Adaptation on English AI Terms Dictionary

Meta-learning

Sat, 18 Jul 2026 10:07:12 +0000

Definition

Meta-learning focuses on designing algorithms that can learn from previous tasks to improve performance on new, unseen tasks. Instead of training a model from scratch for each problem, it optimizes the learning process itself. This often involves few-shot learning, where the model generalizes from very few examples. Key strategies include gradient-based methods like MAML and memory-augmented networks. It is crucial for developing efficient, adaptable AI systems capable of rapid adaptation in dynamic environments without extensive retraining.

Fine

Sat, 18 Jul 2026 09:32:26 +0000

Definition

Fine-tuning involves taking a general-purpose model trained on large datasets and further training it on a smaller, specialized dataset to improve performance on specific tasks. This technique leverages existing knowledge while adjusting weights to fit new contexts, making it cost-effective and efficient. It is widely used in natural language processing and computer vision to achieve high accuracy without training models from scratch.

Summary

Fine-tuning refers to the process of adapting a pre-trained AI model to a specific task or domain with additional data.

Evolving

Sat, 18 Jul 2026 09:32:12 +0000

Definition

The term ’evolving’ characterizes dynamic AI models that undergo continuous learning and adaptation rather than remaining static after initial training. This concept is central to lifelong learning and online machine learning, where models update their parameters in real-time as they encounter new information. Evolution ensures that AI remains relevant and accurate in changing environments, mimicking biological adaptation processes to handle drift and novelty effectively.

Summary

Describes AI systems or algorithms that continuously adapt and improve over time through new data or feedback.

In-Context Learning

Sat, 18 Jul 2026 07:39:00 +0000

Definition

In-context learning (ICL) allows large language models to adapt to new tasks without updating their weights. By providing input-output pairs within the prompt context, the model infers the pattern and applies it to new queries. This zero-shot or few-shot capability enables rapid prototyping and flexibility, serving as a powerful alternative to traditional fine-tuning for tasks requiring quick adaptation to novel domains.

Summary

A technique where models learn to perform tasks by observing examples provided in the prompt.