Artificial intelligence means non-natural ability to think, learn and understand on their own. Artificial intelligence is in the context of humans as humans are the most intelligence on earth.
Artificial intelligence system means that to create some sort of systems that can work and thinks on their own and works independently and intelligently.
Artificial intelligence is not a new field in history of computer science it has a long history and AI was first introduced in 1950’s for the first time at the time this concept is said by “narrow AI” at that time, AI is used for or designed for doing specific tasks like facial recognition etc and that time this concepts lacks of thinking power and learning power on their own.
But now a day’s scientists are working for this field that artificial intelligence power system can not only think and learn like humans but more efficiently than humans.
For the ability of thinking and learning on their own by any system for that we have to turn on mode of machine learning of system for the machine learning different techniques and algorithms implemented in the system so that by the help of these algorithms and techniques system can make prediction about the given data and learning of that data for future use.
With the help of machine learning programmers no longer need to write code of everything and even some time machine remove the bugs of code on their own.
Major branch is Artificial Intelligence and in this major branch there is two sub branches which are or you can say that AI works on two principles one is symbolic based and another is data based.
- Symbolic learning
- Machine Learning
Symbolic learning is also known as symbolic artificial intelligence and this is collection of methods in artificial intelligence research that are based on high-level “symbolic” that can be readable by humans and the representations of problems, logic and search. Symbolic AI was one of the main pilgrim of AI research.
Further, there are two main branches of symbolic learning
Robotics is mixture of science and engineering that cover many fields like mechanical, electrical, Information technology & computer science. Robotics mainly deals with the construction, operation, and use of robots.
Robots technology was designed to replace humans or replicate the humans for performing routine tasks. As humans can understand their environment and move fluidly this all falls under the umbrella of intelligence.
Humans can see things with their eyes and can process on that and concludes or analysis something from the data and in computer science if computer analysis some pattern and process on it is call as computer vision
Humans see the things with the help of their eyes but for machines we use image processing for that purpose so that machine can recognize any pattern or image and can process it although it is not directly related to artificial intelligence but it is important pilgrim in computer vision.
Machine learning is study of algorithms, statistical modeling and different techniques through which computer systems or machines can learn and improve their efficiency of performing the assigned tasks.
In machine learning the mathematical modeling for training data by the help of which prediction and guesses are made without programmed to perform that task.
Most common application of machine learning is email filtering in email filtering spam emails are filtered out on the basis of prediction with the help of machine learning technology.
The two main sub branches of Machine learning are:
- Statistical Learning
- Deep Learning
Statistical learning is framework of machine learning in this stats and faction are used for analysis of any data. Speech recognition and NLP is the application of statistical learning.
Speech recognition is capability of machine to recognize the words, which are spoken, and covert that words to assembly language, which is understandable by machine and NLP (natural language processing) is defined as identification, understanding and analyzing of human natural language.
Human brain is network of neurons and with the help of these neurons, we learn things so if we replicate the system and working of the human brain then we can achieve the capability of learning like human brain this net work is complex and very deep and this is known as Deep learning.
There are different types of deep learning in computer science based on different techniques to replicate the working of human brain working. For example for analyzing any image you can read it from different angles or direction like top to bottom or from left to right etc.
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