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Synthetic Data in AI: Opportunities and Challenges
콘
콘텐주
Current situation
1.
Model Collapse Risk :
AI models trained with AI-generated content may degrade over time
Loss of information about the actual data distribution
As a result, it produces less diverse output that is biased and erroneous.
2.
Insufficient data :
The Internet Is Overflowing with AI-Generated Content
Lack of new human creation or natural data
3.
Solution for synthetic data :
Mimic the statistical properties of real data
Provide sufficient amount of data required for AI training
Ensures inclusion of multiple data points
Applications of synthetic data
Healthcare: Analyzing patient trends, developing diagnostic tools
Finance: Market trend forecasting, risk management
Customer Service: AI-Based Support System
Various Industries: Solving Model Collapse, Improving Data Privacy
Challenges and Risks
1.
Data Quality :
Ensuring accurate reflection of actual data characteristics
Ensuring privacy while maintaining statistical relevance
2.
Privacy Threats :
Reverse Engineering and De-anonymization Risks
Compliance issues such as GDPR
3.
Bias Amplification :
Potential to replicate and amplify existing biases
The difficulty of capturing rare but important nuances
4.
Limit emotional nuance :
Difficulty in capturing the full spectrum of human emotions
Emotions-Affecting AI Applications
Future impact
1.
Competitive Advantage :
Organizations with access to human-generated data have an advantage
2.
Need for guidelines :
Clear standards for responsible use of synthetic data
Strong security measures to prevent reverse engineering
Ensuring unbiased dataset
3.
Ethical Considerations :
Addressing the ethical implications of data sourcing
Adopting Fair Labor Practices in the AI Industry
4.
Data classification changes :
Going beyond the dichotomous classification of personal/non-personal data
Developing a granular approach to data regulation
5.
Balancing :
Managing the use of synthetic data while maintaining AI’s accuracy, diversity, and ethical standards
