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In recent years, Transformers have become one of the pillars of modern artificial intelligence. But what are they and why have they revolutionized the way machines "understand" and generate information?
📌 1. What are Transformers?
A Transformer is a neural network architecture designed to work with sequential data—such as text, audio, code, or even images—much more efficiently than traditional models like RNNs (Recurrent Neural Networks).
The key feature is that they don't process data one after the other, but all at once, using a mechanism called attention (attention mechanism).
🧠 2. The key innovation: attention
The heart of Transformers is the self-attention mechanism.
- This allows the model to evaluate the importance of each element in the sequence relative to all the others. For example, in a sentence, the model can understand that a distant word can give fundamental meaning to another.
- Rather than reading word by word, the Transformer looks at the entire sentence at the same time, assigning “weights” to the most relevant parts.
This parallel attention is what makes the technology so powerful and scalable.
🚀 3. Advantages over traditional models
Compared to RNNs or LSTMs, Transformers offer:
- Parallelization: They can process many parts of the sequence simultaneously, speeding up training.
- Understanding even distant dependencies: They can connect distant concepts in the sequence with greater precision.
- Scalability: They work well on large amounts of data and can be easily expanded for complex tasks.
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- The rising line represents RNNs, which process a sequence element by element.
- The flat line represents Transformers, which process all elements in parallel.
👉 Transformers are much faster to train because they don't have to wait for the previous step to continue processing. This is why Transformers have replaced RNNs in modern models.
🧩 4. How they are made internally
The basic architecture includes:
- Input/embedding: text or other information is transformed into numerical vectors.
- Positional encoding: since Transformers do not read sequentially, a code indicating the position of the elements is added.
- Multi-head self-attention: multiple "attention heads" work in parallel to capture any type of relationship between elements.
- Feed-forward networks and normalization: after attention, each element is further processed using neural networks classic.
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- An attention map in which each cell indicates how much a word "looks" at another word in the same sentence.
- Higher values indicate greater importance.
👉 Each word can focus on multiple parts of the sentence, even distant ones, capturing the global context. Here's how Transformers understand the meaning of a sentence.
📊 5. Why Transformers are important in modern AI
Transformers aren't just theory: they power some of the most advanced AI models available today:
- Generative language models, such as GPT, BERT, T5, and others, which understand and generate text in a sophisticated way.
- Machine translation, with the ability to capture complex meanings in very long sentences.
- Vision Transformer (ViT): applications also in computer vision, where Transformers analyze images.
- Multimodal models, which combine text, images, and audio for tasks More complex tasks, such as interpreting scenes or answering questions about multimedia content.
In practice, most virtual assistants, text and image generators, and natural language understanding systems are based on Transformers or their derivatives.
🔍 6. Limitations and Challenges
Despite their great successes, Transformers are not perfect:
- They require a lot of computing power and resources for training.
- Attention complexity grows rapidly with very long sequences, leading to scalability challenges.
These limitations are driving research towards more efficient variants or new ways of managing attention.
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The graph shows computational scalability:
- The computational cost of RNNs grows linearly.
- The cost of Transformers grows faster with very long sequences (due to attention).
👉 Transformers are powerful but have a high computational cost, especially with long sequences. This is why research is working on more efficient models.
Example Applications of Transformers
🔤 Example 1: Understanding the Meaning of a Sentence
Sentence:
“The bank closed because it was on the riverbank.”
Problem
The word bank can mean:
- financial institution
- riverbank
How a Transformer Works
The model:
- relates bank to river
- pays more attention to these related words
- understands that bank = river bank
👉 A traditional model would have more difficulty, especially if the relevant words are far apart in the sentence.
🌍 Example 2: More accurate machine translation
Italian sentence:
“The book you lent me yesterday is very interesting.”
Correct English translation:
“The book that you lent me yesterday is very interesting.”
What the Transformer does
- Connects book with is
- Ignores the distance between subject and verb
- Maintains the correct structure even in sentences long
👉 RNNs often lost information when sentences became complex.
💬 Example 3: Chatbots and Virtual Assistants
Conversation:
- User: “I missed the train to Milan.”
- User: “When does the next one leave?”
How the Transformer Works
- It understands that the next one refers to the train to Milan
- It maintains context across different sentences
- It responds consistently
👉 This is why modern chatbots seem to “understand” conversations.
🧑💻 Example 4: Code Generation
Request:
“Write a Python function that calculates the average of a list.”
Result
The Transformer:
- recognizes the language (Python)
- connects average with sum / number of elements
- generates syntactically correct and consistent code
👉 Self-attention helps maintain consistency between variables, functions, and structure.
🖼️ Example 5: Vision Transformer (ViT)
Scenario
An image Contains:
- a dog
- a meadow
- a person throwing a ball
How the Transformer Works
- It divides the image into "pieces" (patches)
- It analyzes the relationships between the pieces
- It understands that the ball is connected to the person and the dog
👉 It doesn't just look at neighboring pixels, but the entire scene as a whole.
🎵 Example 6: Audio and Speech Analysis
Case
A voice assistant listens:
"Turn on the kitchen light after dinner."
What it does The Transformer
- Connect turn on → command
- light → object
- after dinner → temporal information
👉 It can handle complex commands without getting confused about word order.
🧠 Why these examples work
In all these cases, the Transformer:
- looks at the whole context
- relates distant elements
- decides what's important through attention
This is what makes modern AI more "smart" and less rigid.
Conclusion
The Transformer is one of the most important architectures of modern AI: thanks to its ability to process entire sequences, capture complex relationships, and scale to enormous amounts of data, it has radically changed the way we build intelligent systems.
Understanding the role of Transformers means understanding the heart of contemporary artificial intelligence.
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