Combining deterministic machine learning models with probabilistic large language models (LLMs) helps us reach the accuracy levels we target.

Large language models (LLMs) are primarily designed to regurgitate information based on statistical patterns in the text they've been trained on, which limits their ability to invent new concepts or solutions.

Large Language Models (LLMs) function like a new operating system, being developed by labs and distributed like utilities, reminiscent of computing in the 1960s.

Achieving high accuracy in enterprise use cases is challenging, especially when integrating SQL with LLMs, which do not work well together.

The introduction of large language models (LLMs) provides a language interface for robots, allowing for easier task descriptions and enabling robots to perform generic tasks more effectively.

Large language models should be referred to as large event models because they are not only effective in language but also applicable in pharmaceuticals and various other fields.

The advancements in large language models (LLMs) have enabled a language interface for robots, allowing them to perform a wider range of tasks more efficiently, such as laundry folding and packing egg crates.

Many highly skilled professionals are leaving their jobs to start new ventures because they believe large language models (LLMs) are powerful tools for innovation.

As we scale large language models (LLMs), we will eventually encounter a data wall, which limits the effectiveness of current scaling techniques.