Introduction: The Emergence of Transformer-Driven Copilots

Get an overview of what we will cover in this chapter.

We'll cover the following

When Industry 4.0 (I4.0) reaches maturity, it will all be about machine-to-machine connections, communication, and decision-making. AI will be primarily embedded in ready-to-use, pay-as-you-go cloud AI solutions. Big tech will absorb the most talented AI specialists to create APIs, interfaces, and integration tools.

AI specialists will go from development to design to becoming architects, integrators, and cloud AI pipeline administrators. AI is becoming a job for engineer consultants more than engineer developers.

Chapter overview

We've described foundation models, or transformers that can do NLP tasks they were not trained for. In addition, we expanded foundation model transformers to task-agnostic models that can perform vision tasks, NLP tasks, and much more.

This chapter will extend task-agnostic OpenAI GPT-3 models to a wide range of copilot tasks. A new generation of AI specialists and data scientists will learn how to work with AI copilots to help them generate source code automatically and make decisions.

This chapter begins by exploring prompt engineering in more detail. The example task consists of converting meeting notes into a summary. Transformers boost our productivity. However, we will see how natural language remains a challenge for AI.

We will learn how to use OpenAI Codex as a copilot. GitHub Copilot suggests source code as we write our programs using Codex. Codex can also convert natural language into code.

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