Understanding the Basics of Artificial Intelligence:

Introduction

In the last two years, the term "artificial intelligence" has been everywhere—in the media and across the internet—largely thanks to the launch of ChatGPT, which quickly became the most popular AI tool.

While generative AI like ChatGPT is widely discussed, there’s another equally important aspect of AI that often gets overlooked: traditional artificial intelligence.

Generative AI, with its ability to create text, images, music, and video, regularly takes the spotlight. However, AI has also been quietly working behind the scenes for years.

This traditional AI powers everyday tools you might overlook, such as text autocorrect on your phone or product recommendations on Amazon.

In this blog, we will explain the fundamental difference between these two types of AI.

Generative AI: Bringing Ideas to Life with Prompts

Generative AI, like ChatGPT, works by responding to prompts—a question, a request, or a simple description you provide. Think of a prompt as giving instructions to a very smart assistant.

For example, you might say, “Write an email to a colleague,” or “Summarize this article.” The AI then takes your input, analyzes it, and produces a response that fits your request. This makes it incredibly useful for tasks that require creativity, speed, or precision, such as writing emails, essays, or reports.

While ChatGPT can do much more than just generate text, we’re focusing on the basics here to make it easier to understand. To show how generative AI works with prompts, let’s look at an example that combines text and visuals.

Using ChatGPT with DALL-E (an AI that generates images from text descriptions), a prompt like “a realistic tabby cat skateboarding in a busy urban setting with people in the background” was provided. Based on this input, the AI created the image you see here—a cat riding a skateboard in a bustling city.

This example shows how generative AI turns written instructions into creative outputs, whether it’s crafting a blog post or illustrating a unique idea.

Traditional AI: The Unseen Support Behind Algorithms

The key element of traditional AI is the algorithm—a step-by-step set of instructions that processes information to make decisions. Algorithms analyze a variety of data, such as:

Text Data: For text predictions, AI uses language patterns, grammar rules, and past user inputs to suggest or correct words.

Search Queries: Search engines process words and phrases, even if they are misspelled, using close matches and browsing habits to help users find the information they need.

How Email Algorithms Organise and Prioritise Your Inbox: Email algorithms analyse user behaviour, such as which senders' messages are opened most frequently, to prioritise the most important emails. Additionally, traditional AI identifies suspicious keywords or unusual content to block potential spam, unwanted advertisements, and email scams.

Product Recommendations: Platforms like Amazon suggest related products by analyzing the most common purchases among customers. Based on the purchases of other users for the same smartphone model, sites like Amazon might recommend a fast charger or wireless headphones.

Navigation Suggestions: When travelling to a new location, GPS systems use AI to analyze different data, including real-time traffic conditions and road closures, to suggest various routes to reach your destination. They often provide multiple route options, giving users flexibility in choosing their preferred path.

Videos and Music: Platforms like Netflix, YouTube, or Spotify analyze user preferences—such as watching specific genres (news, comedy, documentaries) or listening to particular music styles (pop, rock, dance). They then use data from similar users to recommend videos or songs, keeping users engaged with the app.

By recognizing patterns in this diverse data, traditional AI generates outputs like text predictions, relevant search results, personalized recommendations, or email classifications, optimizing our daily interactions with technology that often works quietly behind the scenes.

Conclusion

While Traditional AI has been integrated behind the scenes into everyday tools for decades, powering autocorrect, navigation systems, and recommendation engines, Generative AI, like ChatGPT, has had a strong media and online impact. However, it is still not part of most people's daily lives.

As debates continue—mainly since the launch of ChatGPT 3.5 in November 2022—about its positive and negative impact on our lives and the future, Traditional AI has long been an integral part of daily life. Yet, while Generative AI dominates discussions, it is often primarily associated with ChatGPT despite many other models capable of generating images, videos, and music.

At the same time, Generative AI and Traditional AI are sometimes confused, leading to misconceptions about their distinct roles and capabilities. Meanwhile, the crucial role of Traditional AI remains largely overlooked, even though we rely on it throughout the day without giving it a second thought.

Resources

Traditional AI vs. Generative AI: What’s the Difference? (deltek.com)

Exploring the Shift from Traditional to Generative AI (mit.edu)

(1) Generative AI vs. Traditional AI: What’s Better? | LinkedIn

Generative AI vs. Traditional AI: A Simple Breakdown (agilisium.com)

Traditional AI vs. Generative AI: A Breakdown | CO- by US Chamber of Commerce

The Difference Between Generative AI And Traditional AI: An Easy Explanation For Anyone (forbes.com)

Traditional AI vs Generative AI: Main Differences and Synergies | Calls9 Insights

What’s the Difference Between AI and Regular Computing? | Royal Institution (rigb.org)

Generative AI vs Traditional AI: Understanding the Differences (talentelgia.com)

Generative AI vs. Traditional AI: What's the Difference? | SS&C Blue Prism

Generative AI vs. Traditional AI: Differences, Strengths, Challenges, and Use Cases (hivekind.com)