Advanced Chatbots with Deep Learning and Python - Conventional Versus AI Chatbots

The video tutorial compares conventional and AI-driven chat bots. Conventional chat bots rely on manually created keyword-driven rules, making them difficult to train and scale. In contrast, AI-driven chat bots use deep learning, allowing them to learn from interactions, handle spelling errors, and scale easily. The video highlights the differences in training, scalability, and maintenance between the two types of chat bots.

What is a key characteristic of conventional chat bots?

• They are driven by deep learning.

• They are easy to scale.

• They automatically learn from interactions.

• They rely on manually drafted rules.

Why are conventional chat bots difficult to train?

• They require a lot of computational power.

• They are based on AI algorithms.

• They lack coherence between rules.

• They are too flexible.

Which type of chat bot is more likely to require frequent maintenance?

• Neither requires maintenance

• Conventional chat bots

• AI-driven chat bots

• Both require the same level of maintenance

What is a major advantage of AI-driven chat bots over conventional ones?

• They require more manual input.

• They are keyword-driven.

• They learn from real interactions.

• They are harder to scale.

How do AI-driven chat bots handle spelling mistakes?

• They cannot handle spelling mistakes.

• They require manual correction.

• They learn from a database like Wikipedia.

• They ignore them.

What makes deep learning-based chat bots easy to scale?

• They require constant manual updates.

• They have a sophisticated system that learns over time.

• They are limited to specific keywords.

• They do not interact with users.

What is a benefit of deep learning-based chat bots in terms of audience?

• They can only handle a limited number of questions.

• They can understand a wide variety of questions.

• They are restricted to specific fields.

• They require manual intervention for each question.