Artificial Intelligence is developing a system that imitates the human Intelligence or conduct processes in the day to day life or in an organization. Artificial Intelligence system is based on the principle of learning, reasoning, and self-correction. Learning takes place when the machine acquires information from a defined set of data and operates according to the information fed in the system. Reasoning and self-correction are the outcomes of the learning process where the machine decides how to respond to a particular action without any human intervention.
Some of the applications of Artificial Intelligence are:
- Image recognition
- Speech recognition
- Sentiment analysis
- Chatbots
- Natural language generation
- Personal Assistants
- Predictive Analysis
- Machine Vision
Artificial Intelligence system can be a weak Artificial Intelligence system or a strong Artificial Intelligence system. The Weak Artificial Intelligence system is also called a Narrow Artificial Intelligence system and is defined as a system that is particularly designed and trained for a specific pre-determined task. This type of Artificial Intelligence includes the speech recognition system like the personal assistants E.g. Apple’s Siri, Microsoft’s Cortana, Google’s Alexa.
On the other hand, Strong Artificial Intelligence system is designed to work with generalized human cognitive abilities. Strong Artificial Intelligence system is trained to act and react like a human and are trained accordingly. Machine learning plays an important role in the Strong Artificial Intelligence System as the machine analyses and reasons the actions it performs. Strong Artificial Intelligence is also called Artificial General Intelligence.
Types of Artificial Intelligence Systems:
Artificial Intelligence systems are categorized into 4 types by Arend Hintze. Arend Hintze is an assistant professor for integrative biology, computer science, and engineering at Michigan State University. Some types of Artificial Intelligence systems can be seen in the market today, while some still need to be explored.
Reactive Machines:
This type of Artificial Intelligence system is designed to perform a particular task and focusses on the output. The system is not designed to be trained through machine learning, but pre-determined set of rules are fed in the system to perform the desired task. The system responds according to the rules fed in the system and the system can only be used for that specific task.
One of the examples of the Reactive Machine is the Deep Blue chess program. The system was designed by IBM to identify and predict the elements and moves in a chess game. The system does not have any memory or past data to analyze and make decisions accordingly. The system only analyses the moves that are possible in the present, during the game. As per the programming rules set, the system analyses its moves and the opponent’s moves and proceeds in the game of chess.
Limited Memory system:
Unlike the reactive system, this system has memory to store in the information and can use their recent experiences to respond or take future decisions. As the name suggests, the system has a limited memory or temporary memory which means the instances or experiences are not saved permanently in the system. One of the examples of the limited memory system is self-driving cars. The observation in this type of system can be stored for a short period which is predetermined, or the duration of the memory is based on a set of rules.
Theory of Mind:
This type of system is based on the psychological aspects which includes an understanding of the beliefs, intentions, actions, reactions and other things that impact the decision of an individual. Such kind of system has not been developed yet.
Self-Awareness:
This type of Artificial Intelligence system is more about identifying and analyzing the senses. The Self Awareness Artificial Intelligence systems is a being on its own with a preset and well-defined character. They can sense emotions and are aware of what they feel and their current state of mind. This kind of Artificial Intelligence system is not in existence yet.
Application of Artificial Intelligence technology:
Automation:
This is one of the basic applications of AI technology which includes developing a system that performs the repetitive and sequential tasks performed by humans. Automation can be of different types and as the task or process becomes more complex a more advanced version or level of the AI technology is used.
Natural Language Processing (NLP):
This technology is concerned with the processing of human language and taking actions accordingly. One of the examples is the spam detection. Spam detection is done by analyzing the subject line and the text body of the email. If the Natural language processing (NLP) system detects malicious, fraudulent or harmful content, the mail is marked as spam and is sent to the junk folder. The Natural language processing (NLP) system includes text translation, speech recognition, sentiment analysis and the analysis is based on the machine learning process.
Robotics:
We all have seen smart machines in the form of robots in the movies that conduct processes and tasks by themselves, or Robots that act as a digital or artificial human with a sense of Self being. In reality we do have robots to help humans with the tasks that are not feasible or are difficult to be conducted regularly. These robots are programmed to conduct the task as per the defined rules and learn with the experiences and encounters.
Self-driving cars:
Self-driving cars are Artificial Intelligence-based systems that use techniques like computer vision, image recognition, and deep learning. These systems have limited memory and preset functionality that is required for that particular purpose.
Machine Vision:
Machine vision is used to capture and analyze visual information. This can be done using a camera or digital signal processing. One of the application of machine vision is signature identification.
What is Machine Learning?
Machine Learning is training the system to learn from the past experiences and take future decisions accordingly without any human intervention. Initially, huge data or information if fed into the system for the machine to read and analyze the instances. Once the machine analyses the data it is tested in a closed environment. The machine is trained to make decisions based on the data fed in and with events and experiences learn from those instances.
Machine learning can be of various types as follows:
Supervised learning:
In this type of learning the data sets are labeled. Labeling helps in detecting patterns which can be used to label new data sets.
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Unsupervised learning:
In this type of learning the data sets aren’t labeled. The data is sorted according to similarities or differences.
Reinforcement learning:
In this type of machine learning, the data sets aren’t labeled. This learning is based on the feedback given to the AI system after performing actions.
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