Defining concepts like artificial intelligence (AI), computational intelligence or machine intelligence is not a trivial task. A lot of authors have tried to formalize the concept providing a definition, but it has not been possible to normalize an accurate definition due to the huge breadth of the concept and its fuzzy boundaries with other areas. I usually refer to AI concept as those systems or methods that analyze information and takes actions to handle complexity occurring in diverse application areas, in my case diabetes management or stock market investments.
In short, Artificial Intelligence (AI) is the field of computer science trying to make machine intelligent. One of the major cores of AI is related to intelligence and the difficulty where intellectuals are still trying to make consensus is in defining it. We may define "intelligence" as capabilities of learning, acquiring knowledge, and generalizing. An approach of AI is also devoted to understand intelligence form nature and its modelling.
The second of the major cores of AI is related to computation which is required to mimic or design intelligence. Here, comes the role of computer science, data, algorithms, programming and engineering to deal with the essential complexities of the design. A most popular approach of AI is to design intelligence agents, with a (set) of intelligence function (s). Now a days, its a rich interdisciplinary field, sharing and merging with fields like maths, statistics, economics, engineering, natural and basic sciences, psychology, philosophy, neuroscience etc.
Though the field is continuously evolving and new sub fields are emerging. Few of the well-known pillars of AI include machine learning, knowledge/logic based systems, machine vision, robotics, and natural language processing etc.. Data science and analytics is one of the recent trends related to advanced machine learning applied on big data making huge impact in decision making and business intelligence etc.. Among other interesting applications few are game playing, machine translation, robotics vehicles, spam fighting, planning, scheduling and many more.
I will give you a simple example. Imagine that you have a Robot into a maze(for example an Android). If you program it to find its way into the maze then that program is AI.
The idea is that with simple commands you managed the robot to find the exit into the maze.
For example: if the robot has to make 3 steps forward and 1 step right to find the exit, then you have to give 2 simple commands: "1) Move 3 steps forward" and "2) move 1 step right". The robot doesnt know what it did, it just moved 3 steps forward and 1right (but it found the solution). Thats AI and yes it is difficult.
Is a series of mathematical models to manage the knowledge. It is presented in the form of historical data and is used to generate new scenarios as answers by different combinations of the data that serves to multiple purposes such: control, approximation, optimization and decision making among others.
Artificial intelligence is a branch of computer science that aims to create intelligent machines. It has become an essential part of the technology industry.
Research associated with artificial intelligence is highly technical and specialized. The core problems of artificial intelligence include programming computers for certain traits such as:
Knowledge
Reasoning
Problem solving
Perception
Learning
Planning
Ability to manipulate and move objects
Knowledge engineering is a core part of AI research. Machines can often act and react like humans only if they have abundant information relating to the world. Artificial intelligence must have access to objects, categories, properties and relations between all of them to implement knowledge engineering. Initiating common sense, reasoning and problem-solving power in machines is a difficult and tedious approach.
Machine learning is another core part of AI. Learning without any kind of supervision requires an ability to identify patterns in streams of inputs, whereas learning with adequate supervision involves classification and numerical regressions. Classification determines the category an object belongs to and regression deals with obtaining a set of numerical input or output examples, thereby discovering functions enabling the generation of suitable outputs from respective inputs. Mathematical analysis of machine learning algorithms and their performance is a well-defined branch of theoretical computer science often referred to as computational learning theory.
The ability of a machine to classify data, speech, images, tweets etc. on the basic of their intelligence with the help of "piece of program" is known as artificial intelligence. This piece of computer program along with sufficient data helps machine to learn and develop intelligence. Hence, the learning approach is known as machine learning and the intelligence is artificial intelligence.
AI include machine learning, knowledge/logic based systems, machine vision, robotics, and natural language processing etc.. Data science and analytics is one of the recent trends related to advanced machine learning applied on big data making huge impact in decision making and business intelligence etc.. Among other interesting applications few are game playing, machine translation, robotics vehicles, spam fighting, planning, scheduling and many more.