Summary
The field of Natural Language Processing (NLP) has seen tremendous progress. In particular, algorithms for Natural Language Understanding (NLU) are evolving and improving at a stunning pace. Systems that seemed like science fiction as recently as ten years ago are now commonplace. One such system is GPT-3 (Generative Pretrained Transformer 3), a state-of-the-art model capable of generating human-like text for a wide range of tasks, such as conversation modeling, summarization, and question answering.
In this webinar Professor Christopher Potts will discuss the significance and implications of recent NLU developments including GPT-3. He will outline the fundamental building blocks of these new systems and describe how we can reliably assess and understand them.
- Technical capabilities, limitations, and applications of new Natural Language Understanding systems
- Analysis of the performance of GPT-3 on various language tasks
- Potential future developments in Natural Language Processing
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