What is GPT-3?

GPT-3 is a neural network machine learning model trained using internet data to generate any type of text. Developed by OpenAI, it requires a small amount of input text to generate a large volume of relevant and complex machine-generated text.

GPT-3’s deep learning neural network is a model with over 175 billion machine learning parameters. To put things at scale, the largest trained language model before GPT-3 was Microsoft’s Turing NLG model with 10 billion parameters. As of early 2021, GPT-3 is the largest neural network ever produced. As a result, the GPT-3 is better than any previous model for producing enough persuasive text to appear as if it was written by a human.

What Can GPT-3 Do?

Natural language processing includes natural language generation as one of its main components, which focuses on natural text rendering in human language. However, creating human-intelligible content is a challenge for machines that don’t really know the complexity and nuances of the language. Using text on the Internet, GPT-3 is trained to generate realistic human text.

GPT-3 has been used to create articles, poems, stories, news and dialogues using a small amount of input text that can be used to produce large volumes of quality copies.

The GPT-3 is also used for automated speech tasks, responding to any text a person enters into the computer with a new context-appropriate piece of text. GPT-3 can render anything with text structure, not just human-language text. It can also automatically generate text summaries and even programming code.

GPT-3 Examples

As a result of its powerful text rendering capabilities, GPT-3 can be used in a wide variety of ways. GPT-3 is used to create creative writing such as blog posts, ad copy and even poetry that mimics the style of Shakespeare, Edgar Allen Poe and other famous writers.

Using just a few sample code text snippets, GPT-3 can generate workable code that can run without errors because the programming code is a text-only format. GPT-3 has also been used to spoof websites to strong effect. One developer combined UI prototyping tool Figma with GPT-3 to create websites using only a bit of suggested text, defining just one or two sentences. GPT-3 has even been used to clone websites by providing a URL as suggested text. Developers use GPT-3 in a variety of ways, from creating code snippets, regular expressions, graphs, and charts from text descriptions, Excel functions, and other development applications.

GPT-3 is also used to create realistic chat dialogues, quizzes, images and other graphics based on text suggestions in the game world. GPT-3 can also generate humor, recipes and comics.

How GPT-3 Works?

GPT-3 is a language prediction model. This means it has a neural network machine learning model that can take the input text as input and transform it into what it predicts will be the most useful outcome. This is accomplished by training the system on the large body of internet texts to detect patterns. More specifically, GPT-3 is the third version of a model that focuses on pre-training text generation on large volumes of text.

When a user provides text input, the system analyzes the language and uses a text predictor to generate the most likely output. The model produces high-quality output texts similar to what humans would produce, even without extra settings or training.

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What Are the Benefits of GPT-3?

When a large amount of text needs to be produced from a machine based on a small amount of text input, the GPT-3 offers a good solution. There are many situations where it is not practical or efficient to have a human on hand to generate text output, or there may be a need for automated text generation that looks human. For example, customer service centers can use GPT-3 to answer customer questions or support chatbots; sales teams can use it to connect with potential customers and marketing teams can write text using GPT-3.

What Are the Risks and Limitations of GPT-3?

While GPT-3 is quite large and powerful, it has several limitations and risks associated with its use. The biggest problem is that GPT-3 is not constantly learning. It is pre-trained, which means that learning from every interaction does not have a long-term memory that continues to learn. Additionally, GPT-3 suffers from the same issues as all neural networks. This, in turn, is the lack of the ability to explain and interpret why certain inputs result in certain outputs.

Additionally, transformer architectures where the GPT-3 is one suffer from limited input size issues. A user cannot provide a lot of text as input for output, which can limit certain applications.

GPT-3 can especially deal with introductory text that is only a few sentences long. The GPT-3 also suffers from slow extraction time as it takes a long time to generate the model from the results.

More importantly, GPT-3 suffers from a wide variety of machine learning biases. Because the model was trained on internet text, it exhibits many of the biases that people display in their online texts. For example, two researchers found GPT-3 to be particularly adept at generating radical texts such as rhetoric that mimicked conspiracy theorists. This offers extremist groups an opportunity to automate their hate speech. Also, the quality of the generated text is so high that people are starting to worry that GPT-3 will be used to create “fake news” articles.

History of GPT-3?

Founded in 2015 as a nonprofit, OpenAI developed GPT-3 as one of its research projects to tackle the larger goals of promoting and developing friendly AI in ways that benefit humanity as a whole. The first version of GPT was released in 2018 and contained 117 million parameters. The second version of the model, GPT-2, was released in 2019 with approximately 1.5 billion parameters. The latest version, GPT-3, puts a big difference over the last model with over 175 billion parameters, which is more than 100 times the previous model and more than ten times that of comparable programs.

OpenAI has gradually released access to the model to see how to use it and avoid potential problems. The model was initially released for free during a beta period that required users to apply to use the model. However, the beta period ended on October 1, 2020, and the company has released a pricing model based on a tiered credit-based system, ranging from free access for 100,000 credits or three-month access to hundreds of dollars per month for larger loans. In 2020, Microsoft invested $1 billion in OpenAI to become the exclusive licensee of the GPT-3 model.

The Future of GPT-3?

OpenAI and others are working on even more powerful and larger models. There are a number of open source efforts at play this one joins in to provide a free and unlicensed model as a counterweight to Microsoft exclusive ownership. OpenAI plans larger and domain-specific versions of its models trained on different and more diverse text types. Others are looking at different use cases and applications of the GPT-3 model. However, Microsoft’s proprietary license poses challenges for those who want to embed the capabilities into their applications.

Source: https://statmodeling.stat.columbia.edu/2022/03/28/is-open-ai-cooking-the-books-on-gpt-3/