Exploring the Variants of Artificial Intelligence & Their Uses

The common goal of all AI technologies is to incite human-like decision-making capabilities in computers, machines, applications, and systems. With the help of this technology, systems are programmed to think like humans and impersonate their actions. Artificial Intelligence comprises many technologies such as cognitive computing, machine learning, deep learning, natural language processing, expert system, and IBM Watson. Experts believe that Artificial Intelligence will be quickly acquired as it might be a troublesome technology across various industries.

AI has shown its impact already in various fields. For example:

• Agriculture – In agriculture, AI is assisting farmers in predicting their corn yielding rates more accurately. It has enabled them to increase their tomato production rate by 30% while decreasing their fertilizer consumption rate by 20%.
• Healthcare – In healthcare, both patients and doctors are benefitting from AI. For instance, their predictive models are predicting Sepsis in Pediatric patients, and the real-time audio signal analysis helps in delivering more audible sounds to people using hearing aids.
• Retail – AI is being used in the entire retail product cycle from product designing to selling. Online merchants and retailers are investing in AI to enhance their customer experience. They aim to improve suggestions on the basis of real-time information when a customer is on a call, visiting a website, or in the store, using AI insights. Considering the increasing demand for AI, technology providers are integrating AI in the Customer Relationship Management (CRM) system that retailers use.
• Financial services – From speeding routine tasks, allowing organizations to provide better services, detecting and preventing real-time frauds, and meeting the worldwide regulatory requirements, AI is improvising all operation aspects.

Subcategories of AI

As mentioned earlier, AI comprises many technologies or subcategories. Each of them is aimed at specific applications and uses specific technologies for them.

Cognitive Computing

Cognitive computing refers to the imparting of the human thought processes in a computerized model during complex situations where the answers can be equivocal and undetermined. It imitates the way humans think, learns, and adapt, thus, allowing a broader range of real-time insights and actions.

For instance, cognitive computing helps the HR department in making hiring decisions, assists in determining the diagnosis and treatment based on the patient’s data provided by doctors, and helps the call centres in improving their customer experience.

The technologies that Cognitive Computing uses to enable these actions are:

1. Cognitive Intelligence – It comprises tools and technologies that enable bots, applications, and websites to view, listen, speak, and understand the user requirements via natural language.
2. Emotion Detection – It tries to determine people’s feelings by utilizing capabilities such as expression analysis in picture or video, change of pitch in speech, and much more.
3. Sentiment Analysis – It uses natural language processing, text scanning, and computational linguistics to recognize, extricate, measure, and study affective conditions and personalized information in messages, images, voice notes, and videos.

Machine Learning

This application enables systems to learn and upgrade automatically from experience without being directly programmed. The goal of machine learning is to develop computer programs that can obtain data and use it for learning on their own.

Basically, machine learning is used in:

1. Machine Vision – It uses machine-learning-based image analysis and detection or automated AI to give image detection capabilities to systems like humans.
2. Pattern Matching – Its algorithms examine the existence of a given series of data in a bigger set of data.

Deep Learning

Deep Learning is a sub-unit of machine learning which has networks capable of unsupervised, semi-supervised and supervised learning from untagged and unformed data. These systems continue learning and directing themselves as new data moves in.

Deep Learning can aid in interactive real-time applications that include machine translation and visual and voice recognition using the following technologies:

1. Neutral Network – These are data and computational systems that imitate the human brain for interpretation of data and patterns recognition.
2. Fuzzy Logic – It uses arithmetic methods and tries to estimate human reasoning to make conclusions on raw and equivocal data.
3. Image Recognition – It uses data to recognize objects such as building, people, places, etc. in pictures or video clips.
4. Inference Engine – It uses rules and facts in an expert system’s knowledge base or Deep Learning AI data obtained from Deep Learning AI System.

Natural Language Processing

It uses linguistics and artificial intelligence to enhance communication between humans and computers. Natural Language Processing is generally used to answer a question or direct the user to an appropriate resource as per the voice command.

It uses fundamental technologies to provide facilities such as:

1. Text-to-Speech– It uses software to generate artificial human speech. These systems produce audio output in the spoken voice form.
2. Speech-to-Text– It uses natural language processing and machine learning to convert spoken word from voice command, audio or video into text.
3. Translation– It instantly translates spoken words from one language to another.

Expert systems

Expert systems in AI mimics a human expert’s decision-making abilities. These computer systems are designed to resolve complicated problems using reasoning through its extraordinary knowledge expertise. It is capable of giving advice, interpreting inputs, as well as making decisions.

One of its key features is that it can automate many tasks and work collectively with external information such as a text. Expert systems are used as:

1. Intelligence Agents– These are free entities which take actions using data from voice commands, sensors, messages and other sources.
2. Personal Assistant– This application enhances the user’s productivity by automatically handling routine tasks such as scheduling a meeting, sending an email or text message, setting the alarm, and much more.
3. Chatbots– These are software or applications that carry out verbal or textual conversations with humans using voice recognition, natural language processing, and text-to-speech. Sometimes, chatbots perform better than their human fellow.
4. AI for IT operations- These use cognitive computing as well as AI and machine learning to automate and enhance their IT operations.