Generative Pre-Trained Transformers (GPT) models are the most advanced language models in Artificial Intelligence (AI).
They can learn from huge amounts of text data and produce coherent and diverse texts on any topic.
What's more, they can create relevant answers to questions, translate, summarize, classify, and more. All without additional training!
But how did GPTs become a powerful and versatile member of the family of language models?
In this blog post, we will explore the rapid evolution of GPTs from GPT-1 to GPT-4 and beyond.
Are you ready to dive into the fascinating world of GPTs? Let's get started!
What is GPT?
GPTs are Deep Learning-Based Large Language Models (LLM), meaning they can learn from text data and generate new human-like texts.
They are Generative because they can create something out of nothing. The Pre-Trained comes from GPT's ability to undergo the training process before being used for specific tasks. Lastly, they're Transformers because they use a special Neural Network architecture that can process long sequences of words.
GPTs perform various tasks without additional training, such as writing essays, summarizing articles, translating languages, and even making jokes.
They are the brainchild of OpenAI, a research organization dedicated to creating and promoting beneficial Artificial Intelligence.
The History of GPT Models
GPT-1: The First GPT
In 2018, OpenAI introduced the GPT-1 Model, the first of its series of generative pre-trained transformers.
GPT-1 was a breakthrough in NLP tasks, as it could generate fluent and coherent text given a prompt or context.
Its Transformer architecture relied on a novel NN design with self-attention to process long sequences of words.
Unlike prior training language models, which used recurrent or Convolutional Networks, GPT-1 used a decoder-only architecture.
GPT-1 had 117 million pre-training parameters. The number of weights or connections was significantly larger than previous state-of-the-art language models. Before GPT1, language models had just around 10 million parameters.
This massive amount of text data had two main datasets. On the one hand, there was the Common Crawl, which contains billions of words from web pages. On the other hand, there was the BookCorpus, with over 11,000 books from various genres.
GPT-1 also had some limitations, which prevented it from being a perfect language model. It was prone to repetitive or nonsensical text, especially when given prompts outside its training scope. This model also failed to reason over multiple dialogue turns or track long-term dependencies in text.
GPT-2: Language Modeling Leap
In 2019, OpenAI released GPT-2, the second of its series and a giant leap in Natural Language Processing.
GPT-2 utilized the same Transformer model as GPT-1 but was much larger and more advanced. It had 1.5 billion parameters, more than ten times larger than GPT-1! It also had more than 100 times larger than previous state-of-the-art language models.
They trained it on massive text data, consisting of over 40 GB of text from the Web. Further, this data was collected by crawling and filtering the common crawl dataset.
GPT-2 learned to model natural language at a high level using this diverse and rich data source. Yet, the model was prone to false or misleading information, especially when given ambiguous, biased or malicious prompts. It also failed to capture the nuances and subtleties of natural language, such as humor or irony.
Moreover, its cohesion and fluency were only limited to shorter text sequences. Longer passages would lack structure and coherence.
GPT-3: Cutting-Edge NLP
In 2020, OpenAI launched GPT-3, the 3rd generation of its series of generative pre-trained transformers.
GPT-3 was the cutting edge of NLP., following the same architecture as the previous models. Still, it was a much larger and more advanced model, with 175 billion parameters. That was more than 100 times larger than the GPT-2 model!
The GPT-3 model could also perform various tasks using a few-shot learning technique. This technique allowed it to learn from a few examples or instructions.
OpenAI's GPT-3 was not just a language model. it was a general-purpose AI system able to perform a vast amount of generation tasks and NLU. To name a few, there were writing code, language translation, solving arithmetic operations and designing websites.
However, the model was complex and opaque. This made it difficult to understand, explain or verify its behavior, outcomes or impacts. Due to these implications, OpenAI decided to release GPT-3 in a controlled manner through a private beta program.
It also built a commercial product, the OpenAI API. The OpenAI API allows developers and researchers to access GPT-3 and other models and build applications and tools.
GPT-4: The Future of AI
GPT-4 is OpenAI's latest and most powerful language model in the GPT series because it has multimodal capabilities.
It can understand textual and visual inputs to generate text and images. Moreover, it can do advanced text generation, answering questions and creating art.
It's also safer and more reliable than previous versions because it has undergone training with more data and feedback.
The GPT-4 model is still being improved and updated by OpenAI.
The team is also working on a faster and more efficient version called GPT-4 Turbo, which they introduced in DevDay.
GPT-4 Turbo is a supercharged and more advanced version that will allow for expanded capabilities.
It has a 128k input sequence context window and a JSON mode for developers.
At DevDay, OpenAI's first developer conference, the then CEO, Sam Altman, also announced custom GPTs.
This new feature of ChatGPT allows users to create their own versions of the GPT-4 model for specific tasks or domains.
What's more, they can be made without coding skills using a conversational interface.
This interface guides the user through defining the GPT's behavior, knowledge, and functionalities.
How Do GPTs Work?
You might wonder how GPTs can do many amazing things, such as teaching, designing, or making jokes.
How do GPTs know what to say and how to say it? How do they learn from text data and generate new texts on any topic?
They use Natural Language Processing (NLP), an AI field that deals with the interaction between computers and human language.
This enables a comprehensive understanding of language for tasks like English, Spanish or Mandarin translation.
NLP works for a broad range of applications, such as speech recognition, machine translation, sentiment analysis, etc.
GPT models use self-attention, allowing them to pay attention to different parts of the input and the output.
When you give a "Write a poem about love" prompt, it uses self-attention to understand the prompt's meaning, structure, and theme of love.
It then uses self-attention to generate an accurate response, such as:
What are the Challenges of GPTs?
GPTs are amazing, but they are not perfect. There's a wide range of challenges that limit their potential and raise some concerns:
GPT Data
GPTs rely on large amounts of text data to learn and generate natural language.
However, not all text data is reliable, relevant, or representative.
Some data may be outdated, inaccurate, biased or incomplete.
Moreover, some languages or domains may have less data available than others.
All this results in lower performance or coverage.
GPT Ethics
One of the biggest challenges is the influence they can have on human behavior, opinions and emotions.
For example, GPTs can generate fake news, reviews, or profiles or impersonate someone.
They can also produce offensive, harmful, or inappropriate content, such as hate speech, violence or pornography.
Therefore, GPTs must adhere to ethical and social norms and respect human values.
GPT Privacy
These models process and generate sensitive and personal information, such as health records, financial data or identity details.
They can also reveal or leak information, such as secrets, preferences, sensitive questions, or opinions.
Moreover, GPTs can get hacked, corrupted, or misused by malicious actors, such as cybercriminals or terrorist organizations.
GPTs must protect and preserve user data and prevent unauthorized or harmful access or potential misuse.
GPT Accountability
GPTs are complex and opaque systems, which makes it difficult to understand, explain or verify their outcomes.
They can also make mistakes, errors, or failures, which can have serious consequences or costs.
For example, GPTs can give wrong answers, misleading advice or inaccurate predictions.
They can also cause confusion, frustration, or dissatisfaction among the users or the stakeholders.
GPTs must be evaluated and monitored regularly and accountable for their actions and effects.
Conclusion
GPTs are a technological marvel and a powerful tool due to their impressive capabilities and advanced techniques.
They have sparked a lot of interest and curiosity among the public, as well as a lot of debate and controversy.
Yet, they have also inspired a lot of creativity and innovation in different areas.
To name a few, we're seeing it in AI-powered tools, chatbots, information-gathering tools and cybersecurity services.
These GPTs are even called the future of Natural Language Processing, Natural Language Generation and Machine Learning models!
Their generative capabilities are changing how we communicate, learn and create.
They are valuable tools that are opening new possibilities and challenges for humanity. They are, indeed, something to be excited about.
