Natural language processing (NLP) is a multidisciplinary field spanning parts of linguistics, computer science, information engineering, and artificial intelligence (AI) concerned with the interactions between computers and human (natural) languages, how to program computers to process and analyse large amounts of natural language data.

NLP research is divided into three main tracks:

1. Computational linguistics: this track focuses on the design and implementation of algorithms that can automatically process and analyse natural language data.
2. Human language technologies: this track focuses on the development of practical applications of NLP algorithms, such as spell checkers, machine translation systems, and speech recognition systems. Applications of these methods could be used in an AI driven call centre.
3. Theoretical linguistics: this track focuses on the study of the linguistic properties of natural language data, and the development of models of human language understanding and production.

NLP algorithms are used in a variety of applications, including machine translation, speech recognition, information retrieval, and question answering. In recent years, there has been a surge of interest in NLP, due to the increasing availability of large amounts of digital text data, such as online news articles, social media posts, and digital books. NLP techniques are also being used in a variety of other domains, such as bioinformatics, finance, and medicine. The goal of NLP is to enable computers to understand and generate human language.

NLP algorithms are typically based on statistical models, which are trained on large amounts of data. The most successful NLP applications are those that combine multiple techniques, such as machine translation systems that use both statistical and rule-based methods.

What is GPT-3?

GPT-3 (General Pre-trained Transformer 3) is the third-generation AI platform from OpenAI. It is a result of years of research and development in machine learning. GPT-3 is designed to be more powerful and efficient than its predecessors, and it can handle a wider range of tasks. GPT-3 has already shown itself to be a valuable tool for businesses and organisations. It has been used to create chatbots, to improve search results, and to make recommendations.

GPT-3 is just the beginning of what AI can do. With continued research and development, the possibilities for GPT-3 and other AI platforms are endless.

How does it work?

GPT-3 is a type of model known as a transformer model. In recent years, transformer-based models have revolutionised the field of NLP. These models, which are built on the transformer architecture, have achieved state-of-the-art results on a variety of NLP tasks. One of the key advantages of transformer-based models is that they can be trained on large amounts of data very efficiently. This is because transformer models can be parallelised in a way that traditional recurrent neural networks cannot.

Another advantage of transformer models is that they have a much better capacity for modelling long-range dependencies, such as understanding a large corpus of text than recurrent neural networks – which in turn enables them to learn the complexities of text data better. This is because the transformer architecture allows for a much greater depth of interaction between the input and output sequences.

Some of the most popular transformer-based models for NLP are the BERT and GPT-2 models.

BERT

BERT is a transformer-based model that was developed by Google for the task of pre-training deep bidirectional representations from unlabelled text. The BERT model is based on the transformer architecture and is trained using a masked language modelling (MLM) objective. This objective randomly masks a percentage of the input tokens and then attempts to predict the masked tokens from the rest of the sequence. The BERT model has been pre-trained on a large corpus of English text and is then fine-tuned for a variety of downstream tasks.

GPT-2

GPT-2 is a transformer-based model that was developed by OpenAI for the task of generating text. The GPT-2 model is based on the transformer architecture and is trained using a language modelling objective. This objective is to predict the next token in a sequence, given the previous tokens. The GPT-2 model has been pre-trained on a large corpus of English text and is then fine-tuned for a variety of downstream tasks similarly to the BERT model.

GPT-3 is the newest, third iteration of the series of GPT transformer models and works very similarly to the previous GPT-2 model. It has several different applications to the real world.

Uses for GPT-3

GPT-3 is a powerful AI system that can be used for a variety of tasks. Some of the most popular uses for GPT-3 include:

1. Generating text: GPT-3 can be used to generate text for documents, websites, or even books.
2. Translating text: GPT-3 can be used to translate text from one language to another.
3. Creating new websites: GPT-3 can be used to create new websites from scratch.
4. Creating new products: GPT-3 can be used to create new products, such as mobile apps or software.
5. Analysing data: GPT-3 can be used to analyse data to find trends or patterns.

For example, if we take the text generation use case for GPT-3 and pass “Write the history of GlobalLogic company” into the model we get:

“GlobalLogic is a leading global provider of product development and digital experience services. We help companies imagine, design, engineer, and deliver digital experiences that change the world. Founded in 2001, GlobalLogic has over 12,000 employees and 20+ global delivery centres. We partner with the world’s most innovative companies, including Cisco, Google, Honda, Microsoft, Motorola, and Samsung. GlobalLogic is a growth-stage company with a proven business model and a track record of delivering results for our clients. We have a strong balance sheet and a team of passionate, innovative, and collaborative people.”

It is important to note that this is a completely unique statement, it seems as if GPT-3 has copied and pasted this from somewhere but in-fact it is uniquely written by the model and would pass all anti-plagiarism tests. You should not be able to find this exact paragraph published by someone else the internet. (Try to look for it!).

Conclusion

We have seen the ground breaking performance of GPT-3’s performance and uses to NLP tasks. Its ability to create and write detailed corpuses of text show the extent of its grasp of the English language and it will continue to be one of the industry leading tools in text generation and NLP.

For more information about GPT-3 check out the OpenAI website: https://openai.com/

A bit about the author

I’m Nikhil Modha and I’m from London. I’ve worked in the tech industry for four years, specialising in Data Science and MLOps. I love to work with NLP (Language Data) and productionising models in the cloud.