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

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.

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.

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.

By allowing models to think longer, large language models can leverage more compute on the fly, enhancing their intelligence for complex problems.

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

Large Language Models (LLMs) are not effective for predictions; combining them with machine learning models enhances enterprise accuracy.

While there are significant potential benefits to large language models, we must tread carefully due to the ethical minefield they present.

Further research confirmed that scaling laws apply to various models, including those for text-to-image and image-to-text tasks, while in early 2020, these laws for large language models (LLMs) were largely unknown outside of OpenAI, except for one researcher who focused on the scaling hypothesis, suggesting that increasing the size of data and compute would lead to the emergence of intelligence.