Note of GPT-3
GPT-3 is a neural network machine learning model trained using internet data to generate any type of text. It requires a small amount of input text to generate large volumes of relevant and sophisticated machine-generated text.
How does GPT-3 work?
When a user provides text input, the system analyzes the language and uses a text predictor to create the most likely output. This is accomplished by training the system on the vast body of internet text to spot patterns.
GPT-3 is focused on text generation based on being pre-trained on a huge amount of text.
Capabilities of GPT-3
- Generate various text content depending on the context and predict statements according to available sentences.
- The text predictor of GPT-3 processes all the text existing on the internet and can calculate the most statistically expected output.
- Write poetry, blogs, PR content, resumes, and technical documentation.
- Analyze the context in the provided text and based on that it can generate business idea pitches, fan fiction, memes, and so on.
- Create workable code that can be run without error.
- One developer has combined the UI prototyping tool Figma with GPT-3 to create websites just by describing them in a sentence or two.
- Clone websites by providing a URL as suggested text.
- In the gaming world, create realistic chat dialog, quizzes, images and other graphics based on text suggestions.
History of GPT-3
The version of GPT:
- The first version of the GPT (117 million parameters):
- Showed that the language prediction model could capture global knowledge.
- Proposed that a language model must be learned using unlabeled data, then, improved through samples of natural language processing tasks such as text classification, sentiment analysis, and word segmentation.
GPT-2 (1.5 billio parameters):
- Able to generate realistic text that was refused by OpenAI to be made open-source as the company was concerned about the development and spread of fake news.
GPT-3 (175 billion parameters)
Benefits of GPT-3
Whenever a large amount of text needs to be generated from a machine based on some small amount of text input, GPT-3 provides a good solution. There are many situations where it’s not practical or efficient to have a human on hand to generate text output.
Risks and limitations of GPT-3
- The biggest issue is that GPT-3 is not constantly learning. It has been pre-trained, which means that it doesn’t have an ongoing long-term memory that learns from each interaction.
- Limited input size: A user cannot provide a lot of text as input for the output. GPT-3 can only deal with input text a few sentences long.
- A wide range of machine learning bias: Since the model was trained on internet text, it exhibits many of the biases that humans exhibit in their online text.
Using GPT-3
- OpenAI has released an API for accessing AI models developed by them.
