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In this article, we will explore in detail what artificial intelligence is, tracing its historical origins and analyzing the main developments that have led it to become a fundamental technology in our daily lives.
We will try to understand how, thanks to sophisticated algorithms and machine learning techniques, AI is able to simulate and, in some cases, emulate human reasoning, tackling complex tasks in an increasingly autonomous and precise way.
What is Artificial Intelligence?
Artificial Intelligence (AI) is a technology that allows computers to perform tasks that normally require human intelligence, such as recognizing images, understanding natural language, making decisions, and solving problems. Simply put, it is a way of teaching computers to "think" and learn from data.
Where does AI come from?
The idea of Artificial Intelligence has roots in the past, but it began to develop concretely in the 1950s with the first studies on algorithms and neural networks. In 1956, the Dartmouth conference marked the official beginning of the field of AI. In the 1980s and 1990s, with the progress of computers and the increase in computing power, the first expert systems and artificial neural networks were developed.
In the 2000s, thanks to the exponential growth of available data and the power of new processors (especially GPUs), AI made enormous progress, leading to the birth of deep learning, a method based on deep neural networks. This made it possible to develop AI capable of surpassing humans in specific tasks, such as image recognition or natural language understanding.
In 2018, OpenAI began developing advanced language models based on large neural networks. Among them, the Generative Pre-trained Transformer (GPT) family of models revolutionized the field of natural language processing. The first GPT model was followed by increasingly powerful versions: GPT-2 in 2019, GPT-3 in 2020, and finally ChatGPT, released in 2022, which made AI accessible to the general public thanks to its ability to understand and generate text in a fluent and natural way.
How does AI work?
Artificial Intelligence works by using algorithms and mathematical models that analyze huge amounts of data to learn to recognize patterns and make predictions. The key process is training, which occurs in several phases:
Data collection: The AI model needs a lot of data to learn. For example, a facial recognition system is trained with millions of images of faces.
Creating a model: The AI model is built using mathematical algorithms, often based on artificial neural networks, which mimic the functioning of the human brain.
Model training: This is where the concept of weights, biases and parameters comes into play:
Weights are numerical values that determine the strength of the connection between the neurons of the neural network. During training, they are updated to improve the model's predictions.
Example 1: If an image recognition AI model learns to distinguish between cats and dogs, the weights of some neural connections will be strengthened to better recognize the pointed ears of cats or the faces of dogs.
Example 2: In a speech recognition model, some weights will be adjusted to better identify the vowels and consonants of spoken words.
Example 3: In a recommendation system like Netflix, weights influence how much a movie is suggested to a user based on their previous preferences.
Biases are additional parameters that help the model make more accurate predictions by introducing adjustment independent of the weights.
Example: In an AI-based bank lending system, a poorly calibrated bias could cause the model to automatically favor customers with certain characteristics (e.g., a certain zip code or age), unfairly excluding other users. To avoid these problems, the bias must be controlled and corrected.
Parameters include both weights and biases and represent everything the model learns during training to reduce errors and improve its performance.
Example: In a chatbot like ChatGPT, parameters influence how the model generates responses. If the model has billions of parameters, it means that it has memorized a lot of linguistic patterns to respond consistently and fluidly.
Bias and Optimization: Bias is also a tendency for a model to favor certain outcomes over others, sometimes unintentionally. If the training data is not well-balanced, the model may be biased. To avoid this, optimization techniques are used to improve performance and ensure fairness.
Testing and improvement: After training, the model is tested on new data to verify its accuracy. If necessary, it is recalibrated and improved.
These are all the concepts related to the world of AI but if you are interested in learning more, I recommend you continue reading to better understand what neural networks are and how they actually use weights and biases.
What is a neural network?
A neural network is a computational model inspired by the human brain, designed to recognize patterns and learn from data. It is a type of artificial intelligence that attempts to emulate the way humans acquire knowledge, make decisions, and solve problems. Neural networks are composed of nodes (called artificial neurons) arranged in layers: an input layer, one or more hidden layers, and an output layer.
Here's how it works in broad terms:
- Input layer: Data (e.g., an image or a set of numbers) is fed into the network through the first layer of neurons.
- Hidden Layers: The middle layers process data through connections that mimic neurons in the brain. Each neuron in a hidden layer is connected to those in the layers above and below it, and these connections have weights that determine the importance of each input.
- Output layer: After processing the data, the network provides an answer, which can be a label, a prediction, or a classification.
Neural networks learn from data through a process called training, during which the weights of the connections are adjusted to reduce the error in the prediction. This process is iterative and occurs through optimization algorithms, such as the backpropagation algorithm.
Neural networks are fundamental to many modern applications, such as speech recognition, machine translation, computer vision, and many other areas of artificial intelligence.
Let's take a closer look at how weights work and why they are important
Technically, a weight in a neural network is a numerical value that represents the importance of a connection between two neurons. Each connection between neurons has a weight that determines how much the input signal will affect the output of the next neuron.
Mathematically, a weight is a multiplicative coefficient in a linear equation. When a neuron receives an input, that input is multiplied by the connection's weight before being added to other inputs and then passed through an activation function.
🔹 Simplified mathematical example:
If a neuron receives an input of 2 and the weight associated with the connection is 0.5, the value transmitted will be:
If instead the weight were 1.5, the transmitted value would become:
By changing the weights during training, the neural network learns to better recognize patterns in the data.
All clear? Not really 😎1️⃣ Data collection
Immaginiamo di voler addestrare un'AI a riconoscere le cifre scritte a mano (0-9). Per farlo, raccogliamo un dataset con migliaia di immagini di numeri scritti da persone diverse.
2️⃣ Creating the neural network
Let's build a neural network with three layers:
- Input layer (takes image pixels as input)
- Hidden layer (where the processing takes place)
- Output layer (which generates the prediction: which number is written in the image)
3️⃣ Initialization of weights and biases
At first, the weights of the connections between neurons are assigned random values, and biases are small numbers that help the AI correct its predictions.
4️⃣ Model training
We show the network an image of a number (say, 5). The pixels in the image are transformed into numbers and sent through the neural network, where they are multiplied by the weights and added to the biases.
🔹 Initial case (with random weights):
The neural network might say: "This is a 3" ❌ (mistake!).
🔹 Weight correction with backpropagation:
The model compares the prediction with the correct answer (5) and calculates the error. It then updates the weights with gradient descent to make the prediction more accurate next time.
5️⃣ Repeat the process
This cycle is repeated on thousands of images. Each time, the weights are adjusted to reduce the error. After many iterations, the network becomes more and more accurate.
6️⃣ Model Testing
Now let's test the network with never-before-seen images. If everything works, the network will be able to recognize numbers with high precision.
Let's make a comparison
🔹 The weights indicate how "easy" or "difficult" it is to follow a given path. If a weight is high, that path will have more influence on the final decision; if it is low, the path will be less relevant. During training, the system changes the weights to understand which paths lead to the correct answer more often.
🔹 Biases, on the other hand, are like road signs that can slightly divert your path. If a bias is too strong, it might cause the system to take the wrong shortcut or prefer one path over another, even when it shouldn't.
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