Artificial Intelligence has been making headlines, and stoking debates as news reports of the capabilities of language models like GPT-3 have become more widespread.
AI language models can produce human-like text and answer questions posed to it. Students have been using the technology to write essays, which has stoked debates around college campuses on the ethics behind using a language prediction model like GPT-3 in academics.
In addition, organizations have been using GPT-3 and other language models to create social media posts, generate news articles, translate text, and more.
Before you trust a language model to generate content for your organization, you will want to know the basics. What is GPT-3, and how can it help my organization?
This post will explain GPT-3, how it works, the benefits it can have for your business, and some of the risks you should consider before using it.
Understanding GPT-3: The World’s Most Popular Language Model
GPT-3 is an acronym that stands for third-generation generative pre-trained transformer. GPT-3 is a neural network model using the Internet as training data. GPT-3 was developed by the team at OpenAI.
With the GPT-3 language model, users can input a small amount of text, and GPT-3 generates text in large volumes. The deep learning neural network model used by GPT-3 utilizes over 175 billion Machine Learning parameters.
As a result, GPT-3 is the largest artificial neural network. The next largest artificial neural network is Microsoft’s Turing Natural Language Generation model, which only uses 10 billion Machine Learning parameters.
Since GPT-3 utilizes such a vast body of training data, it is the most powerful natural language processing model currently available and can produce text that seems like a human wrote it.
GPT-3 can create anything with a text or language structure, including human and machine language. This means it can be used to create computer code, not just words we use in speech or text.
How Does GPT-3 Work?
GPT-3 is a neural network Machine Learning model that takes input text and transforms it into the result it predicts to be most useful or successful. At the core of GPT-3 is generative pre-training.
The system, GPT-3, is trained on the immense amount of text content on the Internet to spot patterns. The generative pre-training of GPT-3 was accomplished by using several immense data sets such as Wikipedia, Common Crawl, and WebText2.
Initially, GPT-3 undergoes supervised training, in which a data scientist oversees the learning. For example, a data scientist or training team will ask GPT-3 a question with a specific answer in mind. If GPT-3 answers incorrectly, the model is tweaked to teach it the correct answer.
In addition, GPT-3 produces several responses, which are then ranked best to worst by the training team. GPT-3 is significantly larger than the large language models that have preceded it.
A large language model performance scales as more parameters and input data are consumed by the model.
The Business Benefits of Using GPT-3
GPT-3, like many Machine Learning and Artificial Intelligence solutions, helps reduce the number of rote tasks humans have to perform.
If your organization must generate a large amount of text content with little input, a tool like GPT-3 is perfectly suited to the task.
In addition, GPT-3 is considered task-agnostic, which means it can perform various tasks without fine-tuning.
For example, GPT-3 can be used by customer service teams to handle customer questions and reduce the workload of the service center. In addition, marketing teams can use GPT-3 to write copy, and sales teams can use it to connect with potential customers.
GPT-3 is best used for content that requires quick production with low risk, which means the consequences are not significant if there is a mistake in the copy.
The Risks of GPT-3
GPT-3 is a great tool, but that doesn’t mean it is free from risks and challenges. Before using GPT-3, it is important to understand the problems and limitations of the model. If you are looking for a perfect solution, you won’t find it yet, but GPT-3 has made enormous strides.
The most significant risk of GPT-3 is accuracy. Yes, this model is proficient in imitating human-generated text. However, this doesn’t mean that it is always accurate. In many cases, GPT-3 struggles with factual accuracy.
If your organization uses GPT-3 to generate text content, it is important to check it for factual accuracy before publishing it to ensure you have the correct output.
The other significant issue with GPT-3 is bias. Like all Machine Learning models, GPT-3 is subject to model bias. The language model was trained on data from the Internet. As a result, the model has the potential to learn and exhibit the same biases humans exhibit online.
GPT-3 has not been studied as much as its predecessor GPT-2 since it is newer. However, researchers at the Middlebury Institute of International Studies found that GPT-2 was skilled at generating radical texts that imitated conspiracy theories and white supremacy hate speech.
The last thing your business wants to do is accidentally generate and disseminate hate speech or coded language. GPT-3 aims to reduce the flaws in GPT-2 with additional training and greater oversight.
GPT-3 is an incredible tool businesses can utilize to simplify content creation. However, there are limitations to this tool’s capabilities. Therefore, it is important to understand these limitations before using them.
GPT-3 is an impressive leap forward in the capabilities of AI. You can expect this technology to improve and be more accurate and efficient.
If you want to learn more about GPT-3 and other Artificial Intelligence tools, contact an experienced technology partner like Koombea. We stay abreast of the latest tech news and developments, so you don’t have to.