it is with the language models discussed in this book. They promise to be a powerful tool for enhancing communication and access to knowledge, but they also raise important questions about safety, ethics, and responsibility.
This book invites you on a journey to explore these complex and multifaceted issues together.
It is important to understand that technology itself is neither good nor bad. It all depends on how we use it.
That is why it is so crucial to approach its application consciously, knowing its capabilities and limitations.
In this book, I want to share with you my vision of how language models can change our world, the opportunities they open up for us, and the challenges they pose.
Remember the feeling when you first sat behind the wheel of a car or when you first saw color television? Those moments were pivotal; they opened new horizons and possibilities for us.
In the same way, language models offer us a new perspective on the world of communication. Today, they may seem innovative, but very soon, they will become the standard to which we will all become accustomed.
I am confident that together, we can find a balance between the opportunities and risks, to use this technology for the benefit of all humanity.
Chapter 1: The Fundamentals of Language Models.
We have slightly lifted the veil over the greatness of human language and saw that a word is not just a set of letters in a single palette of sounds.
It is a powerful source of information, a tool through which we can convey our thoughts, feelings, and knowledge.
A word is the key to understanding the world around us and ourselves; when words form sentences, they connect us with other people, allowing us to convey our ideas to them and understand their perception of the world.
In every word we say, there is immense potential. Words shape our world, set the tone for our relationships, and even determine our business success.
With words, we share ideas, inspire teams, and close million-dollar contracts. Words are our tool for influencing the world around us.
Now imagine that the same power inherent in words is enhanced by the latest technological achievements. What if machines could not only listen but truly «understand» us?
What if artificial intelligence could process and analyze our language, making our words even more powerful?
Meet the new era of human-machine interaction – the era of language models. These models are not just code or algorithms.
They are complex systems, trained on billions of words and phrases, capable of understanding human language, its nuances, and context.
Language models are a real breakthrough in the field of artificial intelligence. Remember how you learned a language: starting from simple words to complex sentences and texts.
Imagine that you had billions of books and documents to study and only a few minutes for it. This is how language models work.
Based on machine learning methods, these models analyze vast volumes of text.
They «see» patterns, learn sentence structures, and become capable of creating new texts based on this learning.
In simple terms, a language model predicts the probability of the next word based on the previous context. Take, for example:
«In a distant galaxy…". This is our context. We feed it into the language model, and it predicts the next word. In this case, it could be «lives», «is located», or «evolves».
Why is this so important? Recall the Turing test. This test was created to determine a machine’s ability for human-like thinking.
In it, a person communicates with a machine and another human, and their task is to determine which of them is the machine.
If the machine passes this test, it means that it can mimic human thinking so well that a person cannot distinguish it from another human.
This is the essence of language modeling. If we reach a high level in this area, machines can become «conscious» in a certain sense.
In our everyday world, language models are already actively used. For example, when you write a message on your smartphone, and it suggests the next word to you. This is the work of a language model.
For instance, you write «On the horizon appeared…", and the model might suggest «castle», «ship», or «rainbow» as the next word.
How can this be useful for you? Let’s consider a simple example. Suppose you are a company owner and want to create an advertising text for a new product.
With a language model, you can get several text options in seconds! This saves time and resources.
The architecture of the language model determines how the model processes and generates text based on the data provided to it.
In the context of machine learning and artificial intelligence, architecture is the foundation on which the model is built and defines its structure, functioning, and learning ability.
Let’s consider the main components:
Embedding Layer: This layer transforms words or characters into numerical vectors. These vectors are dense representations of words that the model can easily process.
Imagine you have a book with pictures of different animals: a cat, a dog, a lion, and so on. Now, instead of showing the entire picture, you want to give a short numerical description of each animal.
The Embedding Layer does something similar, but with words. When you tell it the word «cat,» it can transform it into a set of numbers, like [0.2, 0.5, 0.7].
This set of numbers (or vector) now represents the word «cat» for the computer. Thus, instead of working with letters and words, the model works with these numerical representations, making its processing much faster and more efficient.
For example, the word «dog» might be [0.3, 0.6, 0.1], and «lion» – [0.9, 0.4, 0.8]. Each word gets its unique numerical «portrait,» which helps the model understand and process the text.
Recurrent Layers: They are used for processing sequences, such as sentences or paragraphs.
Recurrent Neural Networks (RNNs) and their variations, like LSTM (Long Short-Term Memory) and GRU (Gated Recurrent Units), are popular choices for these layers, as they can «remember» information from previous parts of the sequence.
Imagine reading a book and every time you turn the page, you forget what happened before. It would be hard to understand the story, wouldn’t it?
But in real life, when you read a book, you remember the events of previous pages and use this information to understand the current page.
RNNs work in a similar way. When they process words in a sentence or paragraph, they «remember» previous words and use this information to understand the current word.
For example, in the sentence «I love my dog because she…» the word «she» refers to «dog,» and the RNN «remembers» this.
Variations of RNNs, like LSTM and GRU, are designed to «remember» information even better and for longer periods of time.
Transformers: This is a modern architecture that uses attention mechanisms to process information.
Models based on transformers, such as GPT (Generative Pre-trained Transformer) and BERT (Bidirectional Encoder Representations from Transformers), have shown outstanding results in language modeling tasks.
We will talk about these two models in more detail in the following chapters, compare their principles of operation, and try to give them our assessment.
Output Layer: Usually, this is a fully connected layer that transforms the model’s hidden states into probabilities