In the rapidly evolving field of AI, the distinction between foundation models and task models is critical for understanding how modern AI systems work. Foundation models, like GPT-4 or BERT, provide…
Pretraining vs fine-tuning in AI: what each stage does, masked and causal language modeling, key differences, why they are separate, and real uses.
An embedding is the “translator” that converts language into numbers, enabling AI models to understand and process human language. AI doesn’t comprehend words, sentences, or syntax—it only works with…
Fine-tuning is a key process in AI training, where a pre-trained model is further trained on specific data to specialize in a particular task or domain.
Today’s topic might seem a bit technical, but don’t worry—we’re keeping it down-to-earth.
This was covered in a previous issue: What Are Parameters? Why Are “Bigger” Models Often “Smarter”?
Prompt Engineering is a core technique in the field of generative AI. Simply put, it involves crafting effective input prompts to guide AI in producing the desired results.
In deep learning, parameters are the trainable components of a model, such as weights and biases, which determine how the model responds to input data. These parameters adjust during training to…