The release of the GPT-3 (Generative Pre-trained Transformer 3) language model by Open AI has been greeted with enthusiasm and excitement. Several commentators have hailed the development of the language solution as a crossroads in natural language processing (NLP).

Many are confident that the language model will help businesses make better use of their AI models. The model, which has only been released to a few beta users has shown how efficient it could be in generating quick responses to emails, speaking to your favorite thinker, or generating code.

As always, there have been concerns about how dangerous the application of such a model can be if not well-managed. The team that built the model has also acknowledged this fact. Stating that the broader impact of the model includes misinformation, spam, phishing, and the abuse of legal and governmental processes. Having recognized this possibility, the project team has put measures in place to prevent the abuse of the model.

State of text applications in machine learning

Today, businesses deploy text applications in varying use cases. We have simple applications such as chatbots, digital assistants, document organization, talent recruitment, and sentiment analysis. These applications vary in terms of business impact. The adoption of solutions leads to productive processes and profit-friendly business verticals.

Currently, NLP deep learning models have gradually shifted from task-specific structures into more advanced pre-trained language models. Functions such as chatbots used to be restricted in terms of learning models, with pre-determined functions purpose-built. However, the GPT-3 solution has notably crossed this threshold. Trained on a larger body of dataset, it has a bigger reach in terms of solutions it can provide. The GPT-3 model has 175 billion parameters making it a more advanced model.

Using customer support software as an example, solutions built on GPT-3 may prove revolutionary. Ecommerce businesses require the help of a chatbot in answering simple queries and other products’ inquiry. User experience is therefore bound to improve where a GPT-3 model is deployed.

GPT-3 as a big step for NLP

The GPT-3 model is trained in both syntax and semantic techniques. Making it an effective tool to comprehend, complete, answer questions, and understand the context of words. One of the most hailed innovations of the GPT-3 has been the ability to operate with very little fine-tuning needs as against other previous NLP models. With evaluation conducted in 3 different settings, namely the zero-shot, one shot, and few shots settings, the GPT-3 model has been described as very proficient in providing reliable answers.

While its innovation has been hailed, it is important to also note that the new model is not without its weaknesses. One of the points of criticism concerning the model has been the fact that cases of its failures have not been widely discussed as the successes of usage. For example, in two cases discussed by The Verge, the model’s application made certain errors which no human would reasonably. One such error was made by an application called Learn From Anyone. In a chat with Steve Jobs, after being asked “Where are you right now?”, Steve Jobs (the bot persona) responds “I’m inside Apple’s headquarters in Cupertino, California”. Except for conspiracy theorists, this kind of reply is inaccurate.

Beyond some of these errors made in the output, certain challenges also emerge from its application for NLP. The study on which the creation of the model was based notes that a challenge of the model is data contamination which could result in distorting effects on the model. Though the study addresses this problem, there is also a potential that this may affect the application of the model in certain cases such as text synthesis. Other broader challenges which have been touted by commentators is that the model may be susceptible to some challenges in bias, especially since the data source was obtained from the public. According to the researchers, the dataset used to train the model was obtained from Common Crawl, WebText2, Books1, Books2, and Wikipedia. The researchers themselves admit that the presence of bias in the training data could lead to a challenge in fairness, representation, and bias. For example, the model was found to associate certain words such as “lazy”, “fantastic”, “eccentric”, “protect”, “survive”, co-occurred more frequently as male descriptive words, while “optimistic”, “naughty”, “easy-going”, “tight”, “gorgeous”, co-occurred more frequently as descriptive words for females. These biases reflect the challenges that may be associated with large-scale generative projects. For a project as large scale as the GPT-3, it is certainly susceptible to bias based on the fact that the data used to train it is also infused with bias. Would a small business want to deploy a chatbot that is likely to respond to its consumers when answering queries? Expectedly not.

This exercise reflects that there are still questions for the GPT-3 as a solution. While it is a milestone in the field of NLP, the model still needs fine-tuning before it can be applied to practical solutions such as chatbots or digital assistants.

Data is the answer to better AI models

The questions of bias still continue to nag the deployment of excellent AI models just as exemplified in the GPT-3 deployment. This brings to the fore the discourse of data cleaning and preparation for small businesses and companies looking to deploy AI. Engaging in unsupervised learning is efficient when the data used are well prepared and fashioned to the task. While the GPT-3 model is an ambitious project which has broader tasks, and its deployment for small businesses is not anytime soon, it is important for data scientists, data engineers, and chief technology officers to study the current trend and position their models to perform efficiently.

Data cleaning and preparation companies like Datix AI are essential for the creation of efficient AI models. Datix AI provides wide-ranging services for small and medium businesses looking to deploy their AI models. We provide structured datasets on text data. Our text data collection mechanisms take into context, issues such as contextualized conversations for chatbots. We also help your AI teams train and test their datasets. Therefore, where your company already has data points from previously-deployed chatbots, Datix AI can clean, train, and test that data to make subsequent deployments more useful and results-driven.