Artificial Intelligence Basics And Sub-Practices

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In this article, we’re going to dive into the basics of the artificial intelligence, its main sub-practices and how they’re affecting our lives.

While the term isn’t new and has been used in research laboratories for many years, artificial intelligence has become a buzzword due to the increased data volume, advanced algorithms and improvements in computing power and storage. In fact, the creation of the Logic Theorist is an important step towards the development of modern AI.

What Is Artificial Intelligence? 

Machines demonstrate artificial intelligence with the simulation of human intelligence. Trained by humans to do so, machines can demonstrate certain aspects of human intelligence, such as learning, reasoning, self-correcting. Real-life examples of AI are problem solving,speech and text recognition, learning and planning.

There are two major type in research and development work in AI:

General artificial intelligence (AGI)

Seeks to develop machine intelligence that can turn their hands to any task, much like a human or even better. But, the truth is, most experts say we’re still a decade or two away from achieving true general AI because of its complexity.

Narrow artificial intelligence (ANI)

Which is highly focused and permeates our world today. It uses principles of simulating human thought to carry out few specific things. However, if you give it another different task, it will fail miserably. 

So, let me introduce you to the three basic AI concepts, which we’ll explore together in this article: Machine Learning, Deep Learning, and Big data.

1- Machine learning

artificial intelligence and machine learning

Although AI and machine learning seem to be interchangeable terms, AI is usually considered a broad term, of which machine learning is a subset. So, the main difference between the two is that machine learning is just one of the AI applications, which enables machines/robots to learn automatically and improve from experience.

How it works:

Above all, the main idea is that computer systems can learn on their own from the information acquired by performing past assignments and experience. So, this implies you don’t have to pre-program the AI ​​device each time you need to work on a specific task.

Machine learning algorithms use statistical information to find patterns in large amounts of data. This data contains a lot of numbers, words, images, clicks, things you own. So, if we can store them digitally, then it can enter into a machine learning algorithm.

Real life application:

  • Speech recognition

Speech recognition is the ability for a machine or program to convert into text by identifying spoken words. Vocal assistants generally use this technology, like Apple’s Siri, Google Assistant and Microsoft’s Cortana, which can respond to voice commands and provide users with relevant information about their queries. Currently, voice assistants can perform services such as processing product orders, answer questions and perform tasks such as playing music, or make simple phone calls with friends.

  • Medical diagnosis

Machine learning can be employed in procedures and devices that help diagnose diseases.

  • Statistical Arbitrage

Statistical arbitrage is a short-term trading strategy that uses a mean regression model. In these strategies, users focus on implementing trading algorithms reached from a set of securities. Based on quantity like historical correlation and general economic variables, rather than implying directional bets or exposure to broader market moves.

  • Financial Services

Machine learning plays a major role in the financial and banking sector, from approving loans, to credit scores, to managing assets, and assessing risks.

It can help banks and financial institutions to make smarter decisions, spot an account closure before it occurs, track the spending pattern of the customers and perform the market analysis.

2. Artificial Neural Network (ANN)

Artificial neural networks are one of the basic means used in machine learning. They’re brain-modeling systems designed to replicate the way biological neural networks learn.

How it works:

Input and output layers compose ANN. The input layer collects different forms of information from the external environment. Then, a hidden layer composed of units, converts the input into a format that the output layer can operate. They are the feedforward network. They’re excellent tools for finding patterns that are too complex or too much for human programmers to extract.

Real life application:

Many fields apply ANN:

  • Handwriting Recognition: ANNs can deploy in character recognition systems that have several uses. By instance, as receipt and invoice character recognition and legal billing documents character recognition.
  • Image Compression: The large data size of a high-resolution image brings difficulties in dealing with it, so using neural networks for image compression is worth seeing.
  • Stock Exchange Prediction: ANNs widely use it to predict the trend of stock prices because they can quickly check and sort out large amounts of information

3- Deep learning 

This subset of AI refers to the ability to take unstructured data from multiple sources, analyze it, and apply it to solve new problems.

How it works:

Most deep learning methods use neural network architecture, just like neurons compose the human brain. It’s said that the network is deeper according to the number of hidden layers it has. The traditional neural network only contains 2–3 hidden layers, while the deep network can contain up to 150.

Deep learning systems require powerful hardware because the models are trained by using a large amount of labeled data and neural network architecture. Which can learn features directly from the data without manually extracting features.

Real life application:

  • Automated Driving: Deep learning can be used to detect objects such as stop signs and traffic lights automatically. When driving along the road, millions of AI models can provide information for a car. Besides, deep learning can detect pedestrians, thereby helping to reduce accidents.
  • Aerospace and Military: Deep learning is used to distinguish objects from satellites that locate areas of interest, and recognize protected or dangerous zones for troops.
  • Industrial Automation: Deep learning helps improve the safety of workers around heavy machines by automatically detecting when people or objects are within dangerous machine distance.

4- Big data

Big data refers to massive complex structured and unstructured data sets that have to be processed and analyzed. It includes data mining, data storage, data analysis, data sharing, and data visualization.

And that leads us to the five Vs behind big data: 

  • Volume
  • Velocity  
  • Variety
  • Value
  • Veracity

Read here to learn more about the subject.

How it works:

  • Integration: This refers to blending data together—collected from many sources—and transforming it into the right form that your business needs and that your customers can understand.
  • Management: Now you need a place to store your data. So, you have to find a storage solution that can be in the cloud, on-premises, or both. 

Most Big Data is unstructured, for that reason, Big Data requires specialized NoSQL databases with the ability of storing data in a way that doesn’t require strict adherence to a particular model analysis. Organizations and businesses analyze Big Data for reasons like discovering details on buying patterns and trends related to consumer behavior and our interactions with technology, which can then be used to make decisions that affect our lives.

Real life application:

  • Healthcare 

The importance of big data in healthcare and medicine can’t be ignored. It helps doctors, physicians, etc. to maintain reliable tracking of the entire patient’s medical history in some way. For example, if one of the patients goes to the doctor, he can easily obtain the patient’s medical history. In addition, all data obtained for any patient can be permanently saved and stored and doctors can access the data at any time in the future.

  • Education

Nowadays, in the age of the internet and technology, most courses are available online for students. It’s used to identify educational problems that students must face. Moreover, it can help educational institutions to obtain and store student data. Big data also enables educational institutions to assess student progress and academic development. Also, it helps to collect feedback from students and improve the performance of the institution based on the collected feedback.

  • Banking

One of the major uses of big data is in the banking sector. With big data, banks are able to spot fraud and reduce gaps in their systems. Besides, it helps the banking sector in finding out any misuse of debit cards, credit cards and other services such as enhanced compliance reporting, personalized product offerings and other areas improved thanks to Big Data.


From machine learning to big data, artificial intelligence is changing the world in several ways, having a huge impact on many industries today, and playing a greater role in our daily lives. 

As these different sub-practices gradually develop into more powerful functions, artificial intelligence will eventually be able to solve more complex tasks completely and independently and will continue to grow in future for us to leverage them.

Manny Henri
Manny Henri
Emmanuel Henri grew up in Chambly, a city in the tail of Quebec (Canada) near Montreal. He’s an established technologist with 25 years of experience in the world of programming and design, and also published 125 courses on several platforms such as Linkedin Learning, Pluralsight and O’reilly. Since his teens, he always had a knack for storytelling, especially monster-driven tales, and has compiled a boatload of Sci-Fi, Fantasy and horror ideas he’s thrilled to put into words. To keep his head sane and healthy, especially after his close call with cancer in 2020 (now in remission), he’s pledged his body to a strict diet and rigorous exercise plan. He’s currently working on his novel "Ashes" and editing “From the mist” and several short-stories, such as “The Agency”.
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