Artificial intelligence (AI) has become a hot topic over the last decade. From self-driving cars to smart homes, AI is changing our lives. But what exactly is artificial intelligence? And where does it fit into the five major branches? We’ll discuss all that right here, so keep reading!
What Exactly is AI?
Artificial Intelligence is the science that enables machines to perform tasks better than humans. Similarly to people, these systems also learn from their experiences. Machine learning is an umbrella term for many subfields within AI, including neural networks and more.
The history of AI dates back to ancient times, with the earliest known examples dating back 3,500 years BC. Artificial intelligence can be defined in several different ways. Some believe that a system must exhibit self-awareness, whereas others consider it enough that a system exhibits intelligent behavior.
Still, others define intelligence as learning, reasoning, planning, solving problems, communicating, and adapting. A single definition of these terms does not exist, and there is no consensus among them.
Artificial intelligence works in several industries, including cryptocurrency, healthcare, and government. Finixio AI is one significant crypto company that uses AI to run its operations. However, healthcare companies use AI to diagnose diseases and provide treatment recommendations.
The 5 Major Branches of Artificial Intelligence
1. Robotics
Robotics is one of the essential branches of AI that involves designing, developing, and deploying robots. Developing a functional robot requires a great deal of research.
Robots pick up objects, move around, and perform other basic actions. There are many industries that use them, including manufacturing, home automation, healthcare, etc.
The knowledge of various fields, such as computer engineering, applies to robotics. For example, software engineers can develop algorithms that control the movements of robotic devices. In this case, the robot is programmed to perform a certain action.
The aim of deploying robots is to assist people and help humans with mundane and boring jobs. For example, factories use robots to do repetitive tasks like welding and packaging. They can even work alongside humans in dangerous environments such as nuclear power plants and oil rigs.
2. Machine Learning
Machine learning is one of the most critical areas of artificial intelligence. This field is about teaching computers how to perform tasks that need human expertise. In simple terms, machine learning is the ability of software programs to teach themselves. It is a type of AI where the program learns from the data.
Machine learning encompasses three broad categories of technologies: supervised, unsupervised, and reinforcement learning.
Supervised learning involves giving a system example of correct outputs and incorrect outputs. Then, the system tries to figure out what causes the bad result. Unsupervised learning does not provide feedback; instead, it looks for patterns in the data. An algorithm learns to act correctly through reinforcement learning without being ordered to do so.
There are many applications of machine learning technology today. For example, some companies use it to recommend movies or music. Other companies use it to find trends in customer behavior and recommend products to customers. Even companies use it to improve security by identifying suspicious activity.
3. Expert Systems
An expert system is an artificial intelligence system for making decisions and solving complex problems. They are knowledge-based systems that make decisions based on stored data. Medicine, finance, and manufacturing often use AI technology.
Correlative analysis and pattern recognition are typically performed by expert systems that mimic human cognitive behavior. Expert systems are goal-oriented as well as specific in their outputs.
The mechanism by which expert systems operate depends on a “result-oriented” process. Unlike traditional programming methods, expert systems deliver results based on inputs.
4. Neural Network
A neural network is a method used in artificial intelligence that mimics how our brains work. This form of AI learns by adapting to new situations and improving.
Neural networks depend on several layers of processing units—nodes or neurons—on several levels. These layers represent different types of information and allow the computer to understand the world around it. For example, a neural network might recognize objects in images by analyzing the pixels in those images.
The basic principle behind artificial neural networks involves training a computer to perform tasks with known input and output patterns.
Numbers or text strings are usually inputs, and outputs indicate whether a task was completed. To train the neural network, you must provide examples of both input and output. Then, once trained, the neural network can then analyze new data and make predictions about the output.
Moreover, you can take a structural approach to classify data and draw connections with neural networks. Applications of this type of artificial intelligent technology include fraud detection, voice recognition, and market analysis. These applications rely on the ability of neural networks to learn patterns within large amounts of data.
5. Natural Language Processing
The field of natural language processing (NLP) is sometimes called “computational semantics. It involves how computers can interpret and use human language. This includes translating the written text into different languages and summarizing documents.
Thanks to OCR, computers already understand written language well. But even though OCR enables machines to read printed documents, it doesn’t work well with handwritten notes or informal emails.
In contrast, NLP systems can recognize speech patterns and convert them into text. They’re able to do this because people speak in predictable ways. For example, you might say, “I’m hungry,” or, “My dog ate my homework,” and a computer could use these phrases to determine your meaning.
In addition to speech, NLP systems can analyze unstructured text such as email messages, chat transcripts, blogs, articles, and social media posts. These tools help companies manage customer support requests, identify spam, detect plagiarism, and find errors in reports and financial statements.
Conclusion
The concept of AI is interesting. The most exciting part about it is that it has several branches. Each branch uses its own set of techniques to solve problems. So, to create an AI system, you must choose one of the aforementioned significant branches.