Researchers Develop Method to Allow AI Models to "Forget" Previously Learned Data

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About the Author

By Ryan Daws

December 10, 2024

https://twitter.com/gadget_ry

Categories:

Artificial Intelligence
Ethics & Society
Machine Learning
Privacy

Ryan Daws is a senior editor at TechForge Media with over a decade of experience in crafting compelling narratives and making complex topics accessible. His articles and interviews with industry leaders have earned him recognition as a key influencer by organisations like Onalytica. Under his leadership, publications have been praised by analyst firms such as Forrester for their excellence and performance. Connect with him on X (@gadget_ry), Bluesky (@gadgetry.bsky.social), and/or Mastodon (@gadgetry@techhub.social)

Researchers from the Tokyo University of Science (TUS) have developed a method to enable large-scale AI models to selectively ‘forget’ specific classes of data.

Progress in AI has provided tools capable of revolutionising various domains, from healthcare to autonomous driving. However, as technology advances, so do its complexities and ethical considerations.

The Paradigm of Large-Scale Pre-Trained AI Systems

The paradigm of large-scale pre-trained AI systems, such as OpenAI’s ChatGPT and CLIP (Contrastive Language–Image Pre-training), has reshaped expectations for machines. These highly generalist models, capable of handling a vast array of tasks with consistent precision, have seen widespread adoption for both professional and personal use.

However, such versatility comes at a hefty price. Training and running these models demands prodigious amounts of energy and time, raising sustainability concerns, as well as requiring cutting-edge hardware significantly more expensive than standard computers. Compounding these issues is that generalist tendencies may hinder the efficiency of AI models when applied to specific tasks.

The Importance of Task-Specific Models

For instance, ‘in practical applications, the classification of all kinds of object classes is rarely required,’ explains Associate Professor Go Irie, who led the research. ‘For example, in an autonomous driving system, it would be sufficient to recognise limited classes of objects such as cars, pedestrians, and traffic signs.’

We would not need to recognise food, furniture, or animal species. Retaining classes that do not need to be recognised may decrease overall classification accuracy, as well as cause operational disadvantages such as the waste of computational resources and the risk of information leakage.

The Concept of ‘Forgetting’ in AI

A potential solution to these challenges is the concept of ‘forgetting’ in AI models. By selectively removing specific classes of data from a model’s training dataset, researchers can create task-specific models that are more efficient and effective.

This approach has several benefits, including:

Improved accuracy: Task-specific models can achieve better results on their target tasks by focusing on the most relevant data.
Increased efficiency: By removing unnecessary classes of data, models can process information faster and with fewer resources.
Enhanced privacy: Selective forgetting can help protect sensitive or outdated information from being used in AI decision-making processes.

The Tokyo University of Science’s Approach to Black-Box Forgetting

The Tokyo University of Science’s researchers have developed a method for inducing selective forgetting in black-box vision-language models. This approach, which they call "black-box forgetting," allows them to remove specific classes of data from the model without direct access to its internal architecture.

Key Findings

Their research has shown promising results:

They successfully induced selective forgetting in a black-box vision-language model, removing 40% of target classes.
The approach demonstrated improved performance on task-specific tasks and reduced computational resources required for training.

Applications of Selective Forgetting

The benefits of selective forgetting extend beyond the technical realm. This innovation has significant potential for real-world applications where task-specific precision is paramount.

Real-World Implications

Some possible applications include:

Image generation: Forgetting entire categories of visual context could prevent models from inadvertently creating undesirable or harmful content.
Privacy: Selective forgetting addresses one of AI’s greatest ethical quandaries: privacy. AI models, particularly large-scale ones, are often trained on massive datasets that may inadvertently contain sensitive or outdated information.

Conclusion

The Tokyo University of Science’s black-box forgetting approach charts an important path forward in the development of AI technology. By making AI more adaptable and efficient while adding significant safeguards for users, researchers can tackle both practical and ethical challenges.

References