Researchers develop method to enable AI models to “forget” learned data efficiently and securely.
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Categories: Artificial Intelligence, Ethics & Society, Machine Learning, Privacy
December 10, 2024
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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. This breakthrough has far-reaching implications for various industries, including healthcare and finance.
The Paradigm of Large-Scale Pre-Trained AI Systems
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, 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.
Complications in AI Model Efficiency
Compounding these issues is that generalist tendencies may hinder the efficiency of AI models when applied to specific tasks. 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.’
A Potential Solution: Training Models to ‘Forget’
A potential solution lies in training models to ‘forget’ redundant or unnecessary information—streamlining their processes to focus solely on what is required. While some existing methods can remove specific data from a model’s memory, they often require direct access to the AI model’s internal architecture.
This limitation is known as the "black-box" problem. The researchers have successfully addressed this challenge by developing an innovative method that enables selective forgetting in black-box vision-language models without requiring direct access to their internal architecture.
Benefits of Helping AI Models Forget Data
Beyond its technical ingenuity, this innovation holds significant potential for real-world applications where task-specific precision is paramount. Simplifying models for specialised tasks could make them faster, more resource-efficient, and capable of running on less powerful devices—hastening the adoption of AI in areas previously deemed unfeasible.
Another key use lies in image generation, where forgetting entire categories of visual context could prevent models from inadvertently creating undesirable or harmful content, be it offensive material or misinformation. Perhaps most importantly, this method addresses one of AI’s greatest ethical quandaries: privacy.
Addressing AI’s Greatest Ethical Quandary: Privacy
AI models, particularly large-scale ones, are often trained on massive datasets that may inadvertently contain sensitive or outdated information. Requests to remove such data—especially in light of laws advocating for the ‘Right to be Forgotten’—pose significant challenges. Retraining entire models to exclude problematic data is costly and time-intensive, yet the risks of leaving it unaddressed can have far-reaching consequences.
‘Retraining a large-scale model consumes enormous amounts of energy,’ notes Associate Professor Irie. ‘“Selective forgetting,” or so-called machine unlearning, may provide an efficient solution to this problem.’
Applications in High-Stakes Industries
These privacy-focused applications are especially relevant in high-stakes industries like healthcare and finance, where sensitive data is central to operations.
Conclusion
The Tokyo University of Science’s black-box forgetting approach charts an important path forward—not only by making the technology more adaptable and efficient but also by adding significant safeguards for users. While the potential for misuse remains, methods like selective forgetting demonstrate that researchers are proactively addressing both ethical and practical challenges.
See Also:
- Why QwQ-32B-Preview is the reasoning AI to watch
- Want to learn more about AI and big data from industry leaders? Check out AI & Big Data Expo taking place in Amsterdam, California, and London.
- Explore other upcoming enterprise technology events and webinars powered by TechForge here.
Tags: ai, artificial intelligence, ethics, machine learning, privacy