Artificial intelligence was once viewed as a hyped technology. However, as time goes by, the ever-advancing AI technology is now becoming the trend in many business setups. Through AI, machines can easily learn and operate effectively without depending on manpower. This idea has helped many businesses simplify many tasks.

Even though AI technology is ruling the airwaves when it comes to the best technologies in business, there are some elements that are a bit confusing, and many other establishments cannot define or understand deeply how to apply AI technology in their business activities to simplify operations. In this guide, we dig deep into the six branches of AI technology to help you have a clear picture of AI applications in organizations.

Is there a difference between machine learning and Artificial Intelligence?

In most cases, the two terms are used interchangeably, and it is common for people to misinterpret or misunderstand the key difference.

In the real sense, AI is the umbrella of machine learning. Through AI, machines can perform actions that require manpower. For instance, solving complex problems, understanding natural language, and interpreting sounds or objects, among others.

Ideally, the advanced AI technology is because of the machine learning model. Did you know that the apps we interact with daily, such as Netflix, Amazon, YouTube, and Google Search, are all AI-Powered through machine learning? Well, that is how powerful machine learning AI is.

In YouTube, for instance, machine learning helps suggest videos to users. Google search, on the other hand, uses machine learning to enable billions of people to perform different actions within a minimum time. Netflix and Amazon also depend on machine learning to recommend TV shows & movies and product recommendations, respectively.

Did you know? Currently, more than 95% of establishments are already adopting AI technology.

What are the 6 subsets of AI?

As AI continues to grow and advance and become more powerful, there are some subsets of the same AI you should know. They include the following:

• Machine Learning
• Deep Learning
• Natural Language processing
• Expert System
• Robotics
• Speech Recognition

Subsets of AI

• Machine Learning

As we’ve described above, machine learning is a section of AI that makes it possible for machines to learn and perform human actions without depending on manpower.

It has a systematic algorithm that enables the machine to depend on form historical data to interpret information.

Through the algorithm, machine learning can easily make decisions based on past details and recognize patterns.

The algorithm of machine learning is made in a manner that it can interpret information and perform better automatically.

Types of Machine Learning

Machine learning is divided into three sections, as described below:

Supervised learning – Here, the machine learns the patterns through the established database to predict the results. Therefore, this calls for a supervised learning agent to match the right function with the right set of samples. Under supervised learning, there are two sets of algorithms, namely regression, and classifications.

Reinforcement learning – Reinforcement learning requires a trained AI agent to give required commands to get the required feedback. The feedbacks help the agent enhance the performance. Importantly, the feedback can either be negative or positive. The wrong action calls for negative feedback, and the correct action is equal to positive feedback. Therefore, under reinforcement learning, there are negative reinforcement learning and positive reinforcement learning.

Unsupervised learning – Under unsupervised learning, there is no training or supervision. The algorithms depend on data that isn’t classified or labeled. Under unsupervised learning, we have association and clustering groups.

• Natural Language Processing

NLP is a section of AI n computer science that enables the computer system to interpret and process human-based language. A good example is the English language.

NLP is crucial in AI because an agent cannot handle human instructions and command the system to operate as anticipated without NLP. Nowadays, AI, through NLP, has made it possible for us to seek help from apps such as Cortana, Google, and Siri.

With NLP, users can communicate with the system either through text or speech without any possible troubles.

• Deep Learning

Deep learning is a technology achieved through neural network architecture, and it enables machines to perform human-based tasks without the supervision of a person or manpower.

Through deep learning, an AI agent can copy and act like the human brain. This AI technology depends on both unsupervised and supervised learning to teach an AI agent.

Examples of deep learning applications include automatic machine translation, recognition of images, speech recognition, and self-driving cars, among many others.

While this technology is powerful, there must be a huge volume of data and more computerized technology.

The functioning of deep learning:

Deep learning technology depends on deep neural networks, hence the name deep learning. Several layers from the deep learning networks as described below:

Input layer – This is the first layer, and its main purpose is to receive data input where the neurons broadcast the signal layers above.

Hidden layers – These are all the layers falling in the middle, between the first and the last layer. These layers help to perform computations on inputs; then, the data is transmitted to the last layer.

Output layer – this is the last layer of deep learning architecture. This layer only sends the output back to the user.

Note: the deep neural network has several hidden layers, with each layer made up of neurons that help in transmitting data or information.

• Expert Systems

Expert systems are simply computer programs that depend on human knowledge and programming knowledge for the system to operate.

These systems use the human expert ability to make decisions. The purpose of expert systems is to create solutions to complex problems by obtaining knowledge from manpower instead of coding procedures.

Spelling errors suggestion when typing in the Google search box, for instance, is a perfect example of expert systems.

With the above insights, expert systems have the following characteristic:

Understandable, highly responsive, highly reliable, and high performance.

• Robotics

Another subset of AI is robotics, which is used in making robots after the design process. Robots are simply machines that are programmed to do specific actions either semi-automatically or fully automatically.

Through AI, it is possible to make smart robots perform given actions based on their capabilities. Therefore, Artificial intelligence algorithm is crucial in helping robots perform complex actions.

Currently, high-tech companies use machine learning and Artificial intelligence to make high-tech robots that have human-like abilities. Sophia robot is a perfect example in this case.

• Speech Recognition

Also known as computer speech recognition or automatic speech recognition, this is a technology that makes it possible for machines to interpret and translate spoken language into a format readable by the machine. It is simply talking to a computer, where the computer understands what you are saying and performs the required action.

While we have sophisticated speech recognition softwares, some softwares need simple-to-understand language to interpret the meaning before performing the required action. This is because such softwares have less vocabulary. Apple Siri, Google virtual assistant, and Cortana are among the devices or softwares that have speech recognition technology.

Unlike the previous decades, where speech recognition systems could only convert speech to text, nowadays, it is possible for such systems to understand our language and perform the required tasks, thanks to the evolution of high-tech devices that performs such commands with ease.

Speaker Independent and Speaker Dependent are the two main forms of speech recognition you should know about.

Note: voice dialing systems, industrial applications, and system control/navigational system are some of the areas where speech recognition technology can be applied.

Conclusion

AI is a wide field that is fascinating, giving you something to explore and make discoveries in the ever-changing tech world. Therefore, if you plan to take a career path through AI technology, make sure you explore and understand the subsets of AI as described in this guide.

To know more connect with Artificial Intelligence Development Company : Aalpha information systems!