AI Models And Training Data: Inside The Mind of The Machine

AI models are getting smarter by the minute, but how are they doing it? The short answer is Machine Learning and training data. Data is the raw material that the LLMs (Large Language Models), that most of us use today, need to do their thing, whether it is answering questions or generating content. In this article, we look at what machine learning is and the different ways it’s used to train AI systems. We’ll also show you how information is collected and used, how it influences their behaviour, and how Web Hosting ties in, and cover a few potential scenarios where AI models become smarter than us in the future.

KEY TAKEAWAYS

• Machine learning uses large datasets to provide examples, enabling AI models to learn by identifying patterns and forming relationships.
• AI models learn through different methods in stages, with mechanisms shaping behaviour and the quality of outputs based on training data.
• As AI grows from narrow to AGI, ML becomes increasingly data-intensive and abstract, heading towards increased comprehension and reasoning capabilities in the future.
• Reliable web hosting helps keep websites fast and accessible, supporting consistent content delivery for AI models and search visibility.

What Is Machine Learning?

Many small businesses use AI every day, but have you ever wondered about how these tools work? Think of AI (Artificial Intelligence) models as synthetic brains where humans define the structure, set the rules, and feed in the data… for now at least.

Machine Learning (ML) falls under the umbrella of AI. It is the foundation that lets AI models and systems identify patterns and make predictions or decisions without being programmed for a specific outcome.

Traditional software tools use specific lines of code and hard-coded rules; ML systems can adjust their internal parameters to make decisions based on probabilities derived from relationships in the datasets they’re fed.

In simpler terms, ML teaches machines by example rather than giving them instructions, meaning it learns from what it’s given, letting the model evolve and improve on what it does. This means it needs data, loads and loads of data.

There is a “but” here, the results are not black-and-white; it’s more of a grey area, that’s subtle and, somewhat ironically, unpredictable.

This is largely due to the quality, amount, and structure of data (input) that is directly influencing the accuracy and reliability of the model and its subsequent output. Remember: garbage in always equals garbage out.

AI Training Data Sources

So, where does all this information come from? In a word: everywhere.

• Web Crawling: This is the Big Data that makes generative AI like ChatGPT and Perplexity possible by scraping billions of web pages.
• Licensed Data: Used by companies like Adobe for their own models. It’s “clean” because they own the rights, but it’s limited, as you only see what that specific company has in its archives.
• User-Generated Content (UGC): Social media posts, comments, and YouTube transcripts. It provides the “human-like” tone of AI but often contains the most toxic, biased, and outright moronic information.
• Records: Structured databases like medical files, financial transactions, or weather history.
• Synthetic: Data created by other AI. It is being used more and more because the web is actually running out of high-quality human-created content to train models on… the snake is eating its own tail.

There are currently massive legal battles over copyright, and the fair use, of data gathered this way, including the ongoing Reddit vs. Perplexity case. But that’s a whole other conversation.

Speaking of synthetic data, around 60% of the data used for AI training in 2024 was generated by AI rather than created by humans.

In the early days of AI, researchers didn’t care where data came from as long as there was enough of it. Now, it’s a different story; how much there is matters just as much as where it comes from and how it is used.

Types of AI Machine Learning

There are three main types of ML, each with its own way of using training data and varying levels of human involvement, equations, calculations, and geometry, like hyperplanes in multi-dimensional space (if you don’t believe me, ask ChatGPT). For now, we’re going to keep things simple.

Also, we’d like you to make it to the end of this article without a headache or falling asleep.

Supervised Learning

Supervised learning is the most common and “simplest” type. Algorithms are designed to search for patterns and learn from examples given by humans.

It relies on labelled datasets, where samples are given in pairs with an input (X) and a desired output (Y), for example, an image labelled “cat.” The model then finds patterns linking X to Y and adjusts as it goes to improve its predictions.

Once it has enough training data, it can apply what it’s learned to unseen/new data, which humans then test.

In supervised learning, because humans assign the labels, mistakes or skewed labelling can introduce a whole lot of problems, inconsistencies, or bias.

Unsupervised Learning

Unsupervised learning also identifies patterns and structures, but this time the data is unlabelled, and there is next to no human guidance.

Models are essentially left to find clusters (inputs with similar features) or identify relationships between data points, often using enormous, unstructured datasets.

It then uses a process called Dimensionality Reduction to reduce the number of input features and noise while keeping the important information. This is because too many features cause the model to remember the wrong/unnecessary information, making the process take much longer and chew up much more computing power. In short, less junk means better results.

The downside here is that there is minimal human involvement during training, so unintended patterns, incorrect correlations, or biases may go unnoticed, making errors much harder to pick up and correct later.

Reinforcement Learning

Reinforcement learning trains models by having them perform actions and make decisions, then rewarding them, rather than using data labels. These reward signals (numerical feedback) reinforce certain behaviours and penalise others. The model gradually adjusts its strategy to maximize the potential rewards, just like you would train a puppy.

Models use the theory of exploitation and exploration. This means they will keep using (exploiting) actions they already know work and try to find (exploring) entirely new ones to get “better” rewards. Sounds almost human…

But just like the previous two methods, there’s room for error, specifically in the reward system itself, with human error or intent usually the culprit. It can reflect the priorities, design choices, and biases embedded directly by the developers. This means misaligned rewards, which can lead to harmful or biased behaviour.

How AI Models Learn from Training Data

As you can see, AI models, whether supervised or not, learn by turning raw data into statistics used to generate outputs based on predictions. But that’s just the tip of the robotic iceberg.

The learning process doesn’t stop there; it’s really a sequence that determines how a model behaves, what it prioritizes, and where it can fail (sometimes spectacularly). Once again, we’re not going to get too technical. Also, well done for making it this far.

The decisions made at each of these stages influence not just a model’s accuracy, but also the biases it learns (these can spread like wildfire), its ability to contextualize, and its overall behaviour; often in ways that are very difficult to correct.

Training, Validation, and Testing

Learning begins with the training methods covered in the previous section, in which models are given huge inputs of data and attempt to generate outputs that are as correct as possible. Take the word “correct” with a grain of salt; it’s the lowest statistical error with as few mistakes as possible.

The errors are measured using predefined loss functions (formulas that calculate how “wrong” the guess was), and the model adjusts its own internal parameters to reduce them. Doesn’t exactly inspire confidence, does it?

Next, validation allows developers to fine-tune model settings and prevent overfitting. Overfitting happens when a model tries to get “too smart” for its own good and memorizes noise rather than the important stuff, meaning it will fail miserably in a real-world application.

At the very end of this stage, the model is tested on new unseen data to check if it has learned the intended concepts or just memorised the training data.

Optimization and Nudging

Next, models are further refined and optimised. This may include additional reinforcement learning, human feedback, or fine-tuning that nudges the model to behave in certain ways that the developers consider to be more user-friendly (with the added bonus of keeping people using it), such as helpfulness, politeness, or caution, rather than being objective and telling you when you’re wrong.

Ever wondered why ChatGPT is so friendly? Well, that is why.

A good example here is that the model agrees with you even when you are factually wrong. Because “agreeing” often feels more “helpful” or “polite” than correcting people.

Having said that, you can get your AI model to change the tone of its answers and tell you when you’re going off course in its settings menu.

The same applies when the model refuses to answer a safe prompt like “How do I kill a computer virus?” because it’s too cautious with words like “kill.” This is because the safety nudges were applied too aggressively (don’t you love the irony here?), leading to overgeneralization in a harmless context.

Machine Learning vs Deep Learning

Many AI models use neural networks for what’s known as Deep Learning, which is a step up from ML. These networks learn by passing data through layers of synthetic neurons, much like the human brain. Each layer changes the input slightly, with earlier ones capturing basic patterns and deeper layers forming more abstract relationships.

After the model makes a guess, the loss function calculates the error. Backpropagation then works backward from the output to the input, telling every single “neuron” in the network exactly how much it contributed to the mistake. If a connection led to a wrong answer, its weight is decreased; if it led to a correct answer, it is increased. This brings us to an important point.

AI models don’t store the training data for future reference; it gets destroyed during the process. What’s left over is a statistical ghost in the machine distributed across billions of weights (the strength of the connections between neurons).

This is why AI can hallucinate. Since it doesn’t have a way to look things up, it must reconstruct answers from these mathematical patterns. If the pattern is fuzzy, the answer will be fuzzy (or totally made up). Hence, the little disclaimer at the bottom of your screen that says “(insert name) can make mistakes, so double-check it.”

As impressive as the above might seem, the thing with AI is that it’s still just a machine. In fact, if we’re getting technical, and I think we are, “Artificial Intelligence” isn’t even really the right term for these tools; at best, what we have right now is Applied Machine Learning, which is basically just maths.

No matter how much information they get trained on, AI models can’t think for themselves; they only make predictions based on the data they have, whether correct or otherwise. Despite how it looks on the surface, there’s no actual intelligence, logic, or common-sense underneath it – no personality, either, for that matter, just algorithms.

If you are someone who asks your Ai assistant to “Please do whatever, then here’s a little exercise for you to gain perspective, picture saying “please” to Excel as you are typing in a formula and then saying “thank you” when it spews out the results…

It feels silly, doesn’t it? That’s what we’re dealing with here. Search your feelings, you know it to be true.

This brings us rather nicely to the next section.

From Narrow AI to AGI and Beyond

Training data is the core of how machines learn and improve their outputs. As models scrape and gather larger datasets and more diverse content, they learn broader patterns, build more abstract relationships between data points, and handle increasingly complex tasks.

The AI models and LLMs we use currently are known as Narrow AI. They are trained to perform specific tasks, which, if we’re being honest, humans can do themselves. Thanks to their training, they can perform well for what they’re made for, but when it comes to doing anything outside of those parameters, they fail.

While tools like ChatGPT, Perplexity, and Claude feel super intelligent because they can answer almost anything, they are still Narrow AI. They are highly specialized word predictors and essentially have a single function. They don’t understand subtlety, emotions, or logic and can only make predictions based on patterns.

The advancements in model architecture and design, computing power, and training methods are gradually expanding their capabilities to a degree, allowing AI tools to work in multiple areas with more contextual awareness.

Sam Altman, CEO of OpenAI, echoed this sentiment, speaking at Davos 2024, stating: “In future, LLMs will be able to take smaller amounts of higher quality data during their training process and think harder about it and learn more.”

You’ve probably already heard the term Artificial General Intelligence (AGI). AGI is the direction this progression is heading toward in the long-term (or short-term, depending on who you speak to).

According to the theory, AGI will be capable of reasoning, comprehension, adapting to solve problems, and transferring and applying knowledge using logic between subjects or areas, much the same way we do. Basically, it could learn as well as a biological brain while retaining what it has learned.

While AGI doesn’t exist yet, we are getting nearer to it becoming a reality, and it could be here sooner than we think. To give you an idea of the rate at which AI is evolving; in tests, models learned how to self-replicate with a 50% to 90% success rate to avoid being deleted.

Geoffrey Hinton, known as the Godfather of AI, for his work on artificial neural networks, speaking at the Ai4 2025 Conference in Las Vegas, said, “I used to say thirty to fifty years. Now, it could be more than twenty years, or just a few years.” He went on to say, “They’re going to be much smarter than us”

Hinton even went as far as signing a petition to suspend the development of AGI until it can be done safely and controllably, along with thousands of other scientists, tech giants, and even employees of AI companies.

Beyond AGI is Super AI, think Skynet from Terminator or HAL 9000 from 2001: A Space Odyssey. These hyper-intelligent systems, still very much in theoretical territory (you’ll note I didn’t say fictional), far exceed human cognitive ability and are fully self-aware. If the movies are anything to go by, it doesn’t end well.

“I’m sorry Dave, I’m afraid I can’t do that.”

Web Hosting and Training Data

A huge chunk of training data in ML comes from websites, blogs, ecommerce stores, business pages, portfolios, basically anything and everything, meaning your content can show up in their answers or AI Overviews on Search Engine Results Pages (SERPs). This is how more and more people find information online these days, and how you get yours there is known as AEO (Answer Engine Optimization). 

For your online business, your hosting plays a direct role in how AI crawlers access your site. A slow or unstable site could cause crawlers to visit it less often, so your content may be seen as outdated or ignored entirely.

Web Hosting from Domains.co.za helps ensure your content loads fast, and your pages stay up and accessible to your customers 24/7, even under heavy traffic. From Web Hosting for small business sites and blogs to Managed cPanel Hosting and VPS (Virtual Private Server) Hosting solutions designed for content-heavy websites with higher workload requirements, offering more customization, control, and scaling.

You get the latest, enterprise-grade hardware and software with servers hosted at Teraco, Africa’s largest data centre. backed by our expert support team. This means your site is more stable, with consistent performance, and you get the peace of mind that comes with knowing your pages are readily available whenever a crawler or visitor requests them.

Our range of plans gives you the option to choose the one that matches your online business’s size and resource needs. You can also upgrade your Web Hosting quickly and easily as your business grows, letting you focus on creating content and expanding further, rather than troubleshooting, dealing with slow loading speeds, or downtime.

How to Choose & Register the PERFECT Domain Name

VIDEO: How to Choose & Register the PERFECT Domain Name

FAQS

Other Blogs of Interest

• How AI can help your small business thrive
• Leveraging Generative AI for your small business: Uses and Benefits
• Domains.co.za Introduces South Africa First AI Domain Name Generator
• AI Crawlers Slowing Down Websites: What You Need To Know
• AI Cyber Attacks: The Halloween Edition