AI: It's Not What You Think

When people think about Artificial Intelligence (AI), they often envision a world filled with complicated algorithms, impenetrable code, and teams of expert mathematicians working with massive servers. For years, this intimidating image has kept non-technical people at bay, making AI seem like a realm accessible only to those with deep technological expertise. Yet, in 2024, many of these assumptions no longer hold true and are, quite frankly, absurd. Here’s a look at the myths that need debunking and the unexpected truths that may surprise you.

You Need to Be in IT or Know Programming to Use AI

The idea that AI is solely for IT professionals or those who can code is a relic of the past. Today, tools like ChatGPT, Midjourney, and other AI platforms have simplified user interfaces and accessible functionalities that cater to everyone, from writers and marketers to students and retirees. These platforms enable users to benefit from AI with just a few clicks, dragging and dropping content or engaging in natural language conversations.

ICYMI: You can actually use AI to write code even if you’re not a programmer. Platforms like ChatGPT can generate snippets of code in Python, JavaScript, or even obscure languages like COBOL—all based on simple prompts. This means non-technical users can dabble in coding projects, troubleshoot errors, or automate mundane tasks without ever opening a coding textbook.

You Need to Be a Mathematician or Statistician to Use AI

It’s true that AI was built on complex mathematical principles and algorithms, but that’s where its association with math should end for most users. The AI systems we interact with today are designed to hide this complexity, providing users with straightforward, intuitive interfaces. You don’t need to understand calculus or linear algebra to appreciate the benefits of AI any more than you need to know how an internal combustion engine works to drive a car.

ICYMI: Math was used to build these models, but math is arguably the worst place to use them. In fact, LLMs occasionally get math problems wrong. AI excels in creating dynamic conversations, generating creative content, and solving problems where human intuition would typically trump formulas. The irony is that for most business use cases, attempting to use AI purely for mathematical functions undermines its true potential.

You Need a High-End Laptop, Servers, or Cloud Subscriptions to Use AI

It’s natural to assume that cutting-edge technology requires cutting-edge equipment. However, with cloud-based services and AI applications that run on browsers, almost anyone with an internet connection can access the most powerful AI models in the world using a basic laptop, tablet, or even a smartphone. Free or low-cost subscription plans on platforms like Google Colab or OpenAI’s ChatGPT make experimentation affordable and accessible.

You Need Clean, Curated, Warehoused Data to Use AI

A common misconception is that AI systems need pristine, perfectly formatted datasets stored in high-end data warehouses to function. This was true for traditional machine learning models. Today’s generative AI models can work with natural language inputs, making them vastly more adaptable and flexible. You can input unstructured information like casual text, incomplete data, or even ideas you’re still developing, and AI will still deliver usable outputs.

AI Hallucinates

AI hallucinations—where the system generates plausible but false or nonsensical information—are a real concern, but they are often misunderstood. People blame the AI itself, but hallucinations occur mostly because of poorly framed prompts or insufficient context. When properly guided through techniques like prompt engineering, hallucinations can be minimized, making the AI’s outputs significantly more accurate.

ICYMI: Hallucinations occur more often if you do not use prompt engineering. Understanding how to communicate with the AI, how to set constraints, and how to refine prompts leads to more reliable outputs and reduces the risk of confusing or incorrect information.

Prompt Engineering is Useless

The role of prompt engineering is often dismissed by skeptics who see it as trivial or unimportant. Yet, this skill—knowing how to communicate effectively with AI models—can make a significant difference in the quality of the AI’s outputs. Prompt engineering is akin to knowing how to ask the right questions in a search engine; it’s not a superfluous task but a critical competency. In fact, industries are beginning to see prompt engineers as an essential part of AI project teams, guiding the models to produce business-ready results.

Final Thoughts: The Democratization of AI

What we’re witnessing today is the democratization of AI—an era where AI isn’t just for techies or mathematicians. People from all walks of life are embracing AI to write books, create art, develop recipes, and even enhance personal productivity. The absurdity lies not in these widespread applications but in the stubborn myths that prevent more people from exploring AI’s potential.

As AI continues to become more accessible and versatile, it’s time to lay these misconceptions to rest. Whether you’re a business owner looking to streamline operations or a teacher seeking new ways to engage students, AI has something to offer. Embrace it, experiment with it, and discover its real value—no technical degree is required.

About Me: 25+ year IT veteran combining data, AI, risk management, strategy, and education. 4x hackathon winner and social impact from data advocate. Currently working to jumpstart the AI workforce in the Philippines. Learn more about me here: //docligot.com