Artificial Intelligence is a branch of computer science that emphasizes the development of machine intelligence, thinking patterns and working like humans. For example, voice recognition, problem solving, learning, and planning. Artificial Intelligence is a very popular discussion material that is widely discussed in technology and business circles.
Artificial Intelligence abbreviated AI is a scientifically intelligent entity created by humans. The entity is implanted into a machine, so that the machine seems to be able to think for itself to make a decision.
AI is arguably not only a computer device, because computers can only make decisions and produce functions when directed by the user. While AI or artificial intelligence is apparently able to determine for themselves what decisions will be taken based on the experience that has been recorded into a knowledge. The recording is stored in the AI device database itself, and then applied if needed.
The emergence of this artificial intelligence device is arguably a very remarkable technological progress, because the concept of AI-based devices has slowly begun to be applied in various fields such as multimedia, search engines, robotics, smart home, and so forth. In essence, AI is an artificial intelligence program that is implanted in a machine or device to make it easier for humans to do their jobs.
The history of the development of intelligence begins in the early 17th century, in which a scientist named René Descartes argued that animal bodies are nothing but complicated machines. Then proceed with Blaise Pascal who invented the first mechanical digital calculating machine in 1642. In the 19th century, Charles Babbage and Ada Lovelace worked on programmable mechanical calculating machines.
A few years later, Bertrand Russell and Alfred North Whitehead published Principia Mathematica, which could overhaul formal logic. This prompted the idea for Warren McCulloch and Walter Pitts in publishing the “Logical Calculus of Ideas that remained in Activity” in 1943 which laid the foundation for neural networks.
The 1950s were a period of active effort in AI. The first AI program that worked was written in 1951 to run the Ferranti Mark I engine at the University of Manchester (UK): a script play program written by Christopher Strachey and a chess game program written by Dietrich Prinz. John McCarthy coined the term “artificial intelligence” at the first conference provided for this subject, in 1956. He also discovered the Lisp programming language. Alan Turing introduces “Turing test” as a way to operationalize intelligent behavior tests. Joseph Weizenbaum built ELIZA, a chatterbot that implemented Rogerian psychotherapy.
During the 1960s and 1970s, Joel Moses demonstrated the power of symbolic reasoning to integrate problems into the Macsyma program, the first successful knowledge-based program in mathematics. Marvin Minsky and Seymour Papert published Perceptrons, which demonstrated the limits of simple neural networks and Alain Colmerauer developed the Prolog computer language. Ted Shortliffe demonstrated the power of a rule-based system for the representation of knowledge and inference in medical diagnosis and therapy which is sometimes called the first expert system. Hans Moravec developed the first computer-controlled vehicle to overcome the tangled road independently.
In the 1980s, neural networks were used extensively with the back propagation algorithm, first explained by Paul John Werbos in 1974. In 1982, physicists such as Hopfield used statistical techniques to analyze the properties of storage and optimization in tissue nerve. Psychologists, David Rumelhart and Geoff Hinton, continue research on neural network models in memory. In the 1985s at least four research groups rediscovered the Back-Propagation learning algorithm. This algorithm was successfully implemented into computer science and psychology. The 1990s marked major gains in various fields of AI and demonstrations of various applications. More specifically Deep Blue, a chess game computer, defeated Garry Kasparov in a famous match 6 game in 1997. DARPA stated that costs saved through applying AI methods for scheduling units in the first Gulf War had replaced all investments in AI research since 1950 in the US government.
Nowdays, the usage of artificial intelligence technology has been applied in various fields that are useful to facilitate human work. Therefore artificial intelligence is divided into three types namely Symbol-manipulating AI, Neural AI, and Neural Networks. Of the three types have their respective functions and objectives.
Symbol-manipulating AI works with abstract symbols. Symbol-manipulating AI is the most experimental type. The essence of the experiment is that humans are reconstructed at a hierarchical and logical level. The information is processed from above, then works with symbols that can be read by humans / the developer, abstract connections and logical conclusions.
Neural AI was very popular among computer scientists in the late 80s. With Neural AI, knowledge is not represented through symbols, but rather artificial neurons and their connections – a kind of reconstructed brain. The collected knowledge will then be broken into small pieces (called neurons) and then linked and built into groups. Well, this approach is known as a bottom-up method that works from the bottom. Unlike the Symbol-manipulating AI the first author described. So, the nervous system must be trained and stimulated so that the neural network can gather experience and grow so that it can gather greater knowledge.
Meanwhile, Neural Networks are organized into layers which are connected to each other through simulations. The top layer is the input layer, which functions like a sensor. The sensor in question is the recipient of information that will process and forward it to the system. There are at least two systems – or more than twenty layers in a large system – layers arranged hierarchically. These layers send and classify information through connections. At the very bottom is the output layer, which generally has the fewest number of artificial neurons.
Artificial intelligence that can play a role in all these fields makes it widely applied in the company. The following are applications of artificial intelligence. They are :
Siri is one of the most popular virtual personal assistants offered by Apple on the iPhone and iPad. The assistant who is activated as a friendly female voice interacts with users in daily routines. It helps you find information, get directions, send messages, make voice calls, open applications, and add events to your calendar.
Siri uses machine learning technology to get natural language questions and requests that are smarter and better able to understand. This is definitely one of the most iconic examples of learning ability on a smartphone machine.
Nest is one of the most famous and successful startup examples of applying Artificial Intelligence and was acquired by Google in 2014 for $ 3.2 billion. The Nest Learning Thermostat uses behavioral algorithms to save energy based on your behavior and schedule.
It uses a very intelligent machine learning process that studies the temperature you like and the program itself in about one week. In addition, it will automatically die to save energy, if no one is at home.
In fact, this is a combination of both – artificial intelligence and Bluetooth low energy because some of the components of this solution will use BLE services and solutions.
Cogito was originally founded by Dr. Sandy and Joshua is one of the best examples of a behavioral version of the artificial intelligence application to improve corporate customer service. The company is a synthesis of machine learning and behavioral science to enhance customer collaboration with call centers.
Cogito is used on millions of voice calls that are made every day. Examples of implementing Artificial Intelligence by analyzing human speech and providing guidance to provide maximum service.
Pandora is one of the most popular and highly detailed artificial intelligence technology solutions. This is also called music DNA. Depending on 400 musical characteristics, a team of expert musicians individually analyzes the song. This system is also good at recommending track records to recommend songs that have never been noticed, even if people like them.