The major goals of artificial intelligence also correspond to the traditional problems AI research intends to solve, as well as to the subsets and specific fields of AI. These goals are knowledge reasoning, planning, machine learning, natural language processing, computer vision, robotics, and artificial general intelligence.
The Major Goals of Artificial Intelligence: AI Research Problems and Fields of AI
1. Knowledge Representation and Reasoning
Knowledge representation and reasoning, also known as KR&R or KR, is a specific field within artificial intelligence focusing on designing and implementing computer representations that can process information about the world to solve complex problems.
As one of the major goals of AI, KR&R specifically aims to automate different kinds of reasoning. It involves the codification of factors or relationships between ideas, or rules or transitions between sets of facts in a manner that can be interpreted by a computer system.
Some of the applications of knowledge representation and reasoning include computer-aided diagnosis for assisting physicians interpret medical images, as well as natural-language user interface that involves using human language as input for interacting with computers or software.
2. Automated Planning and Scheduling
Some times called as AI planning, automated planning and scheduling is another subset and field of artificial intelligence concerned with the automated generation of action sequences that correspond to strategies that can be executed by an AI system such as unmanned vehicles or autonomous robots.
As one of the traditional problems of AI research, AI planning aims to mechanize and automate the generation of a plan based on predetermined goals and objectives, and a set of possible actions. Note that it is also one of the fundamental abilities needed to increase the autonomy and flexibility of AI systems.
Examples of AI planning include self-correcting computer programs or software applications, robots that serve as autonomous agents, automated information gathering, and computer-aided suggestions.
3. Machine Learning
Machine learning is both a specific field of artificial intelligence and an actual application of AI. As a field of discipline, it involves the study of algorithms and statistical models that computer systems use to perform a specific task without using explicit instructions effectively.
Central to the concept of machine learning is a different approach to computer programming. To be more specific, it involves the development and use of computer algorithms that can process and analyze Big Data and learn from the outcome without being explicitly programmed.
The benefits of machine learning include supplementing data mining, continuous improvements, and automation of tasks. Real-world applications include search engine ranking, content delivery, online advertising, autonomous driving, and intelligent assistant, among others.
4. Natural Language Processing
Another major goal and subset of artificial intelligence is natural language processing or NLP. It deals with the analysis and generation of languages that humans use naturally to interface with computers. In other words, NLP is about human-computer interaction using natural language instead of computer language.
Because it is also one of the traditional problems of AI, NLP aims to develop and implement computer systems, particularly computer programs that can process large amounts of natural language data. Achieving this aim means overcoming challenges such as speech recognition, natural language processing, and natural language generation.
Intelligent assistant services such as Google Now and the Siri application from Apple use natural language processing, along with other subsets of artificial intelligence such as machine learning and knowledge representation and reasoning.
5. Computer Vision
Computer vision involves acquiring and understanding visual data from still or moving digital images or live images from the real world to process them in a manner that AI systems can utilize to make decisions.
The aim of computer vision, as one of the major problems in AI research, centers on the computerization and automation of tasks that can be performed naturally by human vision, as well as the development of systems that can process, interpret, and utilize visual data.
Facial recognition is one of the notable applications of computer vision. Other applications include video tracking, automated image manipulation, object recognition, and integration with virtual reality and augmented reality.
6. Robotics
Another one of the major goals and thus, one of the subsets and fields of artificial intelligence is robotics. Note that robotics is an interdisciplinary branch of engineering and science that include computer science, electronic engineering, mechanical engineering, and information engineering.
Robotics is about the design, construction, and operation of machines or robots that can replicate human actions and replace human tasks with mechanical tasks. The integration of computer science and AI in robotics corresponds to equipping robots with sensory feedback and information capabilities to allow them to operate autonomously.
Current research in robotics aims to introduce commercial, domestic, and military applications. Online retailer Amazon has been using autonomous robots in its warehouse facilities with the task of organizing items and making warehouse operations more efficient.
7. Artificial General Intelligence
The utmost and long-term goal of artificial intelligence as a field is artificial general intelligence or AGI. By definition, AGI demonstrates the capability of an AI system to perform any intellectual task that a human can perform.
In consideration of the three types of AI systems introduced by Andreas Kaplan and Michael Heinlein, AGI demonstrates a humanized AI system equipped with cognitive intelligence, emotional intelligence, and social intelligence.
An AI system with artificial general intelligence is either self-conscious, self-aware, or both. This system will demonstrate seamless capabilities of machine-to-human and machine-to-machine interactions that replicate normal human-to-human interactions.
