ChatGPT 4. Guide Language Models of the Future

They are trained on such a vast dataset that they can account for nuances, idioms, and language specifics.

Language models are a tool that may soon become an integral part of your business process. They offer new possibilities, making text processing and creation more efficient, faster, and innovative.

The first steps in the field of language models were taken decades ago. If we could go back in time to the beginnings of the computer era, we would see that the initial language systems were primitive and limited.

They were based on simple rules and templates. But, as in many areas, progress did not stop. In the 1980s, statistical language models were developed.

They used probabilistic approaches to predict the next word in a sequence. This was a big step forward, but still far from perfect.

With the advent of the 2000s, thanks to increased computing power and the availability of large volumes of data, the era of deep learning began.

It was during this period that we began to see real breakthroughs in the field of language models. Networks, such as LSTM (Long Short-Term Memory) and transformers, implemented new approaches to language processing.

A significant milestone was the creation of the BERT model in 2018 by Google. This model was capable of understanding the context of a word in a sentence, which was considered a revolutionary achievement.

But an even bigger resonance was caused by the appearance of GPT models, especially GPT-3 and GPT-4, from the American startup OpenAI.

With its ability to generate quality texts based on a given context, it represented a real revolution in the field of language models.

Each stage in the history of language models carried its own lessons and challenges. But the general trend was clear: from simple rules to complex algorithms, from limited models to systems capable of «thinking» and «creating».

Looking back on this journey, we can only marvel at how far we have come. But, as in any business, the key to success lies in understanding the past to better see the future and understand how they work.

When we, as humans, learn something new, we rely on our experience, knowledge, and understanding of the world. And what if language models learn in a similar way, but on a much larger and accelerated scale?

Let’s imagine that every book, article, or blog you have ever read is just a small part of what a language model is trained on.

They «read» millions and billions of lines of text, trying to understand the structure, grammar, stylistics, and even nuances such as irony or metaphors.

At the heart of this process lies a neural network. This is an architecture inspired by the structure of the human brain.

Neural networks consist of layers, each of which processes information and passes it to the next layer, refining and improving the result.

Transformers, which I mentioned earlier, are a special type of neural networks. They can process different parts of the text simultaneously, allowing them to understand the context and relationships between words.

Think of language models as musicians playing instruments. The texts are the notes, and the algorithms and mathematics are the instruments.

With each new «composition,» the model becomes more skilled in its «performance.»

The work of language models is based on analyzing and understanding language in its deepest details. They literally «immerse» themselves in the text to give us outputs that can sometimes surprise even the most experienced linguists.

The training of models occurs according to certain principles. Here are some, and you will see the similarity with the principles of human learning:

Supervised Learning: This is the primary training method for most language models. Models are trained on examples where they are given both input data (text) and corresponding output data.

The goal here is to learn to make predictions or generate text based on the given examples. Imagine that you are a teacher in a school, and you have a student named Vasya.

You want to teach Vasya to solve math problems correctly. For this, you provide him with examples of problems (input data) and show the correct solutions (output data).

Vasya learns from these examples and, over time, begins to solve similar problems independently, based on his knowledge.

Transfer Learning: After the model has been pre-trained on a large volume of data, it can be further trained (or «fine-tuned») on specialized data for specific tasks. This allows the model to apply general knowledge to specific scenarios.

Fine-Tuning Models: This is when a language model is adjusted or «tuned» for a specific task.

This is often used after transfer learning so that the model can better handle the unique aspects of a specific task.

For example, if you bought a new piano and you already know how to play classical pieces, but you decide to join a jazz band.

Although you already have basic piano skills, jazz requires a special style and technique. To adapt to this new style, you start taking additional lessons and practice exclusively in jazz.

This process of adapting your skills to a new style is akin to «fine-tuning» in the world of machine learning.

In the same way, if we have a language model trained on a large volume of data, and we want it to solve a specific task (for example, analyzing restaurant reviews), we can «retrain» or «tune» this model on specialized review data so that it performs better in this specific task.

Reinforcement Learning: In this method, the model is «rewarded» or «punished» based on the quality of its responses or actions, encouraging it to improve its results over time.

Imagine a child’s game where a child controls a radio-controlled car, trying to navigate a closed track. Initially, the child may frequently veer off the track or collide with obstacles.

But each time the car successfully completes a lap around the track without errors, the child rejoices and feels satisfaction.

This joyful feeling serves as a «reward.» If the car goes off the track or collides with an obstacle, the child may experience disappointment or frustration – this is «punishment.»

Over time, responding to these rewards and punishments, the child improves their skills in controlling the car and makes fewer mistakes.

In the world of artificial intelligence, this is analogous to how reinforcement learning works.

A model, for example, playing a computer game, receives a «reward» for correct actions and a «punishment» for mistakes. Responding to these signals, the model gradually refines its game strategy.

In machine learning, especially in reinforcement learning, «encouragements» are often referred to as «rewards,» and «punishments» are called «penalties» or «negative rewards.»

The model aims to maximize the sum of received rewards (or minimize the sum of penalties) during its training process.

Context Adaptation: Language models do not merely «memorize» words, but they also understand the context in which they are used.

This helps them better interpret queries and generate more accurate responses. For instance, if you are reading a book about space and ask a friend, «How many more planets do you think they will find?» your friend understands that you are talking about celestial planets.

But if you are reading a book about ancient Greece and ask the same question, your friend might think you are referring to gods or mythological characters.

In the same way, language models strive to understand the context of your queries. If you ask the model, «Which poison?» after discussing plants, it is likely to assume that you are talking about poisonous plants.

But if this question is asked after discussing detective novels, the model might think the conversation is about poison used in crimes.

In this era of digitization and automation, the ability of machines to learn, adapt, and evolve is key.

As language models become increasingly advanced, their potential grows, opening new opportunities for business and society as a whole.

In today’s world, where technology literally surrounds us, language models have already become a part of our lives.

Smart Assistants: Siri from Apple, Google Assistant from Google, Alexa from Amazon – these are examples of smart assistants that use language models to process your voice commands and provide responses.

Here’s my personal experience of receiving help. I got not only pleasure but also concrete assistance!

One evening, as I was preparing dinner, I encountered a problem. I wanted to cook a special dish, carbonara, but I forgot the key ingredients.

My hands were covered in flour, and I couldn’t pick up my phone to check the recipe. In desperation, I remembered my smart assistant.

«Hey Siri,» I began, feeling my heart race, «how do I cook carbonara?»

A moment of silence, and then Siri’s soft voice filled the kitchen, telling me each step of the recipe.

I followed the instructions, and soon the scent of freshly cooked carbonara filled the room.

That evening, I deeply felt how smart assistants can be useful in our daily lives.

Customer Support Chatbots: Many companies use chatbots for automated customer service on their websites.

Thanks to language models, these bots can understand your requests and provide relevant responses or direct you to the appropriate specialist.

Personal Recommendations: Services like Netflix or Spotify use language models to analyze your preferences and offer personalized recommendations based on reviews and text descriptions.

Educational Platforms: Platforms such as Duolingo use language models to create grammar and style exercises, helping students learn new languages more effectively.

Automatic Text Completion: When your software or application suggests completing your sentence, that is also the result of a language model at work.

All these examples illustrate how language models have become an important tool, making our daily lives simpler and more efficient.

This technology is constantly evolving, and it is possible that its role in our lives will become even more significant in the future.

The capabilities of these virtual assistants, which are based on language models, are astonishing and even a bit mesmerizing. But these assistants are just the tip of the iceberg.

Let’s dive deeper into a world where machines and programs, trained to understand human language, make our lives simpler.

Microsoft Cortana: An integrated assistant in the Windows operating system. It helps the user organize their workflow, reminds them of important meetings, and can even tell a joke upon request.

Therapeutic Bots: Woebot is a chatbot based on the principles of cognitive-behavioral therapy, designed to help people cope with anxiety and depression.

Returning home after a hard day, you feel down and have no one to talk to.

You open an app on your phone and start a conversation with Woebot. It asks you about how you are feeling and suggests strategies to manage your emotions based on cognitive-behavioral therapy.

After talking to Woebot, you feel relief and gain tools to work with your mood.

Woebot’s website can be found at: https://woebot.io/

Shopping Assistants: Chatbots like «Mona» or «Lark» help users find the perfect product or service by analyzing their preferences and asking clarifying questions.

Banking Consultant Bots: Many banks have started using chatbots for initial customer consultations.

Imagine this scenario: you decide to take out a loan to buy a house and approach a bank. Instead of waiting in line or trying to reach an operator, you start a dialogue with a neuro-consultant – a chatbot created based on artificial intelligence.

You ask the bot a simple question: «What documents are needed to get a home loan?».

Thanks to the trained neural network, the bot instantly analyzes your request and provides a precise and understandable list of required documents.

Then you inquire about the interest rate, and the bot, knowing your interest in a home loan, provides up-to-date information on the rates for this type of loan, as well as information about possible promotions and special offers.

When you have a more complex question, such as the procedure for insuring property during the loan period, the bot understands that a live specialist would be better suited to answer this question and instantly redirects your chat to the bank’s loan consultant.

In this way, the use of a neuro-consultant in the bank significantly simplifies and accelerates the process of communicating with clients, helping them quickly and efficiently obtain the necessary information and save their time.

Assistants in Cars: The latest car models are integrated with voice control systems that allow drivers to ask questions, manage multimedia, and even check the weather without being distracted from the road.

Virtual assistants and language models are actively infiltrating every sphere of our lives, making it not only easier but also more interesting, enabling us to interact with technology as naturally as we interact with each other.

Many believe that art and creativity are the exclusive domain of humans, an area where machines could never replace us. However, thanks to language models, the line between human creativity and machine-generated content is becoming blurred.

Literature: OpenAI, the company behind many cutting-edge language models, has presented projects where models generated short stories, poems, and even essays on given topics. Some of these works were so convincing that they impressed even professional writers.

In the chapter about GPT plugins, you will find examples of how books can be created in a matter of seconds, complete with ready-made illustrations and the ability to be instantly published in the Amazon bookstore.

Of course, not everything is smooth sailing; people are reluctant to surrender their positions to artificial intelligence.

The first ones to raise the alarm were Hollywood screenwriters. They expressed concerns that artificial intelligence could take away their jobs.

They also pointed out the threat that artificial intelligence poses if real movies start being made according to its scripts.

They believe that something fundamental could be lost if Hollywood allows itself to become a wasteland of stories created by AI.

Music: Google’s Magenta is a project that utilizes AI for creating musical compositions.

These algorithms can craft melodies, harmonies, and even entire compositions that sound as if they were composed by a human.

Machine-generated singing and music composition could indeed become a new wave in the world of art. With the advancement of artificial intelligence technologies, the possibilities in this field are growing exponentially.

AI is already capable of generating melodies, accompaniments, and even song lyrics that can compete with the works of human composers.

Imagine a world where your personal AI composer can create a unique song or melody based on your mood or the events of your day.

Or even a world where machines can perform as singers or musicians at concerts, offering an entirely new experience for the audience.

Such a future might seem distant, but considering the current pace of technological development, it could become a reality much sooner than we think.

Artificial intelligence continues to learn and improve, and who knows what new horizons it will open for the world of music in the coming years.

In the book’s epilogue, you will become acquainted with the latest achievements of language models in this direction and be amazed by what modern models are capable of.

Visual Arts: Midjerney, DeepArt, and DALL•E are examples of programs that can create artistic images and illustrations based on given descriptions or styles.

They can replicate the style of famous artists or create entirely new, unique masterpieces.

Imagine you want to delight your friend with a unique birthday gift. You decide to give them a portrait, but with a special twist.