GPT (generative pre-trained transformer) is a type of AI model for understanding and generating human-like text. GPTs employ transformer architecture—a deep learning model that uses self-attention mechanisms to process language—allowing them to create coherent and relevant text based on user input.
The recent and rapid evolution of artificial intelligence has unleashed a cascade of new capabilities for businesses in essentially every industry. Improved computational power and innovative algorithms are dramatically improving tasks such as natural language processing (NLP), image recognition and predictive analytics, making it possible for companies around the globe to understand and target their customers and generate insightful, powerful content at reduced cost more accurately. At the forefront of these advancements are generative pre-trained transformers, more commonly known as GPT.
Developed by OpenAI, GPT models are a breakthrough in the AI field, using a unique architecture known as the transformer. These models are defined by their deep learning framework, which allows them to generate text that is contextually relevant and often indistinguishable from human-generated content. Initially introduced as GPT-1, the technology has since evolved through multiple iterations, with the most recent versions showcasing even greater capabilities in handling complex language tasks.
Building a GPT model is a sophisticated process that requires specific tools and resources. These must be powerful enough to handle the complexities of training large-scale AI systems. Here is an overview of what goes into the creation of a generative pre-trained transformer:
Essential for any AI development, this software simplifies the creation, training and validation of deep learning models. Popular frameworks like TensorFlow, PyTorch and Keras offer strong support for neural network architectures, including the transformer models used in GPT.
GPT models require extensive datasets to learn the subtleties of human language. This can consist of a diverse range of texts from books, articles, website content and other sources to ensure a broad understanding of language from which to draw.
Training GPT models demands significant computational power, usually provided by graphics processing units (GPUs) or tensor processing units (TPUs). These environments speed up the training process and can handle the large amount of data and complex calculations involved.
Understanding the principles of neural networks, optimisation algorithms and model architectures is crucial. This knowledge allows developers to effectively design, train and tweak models to achieve desired outcomes.
Before training, data must be cleaned and pre-processed. This includes tasks like tokenisation, removal of irrelevant data and conversion of text into formats suitable for neural networks. Tools and libraries that assist in this process are vital for preparing the training data.
Tools for evaluating the model
Once a model is trained, it is important to evaluate its performance using metrics like perplexity, accuracy and loss functions. Tools that assist these evaluations help developers refine the model and assess its readiness for deployment.
Libraries such as NLTK, SpaCy or Hugging Face's Transformers provide pre-built functions and models that can accelerate the development of GPT models. These libraries include features for language processing tasks essential for training and deploying sophisticated models.
The development of each new version of GPT by OpenAI marks a significant milestone in the field of artificial intelligence. These models have evolved over time, with each iteration introducing more advanced capabilities and pulling from larger training datasets—becoming 'smarter' (or at least more capable) with every new release.
Major GPT iterations include:
Launched in 2018, GPT-1 was the first version and introduced the foundational architecture for subsequent models. It incorporated 117 million parameters and could perform a variety of language-based tasks with moderate success. This model set the stage for the development of more sophisticated transformers to come.
GPT-2 was released in 2019 and was an upscale from its predecessor, equipped with approximately 1.5 billion parameters. It was not immediately released in full due to concerns over potential misuse (such as generating misleading news articles or impersonating individuals online). GPT-2 demonstrated a significant leap in language understanding and generation capabilities.
Introduced in 2020, GPT-3 is one of the largest and most powerful language models ever created, using an astonishing 175 billion parameters. This iteration marked a major breakthrough in AI's ability to generate human-like text, capable of writing essays, poems and even computer code that are difficult to distinguish from those written by humans.
2022 saw the release of 3.5 which served as a refinement of GPT-3. It improved on several of the issues found in the earlier model, such as response quality and training efficiency. GPT-3.5 offered improved performance, particularly in more nuanced conversations and specialised tasks.
A further iteration within the GPT-3 release, GPT-3.5 Turbo was introduced to further streamline performance and optimise processing speed. This version maintains the model's depth of knowledge while increasing response times and lowering computational costs.
Released in 2023, GPT-4 pushed the boundaries even further, incorporating more data, refined training techniques and multi-modal capabilities—meaning it can now understand and generate content based on both text and image inputs. This version is known for its significantly improved accuracy, improved understanding and creative output capabilities.
The most recent advancement as of the time of this writing is GPT-4 Turbo. This version increases the capabilities of GPT-4 by further improving efficiency and processing speeds and continuing to set new standards for what can be achieved in terms of generative AI (GenAI) language models.
The effectiveness of GPT can be attributed to three core components: generative models, pre-trained models and transformer models. Each of these plays a foundational role in how GPTs understand and produce language
Generative models are a class of artificial intelligence algorithms designed for generating new data instances that are like (yet distinct from) the original data. In the context of GPT, these models are often trained to produce text that mimics human writing styles. By learning from a vast corpus of text data, generative models can compose coherent and contextually relevant content based on the patterns and structures they have absorbed. This capability is not just about replicating text; it's about understanding and generating nuanced responses that cater to specific prompts or questions. This makes them invaluable in tasks ranging from automated customer service to content creation.
The strength of generative models lies in their ability to learn from data without explicit programming for each task. Instead, they use statistical methods to infer the underlying patterns in the data, allowing them to produce a wide variety of outputs from a single model.
Pre-training refers to the method of training a machine learning (ML) model on a large dataset before it is fine-tuned for specific tasks. For GPT, this involves training on a diverse range of internet text. The pre-training process equips the model with a broad understanding of language (including grammar, context and even certain world knowledge) before it is further optimised through fine-tuning on task-specific data. This extensive pre-training is what gives GPT its powerful capabilities in generating high-quality responses that feel natural, informed and applicable to the prompts it is given.
The advantage of using pre-trained models is significant in reducing the time and resources required to develop effective models for specific tasks. Instead of starting from scratch, developers and researchers can leverage the pre-trained model's general capabilities and then fine-tune it with smaller, task-specific datasets.
Transformers, the architecture underpinning GPT, differ from previous models such as recurrent neural networks (RNNs) by employing attention mechanisms. These mechanisms weigh the importance of different words in a sentence, regardless of their positional relationship, making it possible for the model to process all parts of the input data simultaneously. The result is that the GPT becomes more efficient and effective at understanding context over longer stretches of text.
The key feature of transformer models is their ability to manage large-scale inputs and outputs, making them ideal for tasks that involve understanding and generating long-form texts. Their architecture likewise smooths dynamic data handling, allowing for nuanced and context-aware outputs generally beyond the capabilities of other models.
All tools throughout human history have had the same basic function: to reduce the time or effort a human must invest in completing a task. Whether that task involves driving a nail into a wooden board, moving a heavy load, or programming a software application, it all comes down to how much of the job the tool can perform for the human. GPT is no different in this respect; where it becomes significant is in its ability to perform much more of the job with far less manual direction or involvement from its human operators.
Using the aforementioned transformer architecture, GPT models streamline processes such as language translation, content creation and even software development, thereby significantly reducing the time and labour involved. These capabilities make GPT models invaluable tools for enhancing productivity and innovation in various sectors. At the same time, the leap in processing speed and scale represented by this technology opens new possibilities for businesses, researchers and even everyday users, pushing the boundaries of what can be automated.
Because they can produce human-like results with computer-level efficiency and accuracy, it is easy to see why GPT models are considered such a step forward in AI. Here are some of the most impactful use cases:
Code generation
GPT can automate the writing of code, helping developers by suggesting solutions and debugging existing code.Human language understanding using NLP
GPT technology improves machines' ability to understand human language undertones and connotations, enabling better user interaction and service automation.Content generation
From creating articles and reports to generating more creative content, GPT models can produce diverse forms of text clearly and quickly.Language translation
GPT models provide near-instant translation between languages, making global communication more accessible.Data analysis
These models can analyse large datasets to extract insights and patterns, aiding in decision-making processes.Text conversion
GPT can convert text between different formats, such as converting prose into various structured data formats.Production of learning materials
GPTs can generate educational content, tailor-made to suit different learning styles and needs.Creation of interactive voice assistants
GPT powers voice-operated AI, enabling more natural interactions in devices like smartphones and home assistants.Image recognition
Though primarily known for its application in working with written text, GPT models are increasingly being used in image recognition tasks, identifying and categorising visual data.
Given the widespread publicity surrounding ChatGPT, it's no wonder that many people see it as synonymous with the more general concept of generative pre-trained transformers. But GPT and ChatGPT are not the same thing. Instead, one is an application, and the other is the foundational technology that supports it.
GPT refers to a series of increasingly sophisticated AI models. These models are extremely versatile, supporting a wide range of applications beyond conversation—automated writing assistance, coding and visual content creation are improved through GPT solutions.
ChatGPT, on the other hand, is a specific application of the GPT model that is tailored to conversational uses. It employs a GPT base to engage in dialogues and provide intelligent, human-level responses to user's inquiries. This specialisation allows ChatGPT to simulate a human-like conversational partner, capable of answering questions, providing explanations, assisting with written content creation and even engaging in casual discussion. In other words, ChatGPT is an AI-powered chatbot—one that displays advanced capabilities.
Turning unstructured textual and visual data into something a computer system can comprehend and emulate is no simple process. The technical details that go into making GPT function are beyond the scope of this article, but on surface level core processes that power GPT models include the following:
Training on massive datasets
GPT models are initially trained on vast amounts of data from the internet. This training involves deep learning techniques, part of the broader field of machine learning. GPT-3, for instance, was trained on approximately 500 billion tokens, which are essentially pieces of text. This extensive training allows the model to learn a wide variety of language patterns.
Understanding through tokens
Unlike humans, GPT models do not understand text directly. Instead, they break down text into the tokens mentioned above. These tokens can be words or parts of words; they help the model grasp the structure and variety of human language. GPT-3's ability to handle these tokens through its billions of parameters allows for an in-depth understanding and replication of text.
Working within the transformer architecture
The core of GPT lies in its use of transformer architecture, specifically designed for handling sequences of data (such as text). This method is more efficient than earlier RNN solutions and scales better with longer text sequences.
Employing the self-attention mechanism
Within the transformer architecture, the self-attention mechanism allows the GPT to weigh the importance of each token relative to others in a sentence. This process enables the model to focus on relevant tokens when generating responses, ensuring that the output is appropriate to the context.
Applying network training
The transformer model in GPT consists of several layers of neural networks that calculate probabilities and relationships between tokens. By adjusting weights within these networks, GPT models can generate improved responses.
Using encoding and decoding processes
In more detailed transformer models, an encoder processes the input text into a set of mathematical vectors that capture the essence of the words and their relationships. Each vector represents a word or a token, maintaining not only the word's identity and its positional information in the sentence. The decoder then takes these vectors and generates output text. It predicts the next word in a sequence by considering the encoded information and the words it has generated so far, effectively translating the internal representation back into human-readable text.
Creating a GPT model involves a series of steps that require careful planning, significant resources and deep technical expertise. Organisations interested in developing their own GPT models should follow this approach:
Define the scope and objectives
Clearly define what the GPT model is supposed to achieve. This could range from improving customer service with a chatbot to automating specific types of content generation.Assemble a skilled team
Gather a team with expertise in machine learning, data science and software engineering. This team will lead the development and training of the GPT model.Acquire and prepare the data
Collect a large dataset that is relevant to the tasks the model will need to perform. This data must then be cleaned and pre-processed to ensure it is suitable for training the model.Choose the right tools and technology
Decide on the deep learning frameworks and hardware that will support the training of the GPT.Prioritise model training and tuning
Train the model using the prepared datasets. This process involves setting the parameters, training the model iteratively and fine-tuning the results to improve accuracy and performance.Evaluate and iterate
Continuously evaluate the model's performance using appropriate metrics. Make adjustments based on feedback to refine the model's outputs.Deploy and integrate
Once the model meets the desired standards, deploy it into the production environment where it can start performing the designated tasks. Ensure it integrates smoothly with existing systems.
Successfully implementing GPT models involves more than just technical expertise and resources. Organisations must also consider certain ethical and functional aspects to ensure that their models are both effective and responsible. When building a custom GPT, consider the following:
Eliminating bias and other harmful elements
It's crucial to train models on diverse datasets to minimise bias. Regularly testing and updating the model to identify and remove any discriminatory or harmful language is essential for ethical AI practices.Reducing inaccuracies
GPT models can sometimes generate incorrect or misleading information, known as 'hallucinations'. Enhancing training methods and refining model architectures can help reduce these inaccuracies, ensuring the reliability of the generated content. Likewise, human evaluation may be an effective 'last defence' for catching inaccurate outputs.Maintaining data security
Ensuring that the training data does not leak into the outputs is vital for maintaining the integrity and confidentiality of the information. Techniques like differential privacy, careful data management and monitoring, and establishing transparent data-usage policies among developers are critical.
Creating a GPT model in-house can be a complex and time-consuming endeavour. As such, many organisations choose to instead work with third-party vendors who specialise in AI and machine learning solutions. These vendors can provide the expertise and resources needed to develop or use effective models more quickly and with a lower investment upfront.
GPT models, with their ability to generate coherent, relevant text, promise significant value in today's technologically evolving market. In this environment, using the right platform to harness the potential of generative AI and intelligent automation is crucial for businesses interested in remaining at the forefront of innovation.
The award-winning Now Platform®, ServiceNow's cloud-based foundation supporting its diverse range of products and services, provides comprehensive AI solutions designed to integrate seamlessly with GPT models. The Now Platform improves productivity by automating routine tasks and providing advanced analytics, making it a vital tool for businesses looking to implement GPT. ServiceNow's AI capabilities include everything from natural language understanding (NLU) and intelligent search to predictive analytics and process mining—all aimed at simplifying and improving work processes. These tools are built to ensure that businesses can effectively use AI for a wide range of applications, from customer service automation to enterprise data analysis and decision-making.
By incorporating ServiceNow's AI tools, you can transform your operations to meet the growing needs of your business. See how ServiceNow can make AI work for you; schedule a demo today!