Summary: The Emergence of Transformer-Driven Copilots

Get a quick recap of what we covered in this chapter.

This chapter described the rise of AI copilots with human-decision-making-level capability. Industry 4.0 has opened the door to machine interconnectivity. Machine-to-machine micro-decision making will speed up transactions. AI copilots will boost our productivity in a wide range of domains.

We saw how to use OpenAI Codex to generate source code while we code and even with natural language instructions.

We built a transformer-based recommender system using a dataset generated by the MDP program to train a RoBERTa transformer model. The dataset structure was a multi-purpose sequence model. A metahuman can therefore acquire multi-domain recommender functionality.

We then saw how a vision transformer could classify images processed as sequences of information.

Finally, we saw that the metaverse would make recommendations visible through a metahuman interface or invisible in deeply embedded functions in social media, for example.

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Course Introduction

Basics of Transformers

Architecture of the Transformer Model

Fine-Tuning BERT Models

Pretraining a RoBERTa Model from Scratch

Downstream NLP Tasks with Transformers

Machine Translation with the Transformer

The Rise of Transformers with GPT-3 Engines

Applying Transformers for AI Text Summarization

Matching Tokenizers and Datasets

Transformers for NLP—Exam 1

Semantic Role Labeling with BERT-Based Transformers

Your Data Speaks: Story, Questions, and Answers

Detecting Customer Emotions to Make Predictions

Analyzing Fake News with Transformers

Interpreting Black Box Transformer Models

From NLP to Task-Agnostic Transfomer Models

The Emergence of Transformer-Driven Copilots

Transformers for NLP—Exam 2

Conclusion

Appendix

Summary: The Emergence of Transformer-Driven Copilots