Machine Learning and Artificial Intelligence are two buzzwords that have become trendy of late. Some have even used their meanings interchangeably.


But what do they actually mean and how do they work? In this article, we will break down each term and the technologies behind them to help you understand AI and Machine Learning better. 


What exactly is Artificial Intelligence? and how it works?

Artificial intelligence is the ability of a machine or computer system to imitate or simulate intelligent behavior in humans. This includes human traits such as problem-solving and learning.

What exactly is Artificial Intelligence

Artificial Intelligence, or AI, applies logical and mathematical concepts to train computers to mimic human reasoning and decision making often using real-time data.  

With each round of successful data processing, AI systems can test and calibrate solutions much more rapidly compared to humans.

Their effectiveness in adaptive and intelligent information processing makes them a valuable option for business streamlining in the modern world.

What is the importance of artificial intelligence?

Considering the increasing volumes of data available, AI technology enables the programming of computers with the aim of reducing human effort as much as possible.

This is achieved through the automation of models that can analyze and process bigger and more complicated data, producing faster and more accurate results.

This importance of AI is achieved with the following concepts:

  • AI automates repetitive tasks with guaranteed accuracy and precision.
  • Existing products can be made smarter with AI, for example, Apple products added Siri as a feature on their new generation of products.
  • AI uses neural networks to analyze deeper and larger volumes of data, in order to get the most out of it.
  • AI allows for high accuracy in information processing. For instance, AI has improved the accuracy with which cancer can be identified on medical images.

What are some examples of Artificial Intelligence?

There are many applications of AI in the modern world. Some of the most notable include:

Vehicle Automation :

Fully autonomous vehicles use advanced algorithms to process data captured from cameras and sensors. This information is used by the AI system to make real-time navigational decisions that move the car safely without human involvement.

Facial Detection and Recognition :

AI allows us to unlock our phones through face ID and apply filters when taking pictures. The former implements facial recognition, whereby a specific human face is distinguished and granted access. 

Autocorrect & Text Editors :

AI algorithms use natural language processing, deep learning, and machine learning, to identify errors in language use.

This feature is normally implemented in software such as texting apps and word processors. To achieve this, computer systems are taught proper grammar through linguistics, just as you would a child in school. 

Social Media :

Social media applications implement AI technology to achieve objectives such as targeting specific users with ads, monitoring content posted, and suggesting interactions for users. 

Digital Assistants :

When you’re hands are full, you can command digital assistants like Siri to perform tasks on your behalf. This could be texting your mom or searching and calling a contact from your phonebook.

These assistants use statistical analysis, machine learning, and algorithms to identify and execute your commands.

How many jobs will Artificial Intelligence replace?

The World Economic Forum reports that by 2025, AI machines will replace 85 million jobs. While this statistic might be terrifying, there is a silver lining.

How many jobs will Artificial Intelligence replace?
Competition of people and robots for jobs, queue for an interview. Unemployment, robotization, ai. Business people and artificial intelligence want to get a job. Vector illustration, flat isolated.

The same report states that AI will create 97 million new jobs by the same year. This means that we actually stand to gain more from AI implementation.

The best free AI courses

There are numerous free courses available online if you want to understand AI and related technologies. Regardless of your job title or background, the courses are short and simple to understand, and most importantly, they are free! Below we list the best five learning platforms available online.

Google’s Artificial Intelligence Course: Google intends to increase understanding and awareness among the public with this newly launched course.

The course covers everything from the fundamentals of AI to training neural networks. View CourseStanford’s Artificial Intelligence Course – View Free AI Courses offered by Stanford or enroll in the free course by Andrew Ng, founder of Google Brain.

There are also free AI courses available on Udemy. They include:

  1. Learn basics about A.I. (Artificial Intelligence) by Nikola Milosevic View Course
  1. Introduction to Artificial Intelligence in Software Testing by Sujal Patel View Course
  1. Artificial Intelligence Markup Language (AIML) by Steve Worswick View Course
  1. Introduction to AI for Business by Zigurat Innovation & Technology Business View Course
  1. Artificial Intelligence Level 1: Cogito by Gilad James, Ph.D. View Course

Will AI take over humanity? 

Many people fear the hypothetical scenario depicted in movies where robots or computer programs take over and rule the planet.

Will AI take over humanity? 

But is this fear warranted? Some public figures like Elon Musk and Stephen King have urged research into steps that ensure humans retain control of AI when it surpasses our intellect i.e. superintelligent AI. 

In her book, “You Look Like a Thing and I Love You: How Artificial Intelligence Works and Why It’s Making the World a Weirder Place.”, researcher Janelle Shane argues with practical examples how Ai won’t be taking over humanity anytime soon.


What is Machine Learning used for?

Machine Learning is the field of Artificial Intelligence that enables machines to learn automatically and improve from experience without human programming. Machine learning algorithms learn from data in order to independently make decisions and predictions.

What are Machine Learning examples?

Machine learning algorithms have numerous applications where conventional algorithms are difficult to implement.

This includes:

  • Speech recognition – Spoken words can be converted into text using natural language processing (NPL).
  • Stock Trading – Machine learning enables optimization of stock portfolios and high-frequency trading which is AI-driven. This means millions of trades can be automatically made daily.
  • Customer Service – Companies can now use online chatbots to replace humans by implementing features such as FAQs to resolve customer queries.
  • Computer vision – This technology allows computer systems to retrieve information and take action from visual inputs like videos and images.
  • Email filtering – Machine Learning is used to detect and filter spam emails.

What is the best programming language for Machine Learning?

Python is the most popular language used for Machine Learning. Despite being created before Artificial Intelligence became critical for businesses, Python was developed as a powerful data analysis tool mostly used in the big data field. Other programming languages include Lisp, Java, C++, R, and Julia.

What are the four types of Machine Learning?

There are four types of machine learning algorithms. They are: unsupervised, supervised, semi-supervised, and reinforcement.

Supervised learning :

This involves teaching a machine by using an example. The user provides the ML algorithm with desired inputs and outputs from a known data set, and the machine figures out how to arrive at these inputs and outputs.

Although the user knows the correct solutions to the problem, the algorithm looks for patterns in the data, learns from them, and then makes predictions. 

Unsupervised learning :

In unsupervised learning, the machine learning algorithms identify patterns by studying data. Here there is no use to give instructions or the answers required.

The machine is instead left to analyze and interpret large data sets with the aim of organizing and structuring the data. It involves methods like Clustering and Dimension Reduction.

Semi-supervised learning :

This is similar to supervised learning with the only difference being the use of both unlabeled and labeled data. Labeled data refers to information that contains meaningful tags to help algorithms interpret the data, while unlabeled data doesn’t have this information.

Reinforcement learning :

In reinforcement learning an operator provides a machine learning algorithm with rules defining end values, parameters, and actions. The algorithm determines optimal results by exploring various options and possibilities through trial and error.

How much does a Machine Learning engineer make?

According to and, a Machine Learning Engineer in the United States has an average national salary of $131,001 per year. This however varies depending on location.

Will Machine Learning replace programmers?

Although there is code-generating AI software available such as Enzyme and Github’s Copilot, these solutions are far from effective. Most AI programs can only write simple code.

Will Machine Learning replace programmers?

They have no way of determining which real-world problems need solving and which features to prioritize. For now, only a knowledgeable programmer can write code based on certain specifications and requirements.

So the short answer is no, AI and Machine Learning will not replace programmers anytime soon. It may only help programmers improve code and optimize software in the future.


What is the difference between AI and Machine Learning?

Artificial intelligence enables a computer system or machine to mimic human behavior. On the other hand, machine learning is a branch of AI that enables a machine to extract knowledge from past data.

This knowledge is then used to make decisions and predictions without explicit programming.


Artificial Intelligence and Machine Learning technologies have made huge strides in a short period of time. The pace of adoption for these technologies has also accelerated in many industries where speed, efficiency, and accuracy are priorities.

Analysts predict that the demand for AI and Machine Learning is expected to grow sustainably across all fields and have a substantial impact on society.

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