The article also explains the fundamentals of how LLMs work, including the concepts of probability, gradient descent, and fine-tuning. LLMs are probabilistic, predicting the likelihood of words and phrases based on input, and are trained using a technique called gradient descent. This technique involves comparing the model's outputs with training data and adjusting the model's parameters to make the outputs more like the training data. The author likens this process to a sophisticated auto-complete function and emphasizes that while this probabilistic approach has its weaknesses, it also allows for powerful and flexible applications.
Key takeaways:
- Modern AI models like ChatGPT are becoming more general-purpose tools, capable of being used in a variety of fields without needing specific training for each.
- These AI models are probabilistic, predicting the likelihood of words and phrases based on input, which allows for flexibility and adaptability.
- Training these AI models involves a technique called 'gradient descent', which incrementally adjusts the model's parameters to improve its output.
- While these AI models are powerful and versatile, they are not all-knowing and still require careful management and oversight to ensure their effective use.