How Does AI and Machine Learning Work?

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Artificial intelligence combines large data amounts with fast processing alongside intelligent algorithms to allow the software to learn from data or feature patterns. On the other hand, machine learning is a subfield of AI that uses a neural network, operation research, statistics, and physics for analytical model building. It unmasks hidden insights in data without being programmed – a conclusion and where to look for it.

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How does AI and machine learning work?
How does AI and machine learning work?

Is AI and Machine Learning the Same Thing?

Theoretically, machine learning is an AI subfield, i.e., it is one of the many ways of implementing AI. Ideally, AI is the broader concept of machine learning, i.e., machines can carry out tasks in an intelligent manner. Machine learning technology is a modern application of AI-based ideas that implies allowing devices to access data and learn by themselves.  

So, machine learning allows computer systems to predict or decide using historical data. These systems use vast amounts of structured and semi-structured data to create models and give predictions or spawn accurate results based on that data.

However, in practice, AI and machine learning are used interchangeably to mean supervised learning. When you raise the topic of Big Data or analytics, both these tops will come up more frequently.

There is appreciable confusion between the two terms. But majorly, that depends on the usage. Many people think AI is a more fancy word than machine learning; thus, used more often. For instance, in media and marketing, AI is commonly used and covers many areas, including Expert Systems, Machine Learning, Process Automation, Deep Learning, and Reinforcement Learning. 

AI systems don’t require preprogramming; instead, they employ algorithms that use their intelligence – for instance, deep learning neural networks and reinforcement learning algorithms. 

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All devices designed to work intelligently are usually put into two primary groups – general and applied. Applied AI s the commonest of the two, as you will find it in several applications such as autonomous vehicles and smart devices that trade shares and stocks. 

By contrast, generalized AI is a system that can theoretically handle any task. These categories of smart devices are less common, but they come with admirable advancements. Precisely, it is an area that gave rise to machine learning. 

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How Does Machine Learning Actually Work?

By now, you know that machine learning is an AI type that teaches computers to reason just like humans. The concept is that this technology explores data to identify patterns that it can base upon with minimal human intervention. So, machine learning is an application that automatically learns and improves without programming. 

Learning by machines, systems, or devices occurs due to analyzing the ever-increasing data amounts. While the principal algorithms do not change, the internal weight and biases of the code are used to select specific answers to change. 

Machine learning can therefore automate any task that requires a data-defined pattern or a set of rules. Companies can transform and handle processes that were previously only done by humans. 

The main techniques in machine learning:

Supervised learning

This form of learning lets you output data or collect data from previous machine learning deployment. This form of ML is pretty much how humans learn, making it an exciting option. Here, you present the computer with labeled data points known as training sets – for instance, it can be a set of readouts from a system training makers and terminals with delays for the past three months. 

Examples of supervised ML are: 

  • Support Vector Machines (SVMs)
  • Linear or Logistic regression
  • K-Nearest Neighbors (KNN)
  • Naïve Bayes

Regression problems target numeric values, while classification problems target qualitative variables, e.g., tag or class. Regression tasks are vital for functions such as the average price of commodities in a particular area, while classification can distinguish different products based on their structures. For instance, it can distinguish flowers based on petal and sepal measures. 

Unsupervised machine learning

This form of ML lets you find the different types of unknown patterns within data. Here, the algorithms learn some inherent structure data with only unlabeled examples. The two everyday tasks are clustering and dimensionality reduction.

  • Clustering: here, data scientists attempt to group data points into meaningful clusters/groups – similar elements placed together. Clustering has its applications in areas like market segmentation, i.e., where differentiation is needed. 
  • Dimension reduction: in this model, the number of variables in a dataset is reduced through grouping correlated or similar attributes for better interpretation and more effective model training. 

Other ML techniques are: 

Semi-supervised/ machine learning: This type of ML also looks for data patterns but relies on labeled and unlabeled data to perform tasks faster than strictly unlabeled data. 

Self-supervised learning: this ML uses context and does not require labels to perform its tasks. So, if you supply it with labels, it will ignore. 

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How Does Artificial Intelligence Work?

When machines demonstrate intelligence, that is called artificial intelligence. This is something popular today as it is gaining front-page headlines worldwide. Ideally, AI is the simulation of human intelligence in machines programmed to learn and imitate human actions. The devices can learn from human experiences and thus, perform human-like tasks. 

Today, humans create lots of different intelligent entities that can perform tasks intelligently without instructions. That means they can think and act humanly and rationally. 

So, how does AI work?

AI systems are a careful process of conferring human capabilities and traits to a machine. This reverse-engineering process allows the devices to employ their computational prowess to surpass human capabilities. 

You can only understand how AI works if you understand the various subdomains:

  • Machine learning: teaching a machine to make a conclusion based on previous data and past experiences. It identifies and analyses data and identifies patterns to infer meanings without human involvement.  
  • Neural networks: these are a series of algorithms capturing relationships between various variables. These interconnected units process data via responding to external inputs and relaying information between each unit. 
  • Deep learning: This ML technique teaches machines to process inputs via layers to classify, infer and predict results. It utilizes a vast neural network with multiple layers of processing units. 
  • Computer vision: This technique recognizes images by breaking them down into smaller components to respond accordingly. 
  • Natural language processing (NLP): This technique allows computers to read, understand and analyze a language. 
  • Cognitive Computing: This technique features algorithms that imitate the human brain by analyzing text/images/speech similarly to humans to give the desired output. 

The three types of AI are:

  1. Artificial Narrow Intelligence (ANI): designed to solve one problem only – they have limited capabilities, e.g., predicting weather. 
  2. Artificial General Intelligence (AGI): this is still a theoretical concept – its AI is designed to have human-level cognitive functions, e.g., reasoning, language processing, and computational functioning 
  3. Artificial Super Intelligence (ASI): Also, theoretical. It would be able to perform better than humans in terms of decision-making. Also, it would build emotional relationships. 

Artificial intelligence needs supporting technologies to work effectively—for instance, GPUs, the internet of things (IoT), and Intelligent data processing. 

It’s worth noting that the significant purpose of AI is to aid human capabilities and aid in making advanced decisions. This will help humans live a more meaningful life and help manage complex processes. 

Applications of Artificial Intelligence

Artificial intelligence is gaining worldwide popularity. Typically, it is being used in various fields to make multiple processes easy. Also, AI is evolving to offer better services in almost all business sectors. Its primary applications are:


AI is extensively used in different sectors of eCommerce. For example, in personalized shopping, AI technology makes a huge part of recommendation engines to help engage better with customers. The engagements are based on customer preference and history. 

Also, businesses are using AI-powered assistants. These virtual shopping assistants boost user experience while shopping online. The use of Natural Language Processing makes conversations sound as human and personal as possible. Good thing; the engagements are in real-time. 

Ecommerce avenues are prone to fake reviews and credit card fraud. AI uses patterns, which reduces the chances of credit card fraud. 


Many banks are adopting AI-based solutions and systems to offer customers efficient support and detect credit card frauds and any anomalies. The common application used in banks is the EVA (Electronic Virtual Assistant) that addresses customer queries.


AI can make navigation much easier. MIT research shows that GPS technology provides users with accurate, detailed, and timely information for safety improvement. The technology combines Graph Neural Network and Convolutional Neural Network, which automatically detects the number of lanes and road types behind road obstructions. Many logistics companies and Uber use it to improve operational efficiency, optimize routes and analyze traffic. 

Human Resources

Intelligent software is primarily used in the hiring process this – blind hiring. The machine learning software can help analyze applications based on set parameters. These systems can scan the candidate’s profiles and resumes/CVs to offer recruiters insights into the talent pool to select from. 


AI is extensively used in the healthcare sector, where they build sophisticated equipment to identify cancer cells and detect other diseases. Artificial intelligence systems can help analyze chronic conditions using lab and other medical data for better and early diagnosis. Also, it uses historical data alongside medical intelligence to discover new drugs. 

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With AI, it is possible to create intelligent human-like non-player characters (NPCs) with the players. Besides, AI is extensively used in the gaming industry to predict human behavior while playing a game. This information is later used to improve the game design. For instance, the 2014 release Alien Isolation games use AI to stalk the player throughout the game.

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Types of Artificial Intelligence

Four major types of artificial intelligence technology are:

Reactive machines

This is the most basic type and, thus, can only perform minimal operations. These systems do learn; therefore, they cannot form memories from past experiences. The primary example is Deep Blue, IBM’s chess-playing supercomputer. This AI system can identify chess pieces on the board, know each move, and predict movements that the opponent can make. Beyond that, there is no concept.  

Limited memory

This category of AI can store and use previous data to predict future events. The machines have a little more complex architecture. A typical application using the limited memory AI is the self-driving car. The applications within the car observe the direction and speed of other cars. However, these cannot be done in a moment – instead, it requires identifying specific objects and monitoring them over time. The observations are then added to the self-driving cars’ preprogrammed representations of the world. 

The ML models here are:

  • Reinforcement learning: learns from cycles of trials and error 
  • Long short term memory (LSTMs): uses past data to predict the next item in a sequence, where it tags the most recent information piece. 
  • Evolutionary generative adversarial networks (E-GAN): has memory and evolves at each stage.

Theory of mind

Theory of mind is a technique of the future. Currently, it is only in the infant stages, where it is applied mainly in self-driving cars. The technique aims at developing systems that have thoughts and emotions just like humans. While all existing ML models do a lot to achieve the task at hand, the relationship is mostly one way. For instance, AI Siri, Google Maps, and Alexa respond to every command, which is one way. If you shout at Google Maps to change directions, it won’t offer advice or emotional support. 

There is research underway to enhance decision making, as this is deemed the future of AI and ML. 

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Building AI machines that can represent themselves is the highest level. Currently, this technology only exists in stories and movies, injecting immense fears and hope in people. A self-aware AI system has intelligence beyond humans. Most likely, humans will have to negotiate with the systems they create to reach a consensus when issues arise. 

The creation of self-awareness machines may be a farfetched idea. Nonetheless, the basic thing people need to understand is how memory, learning, and the ability to decide based on past experiences work. This will help understand human intelligence better. 

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What Is Artificial Intelligence in Computer?

Artificial intelligence is a vast field in computer science that utilizes many computing technologies and techniques alongside various programming languages. The fundamental purpose of AI is to design and launch systems that behave and work like humans, thanks to a mixture of algorithms.

In computing, there are some things to put in mind while defining Artificial intelligence:

  • Systems act like humans: this category of computer systems performs tasks that otherwise humans would perform. They are efficient even without human interventions. 
  • Systems that reason rationally: these computer systems attempt to mimic the rational, logical reasoning of humans. Precisely, the systems try to locate means by which machines can understand the query at hand and act accordingly. 
  • Computer systems that act rationally: these systems try to imitate human conduct rationally 
  • Systems that act like humans: these systems automate processes, making all other methods very easy. 

So, an AI system will act and think like humans. But that does not occur in all artificial intelligence systems as they are created differently. Some systems have a low level of intelligence, while others have a higher intelligence level. 

The perspectives concerning Artificial Intelligence vary among different computer engineers and scientists. The reason for differences is due to the enormous amounts of techniques and strategies used. Further, these strategies and techniques evolve. 

Low artificial intelligence in computer science: Here, computers are thought to be able to simulate different scenarios in ways that they’ll be deemed to be reasoning as well as acting intelligently. This may seem a farfetched idea as self-awareness computers may not exist anytime soon. 

Strong Artificial Intelligence in computer science: here, a computer has some intellectual states and thoughts. Thus, some days it will construct something with skills of human thoughts. Such computers can imagine, purpose, etc.

What Is Artificial Intelligence With Examples?

AI is a computer science branch that emphasizes the development of intelligent machines that think and work like humans – for instance, speech recognition. 

According to Narrative Science, 32% of executives acknowledge that voice recognition is the most used AI technology in business. AI is a popular technology today because almost every sector employs systems to make operations efficient and accurate. That makes AI the present and not the future.   

Common AI examples are:


Siri is a product of Apple. This female voice-activated assistant is found on both iPhone and iPad. It is very friendly and interacts with users daily in various tasks, including finding information, making voice calls, sending messages, getting directions, etc. Siri uses ML technology to get smarter as well as understand natural language questions and requests. 


Are you a car geek? You should know Tesla. Tesla vehicles have a massive array of features, including predictive capabilities and self-driving potential. Thanks to regular updates, this car is getting better by the day. 


This popular content-on-demand service employs predictive technology to offer recommendations using a customer’s reaction, choices, interests, and behavior. It looks at records and recommends movies. Like other ML technologies, it gets smarter by the day. However, a small film may not be noticed.


This AI tool combines machine learning and behavioral science to offer the best customer collaboration for phone professionals. The tool works on millions of voice calls taking place daily, where it analyzes human voices to provide real-time guidance to enhance performance and behavior. 


If you want to search web information, shop, schedule appointments, and control switches, lights, and thermostats, etc., Echo is a revolutionary tool to use. This Amazon product continually gets new features to improve performance.

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Face Detection & Recognition Technology

You’ll find these AI technologies on social media, e.g., Snapchat, where they use face detection technology. Also, devices such as iPhones use FaceID to unlock them. These technologies use a machine-learning algorithm to detect facial features to determine whether or not to open a phone or an application. 

Text Editor

To offer the best writing experience, most text editors have turned to artificial intelligence. Most text editors use NLP algorithms to identify grammar mistakes and suggest corrections. Beyond auto-correction advanced text, editors offer plagiarism and readability stats. Other text editors, e.g., INK, use artificial intelligence to provide intelligent web content optimization recommendations.

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History of Artificial Intelligence

The term AI was fabricated in 1956 at Dartmouth College, thanks to cognitive scientist Marvin Minsky. However, the idea that machines can think logically existed years before. 

1940-1960 – This period marked the birth of cybernetics and an increased desire to know how machines can be given the capability to think like humans. Norbert Wiener, the founder of cybernetics, aimed at bringing together mathematical theory, electronics, and automation in animals and machines.

In 1950, John Von Neumann and Alan Turing founded the technology behind AI though they did not use the term AI. Typicaly, they invented the 19th-century decimal logic, dealing with values 0 to 9 and binary logic, relying on Boolean algebra. Thus, they formalized the contemporary architecture of computes, demonstrating that it was a universal machine that could execute programs. 

Turing proposed that machines could be intelligent in his 1950 article “Computing Machinery and Intelligence.” AI is attributed to John McCarthy of MIT.

In the 1960s, the popularity of AI fell despite being a fascinating technology. The machines had very little memory; thus, it was hard to use computer languages. 

The popularization of AI was perhaps done by the 1968 film “2001 Space Odyssey”. The computer HAL 9000 summarizes all the ethical questions that AI poses: will it represent high-level sophistication, is it good for humanity or a danger? 

Towards the end of 1970, these first microprocessors ushered AI into the golden age of expert systems. The expert systems were based on the inference engine, programmed to the logical mirror of human reasoning. Ideally, the engine could provide high-level answers when queries were entered. 

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There was massive development during this period, but it lasted up to 1980 and again in early 1990. Programming is so demanding, especially the creation of rules. The reasoning of the machines was not well understood. In some applications, they could work faster but very slow in others. The system needed about 300 rules to function. This made it pretty hard to develop and maintain. In the 1990s, AI was almost forgotten.

But there was a success in 1997 when Deep Blue defeated Garry Kasparov at the chess game. Deep Blue was built on a systematic brute force algorithm. This action remained very symbolic.

Around 2010, there was a boom in AI for two reasons, massive access to data and the development of high-efficiency computer graphic cards. The computer processors made it easy to run algorithms. Since 2010 there has been an enormous success in AI solutions. For instance, IBM’s Watson won against 2 Jeopardy champions. Today, there are thousands of AI solutions in all sectors. 

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Luis Gillman
Luis Gillman

Hi, I Am Luis Gillman CA (SA), ACMA
I am a Chartered Accountant (SA) and CIMA (SA) and author of Due Diligence: A strategic and Financial Approach.

The book was published by Lexis Nexis on 2001. In 2010, I wrote the second edition. Much of this website is derived from these two books.

In addition I have published an article entitled the Link Between Due Diligence and Valautions.

Disclaimer: Whilst every effort has been made to ensure that the information published on this website is accurate, the author and owners of this website take no responsibility  for any loss or damage suffered as a result of relience upon the information contained therein.  Furthermore the bulk of the information is derived from information in 2018 and use therefore is at your on risk. In addition you should consult professional advice if required.