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

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.

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

Large language models, like ChatGPT, demonstrate that language can self-generate based on its internal structure, indicating that the principles governing human language generation may be the same as those in these models.

The development of large language models (LLMs) like GPT-2 marked a significant turning point for OpenAI, showcasing the potential of AI.

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.

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.

Since the release of ChatGPT, we've seen a shift in enterprise interest from how generative AI works to how it can solve specific business problems.

The emergence of large language models has allowed us to create a conversational partner that we couldn't build before, enhancing the user experience.