Generative Pre-trained Transformer (GPT) definition: Applications and use cases

A Generative Pre-trained Transformer (GPT) is an Artificial Intelligence (AI) model that processes and generates text based on the input you provide. By understanding context and language patterns, it can help you with tasks like drafting emails, answering questions‌ and providing recommendations.

Developed by OpenAI, the GPT model has been trained on diverse datasets, enabling it to provide detailed and coherent responses. It enhances your productivity and decision-making capabilities in various applications.

GPT models aren't just revolutionising the AI industry. They’ve also found applications across various other industries 👇

1 - Healthcare

In the field of healthcare, GPT models aid in managing patient databases, creating medical reports and notes and identifying symptoms for diagnosis.

They also support personalised care by answering health questions and providing recommendations.

2 - Retail

GPT models create detailed and engaging product descriptions for ecommerce platforms. They also power chatbots that help customers by providing them with personalised recommendations.

3 - Education

GPT models enhance educational platforms by providing personalised learning and tutoring as well as study aids. They can also generate educational materials and interactive modules.

4 - Finance

In the finance industry, GPT models analyse market trends and generate personalised investment insights.

Advanced GPT models can help in fraud detention and risk management by recognising patterns in transactional data.

5 - Software development

In ‌software development, GPT models can be used to generate code, automate software development tasks‌ and help to create software applications.

This can streamline your development processes, reduce time-to-market‌ and improve the quality of your software products.

GPT works through a combination of pre-training and fine-tuning stages. Here’s a simplified explanation 👇

1 - Pre-training

The model is trained on a large dataset consisting of text from the internet. During this phase, GPT learns grammar, facts about the world‌ and some reasoning abilities by predicting the next word in a sentence. It builds a general understanding of language and context from this extensive data.

2 - Fine-tuning

After pre-training, the model undergoes fine-tuning on a smaller and more focused dataset. This dataset usually contains examples directly related to the intended application.

At this stage, supervised learning is applied to enhance the model's performance. This fine-tuning process allows the model to specialise and significantly improve its accuracy for specific tasks.

3 - Tokenization

The process of breaking down input text into smaller parts, known as tokens, can include words, subwords‌ or individual characters. These tokens are then converted into numerical representations that the model can process.

4 - Transformer architecture

GPT uses the transformer architecture, which includes mechanisms like self-attention. Self-attention enables the model to assess the significance of individual words within a sentence, enhancing its comprehension of context and connections among words.

5 - Generation

During the generation phase, the model receives an AI prompt and generates a coherent and contextually relevant continuation based on its training data.

It predicts one token at a time, using the previously generated tokens as context. This process continues until the desired output length is reached.

While GPT models have found various applications across various industries, they also present some challenges.

1 - Bias and fairness

GPT models can inherit biases from the data they’re trained on, which can lead to unfair results. To make sure results are unbiased, data must be continuously checked, while biased information must be removed.

2 - Contextual understanding

GPT models may fail to fully understand complex contexts, leading to inappropriate or irrelevant responses. This requires human oversight to make sure that ‌outputs are meaningful and contextually appropriate.

3 - Ethical concerns

GPT models have the potential for misuse, such as creating deepfakes, generating misleading content‌ or producing harmful text.

To prevent this misuse, it's essential to establish ethical guidelines and regulatory frameworks. This involves setting clear boundaries for acceptable use and implementing measures to detect and mitigate harmful applications.

4 - Data privacy

Using GPT models with personal data raises privacy concerns. To maintain user trust, it's crucial to ensure compliance with data protection regulations, such as GDPR.

This requires robust data handling practises and security measures to safeguard sensitive information from unauthorised access and breaches.