AI 101 – Your Introductory Guide to the World of Artificial Intelligence
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From James Cameron’s “The Terminator” to the 2013 science fiction movie, “Her”, it’s easy to see that “artificial intelligence” has been with us for what seems like forever. But as many of the presentations of this concept in pop culture appear futuristic, it can be somewhat difficult to have a good grasp on what artificial intelligence is. So, it’s time for AI 101!

Whether it’s immediately apparent or not, the fact is that artificial intelligence, or AI as it’s more commonly known, is actually a huge part of our lives today.

While AI hasn’t presented itself as the advanced technology that fiction portrays it to be, it has slowly permeated in various aspects of our lives.

AI and AI-related technologies are evident in everything from the writing software that helps managers create precise job descriptions to the all-popular, voice-activated Alexa from Amazon.

What’s more, from all indications, artificial intelligence applications are set to play a more predominant role in our lives as time goes by.

With things like complete self-driving cars and fully laboratory automation tools on the horizon, there has never been a better time to learn more about artificial intelligence.

Here, we’ll be discussing the basics of AI, from what it is to the common phrases you should know as well as the potentials it holds for the future.

AI 101 – Just What is Artificial Intelligence?

At the moment, there isn’t one single, universally-accepted definition of artificial intelligence as it’s often defined in philosophical and/or technical terms. That being said, there are a few key points on which virtually all researchers agree.

We’ll be analyzing AI from that point of view.

In the words of the founder of artificial intelligence, John McCarthy, AI is, “…the science and engineering of making intelligent machines, especially intelligent computer programs. It is related to the similar task of using computers to understand human intelligence, but AI does not have to confine itself to methods that are biologically observable.”

Oracle breaks this definition extensively, simply referring to AI as the development of machines or systems that mimic human intelligence when performing tasks while seeking to continually improve (itself).

This is an opinion strongly echoed by tech organizations like MIT-IBM Watson AI Lab.

From these definitions, it can be inferred that artificial intelligence and data research are premised on the fact that there may be limits to what the human brain can achieve.

Machines and systems capable of performing unsupervised learning may, in theory, complement human resources to exceed those limitations.

The MIT-IBM Watson AI Lab goes on to break down artificial intelligence into three categories. They are:

Narrow Artificial Intelligence or Narrow AI

These types of AI are the most popular at the moment. They are characterized by the ability to perform specific tasks at incredible speeds, thanks to their unique composition of algorithms.

Examples of Narrow AI range from voice assistants to advanced chess programs, laboratory automation tools, and translation services, to mention a few.

Broad AI

Beyond Narrow AI, Broad AI is expected to comprise multiple programming systems that can perform unsupervised learning with a greater level of ease and flexibility.

Artificial General Intelligence

The last type of AI that the lab envisions is Artificial General Intelligence which is essentially an AI that’s capable of executing complex reasoning and possesses full autonomy.

In a nutshell, this type of AI would appear as something similar to the autonomous machines often portrayed in archetypal science fiction.

Further down the road of artificial intelligence education, here are some of the most common terms used in the AI space you should know.

The Most Common Terms in AI

With AI-related technologies beginning to feature in virtually every aspect of life, you’ll be hearing a lot of new phrases such as algorithms, machine learning models, and deep learning.

We’ve highlighted some of these popular words as well as their meanings below.

1. Algorithms

People test the new Algorithm

Simply put, these are mathematical formulas that act as a set of processing instructions that are designed to handle a specific task. Algorithms present themselves in various forms in AI.

For example, in many AI, the algorithms require fairly constant and consistent human intervention to function at optimal levels. However, there are some AI systems programmed to learn on their own.

So, let’s say an artificial intelligence team equipped a robot with an algorithm for baking a cupcake.

As this involves mixing ingredients like flour, eggs, and sugar together, over time, after it must have made several attempts (thereby collecting a data set), the robot would be able to (on its own) determine that excess flour will make the cupcake dry.

Essentially AI 101, the complexity and functionality of an algorithm are what ultimately determine just what any artificial intelligence system or machine can do.

2. Deep Learning

Ai 101 technology self driving car

This concept is essentially based on the notion that just as the human brain can recognize different people through experience and knowledge, machines like self-driving cars can be trained to identify and recognize objects like sidewalks and pedestrians while on the road.

For basic users, this won’t only make driving a more comfortable exercise, but it may also actively reduce the risk of loss of life due to traffic accidents.

Deep learning leverages a complex network of computation models referred to as neural networks for this task. These neural networks mimic the human brain such that the more experience the car has the better it’ll get at identifying simple and complex objects.

One of the most accurate representations of deep learning in everyday life at the moment is Facebook suggesting name tags every time an image is uploaded on the platform.

By leveraging facial recognition, it’s able to measure numerous facial features and then use its complex algorithms to locate an accurate match.

3. Machine Learning

man pointing a machine learning

You know how Netflix always seems to specifically recommend great movies you’re sure to like, even though they have thousands of videos in their archives?

Or how that you’re just screening through your Facebook feed as per usual and you stumble across the exact refrigerator you’ve been thinking of buying?

While it might certainly seem like it, those platforms aren’t reading minds or performing magic. They’re simply leveraging unique machine learning models to establish a pattern in your behavior. Using this, they can almost accurately predict what you’d want.

More specifically, platforms like Facebook can connect the dots and make accurate recommendations because they actively collect information on their users.

They use everything from online purchasing habits and browsing history to age and other metrics to make inferences on what you might like in the near future.

There are three different forms of machine learning at the moment:

  • Supervised Learning
  • Unsupervised Learning
  • Reinforcement Learning

Supervised Learning

This type of machine learning is done by feeding annotated images into the software of the AI. An example of this is seen in autonomous vacuums that can clean without bumping into objects.

They’re able to achieve this thanks to the fact that they’ve been fed algorithms labeling the room and its contents so they know what to avoid automatically.

Unsupervised Learning

This form of machine learning is designed to look for patterns and connect dots that don’t seem related on the surface without pursuing a specific goal.

For instance, when people purchase TV sets online, the customer’s information is collected and then pooled in a large database. Unsupervised learning is then introduced here to analyze the data for patterns and then use that information to make inferences on future purchasing habits.

As we mentioned earlier, this type of machine learning has become the convention for the online business process today.

Reinforcement Learning

This is when the system teaches itself through the process of trial and error.

Very similar to what is known as on-the-job learning, the system is exposed to a controlled environment, allowed to interact with it, and then uses the information gathered to make better decisions subsequently.

4. Deepfake Technology

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One of the more recent artificial intelligence applications, deepfake technology involves using complex deep learning models to reconfigure audio and visual text. The aim of this is to create realistic video and audio footage of people saying or doing things they didn’t in reality.

More often than not, this technology is usually used for nefarious purposes. In recent times, this application of AI has been used to cause disruptions on various levels.

It isn’t all doom and gloom though as deepfake has managed to find its way into the movie and art industry, helping these markets create more realistic and entertaining visuals.

5. Natural Language Processing

Siri on the phone

If you’ve ever used Siri to get directions to a friend’s house or get the transcript of a voice mail in your email inbox, then you’ve already witnessed the power of natural language processing at work!

Also commonly known as NLP, this advanced technology uses specific machine learning models and algorithms to tag elements like parts of speech and the relationship and meaning of words to discern the meaning of your audio and text.

The Future of Artificial Intelligence

While artificial intelligence only came into existence less than a century ago, it’s made gigantic leaps in that timeframe. And, in the future, it’s clear it’ll make even more progress.

And even though it’ll no doubt shape every aspect of life as we know it, some areas that are likely to feel its real impacts in the immediate future are:

The Customer Service Industry

One of the greatest challenges that the customer service industry is facing at the moment is the inability of most operatives to maintain their stature and discipline under duress.

The more customers an operative has to deal with, the more difficult it can be for some operatives to stay composed.

But even with this, most people still prefer human help as they don’t feel comfortable with robotic chats.

AI presents an elegant solution here.

Because it imbues the regular chatbot with a human-like level of help and assistance capability without the issue of discipline, more customers will likely come to rely heavily on this feature soon.

The Personal Assistant Industry

Voice assistants like Amazon’s Alexa, Apple’s Siri, and Google’s Voice Assistant are already reshaping the personal assistant landscape.

What’s more, soon, these smart assistants will be able to do much more than handle your playlists and manage your calendars.

For example, even though it may still have a long way to go, chatbots like Amy by are already capable of doing things like scheduling your meetings and scanning your emails.

Ultimately, bots like this will continue to enter and eventually dominate this industry.

The Psychology and Health Care Industry

Artificial intelligence is already playing a major role in these fields even in this day and age. From therapeutic games targeted toward enhancing problem-solving skills and boosting self-confidence to virtual worlds like Second Life designed for autistic individuals, AI is already redefining this landscape.

Apps like Cognito that help veterans by monitoring their phones have also been introduced to help in detecting the first signs of psychosis. Devices like the Apple Watch are constantly being enhanced to detect various health conditions.

AI offers numerous potentials in this area, many of which we haven’t even uncovered yet. With more focus turning to these possibilities each new day, AI is sure to make its impact felt in the medical field.

The Automotive Industry

Tesla car self driving

So far, AI has been able to integrate with automotive technology smoothly. This means that the dream of self-driving cars will soon become a reality.

Proof of this is in the Tesla Model S and Model X which can currently not only auto-drive but even auto park. We can reliably look forward to more advancements in this area in the not-too-distant future.


While our AI 101 guide is certainly concise, it only barely scratches the surface of all that’s happening in the world of artificial intelligence.

New advancements in AI are being recorded almost by the day!

With automation tools and systems poised to reshape life as we know it, you can keep up with all the latest updates in the AI space here.


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