Your Quick Guide to Artificial Intelligence

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Artificial intelligence is considered as the decade’s most controversial topic. Despite the fact that there are many debates upon the advancement of this specific field, we cannot ignore the privileges that humans currently have that were not available before due to applications and machines that have been made based on AI principles.

In, this article we will briefly discuss what is AI, some of AI branches, and what is the future of AI.

What is AI

Artificial intelligence is the ability of a computer or a computer-controlled robot to perform tasks that are normally performed by humans because they require human intelligence and discernment. Although no AI can perform the wide range of tasks that an ordinary human can, some AIs can match humans in specific tasks. 

AI can achieve many tasks as speech recognition, problem-solving, learning and planning and much more.

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AI branches


Machine learning

Machine learning is a sub-field of artificial intelligence (AI) and computer science that focuses on using data and algorithms to imitate how humans learn, gradually improving its accuracy.

How it works?

Machine Learning applications learn from experience (or, to be more precise, data) in the same way that humans do, without the need for direct programming.

When these applications are exposed to new data, they learn, grow, change, and develop on their own. In other words, machine learning is the process of computers discovering useful information without being told where to look. Instead, they use algorithms that learn from data in an iterative process.

They are many methods of machine learning like; supervised learning, unsupervised learning, semi-supervised learning and reinforced learning.

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Types of Machine learning

Natural language processing

The goal of natural language processing is to improve how computers understand human text and speech. NLP processes text and voice data, derives meaning, determines intent and sentiment, and forms a response using artificial intelligence and machine learning, as well as computational linguistics.

How it works?

NLP can be used on both text and speech. In the case of text, it utilises optical character recognition (OCR) to convert text in English or any other language into data blocks that computers can comprehend.
It converts unstructured text, such as PDF forms or social media, for machine processing. In the case of speech, it employs speech recognition techniques to deconstruct the audio into linguistic structures known as phonemes, or distinct units of sound, which are then matched with their text equivalents for machine processing.

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Natural language processing

Adopting the same techniques we know from linguistics. There are four steps to language processing:

  • Morphology – how words are formed and their relationship to other words
  • Syntax – how these words are put together in a sentence
  • Semantics – how the meaning of words is revealed through grammar and lexical meaning
  • Pragmatics – meaning of words in context

Each of these steps adds a new layer of proper understanding to the words.

Computer vision

Computer vision is the study of how computers can imitate the human visual system. It is a subset of artificial intelligence that collects and processes information from digital images or videos to define attributes. The entire procedure involves acquiring images, screening them, analysing them, identifying them, and extracting information.

How it works?

To self-train and understand visual data, computer vision relies heavily on pattern recognition techniques. The widespread availability of data, as well as companies’ willingness to share it, has enabled deep learning experts to use this data to improve the process’s accuracy and speed.
Deep learning and neural network techniques help computer vision make sense of what it sees, bringing it closer to the human visual cognitive system. In fact, in many applications, such as pattern recognition, computer vision outperforms human vision.

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Computer Vision

Computer vision is highly reliant on the quantity and quality of data; more data of higher quality leads to better deep learning models. Every day, visual information from smartphones feeds computer vision algorithms. As a result, computer vision systems will improve and become smarter in the future.

The future of Artificial Intelligence

AI will continue to drive vast innovation, fueling many existing industries while also having the potential to create many new growth sectors, ultimately leading to the creation of more jobs. According to statistics made by the World Economic Forum, the AI market will be worth $190 billion by 2025.

No company is going to survive in the future without implementing, or at least gaining an understanding of, artificial intelligence and how it can be used to better grasp data they collect.

– David Gasparyan, President of Phonexa

Artificial intelligence is the technology of the future. As this technology advances, the world will witness new startups, a multitude of business applications and consumer applications, the displacement of certain jobs and the creation of entirely new ones. Artificial intelligence, along with the Internet of Things, has the potential to dramatically reshape the economy, but its exact impact remains to be seen.

Now you can empower your business with first-class AI Engineers and Data Scientists using our unmatchable hiring technology at SupportFinity, for more, check our Artificial Intelligence page.

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  • Dalia Fawzy

    Content Creator at Supportfinity. A technology enthusiast and knowledge passionate. Fawzy Dalia
Dalia Fawzy

Dalia Fawzy

Content Creator at Supportfinity. A technology enthusiast and knowledge passionate.

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One thought on “Your Quick Guide to Artificial Intelligence

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