What is Artificial Intelligence?
Working on AI in the manufacturing sector, I’ve often encountered people who express an interest in the topic, but also feel it’s mysterious or even magical sometimes. Many say they find AI difficult to grasp, so today, I want to break it down and explain it in simple terms to make this complex subject more approachable. Let’s peel back the layers of mystery and see AI for what it really is—an exciting and accessible field.
What Are the Components of AI? At its core, AI consists of two fundamental components:
- Data
- Models
AI, at its most basic level, is about finding patterns in existing data. By identifying these patterns, AI can help us make useful insights and predictions. But what exactly is “data”? Data isn’t limited to numbers on a spreadsheet—it can take many forms, including text, images, and audio. For example, the fields of Natural Language Processing (NLP) and Computer Vision (CV) have expanded the reach of AI to handle more complex data types like text and images, turning them into numerical data that AI can work with. Think of it this way: words like “dog” and “cat” can be assigned numerical values based on their shared category—pets—which helps the AI recognize similarities between them.
Once you have your data, you need a model to process it. A model is essentially a mathematical structure used to find patterns in the data. A simple example would be linear regression, which you might remember from school: fitting a straight line through a scatter plot. The line helps identify a general relationship between two variables. In more complex AI systems, however, the relationships are rarely this simple. Data often can’t be represented by a straight line or a two-dimensional plot. In the real world, we deal with multi-dimensional, non-linear data, which is where more advanced models come in. These models help us navigate this complexity and identify patterns that might otherwise be hidden.
This is what makes AI so exciting — it enables us to find meaningful patterns in large, intricate datasets, often involving many dimensions and variables, that would be difficult for humans to analyze manually.
Managing Expectations of AI
While AI is powerful, it’s important to set realistic expectations. At its core, AI is about identifying patterns in data, but this process is only effective if the data is of high quality and relevant to the task at hand. A common reason for poor data quality is the absence of patterns within the data we use to train the model. Consequently, the model cannot extract anything meaningful. This happens rather often because not everything we do in real life can be captured in the form of data.
Another challenge is choosing the right model. Different problems require different types of models, and using the wrong one can lead to poor results. This trial-and-error process is a key part of AI development. Building an AI model often requires making intelligent assumptions, testing them, and refining the approach based on the results.
In summary, while AI has great potential, it’s not a magic solution. It’s a process of discovering patterns in data, and its success depends on factors like the quality of data, the choice of model, and the assumptions we make. By managing these expectations and recognizing that AI is a tool for discovering insights rather than a perfect answer, we can use it more effectively.
The Rapid Evolution of AI and the Importance of Fundamentals
The field of AI has advanced at an extraordinary pace, with new breakthroughs emerging all the time. One of the most popular and recent advancements is generative AI, which is capable of not only recognizing patterns in data but also recombining and reimagining them in meaningful ways based on a prompt. This gives the impression of “magic,” as we see in applications like ChatGPT, a model trained on vast amounts of internet data to generate human-like text responses. While generative AI may seem like a magical innovation, it’s important to remember that it still relies on the same fundamental building blocks of AI: data and models.
This foundational knowledge is essential when setting realistic goals and expectations for AI models, especially across different departments. Without understanding the basics, it’s easy to overestimate what AI can do or underestimate the complexities involved. For instance, a marketing team might expect an AI model to instantly predict customer behavior with high accuracy, while a product development team might expect a model to autonomously improve its design without proper training data. By understanding key principles—such as data requirements, model limitations, and the process of model training, teams can set achievable, measurable goals that align with the model’s true capabilities. This clarity leads to better collaboration, avoids miscommunication, and ensures that AI systems are both feasible and aligned with broader business objectives. A shared understanding also helps manage expectations around timeframes, resources, and potential challenges, which ultimately results in smoother execution and more successful outcomes.