Large language models, like ChatGPT, are trained on vast amounts of text data, allowing them to generate human-like responses.

Generative AI and large language models, like ChatGPT, are incredibly powerful technologies that will soon become even more powerful.

Large language models like ChatGPT can answer questions, write stories, and engage in conversation, but to build a business using this technology, you need to customize it for your specific use case to differentiate it from existing solutions.

The evolution of language models, particularly with the introduction of GPT-3, has demonstrated a significant leap in their ability to perform language tasks, even though they do not possess real-world knowledge.

The grounded thesis of language posits that human language is reflected in the operations of large language models, suggesting that the mechanisms for generating language in humans and machines are fundamentally similar.

I believe that if Tesla were tasked with creating a large language model, it would outperform existing models like ChatGPT, given our computing power and resources.

Language is an autoregressive model, and this autoregressive nature is fundamental to how we generate and understand language.

With the release of GPT-3, it seems the sky is the limit for this new paradigm of scaling large language models.

Developers are increasingly finding that a significant portion of their code is being generated by large language models, which raises questions about the future impact of these models on society.