AI Research Paper

Summary:

This AI research paper provides a detailed discussion of Artificial Intelligence (AI), which is the ability of machines to mimic human intelligence. It explains the types and components of AI, including its applications in various fields such as mathematics, business, and designing text-to-image generators. The paper highlights the advantages of AI, such as reducing human errors and avoiding dangers but also mentions its potential disadvantages, such as being costly and making humans lazy. The paper concludes that AI is an expanding field, and researchers are focused on expanding it rather than considering its conclusion. The development of an artificial human mind may lead to the end of AI, but if not, there are many paths for AI to cover.

Excerpt:

Abstract:
Artificial intelligence (AI) refers to machines’ ability to mimic or improve human intelligence, such as thinking and learning from experience. For many years, artificial intelligence has been applied in computer applications. Right from a mathematics base problem solver to a talking personal artificial assistant. Some of the types of AI are used in many fields such as in designing text-to-image generators (weak AI), in business IBM Watson (strong AI), and many more. There are always some advantages and disadvantages we can avoid danger by using AI but automation also make us lazy. The endpoint is AI is an expanding field by the day. If the scientist can develop an artificial human mind then the conclusion is near otherwise AI has many paths to cover.

Introduction:
Artificial intelligence is the ability to create intelligent computers or self-learning software applications that mimic human mind characteristics such as reasoning, problem-solving, planning, optimal decision-making, sensory perceptions, and so on.

The term AI (Artificial Intelligence) was first used by john McCarthy in 1956. The first personal artificial assistant produces by US Defense in 2003. After that GPS (general problem-solving) software was launched that can solve any problem if it is in mathematics form. Then came the IKBS (intelligent knowledge-based system) followed by this the concept of big data, neural networks, and genetic algorithms that are used in machine learning.