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

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.

Despite increasing GPU resources, the intelligence improvements from the current generation of LLMs are not meeting expectations, with reports of failed training runs and diminishing returns emerging from major labs. Additionally, the lack of high-quality data to train new models has become a major bottleneck for AI development.

The trend in machine learning is towards convergence, with many different types of models shifting to a more unified transformer model.

AI models are valuable because they can interpolate between vague questions and provide tailored responses, even when data is messy.

AI models are valuable because they can interpolate between vague questions and provide tailored responses, even when data is messy.

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

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

The distinction between LLMs and AlphaZero lies in their foundational capabilities; LLMs are closer to achieving human-level intelligence with additional refinement.