Can We Teach AI To Talk? | #AI101

The video explores natural language processing (NLP), a subfield of AI focused on understanding and interpreting human language. It covers the history of NLP from rule-based systems in the 1950s to the introduction of machine learning in the 1980s. The video discusses modern techniques like deep learning and unsupervised learning, highlighting models like GPT-2. Challenges in NLP, such as language complexity and the Turing test, are also addressed.

What is the primary focus of natural language processing?

• Building hardware for AI applications

• Creating algorithms for image recognition

• Developing AI that can understand and interpret language

• Designing autonomous vehicles

What was a key characteristic of early NLP algorithms before the 1980s?

• They were based on deep learning

• They relied on preset rules

• They were primarily hardware-based

• They used unsupervised learning

How did the introduction of machine learning change NLP in the late 1980s?

• It shifted focus to image processing

• It introduced statistical methods using annotated corpora

• It focused on hardware improvements

• It eliminated the need for human intervention

What is a significant challenge of using unsupervised learning in NLP?

• It is only applicable to image data

• It is not compatible with deep learning

• It is difficult to develop good models without labels

• It requires a large amount of labeled data

What is GPT-2 known for in the context of NLP?

• Generating text using labeled data

• Being a rule-based language model

• Creating text without labeled data

• Focusing on image recognition