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


You are a robot, but like the scarecrow in the Wizard of Oz, you have no brain. John the human wants to change that, so he filled your brain with a model of a fire engine. 

But John also wants you to identify the fire engine by knowing the components that comprise it, so he provides you with this knowledge.

In addition, he provides you with information as to other variations of the fire engine vehicle, meaning that if the parts do not entirely match that of a fire engine, the components may be more closely matched to that of an ambulance or possibly some other type of vehicle.

Photo of a fire engine
Photo by John Torcasio on Unsplash
Now you have the data necessary to identify a fire engine and know what the parts are that encompass it. You can use this knowledge to compare the model to other objects and determine if any of those objects are fire engines or decide that it is something else entirely, and if so, what else could it be?
Congratulations! You are now a machine that can differentiate between objects, or more specifically, you are artificial intelligence!
Ok, we admit this scenario is quite simplified but the idea is to provide the concept of artificial intelligence. So now, let’s dwell into the details of exactly how this works, but before we continue, here are a few technical terms that you should familiarize yourself with. We will be discussing them in more detail further into this article.
Datapoint = The components that make up the model (parts of the fire engine).
Dataset = The combination of all the components together that make up the model (the vehicle as a whole unit).
Supervised Learning = The ability to look at a particular object and compare it to the object (model) that you have in your possession.

AI is Learning

The basic premise behind AI is to create algorithms (computer programs) that can scan unknown data and compare it to data that it is already familiar with. So let’s start by looking at another example.

Image of a fork
Image by Susann Mielke from Pixabay. Text by SMS.

The AI Mindset

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

AI algorithms scan the unknown data’s characteristics, called patterns. It then matches those patterns to data it already has recognized, called pattern recognition. The data it recognizes is called labeled data or training data and the complete set of this labeled data is called the dataset. The result is that it decides as to what that unknown item is.

The patterns within the dataset are called data points, also called input points. This whole process of scanning, comparing, and determining is called machine learning. (There are seven steps involved in machine learning and we will touch upon those steps in our Artificial Intelligence 102 article).

For example, if you are going to write a computer program that will allow you to draw a kitchen on the screen, you would need a dataset that contains data points that make up the different items in the kitchen; such as a stove, fridge, sink, as well as utensils to name a few; hence our analysis of the fork in the image above.

Note: The more information (data points) that is input into the dataset, the more precise its algorithm’s determination will be.

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

Writing the Computer Program

Computer Program Instructions
Photo: 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 was enough money in the person’s account and not if there wasn’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 the potential for fraudulent activity.

Bottom line – There is no learning involved.

Writing a Learning Program

The ATM example is limited to two options, but AI is much more intensive than that. It is used to scan thousands of items of data 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 examining 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 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 example, if a user has watched science fiction movies, the recommendation might be to suggest other sci-fi films or TV shows that are popular among those 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 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 reads 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 which uses the Natural Language Process (NPL) model.

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

AI algorithms feed on data to learn new things.
The more data (data points) that exist, the easier it will be for the model to identify the patterns of an unknown entity.

AI: How it All Works

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

Click CC above for closed caption

Supervised Learning

This is the most common type of machine learning. It involves feeding a computer a large amount of data to enable it to recognize patterns from the labeled dataset and make predictions when confronted with new data.

In other words, supervised learning consists of training a computer program to read from a data sample (dataset) to identify what the unknown data is. 

How the Machine Thinks with Supervised Learning

Poyab Bridge under construction, Freiburg, Switzerland
Photo: iStock

Show and Tell: A human labels a dataset with data points that identify the sample set to be 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 patterns 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 differences in the 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. The data sample is 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 with data points that distinguish each.

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 result would be that the program would be able to make a distinction of whether the 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 provide 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 those categories.

In other words, it matches patterns of the unknown data to the groups it created 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) learns by trial and error, receiving feedback in the form of rewards or penalties for their actions. Any negative number that gets assigned means it is punished.

The higher the negative number, the more the algorithm will learn not to pursue that particular circumstance and will subsequently try again until positive numbers are assigned, called a reward. It will continue this process until it is properly rewarded. The goal of RL is to maximize its rewards over time by finding a sequence of actions that leads to the highest possible reward. 

One of the defining features of RL is the use of a feedback loop in which the agent’s actions (an agent is the decision-making unit that is responsible for choosing actions in the environment that was provided to it). The loop permits the agent to learn from experience and adjust its behavior accordingly.

The feedback loop works as follows:

  1. The agent takes an action in its environment.
  2. The environment provides the agent with feedback about the action, such as a reward or punishment.
  3. The agent then updates its policy based on the feedback.
  4. The agent will repeat steps 1-3 until it learns to take actions that lead to desired outcomes (rewards).

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 action may not be immediately clear and where exploration is necessary to find the best solution.


Overall, these AI methods are widely used in various industries and applications. We will continue to see growth and development as artificial intelligence technology advances.

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

Machine Language Terms to Know

  • Computer Instruction
  • Computer Program
  • Algorithm
  • Data Points
  • Patterns
  • Labeled Data
  • Dataset
  • Data Model
  • Pattern Recognition
  • Machine Learning
  • Binary Classification
  • Multiclass Classification
  • Supervised Learning
  • Unsupervised Learning
  • Reinforced Learning

How to Optimize for Voice Search in 2023

Illustration of voice seach with man at microphone
Image: iStock

Voice Search Overview

Voice search is here to stay and will only be gaining momentum as we proceed into the future and for those that are in marketing or SEOs, it is important to stay up to date with these features and optimize accordingly.

The processes behind voice and text search are quite different. Voice search queries may be longer and more complex, as people tend to ask questions in a conversational style, while text queries are typically shorter and more direct.

Another difference is in the way search results are presented. In text search, results are typically displayed on a search engine results page (SERP), with a list of links and brief descriptions. In contrast, voice search typically provides only the most relevant result, read aloud by a virtual assistant or smart speaker; such as Apple Siri, Amazon Alexa, Google Assistant and Microsoft Corona. This means that optimizing for voice search requires a different approach to traditional SEO, with an emphasis on providing clear, concise answers to common voice questions.

Searching by sound is an SEO component that cannot be overlooked and with the accelerating advancements in artificial intelligence, it is imperative that web developers and SEOs keep a watchful eye on this evolving technology.

The Statistics

Laptop computer showing statistics
Photo: iStock

As of the writing of this article, 32% of people between the age of 18 and 64 use a voice search medium (Alexa, Siri, Corona, etc.) and that number will only grow as we move to the future. 

Entering standard text search queries on mobile devices is commonplace, with over 60% of cell phone users text searching and 57% of mobile users taking advantage of voice search. 

It should be no surprise that Google is the most successful interpreter of audio searches with a 95% accuracy.

In a study in 2021, 66.3 million households in the US were forecasted to own a smart speaker and that forecast has become a reality as of 2023.

Voice technology stretches beyond search queries as 44% of homeowners use voice assistants to turn on TVs and lights, as well as an array of other smart home devices currently on the market. 

With statistics as these, speaking to robotic assistants is here to stay and will only be growing with new technologies as we proceed through the 2020s and beyond. 

How Does Voice Search Work?

Woman speaking into a moblie phone
Photo: iStock

The Physics Behind It

If you just need to know that there is an analog-to-digital conversion and are not interested in the specifics of how it’s done, you can skip this part and go to the next section, which is Where Does the Data Come From?

We will summarize the process of how the sound of human speech is converted into machine language, which is filtering and digitizing.

Filtering: Smart speakers and voice assistants are designed to recognize the human voice over background noise and other sounds; hence, they filter out negative sounds so that they can only hear our voices.

Digitizing: All sound is naturally created in analog frequencies (use of sinewaves). Computers cannot decode analog frequencies. They must be converted to the computer language of binary code.

Below are the details of how an analog signal is converted to digital. 

The Analog Conversion Process

Illustration of a sine wave
Image by Gerd Altmann from Pixabay

|n order to make this conversion, an Analog-to-Digital Converter (ADC) is required. The ADC works by sampling the analog signal at regular intervals and converting each sample into a digital value. 

The steps are as follows:

    1. Sampling: The first step is to sample the analog signal at a fixed interval. The sampling rate must be high enough to capture all the frequencies of interest in the analog signal. The Nyquist-Shannon sampling theorem states that the sampling rate must be at least twice the highest frequency in the signal. Sampling means taking regular measurements of the amplitude (or voltage) of the signal at specific points in time and converting those measurements into a digital signal. Sampling is necessary in order to convert analog sound waves into digital signals, which are easier to store, transmit, and process using digital systems such as computers or microcontrollers. The rate at which the analog signal is sampled, known as the sampling rate or sampling frequency, is important because it determines the level of detail that can be captured in the digital signal. Sampling an analog signal is an important step in converting it to a digital signal that can be analyzed, manipulated, or transmitted using digital systems.
    2. Quantization: Once the analog signal is sampled, the next step involves assigning a digital value to each sample based on its amplitude. The resolution of the quantization process is determined by the number of bits used to represent each sample. The higher the number of bits, the greater the resolution of the digital signal.
    3. Encoding: The final step is to encode the quantized samples into a digital format. This can be done using various encoding techniques such as pulse code modulation (PCM) or delta modulation.

Overall, the main process of converting analog to digital frequencies involves sampling, quantization, and encoding. The resulting digital signal can then be processed using digital signal processing techniques.

In summary: Smart speakers and voice assistants take in the audio from a person’s speech and convert it to machine language.

Where Does the Data Come From?

Outline of a computer screen wiht a cloud behind it
Image by Gerd Altmann from Pixabay

Information gathered from smart speakers and voice assistants pulls data from an aggregate of sources.

If you want your business to grow, you must be attentive to where content for voice search is collected so that you can make intelligent decisions regarding how to optimize for these devices. 

Amazon Alexa

When Alexa responds to a query, it relies on Microsoft’s Bing search engine for the answer. Why? Because Amazon, as well as Microsoft, are in direct competition with Google, even though Google has the most popular search engine in the world. 

Amazon’s refusal to use Google for audio responses is not something to be concerned about. After all, Bing’s search algorithms are very similar to Google’s.

With that said, if a person speaks to Alexa with a specific request, (e.g. “What’s the weather today?”), Alexa can pull that information from a database associated with that request. In this case, Alexa will connect to Accuweather. The device can access Wikipedia and Yelp if it needs to as well.

Apple Siri

Initially, Apple used Bing as its default search engine, but in 2017, Apple partnered with Google. Now, when you say “Hey Siri”, you can expect Siri to access the immense data repository from Google and supply the answer. This applies to the Safari browser for text searches as well.

There is a caveat though. When it comes to local business searches, Siri will call on Apple Maps data and will use Yelp for review information.

Microsoft Cortona

This one is probably the most straightforward out of all of the search engines, as Cortona relies on what else but Microsoft Bing for its information. 

Google Assistant

OK, this one’s a no-brainer. Google can currently index trillions of pages to retrieve information and since this also applies to Apple’s Siri, this section is of most importance if you want to optimize voice search for these voice assistants.

In most cases, Google and Siri will read from Google’s featured snippet.

So What is a Featured Snippet?

Screenshot of a Google featured snippet
Image: © SMS

Featured snippets are what you see after you run a Google search query. It is a paragraph that appears at the top of the page that summarizes the answer to a question.

The information that Google applies to the snippet is gathered from, what Google determines to be the most reliable source (website) for that information.

How Does Google Determine a Featured Snippet?

For a snippet to be posted by Google, it needs to know that the source is trustworthy via its domain authority, link juice and high-quality content, to name three important organic factors, as any SEOs would already know, but in addition to these factors, Google will defer to “HowTo” and FAQ pages most often to pull in the snippet.

Is Structured Data Needed?

Structured data is extra code that helps Google better understand what the page or parts of the page are about.

One might wonder if structured data has to be used in order to provide the featured snippet? The answer is no. As per Google, as long as the web page is optimized properly and contains the questions that equate to the user’s query or voice search in this case, structured data is not necessary; however, if it wouldn’t hurt to put it in, as we all are aware that nothing is static in the SEO world and this rule can easily change in the future.

The reason why Google focuses on “HowTo” and FAQ pages is that their content reflects that of human speech. For example, an FAQ page on EV cars may have the question “How long do EV batteries last?” – That is exactly how a person would ask a voice assistant that same question!

An ‘Action’ for Google Assistant is created, equivalent to an Alexa Skill and Google will read the snippet back to the user to answer the question he/she asked.

Summarizing Optimization for Voice Search


Bing: If you have not already done so, bring Bing into your scope of work for SEO and start optimizing for this search engine.

Yelp: We all know that reviews are of the utmost importance, so check out Yelp for your or your client’s business and build on those reviews! Legitimately of course.


Google SEO: If you are already optimizing for Google’s search, just keep up the good work.  

Apple Factors: Where you might not be fully optimized is with Apple Maps, so get going. Start by registering with Apple Business Connect.

Yelp: And now Yelp is back in the picture!


Bing: As mentioned, become an SEO Bing expert and you are ready to ask Cortona anything.


Besides the standard organic optimization, focus on schema markup for HowTo and FAQ articles for voice search, which, if you’re lucky, will be shown on the SERP as a featured snippet.

There you have it. How to optimize for voice search. Let’s get these robots configured so that our businesses will be the first thing you hear from your voice assistant!



The Pros and Cons of AI

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


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 whether you feel that the glass is half full or half empty, but the reality is that there are always consequences to technological advancements. Hopefully, we can honestly say a lot of it will be for the good of humankind, but let’s not be naive and think three won’t be those nefarious individuals looking to selfishly benefit at the expense of the rest of us.

One example would be the development of the atom bomb, which was the result of Einstein’s theory of relativity, even though the scientist had no idea of the frightening consequences his theory would bring.

Enter AI 

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

Regardless, artificial intelligence is advancing at an exceptional rate whether we like it or not, as our AI avatars explain below.

So 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 decide.

The Benefits

Advancement on Healthcare

Medical Technology
Photo: Pixabay

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


Photo of traffic
Photo: Free Images

Artificial intelligence can be used to optimize traffic flow and reduce congestion and subsequently, travel time for busy commuters.

Moving not too far into the future are autonomous vehicles – cars that drive themselves. There are some being tested now, such as Teslar and Google and Teslar already has autonomous vehicles on the market, but a driver must remain inside.

When it does become mainstream, 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.


A young man with long hair is working on a laptop. hands close up
Photo: iStock

Artificial intelligence 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.

One caveat is the temptation for students to cheat by using apps such as Chat GPT, but alert teachers should be able to tell the difference by determining if the student’s writing style has changed.  With that said, this will still be a challenge for educators.


Ai can be used to detect fraud, manage risk and optimize investments. By analyzing financial data,  machine learning algorithms can detect patterns that may indicate fraudulent activity, alerting financial institutions to potential threats before they cause significant damage.

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

Law Enforcement

AI-powered surveillance systems can detect potential threats in public spaces, alerting law enforcement and allowing them to respond more quickly.

It can also be used to analyze crime data, helping law enforcement identify patterns and allocate resources more effectively. Indeed, New York City Mayor Eric Adams introduced crime-fighting robots to the Times Square area and if they prove productive, they will be placed all over the city.

The Environment

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

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, it 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 Negatives

Of course, as with any powerful technology, AI also poses some risks and challenges. 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 AI could become too powerful, leading to unintended consequences or even threatening human existence.

To mitigate these risks, we must approach AI development 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, as well as strict laws that prohibit collusion and/or intentionally skewing the algorithms. 

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.

The Labor Question

No doubt, unemployment due to artificial intelligence is a major concern. As this 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.


Another danger is its ability to perpetuate biases and discrimination. 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, such as in hiring, lending, or criminal justice. It can have significant negative impacts on individuals and communities.

The Military

Photo: U.S. Navy photo by Mass Communication Specialist 1st Class Michael Moriatis/Released. Wikimedia CC.

AI could pose a significant threat to global security. With technological advancements increasing in this arena technology, it is becoming increasingly possible for computers 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.

Malicious Financial Behavior

Woman gestering in awe looking at computer laptop
Photo: iStock

The financial markets would most likely be the most affected by artificial intelligence, both for good and bad. We have already discussed the good, but the bad is already a concern. There can be serious consequences that could affect the banks and stock market as nefarious individuals try to override the algorithms with corrupt data and computer instructions. The expression “What’s in your wallet” will have a  much greater significance should malicious AI alter your bank accounts.

A Question of Morals

Finally, the development of AI could also pose ethical and moral dilemmas. As these algorithms 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 an AI system is programmed to harm humans or perform unethical tasks?

AI in a Nutshell

Artificial intelligence can help us solve some of the biggest challenges facing our society. However, we must approach AI with caution and foresight, taking steps to mitigate risks and ensure that this technology is used in ways that prioritize humanity and respect human rights. With careful planning and collaboration, we can harness the power of Artificial Intelligence to create a better future for all!