White Paper

GPT-3: understanding its potential

Autoregressive language models have become a dominant trend in the Artificial General Intelligence (AGI) field: by leveraging deep learning, GPT-3 is able to perform a wide range of tasks. In which real-world contexts will this innovation be truly effective? Reply is actively researching GPT-3’s potential use cases across different fields to answer this question with actual tests.


AGI – what are we talking about?

The development of machines that have human-like intelligence is one of the greatest unresolved challenges in computer science. The feeling in today’s world is that this goal is no longer impossible.

Over the last decade, artificial intelligence has made great strides, witnessing the rise of so-called Artificial General Intelligence (AGI). AGI consists of systems that possess intelligence equal to or greater than humans. AGI technologies are based on complex networks with millions of parameters, based on transformer architectures: deep learning models that mimic cognitive attention.

The models focus more on the most important parts of the input data, diminishing the importance of other parts. Additionally, transformers allow for parallelized training, meaning they can process more words at a time. This leads to reduced training times.


GPT-3: features and business value

GPT-3 stands for Generative Pre-Trained Transformer 3. It’s one of the most straightforward models available for building AI applications. Being Generative, it utilises statistical modelling to construct its output text. It is Pre-Trained on four databases, resulting in 175 billion parameters. Its Transformer architecture allows it to “act” like a human, filtering out extraneous information and focusing on the relevant words based on probabilities.

These features make GPT-3 very attractive in organisations' journey towards automation, highlighting its business potential in relation to 4 areas.


GPT-3 is trained on immense text corpora of data. This means GPT-3 can adapt to countless domains of application.


GPT-3 is ready for any use case that requires some level of cognitive skill. Simple proof-of concepts can be realized and validated within minutes.


The ease with which GPT-3 is accessible has enabled its wide adoption. The API release created a paradigm shift in NLP, attracting a large number of beta testers.


Although Large Language Models require considerable resources for training, their structure makes them highly efficient for inference. Solutions based on GPT-3 are designed to scale up as required.

Reply’s GPT-3 use cases


Sentiment Analysis

We assessed the ability of GPT-3 to extract a customer's sentiment towards a company by having it rank a large number of reviews on a predefined rating scale.


Structuring Data

We evaluated the ability of GPT-3 to extract essential fields and valuable information from a database of unstructured data, consisting of hundreds of e-mails.


Email information capturing

GPT-3's summarization capabilities can be used to streamline project management processes. We tested GPT-3's ability to automate the creation of status reports starting from status information exchanged via email.

How we can help

Reply firmly believes in the potential of new technologies, so it is exploring the tools available in the commercial landscape. This exploration include testing and experimenting through demos to gain a better understanding of the pros and cons of this new trend based on Artificial Intelligence and Machine Learning.

Going forward, Reply can help customers in several ways:

  • Exploration of the main experiences and use cases through custom workshops.

  • Support to customers in selecting the best platform/solution for their individual needs through surveys and assessment sessions.

  • Analysis of the business context to determine the adoption measures for the new GPT-3 and Natural Language Processing tools.

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