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Exploring GPT-3

You're reading from   Exploring GPT-3 An unofficial first look at the general-purpose language processing API from OpenAI

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Product type Paperback
Published in Aug 2021
Publisher Packt
ISBN-13 9781800563193
Length 296 pages
Edition 1st Edition
Languages
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Author (1):
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Steve Tingiris Steve Tingiris
Author Profile Icon Steve Tingiris
Steve Tingiris
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Table of Contents (15) Chapters Close

Preface 1. Section 1: Understanding GPT-3 and the OpenAI API
2. Chapter 1: Introducing GPT-3 and the OpenAI API FREE CHAPTER 3. Chapter 2: GPT-3 Applications and Use Cases 4. Section 2: Getting Started with GPT-3
5. Chapter 3: Working with the OpenAI Playground 6. Chapter 4: Working with the OpenAI API 7. Chapter 5: Calling the OpenAI API in Code 8. Section 3: Using the OpenAI API
9. Chapter 6: Content Filtering 10. Chapter 7: Generating and Transforming Text 11. Chapter 8: Classifying and Categorizing Text 12. Chapter 9: Building a GPT-3-Powered Question-Answering App 13. Chapter 10: Going Live with OpenAI-Powered Apps 14. Other Books You May Enjoy

Introduction to GPT-3

In short, GPT-3 is a language model: a statistical model that calculates the probability distribution over a sequence of words. In other words, GPT-3 is a system for guessing which text comes next when text is given as an input.

Now, before we delve further into what GPT-3 is, let's cover a brief introduction (or refresher) on Natural Language Processing (NLP).

Simplifying NLP

NLP is a branch of AI that focuses on the use of natural human language for various computing applications. NLP is a broad category that encompasses many different types of language processing tasks, including sentiment analysis, speech recognition, machine translation, text generation, and text summarization, to name but a few.

In NLP, language models are used to calculate the probability distribution over a sequence of words. Language models are essential because of the extremely complex and nuanced nature of human languages. For example, pay in full and painful or tee time and teatime sound alike but have very different meanings. A phrase such as she's on fire could be literal or figurative, and words such as big and large can be used interchangeably in some cases but not in others—for example, using the word big to refer to an older sibling wouldn't have the same meaning as using the word large. Thus, language models are used to deal with this complexity, but that's easier said than done.

While understanding things such as word meanings and their appropriate usage seems trivial to humans, NLP tasks can be challenging for machines. This is especially true for more complex language processing tasks such as recognizing irony or sarcasm—tasks that even challenge humans at times.

Today, the best technical approach to a given NLP task depends on the task. So, most of the best-performing, state-of-the-art (SOTA) NLP systems are specialized systems that have been fine-tuned for a single purpose or a narrow range of tasks. Ideally, however, a single system could successfully handle any NLP task. That's the goal of GPT-3: to provide a general-purpose AI system for NLP. So, even though the best-performing NLP systems today tend to be specialized, purpose-built systems, GPT-3 achieves SOTA performance on a number of common NLP tasks, showing the potential for a future general-purpose NLP system that could provide SOTA performance for any NLP task.

What exactly is GPT-3?

Although GPT-3 is a general-purpose NLP system, it really just does one thing: it predicts what comes next based on the text that is provided as input. But it turns out that, with the right architecture and enough data, this one thing can handle a stunning array of language processing tasks.

GPT-3 is the third version of the GPT language model from OpenAI. So, although it started to become popular in the summer of 2020, the first version of GPT was announced 2 years earlier, and the following version, GPT-2, was announced in February 2019. But even though GPT-3 is the third version, the general system design and architecture hasn't changed much from GPT-2. There is one big difference, however, and that's the size of the dataset that was used for training.

GPT-3 was trained with a massive dataset comprised of text from the internet, books, and other sources, containing roughly 57 billion words and 175 billion parameters. That's 10 times larger than GPT-2 and the next-largest language model. To put the model size into perspective, the average human might read, write, speak, and hear upward of a billion words in an entire lifetime. So, GPT-3 has been trained on an estimated 57 times the number of words most humans will ever process.

The GPT-3 language model is massive, so it isn't something you'll be downloading and dabbling with on your laptop. But even if you could (which you can't because it's not available to download), it would cost millions of dollars in computing resources each time you wanted to build the model. This would put GPT-3 out of reach for most small companies and virtually all individuals if you had to rely on your own computer resource to use it. Thankfully, you don't. OpenAI makes GPT-3 available through an API that is both affordable and easy to use. So, anyone can use some of the most advanced AI ever created!

You have been reading a chapter from
Exploring GPT-3
Published in: Aug 2021
Publisher: Packt
ISBN-13: 9781800563193
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