What Is GPT-3 & Why is it so popular?

Understanding the hype behind this humanlike text-generation language model released by OpenAI

In July 2020, OpenAI released a beta testing version of GPT-3, a powerful auto-completion program that would very likely define the next decade of AI programming.

In this blog post, we will learn about OpenAI’s GPT-3 model, and present the strengths, limitations, and potential for this new technology.

What is GPT-3?

Generative Pre-trained Transformer 3 (GPT-3) — an AI technology developed by OpenAI. GPT-3 is the third model in OpenAI’s GPT series of autoregressive(AR) language tools.

GPT-3 is a language model that uses deep learning to create human-like text that has the potential to be practically indistinguishable from human-written sentences, paragraphs, articles, short stories, dialogue, lyrics, and more.

GPT-3 has been trained by OpenAI on a massive corpus of text with more than 175 billion parameters, making it the largest language model — ever.

In non-tech terms, GPT-3 is able to pick up on writing patterns with a small amount of user text and is well trained to understand how people(millions of people) write. Small user input is required to start with, and the GPT-3 model will generate intelligent text following the submitted pattern and structure.

It is quite similar to the auto-completion feature which occurs when you type something in the Google search bar.

How it Works

GPT-3 is a language model, which follows a statistical program that predicts the probable sequence of words. Trained on an extensive dataset (from sources like Common Crawl, WebText2, Books1, Books2, Wikipedia, and more), GPT-3 has been exposed to millions of conversations and can easily calculate & predict which word should come next in relation to the words around it.

At its core, GPT-3 basically works like a transformer model. The transformer model is a sequence-to-sequence deep learning model that can produce a sequence of text given an input sequence. These models are designed for text generation tasks such as text summarization, question-answering, and machine translation.

GPT-3 has a special ability to respond intelligently to minimal input. It has been extensively trained on billions of parameters, and only needs a handful of prompts or examples to perform the specific task you desire — this concept is known as “few-shot learning.”

For example, after analyzing thousands of poets and poems, we can simply input the name of a poet, and GPT-3 can create an original poem based on the author’s style. GPT-3 replicates the texture, genre, rhythm, vocabulary, and style of the poet’s previous works to generate new ones.

GPT-3 works as a cloud-based LMaas (language-mode-as-a-service) offering instead of just a mere download. By making GPT-3 an API, OpenAI seeks to more safely control access and rollback functionality, in order to avoid the manipulation of technology.

Why GPT-3 is so powerful

Recently GPT-3 has emerged as one of the most popular AI tools because it can perform a wide variety of natural language tasks and generates human-like text. The tasks performed by GPT-3 include, but are not limited to:

• Sentimental analysis
• Question answering
• Text generation
• Text summarization
• Named-entity recognition
• Language translation

Based on the tasks performed by GPT-3, we can think of it as a model that can perform reading and writing tasks at a near-human level. In some examples of GPT-3, users have been able to generate an entire story written through AI.

Based on the context, GPT3 can generate the rest of a story.

This is exactly why GPT-3 is so powerful. There are so many startups that have been created with GPT-3 because it can be used as a general-purpose swiss army knife for solving a wide variety of problems in natural language processing.

Use cases of GPT-3

Although GPT-3 is an auto-completion tool, it can be utilized for a number of tasks. The proposed GPT-3 model use-cases include

• Maintaining a conversation.
• Passing a sentence into a mathematical expression.
• Generating news articles.
• Designing/creating interface layouts.
• Creating pieces of code.
• Translating code to different programming languages.
• Improving keyboard predictions on devices.
• Mass marketing across the web.
• Creating characters and stories for video games to allow varied user experiences.

GPT-3 is now in the beta testing phase, its new uses are constantly being discovered. And it is more likely that OpenAI will continue to update the new use-cases in the future, as more users interact with GPT-3.

Downsides to GPT-3

GPT-3 represents a significant step in the evolution of AI technology which delivers robust solutions, but it still has room to grow. However, despite its potential, there are a few downsides to this powerful deep learning model:

• Lack of true intelligence: GPT-3 is a deep learning model that uses machine learning algorithms, but it’s still not “intelligence.” It only uses already existing text to predict future results — it lacks true understanding and meaning as it’s not necessarily coming up with anything truly original.
• Privacy risk: It’s still unclear whether GPT-3 retains any portion of the training data, which can lead to potential privacy issues.
• Bias: GPT-3 can be fooled into creating incorrect, sexist, racist, and biased content that lacks common sense and real-world sensibility. The model’s output is purely dependent on its input: garbage in, garbage out.

Wrapping Up

GPT-3 is the next big thing in the field of deep learning after Netscape Navigator, and it’s expected to bring a change to the world. It is like tapping into a brain that stores the best knowledge and information. In the future GPT-3 will act as a great helper to humankind in all fields, including teaching, software development, writing poetry, & even comprehending large volumes of text.

GPT-3 can also contribute to advanced learning and development, harnessing the information learned from the best reliable sources.

Happy Learning!