ChatGPT and Prompt Engineering With Advanced Data Analysis - Technology behind ChatGPT (In Simple Terms)

The video tutorial explains the technology behind Chart GBT, focusing on artificial intelligence, machine learning, and deep learning. It discusses the differences between weak and strong AI, and delves into neural networks, including artificial, convolutional, and recurrent neural networks. The tutorial highlights the limitations of RNNs and introduces Transformers, emphasizing their self-attention mechanism and encoder-decoder structure. The video concludes with a focus on ChatGPT, a transformer-based language model, and its applications in language tasks.

What are the main technologies that enable artificial intelligence?

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Explain the difference between structured data and unstructured data.

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What is the significance of the hidden layer in a neural network?

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Describe the differences between a feed forward neural network and a recurrent neural network.

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What are transformers and how do they improve upon RNNs?

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What role does the self-attention mechanism play in transformer models?

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Summarize the main applications of the transformer decoder model.

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