Introduction to What is Artificial Intelligence?
Artificial Intelligence refers to machines chiefly computers working like humans. In AI, machines perform tasks like speech recognition, problem-solving and learning, etc. Machines can work and act like a human if they have enough information. So in artificial intelligence, knowledge engineering plays a vital role. The relation between objects and properties are established to implement knowledge engineering. Below are the techniques of Artificial Intelligence.
Top 4 Techniques of Artificial Intelligence
Artificial Intelligence can be divided into different categories based on the machine’s capacity to use past experiences to predict future decisions, memory, and self-awareness. IBM came up with Deep Blue, a chess program that can identify the pieces in the chessboard. But it does not have the memory to predict future actions. This system though useful, but it cannot be adapted to another situation. Another type of AI system that uses past experiences and has the bonus of a limited memory to predict the decisions. An example of this kind of AI system can be found in the functions of decision making in case of the self-driving cars. Here the observations help in the actions to be taken shortly, which does not get stored permanently as the observations change frequently. At the same time with the advancement in technology, it might be possible to have machines with a sense or consciousness where the machines understand the current state of things, which can be used to infer what is to be done. But such systems do not exist.
Below are the various categories of Artificial Intelligence:
1. Machine Learning
It is one of the applications of AI where machines are not explicitly programmed to perform certain tasks; rather, they learn and improve from experience automatically. Deep Learning is a subset of machine learning based on artificial neural networks for predictive analysis. There are various machine learning algorithms, such as Unsupervised Learning, Supervised Learning, and Reinforcement Learning. In Unsupervised Learning, the algorithm does not use classified information to act on it without any guidance. In Supervised Learning, it deduces a function from the training data, which consists of a set of an input object and the desired output. Reinforcement learning is used by machines to take suitable actions to increase the reward to find the best possibility which should be taken in to account.
2. NLP (Natural Language Processing)
It is the interactions between computers and human language where the computers are programmed to process natural languages. Machine Learning is a reliable technology for Natural Language Processing to obtain meaning from human languages. In NLP, the audio of a human talk is captured by the machine. Then the audio to text conversation occurs, and then the text is processed where the data is converted into audio. Then the machine uses the audio to respond to humans. Applications of Natural Language Processing can be found in IVR (Interactive Voice Response) applications used in call centres, language translation applications like Google Translate and word processors such as Microsoft Word to check the accuracy of grammar in text. However, the nature of human languages makes the Natural Language Processing difficult because of the rules which are involved in the passing of information using natural language, and they are not easy for the computers to understand. So NLP uses algorithms to recognize and abstract the rules of the natural languages where the unstructured data from the human languages can be converted to a format that is understood by the computer.
3. Automation and Robotics
The purpose of Automation is to get the monotonous and repetitive tasks done by machines which also improve productivity and in receiving cost-effective and more efficient results. Many organizations use machine learning, neural networks, and graphs in automation. Such automation can prevent fraud issues while financial transactions online by using CAPTCHA technology. Robotic process automation is programmed to perform high volume repetitive tasks which can adapt to the change in different circumstances.
4. Machine Vision
Machines can capture visual information and then analyze it. Here cameras are used to capture the visual information, the analogue to digital conversion is used to convert the image to digital data, and digital signal processing is employed to process the data. Then the resulting data is fed to a computer. In machine vision, two vital aspects are sensitivity, which is the ability of the machine to perceive impulses that are weak and resolution, the range to which the machine can distinguish the objects. The usage of machine vision can be found in signature identification, pattern recognition, and medical image analysis, etc.
Applications of Artificial Intelligence
Below are the various applications of artificial intelligence.
- AI is used in the finance industry, where personal data is collected, which can be later used to provide financial advice.
- AI is used in the field of education, where the grading system can be automated, and the performance of the students can be assessed based on which the learning process can be improved.
- In the field of Healthcare, AI is used to perform a better diagnosis where the technologies used to understand the natural language and respond to the questions asked. Also, computer programs like chatbots are used to assist customers in scheduling appointments and ease of billing process, etc.
- AI is used in Business to automate the repetitive tasks performed by humans with the help of Robotic Process Automation. To increase customer satisfaction, machine learning algorithms are integrated with analytics to gather information which helps in understanding customer needs.
- AI is used in Smart Home devices, security and surveillance, navigation and travel, music and media streaming and video games, etc.
AI is impacting our lives on a great scale. Organizations also are taking steps towards adapting to the AI technology, which can give them new ways of performing the tasks as well as understanding the data pattern to have maximum productivity.
This is a guide to Artificial Intelligence Techniques. Here we discuss What is Artificial Intelligence and its Techniques along with Applications. You can also go through our other suggested articles to learn more –