booming technology about which we all must be aware.