by First of all, I’d like to welcome whoever reads this to the articles I’ll be writing!
I’m excited to be here—and nervous in a way that words can’t quite capture—but I truly hope the data and insights I’ll be sharing, as often as I can, will prove useful.
In this article, I’ll cover the basics of what happens behind the scenes of a functioning Artificial Intelligence (AI), without diving too deep into the technical rabbit hole. After all, we do love a bit of mystery.
What is Artificial Intelligence at its core?
Artificial Intelligence, as the name suggests, refers to computer systems designed to simulate human intelligence. These systems mimic how our brain’s neural networks operate, learning patterns from data and experiences, interpreting natural language, and solving complex tasks that typically require human cognition.
What sets Artificial Intelligence apart from traditional computing is its ability to adapt to new data and improve its performance over time.
It’s fascinating to think that one of the foundational moments in Artificial Intelligence’s history dates all the way back to 1950, when Alan Turing proposed the famous Turing Test in his paper Computing Machinery and Intelligence. This test aimed to determine whether a machine could exhibit intelligent behavior indistinguishable from a human’s. From that point, AI journeyed from speculative fiction to real-world innovation—and eventually to the powerful models we use today.
What makes an Artificial Intelligence ‘intelligent’?
Just like neurons in the human brain transfer information between each other, AI systems process information through interconnected layers of artificial “neurons,” forming what we call Artificial Neural Networks (ANNs).
But these networks alone don’t explain everything. To understand how AI gets smarter, we need to talk about how it “learns”—and for that, we’re going to introduce two essential concepts:
- Deep learning
- Machine Learning
Deep Learning
As technology advances, enabling more powerful hardware, AI has had to evolve to tackle increasingly complex tasks. That’s where Deep Learning comes in—a method inspired by how humans learn, particularly when processing images, symbols, and abstract concepts.
Deep Learning involves deeper, more layered neural networks capable of detecting intricate patterns and subtle relationships in massive datasets. It’s especially prevalent in tasks like facial recognition, autonomous driving, and even artistic style transfer.
Machine Learning
Are they similar or different?
The truth is: they’re related, but not the same.
Machine Learning is a subfield of Artificial Intelligence, and it’s what allows models to learn from data without being explicitly programmed for every outcome. In other words, instead of hardcoding rules, we let the machine identify patterns and optimize its behavior based on examples.
The process typically starts with a curated dataset tailored to the task. Then, developers select an appropriate algorithm, supply the training data, and allow the model to adjust its internal parameters to learn. The goal? The model should generalize from what it has seen and make accurate predictions on new, unseen data.
How does an Artificial Intelligence learn?
At its core, AI is statistics. It doesn’t generate brand-new ideas from thin air—it operates by identifying patterns in data on which it has been trained. No matter how advanced a model is, it still relies on the information it’s been given to make predictions or decisions.
Every AI model operates on a basic principle: input → processing → output.
Straightforward, right?
But here’s the fun part: depending on the kind of data we give it—images, text, sound—each AI model has its own way of processing that input, even if the underlying math and data propagation are conceptually similar. For example, how does an image recognition model process an image compared to how a language model processes text or an audio model processes sound?
Then we have multimodal models, like ChatGPT itself, which can process a combination of text, image, and audio inputs to generate meaningful and relevant outputs for the user.
So, how does that work? Well, I used to think it was magic, too. And, in a way, it is kind of magical to imagine someone programming a machine to think.
But let’s break it down.
🖼️ Image
Let’s start with the visual.
A digital image is, from a computer point of view, essentially a matrix of pixel values ranging from 0 to 255. From a computer’s perspective, this image is just a grid of numerical data. Through a field called Computer Vision, the AI analyzes this matrix, detecting shapes, edges, and textures.
If you were to reverse-engineer the processed data, say, from a TensorFlow matrix, you’d get back an approximation of the original image. The model doesn’t “see” the image the way we do, but it recognizes structures and relationships within that grid of numbers.
📚 Text
Secondly, have you ever wondered how models like ChatGPT or DeepSeek are able to understand the text input they receive?
This is thanks to a field called Natural Language Processing (NLP). The process starts with something called tokenization — the model breaks down the sentence into units such as words, subwords, or even characters.
These tokens are then embedded into a numerical matrix, where each token is represented as a vector in a multi-dimensional space. In this space, tokens with similar meanings or usage patterns appear closer together, allowing the model to capture subtle relationships based on context.
🎧 Audio
Now, last but not least, let’s talk about audio as an input. Ever thought about how an AI can not only mimic but also understand speech?
This is a bit more complex. Unlike images, audio isn’t naturally something a computer can see. But if you think in terms of physics, sound is simply air pressure waves that vary over time — this variation is what we perceive as frequency and amplitude.
To make this data readable for machines, the first step is to convert the audio into a waveform — a graph showing how the amplitude of the sound changes over time.
Then, this waveform is transformed into a spectrogram, which is essentially an image that represents how energy (or intensity) is distributed across different frequencies over time. Each pixel in this spectrogram corresponds to a specific frequency at a specific time, making it possible for Artificial Intelligence (AI) models — especially those trained to work with visual inputs like convolutional neural networks (CNNs) — to process sound in a similar way they process images.
Finally, the spectrogram is passed through the model, and ta-da! — we get an output, whether it’s text, classification, or even voice commands, as accurately as the model was trained to deliver.
And how, pray tell, does an AI process all this data turned math, you ask? Well, it’s simple (well, not so simple for us mere mortals, but alas, we’re not the ones doing the math behind it all).
It all comes down to Computational Math—a mix of probability, algebra, and some pretty clever optimization tricks.
Behind the set: Computational Math
An Artificial Intelligence is built upon several factors that work together to compute a mathematical result that closely aligns with the goal it was trained for. Yes, it’s probabilistics. To understand how, let’s break down each of these components.
Node: A node is the computational counterpart of a human neuron. It receives an input, passes it through a weighted sum plus a bias, then applies an activation function, and sends the result to the next layer of nodes.
Think of a node as a small mathematical function that converts data into an internal decision.Weights: Weights determine how much influence an input has on the output of a node. They’re crucial in training a model, and here’s where it gets a bit magical: the AI adjusts the weights by itself.
If a weight is high, it means the input is more important; if it’s low, the input is less relevant.Bias: The bias is a value added to the weighted sum of the inputs. Its job is to allow the model to make adjustments, even when the inputs are zero.
Without the bias, the model would be stuck at the origin point (0,0), so it essentially helps to “shift” the activation function.Activation Function: This is the mathematical function applied to the result of each node, introducing non-linearity. This non-linearity is crucial, especially in Convolutional Neural Networks (CNNs).
Why?
If each node were just the sum of the weights and bias, we’d end up with something like:
Wx + bwhich is a linear function. And while linear functions are great for simple tasks, for complex problems, the Artificial Intelligence (AI) needs to learn more intricate patterns, like curves on a graph, that linear functions just can’t represent.
Learning Rate: Now, how does the Artificial Intelligence (AI) actually adjust the weights to improve its predictions?
This is where the learning rate comes in.
The learning rate determines the size of the steps the model takes when adjusting its weights.
If the learning rate is too high, the model may “overshoot” the optimal solution, jumping over the best possible weights. If it’s too low, the model may take tiny steps and take forever to converge, or get stuck in suboptimal solutions.
So, finding the right learning rate is key for effective training.
Then, when we bring all of these pieces together, we get something that looks more like this:
Of course, for all of that to validate an accurate output, it’s not some supernatural occurrence; the model had to be trained beforehand for a chosen task, otherwise the user would be receiving some mad babbling that would make no sense.
You can think of this next part as a human learning a new language – you can only speak it once you’ve learned it, correct?
So here we go.
Validation and Training
Any model has to be trained to validate given inputs. The model is given a dataset, and it passes through a series of steps, calculating its weights until they are optimal for the task at hand.
A model can have many nodes that are separated in layers, and each layer adds complexity to the output, supposedly increasing accuracy if done right.
Let’s dive into how it works.
Once the input is in, it passes through all layers of nodes, in a process called a forward pass, which basically means that it’s making all the necessary calculations until it reaches the output layer.
For the recent models, after calculating the mistakes during the forward pass step, backpropagation is used. This technique calculates the gradient of the loss function with respect to each weight, helping the model learn how to minimize errors for the next forward pass.
Additionally, gradient descent is an optimization algorithm used during backpropagation to adjust the weights and bias based on the error. It helps the model decide which direction to adjust the parameters to minimize mistakes.
It looks something like this, though there’s more, but we won’t get too deep into it in this post:
class Classifier(nn.Module):
def __init__(self, hidden_layer, input_layer):
super(Classifier, self).__init__()
self.hidden = nn.Linear(input_layer, hidden_layer)
self.output = nn.Linear(hidden_layer, 102)
self.relu = nn.ReLU()
self.dropout = nn.Dropout(p=0.2)
self.logsoftmax = nn.LogSoftmax(dim=1)
def forward(self, x):
x = self.dropout(x)
x = self.hidden(x)
x = self.relu(x)
x = self.output(x)
x = self.logsoftmax(x)
return xAfter all these steps are taken, the model should be ready to face whatever challenges it was designed for.
But can training go wrong somehow?
Trained Artificial Intelligence
Oh boy, how it can.
One thing that AI engineers have to be very aware of is how to correctly use their activation functions for their models, because not every model needs to add complexity to its output, but at the same time, what if you train it too little?
Overfitting vs Underfitting
Imagine you studied way too much for an exam, and when you finally begin to read the questions, you keep overthinking — “Well, the questions are simple, right? But what if this is a prank by the professor? What if I am wrong?“
That’s overfitting for you: When you train an AI with higher complexity and give it a simple task, it will get stuck, since it’s optimized for the data it saw during training, so it “notices” noise and irrelevant patterns. When applied to another dataset of the same type, it cannot make generalized predictions.
From a more technical viewpoint, you can say that the weights and the bias were so well-adjusted that they fit the training set just right, but that also takes away their ability to ‘think’ in real datasets.
Then there’s the situation where you study too little—this is underfitting. The AI hasn’t been trained enough to understand the patterns given to it, so it predicts wrong most of the time.
Technically, the weights and the bias are wrongly fitted to give any correct answers.
Lastly, when all of the above are done, we arrive at the inference process, that is basically an Artificial Intelligence in action – if well trained, it will output accurate data based on the model chosen, and alas, making our lives easier.
Conclusion
This is a simplified overview of how Artificial Intelligence (AI) works and how it learns, processes data, and improves over time. While the details can get very complex, the basic idea remains the same: AI models are constantly learning from the data they are given and are designed to make predictions or decisions based on that learning, so if trained incorrectly, they will most likely have lower accuracy.
In this context, they are great tools to use in a multitude of areas, including programming Yet still, do remember that they are not infallible, and more often than not, can predict a wrong answer if not given enough context.
Hope this article has helped to improve your understanding of the ‘behind the scenes’ of any given model!
References
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