Category Archives: Artificial Intelligence

AI 101 – How Does Artificial Intelligence Work?

Illustration of computer chips on a wall with a woman in front
Image by Gerd Altmann from Pixabay

Overview: Artificial Intelligence in 100 Words

AI is Learning

The basic premise behind artificial intelligence is to create algorithms (computer programs) that can learn by viewing unknown data and comparing that data to data that it is already familiar with or has learned what it is. (We’ll discuss the difference between “already familiar” with “has learned ” further in this article), so let’s start by looking at a simple example.

Image of a fork

Image by Susann Mielke from Pixabay. Text by SMS.

In the Mind of AI

Is this a fork or a spoon? Well, they both have handles, but this one has spikes. Let me look up what pieces of information I have in my database that looks like this item. Oh, I have a piece that resembles this spike property, so it must be a fork!

These programs are written to scan new data, then they break the data up into the different characteristics that compose the item. It then matches those characteristics to other data it already knows and makes a determination.

The more information (characteristics) it can compare the new item to, the more precise its determination will be.

AI compares new data to old data in order to make a determination of what the new data is. This is called data analytics.

Now, let’s go a bit deeper into how a computer program is written.

Writing the Computer Program

Computer Program Instructions
Phto: iStock

We spoke about how computers are programmed using instructions in our bits and bytes article, but as a refresher, let’s recap!

Computer programs, called algorithms, tell the computer to do things by reading instructions that a human programmer has entered.  One of our examples was a program that distributes funds to an ATM recipient. It was programmed to distribute the funds if there is enough money in the person’s account and not if there isn’t.

But THIS IS NOT AI since the instructions are specific and there are no variations to decide anything other than “if this then that”.

In other words, the same situation will occur over and over with only two results. There is no determination that there may be more issues, such as possible fraudulent activity.

Bottom line – There is no learning involved.

Writing a Learning Program

The ATM example above is limited to two options, but AI is extremely more intensive than that. It is used to scan thousands of items of data in order to determine a conclusion.

How Netflix Does It

Did you ever wonder how Netflix shows you movies or TV shows that are tuned to your interests? It does this by determining what your preferences are based on your previous viewings.

The algorithm analyzes large amounts of data, including user preferences, viewing history, ratings, and other relevant information to make personalized recommendations for each user.

It employs machine learning (we will be discussing this more later in this article) to predict which movies or TV shows the user is likely to enjoy.

It identifies patterns and similarities between users with similar tastes and suggests content that has been positively received by those users but hasn’t been watched by the current user.

For instance, if a user has watched and enjoyed science fiction movies, the recommendation might be to suggest other sci-fi films or TV shows that are popular among users with similar preferences.

The program will learn and adapt as the user continues to interact with the platform, incorporating feedback from their ratings and viewings in order to refine future recommendations.

By leveraging machine learning, streaming platforms like Netflix can significantly enhance the user experience by providing tailored recommendations, increasing user engagement, and improving customer satisfaction.

This can’t be done using the non-learning ‘if-else’ program we previously spoke about in the ATM example.

A Gmail AI Example

As you type your email, Google is reading it and then offers words to accompany the sentence that would coincide with what you are about to type before you have even typed it.

This is called language modeling and is another method by which AI is used.

In language modeling, the algorithm uses a factor of probability that is used to predict the most likely next word in a sentence based on the previous entry.

A Vocabulary Update

Before we continue, let’s get a bit more technical. The word ‘characteristics’ has been used here for simplicity, but the actual term where the computer looks at points of a subject is called “patterns“. And pattern recognition is the process of identifying unique points in the data.

AI algorithms feed on data to learn new things. The more data that exists, the easier it will be for the algorithm to identify the characteristics or patterns of an entity.

AI: How it All Works

There are three main types of machine learning: supervised learning, unsupervised learning and reinforcement learning.

Supervised Learning

This is the most common type of machine learning. It involves feeding a computer a large amount of data with the aim of enabling it to recognize patterns and make predictions when confronted with new data, called training data or a data sample. In other words, supervised learning consists of training a computer program to learn from a data sample that identifies what that data is (called marked or labeled data) and subsequently, the algorithm looks at this sample to see if it matches the unknown sample. 

How the Machine Thinks with Supervised Learning

Poyab Bridge under construction, Freiburg, Switzerland
Photo: iStock

Show and Tell: A human labels a piece of data to be a building and identifies the building with specific characteristics called patterns that distinguish it specifically as a building.

Then the human does the same thing to identify a bridge. This is another classification different from the building classification and is identified with specific patterns that make up a bridge.

The program takes note of the characteristics of each classification. If computer instructions were written in plain English, this is what it would say:

This is a bridge. Look at the patterns that make up the bridge. And this is a building. Look at the patterns that make up the building. I can see distinguishable characteristics between the two. Let me match them up to the unknown data and make a decision on whether this new data is a bridge or a building.

Supervised learning is used in many applications such as image recognition, speech recognition and natural language processing.

Supervised learning uses a data sample to compare unknown data, also called a data model.

It’s Raining Cats and Dogs

A supervised learning algorithm could be trained using a set of images called “cats” and “dogs”, and each cat and dog are labeled as such.

The program would be designed to learn the difference between the animals by using pattern recognition as it scans each image. 

A computer instruction (in simplified terms) might be “If you see a pattern of thin lines from the face (whiskers), then this is a cat”.

The end result would be that the program would be able to make a distinction of whether a new image it is presented with is that of a cat or dog!

This type of learning involves two categories – cats and dogs. When only two classifications are involved, it is called Binary Classification.

Supervised Learning Usining Multi Classifications

An Example

Illustration of a fruit fly
Image by Mostafa Elturkey from Pixabay

Suppose you are studying insects and you want to separate flying insects from crawling ones. Well, that’s easy. You take a bug that you found in your backyard and compare it to the ant and fly you already stored on your insect board. In AI terms, this is supervised binary classification.

You immediately know, based on the pattern configuration of the insect which classification it belongs to – the crawlers or the flies. Now you grab more flies and put them in the fly category and do the same with the creepy crawlers for their category.

Let’s say you want to go deeper in the fly classification and find out what type of fly it is, (e.g. house fly, horse fly, fruit fly, horn fly, etc.); but you only have two classifications to compare them two – flies and crawlers, so what do we do? You create more classifications for the fly class.

This is multi-classifications, or more technically called multi-class classifications, which provides additional labeled classes for the algorithm to compare the new data to.

We will delve more into multi-class classifications and how this works in our next article, but for now, just know what binary classifications and multi-class clarifications are.

Unsupervised Learning

Colorful illustration of AI unsupervised clustering
Photo by Google DeepMind on Unsplash

Unsupervised learning involves training a computer program without providing any labels or markings to the data. The aim is to enable the program to find (learn) patterns and relationships on its own.

It does this by reading millions of pieces of information and grouping them into categories based on their characteristics or patterns, then making decisions on what the new entity is by matching it up to one of the categories it created.

In other words, it finds matching patterns from the groups in the dataset completely on its own and then labels them without human intervention. This is called clustering.

Anomaly detection is the task of identifying data points that are unusual or different from the rest of the data. This can be useful for tasks such as fraud detection and quality control.

Reinforcement Learning

Reinforcement learning (RL) is a type of machine learning that focuses on training agents to make decisions based on experience in an environment. RL algorithms learn by trial and error, receiving feedback in the form of rewards or penalties for their actions.

The goal of RL is to maximize cumulative rewards over time by finding an optimal policy, or a sequence of actions, that leads to the highest possible reward. The agent explores the environment by taking action and receiving feedback, which it uses to update its policy to improve its performance.

One of the defining features of RL is the use of a feedback loop in which the agent’s actions influence the state of the environment, which in turn affects the rewards received by the agent. This feedback loop allows the agent to learn from experience and adjust its behavior accordingly.

RL has been applied to a wide range of problems, such as games, robotics and autonomous driving. It is particularly useful in scenarios where the optimal action may not be immediately clear and where exploration is necessary to find the best solution.

Overall, these AI tasks are all widely used in various industries and applications and we continue to see growth and development as artificial intelligence technology advances.

What are the advances or dangers that AI can bring in the future? Read our article on the Pros and Cons of AI to find out.

Machine Language Terms to Know

Data Sample
Data Model
Data Pattern
Binary Classification
Multiclass Classification
Supervised Learning
Unsupervised Learning
Reinforced Learning

Artifical Intelligence: The Pros and Cons

Human hand touching a brain and AI hand touching a brain
Image by Gerd Altmann from Pixabay

The Quandary of AI

Are you afraid of what AI can do or are you looking forward to the benefits it can provide?  Part of your decision would be based on personality glass is half full or the glass is half empty, but there are always consequences to technological advancements, whether for the good of humankind or for those looking to gain an upper hand in a nefarious manner. The development of the atom bomb was the result of Einstein’s theory of relativity, even though the scientist had no idea of the negative consequences his theory would bring.

Let’s take a look at both the positives and negatives of artificial intelligence and what it can potentially have for us and then you can make a decision.

AI Overview

Artificial intelligence (AI) is a rapidly growing field that has the potential to transform our world in countless ways. From healthcare to finance, and education transportation, AI can benefit mankind in a myriad of ways, but not everyone is on board with this as we will see in this article, the good and the bad of the advancements of artificial intelligence. 

The Benefits

Advancement on Healthcare

Doctor at a laptop
Photo: IStock

One of the most significant benefits of AI is its potential to revolutionize healthcare. AI can analyze vast amounts of medical data, including patient records, lab results, and imaging studies.

With this information, AI algorithms can detect patterns and make predictions that could help doctors diagnose and treat diseases more accurately and quickly. It can also help identify high-risk patients, allowing doctors to intervene early and prevent diseases from progressing.


Cars in traffic
Photo: iStock

Another area where artificial intelligence can benefit us is in the field of transportation. Self-driving cars, buses, and trains have the potential to significantly reduce accidents, traffic congestion, and pollution. By removing the human element from driving, these vehicles can make our roads safer and more efficient.

Additionally, AI can be used to optimize traffic flow, reducing congestion and travel times. This can save time and money for individuals and businesses alike.


AI can also be used to improve education. AI-powered tutoring systems can provide personalized, adaptive learning experiences for students of all ages and abilities. By analyzing a student’s learning style, strengths, and weaknesses, these systems can create customized lesson plans that help them learn more effectively. This can lead to improved academic outcomes and greater educational equity, as students who may struggle with traditional teaching methods can receive tailored instruction that meets their needs.


Graph of gold on the rise
Photo: GraphicStock

Detecting fraud, managing risk, and optimizing investments are just three of the ways AI is being used to advance the financial sector. By analyzing financial data, algorithms can detect patterns that may indicate fraudulent activity, alerting financial institutions to potential threats before they cause significant damage.

Additionally, AI can help them manage risk more effectively by predicting market fluctuations and identifying potential investments that offer high returns with low risk.

AI can also benefit society by improving public safety. AI-powered surveillance systems can detect potential threats in public spaces, alerting law enforcement and allowing them to respond more quickly. AI can also be used to analyze crime data, helping law enforcement identify patterns and allocate resources more effectively.

The Environment

Illustration of the effects of climate change, showing grass and then barren ground
Photo: iStock

Finally, AI can benefit mankind by helping us protect the environment. By analyzing environmental data, AI can help us understand the impacts of human activity on the planet and develop strategies to mitigate them. For example, AI can help us optimize energy consumption, reduce waste, and improve recycling efforts. Additionally, AI can help us predict and respond to natural disasters, reducing their impact on human lives and property.

The Benefits of AI – A Summary

AI has the potential to benefit mankind in countless ways. From healthcare to education, finance to public safety, and the environment. It can help us solve some of the biggest challenges facing our society. However, we must approach AI development with caution and foresight, taking steps to mitigate risks and ensure that it is used in ways that prioritize human welfare and respect for human rights. With careful planning and collaboration, we can harness the power of machine learning to create a better future for all.

Potential Dangers

Unknown person in black sourrounded by binary code
Photo: Pixabay

Artificial Intelligence can pose significant dangers that need to be addressed. Similar to the potential dangers of the use of quantum computers, the same threats are associated with AI.

One concern is the potential for it to be used in ways that violate privacy or human rights. Additionally, the use of AI in decision-making processes could result in biases or discrimination if the algorithms are not carefully designed and monitored. Finally, there is the risk that it could become too powerful, leading to unintended consequences or even threatening human existence.

The Labor Question

As AI technology advances, it becomes increasingly capable of performing tasks that were once done by humans, leading to job loss and economic disruption. For example, self-driving cars have the potential to replace human drivers, which would lead to unemployment in the transportation sector. This could result in a significant reduction in the workforce and an increase in social inequality.

AI and Bias

Another danger of AI is its ability to perpetuate biases and discrimination. AI algorithms are designed to learn from data, and if the data used is biased, the AI will also be biased. This can result in unfair decisions being made by AI systems, such as in hiring, lending, or criminal justice. This can have significant negative impacts on individuals and communities.

Global Security

Furthermore, AI could pose a significant threat to global security. With advancements in AI technology, it is becoming increasingly possible for AI systems to be used in cyber-attacks or even to control weapons systems. This could lead to significant risks and damages, such as loss of life or damage to critical infrastructure.

Nefarious Exploitation

Finally, the development of AI could also pose ethical and moral dilemmas. As machine language systems become more intelligent, questions arise about their autonomy and decision-making capabilities. If an AI system makes a decision that is morally or ethically questionable, who is held accountable? What happens if it is programmed to harm humans or perform unethical tasks?

In a Nutshell

Artificial Intelligence Illustration AI
Image by Tumisu from Pixabay

While AI has the potential to bring significant benefits, it is important to be cautious in its development and use. The dangers of should be taken seriously and addressed through proper regulation and oversight. It is important to ensure that AI systems are developed and used responsibly and ethically to minimize the potential risks and maximize the benefits of this technology.

To mitigate these risks, we must approach AI with caution and foresight. We must ensure that AI is developed and used in ways that prioritize human welfare and respect human rights. This requires ongoing dialogue and collaboration between technologists, policymakers, and the public.

With that said, we do have the opportunity to live better in all aspects of our lives and it is well worth something for all of us to look forward to!