Catastrophic Forgetting: The AI Brain Drain

Catastrophic forgetting is a problem in artificial intelligence where a model trained on one task or dataset suddenly loses its ability to perform well on that original task after being trained on a new task.

Source: Generated by AI - Imagen 3 | Gemini

It occurs when a neural network forgets previously learned information upon learning new data. This is because the network overwrites old memories with new ones. It's like a student who cram for a test and ace it but forgets everything they learned immediately afterward. This makes it challenging to train AI models to perform multiple tasks sequentially.

Types

There are different types of catastrophic forgetting:

Task-Level Forgetting: 

A model forgets a task after learning a new, unrelated one. For example, a model that classifies animals forgets this after learning to classify vehicles.

Domain-Level Forgetting:

It is when a model retrained on new domain data forgets the original domain. Example: A model that recognizes handwritten digits forgets this after training on stylized digits.

Class-Level Forgetting:

A model forgets older classes as new ones are added.

Feature-Level Forgetting:

New training erases important features from earlier training.

Sequential Data Forgetting:

The model overfits recent data and forgets earlier sequences.

Uses

Understanding catastrophic forgetting is important for developing AI models that can learn continuously and adapt to new information without forgetting what they've learned. This is crucial for applications like:

Robotics: A robot learning new skills shouldn't forget previously learned ones.

Natural language processing: A language model should retain knowledge of older texts while learning new ones.

Personalized AI assistants: An assistant should remember user preferences over time, even after updates.

Examples

• An image recognition model trained to identify cats, then trained to identify dogs, may forget how to identify cats.
• A language model trained on English text, then trained on Spanish text, may lose its fluency in English.