Artificial Intelligence (AI)
A Brief Introduction
Artificial intelligence (AI) simulates human intelligence with machines, in particular computer systems. This includes learning (gathering information and rules for using the information), inference (using the rules to draw approximate or definitive conclusions), and self-correcting. Special applications of Artificial Intelligence are expert systems, speech recognition and machine vision.
The term artificial intelligence (AI) was coined by John McCarthy, an American computer scientist, at the Dartmouth Conference in 1956. Today, AI is a generic term that ranges from Robotic Process Automation (RPA) to actual robotics.
Artificial intelligence has grown in importance recently, partly due to big data and the increase in the speed, size, and variety of data that companies collect today. For example, AI can recognize patterns in data more efficiently than humans, giving companies more insight into their data.
Types of Artificial Intelligence
AI can be categorized in a number of ways. Here are two examples:
The first type classifies AI systems as either weak or strong AI. Weak AI (weak or narrow AI) is an AI system that is developed and trained for a specific task. Virtual personal assistants, like Apple’s Siri, are a sort of weak AI.
Strong AI, also known as general artificial intelligence, is an AI system that has generalized human cognitive skills so that when faced with an unknown task, it has enough intelligence to come up with a solution. The Turing test, which was developed by mathematician Alan Turing in 1950, is a method to determine whether a computer can actually think like a human. However, the method is controversial.
Arend Hintze, professor of Integrative Biology and computing from Michigan State University categorizes Artificial Intelligence into four types, from the types of AI systems that exist today to sentient systems that don’t yet exist. Its categories are:
Type 1: reactive machines: One example is Deep Blue, the IBM chess program that Garry Kasparov defeated in the 1990s. He analyzes possible moves — his own and his opponent — and chooses the most strategic move. Google’s Deep Blue and AlphaGO are designed for limited purposes and cannot simply be applied to any other situation.
Type 2: Limited storage : These AI systems can use past experiences to form future decisions. Some of the decision-making functions in autonomous vehicles are designed in this way. Observations that happen in the not too distant future, such as a car changing lanes. These observations are not saved permanently.
Type 3: self-awareness. In this category, AI systems have self-confidence or awareness. Machines with self-confidence understand their current state and can use the information to infer what others are feeling. This type of Artificial Intelligence (AI) does not yet exist.
Examples of AI technology
Automation is the process by which a system or process works automatically. For example, Robotic Process Automation (RPA) can be programmed to handle high-volume, repeatable tasks that would normally be performed by humans. RPA differs from IT automation in that it can adapt to changing circumstances.
Machine learning is the science of making a computer act without programming. Deep learning is a branch of machine learning and can be understood as an automation of predictive analytics. There are three types of machine learning algorithms: supervised learning, in which records are tagged so that patterns are recognized and used to tag new records; unsupervised learning, in which records are not labeled and sorted based on similarities or differences; and reinforcement learning, in which data records are not marked, but rather feedback is given to the AI system after one or more actions.
Machine vision is the science of “letting computers see”. It is often compared to human vision, but machine vision is not tied to biology and, for example, can be programmed to see through walls. It is used in a number of applications from signature identification to medical image analysis. Computer vision, which focuses on machine vision, is often combined with machine vision.
Natural Language Processing (NLP) is the processing of human language by a computer virus . One of the oldest and most well-known examples of NLP is spam detection, which looks at the subject line and body of an email and decides whether it is junk email. Current approaches to NLP are supported machine learning. NLP tasks Pattern recognition may be a branch of machine learning that focuses on identifying patterns in data.
Robotics is an area of mechanical engineering that focuses on the development and manufacture of robots. Robots are often used to perform tasks that are difficult or impossible for humans to complete. They are used in assembly lines for automobile production or in space travel to move large objects in space. More recently, researchers have been using machine learning to build robots that can interact in social environments.
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