Adaptability

As explained in Deep Learning with R, Third Edition

Adaptability is the ability of a system or agent to adjust its behavior in response to unexpected conditions or new information.

Adaptability is the ability to face unexpected conditions, adjust behavior, and learn from new information. It separates intelligence from automation: automation follows situations anticipated during training or programming, while an adaptable agent can respond beyond them. section 1.10

Deep learning models often struggle with novelty because their parameters are fixed after training. At inference time, they retrieve patterns from past data and may fail when situations fall outside that distribution. section 19.1.1

Adaptability becomes visible when a system encounters conditions unlike those represented in its training experience:

• A model trained on ImageNet may classify a leopard-print sofa as a leopard because sofas were absent from its training data. section 19.1.1
• Large language models may reuse memorized text-processing patterns successfully, yet become helpless when a problem has no direct equivalent in their training data. section 19.1.1

Novelty as a test

An easy puzzle can still expose weak adaptability if it presents a configuration not directly represented in training data. Solving such a problem requires more than retrieving a familiar pattern. section 19.1.1

Figure 19.1 An easy yet novel puzzle

Use the figure as a concrete reference for a novel puzzle that challenges pattern retrieval. section 19.1.1

How Deep Learning with R, Third Edition covers it

How deep it goes

Defines adaptability conceptually and illustrates failure on novelty, but does not provide mechanisms for building adaptive systems.

Examples

Uses a leopard-print sofa classification error, memorized LLM programs, and a novel puzzle to demonstrate failure beyond training data.

What you can do after

Evaluate whether a system retrieves familiar patterns or adapts when faced with situations outside its training distribution.

How it connects

Connects adaptability with intelligence, automation, deep learning models, inference, training data distributions, and large language models.

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Also explained in

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  Generative AI in ActionModel adaptation is the adjustment of a generative AI model so that its outputs perform better for particular tasks or domains.
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  The Art of AI Product DevelopmentAdaptability is the capability of a system to detect changing conditions and adjust its behavior in response to them.
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  Effective Conversational AIAdaptiveness is the ability of a conversational system to adjust its response to the user's wording, tone, context, or requested level of detail.
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  The Creative ProgrammerThe capacity to adjust behavior, methods, or knowledge to changing situations and requirements.

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Related concepts

• low-rank adaptationincludes · Low-rank adaptation is a parameter-efficient fine-tuning method that adapts a model by learning low-rank updates to its weights, typically while keeping most original weights fixed.
• fine-tuningincludes · Fine-tuning is adapting a pretrained machine-learning model by training it further on targeted data or objectives for a task, domain, or behavior.
• retrieval-augmented generationproduced by · Retrieval-augmented generation is a technique that retrieves relevant information from external sources and supplies it as context to a generative model when producing an output.
• language modelcontrasts with · A language model is a computational model that represents patterns in linguistic tokens to estimate sequence probabilities and predict or generate language from context.
• foundational modelused by · A base or foundation model is a pretrained model that provides learned parameters or capabilities as a starting point for reuse or adaptation to downstream tasks.

This explainer was generated by Manning's knowledge graph from the text of Deep Learning with R, Third Edition. The section links point to the parts of the book it draws on.