Artificial Intelligence Faces Data Shortage

## Artificial Intelligence Faces Data Shortage: Why It's Getting Harder for Neural Networks to Access Training Data

The world of artificial intelligence (AI) is rapidly evolving, but it faces an unexpected obstacle: a shortage of data for training. According to the latest research by Data Provenance, the volume of content available for use by neural network developers has sharply decreased. This trend has become particularly noticeable in recent years, as reported by The New York Times.

Data Provenance analysts examined around 14,000 domains and found that many online platforms have implemented restrictions on data collection from their sites. This significant change in data access threatens the development and effectiveness of AI models, which rely on large amounts of data for training and improving their algorithms.

### Why Is Data So Important for AI?

Successful training of AI models requires vast amounts of data. This data is used to create algorithms that can recognize patterns, make decisions, and predict outcomes. Without sufficient data, training becomes less effective, and models may suffer from a lack of accuracy and reliability.

### Restrictions on Data Collection

Increasing restrictions on data collection by online platforms are driven by several factors. First, there is growing attention to privacy and data protection issues. With the introduction of laws such as the General Data Protection Regulation (GDPR) in Europe, companies are obligated to protect users' personal data, leading to restricted access.

Second, platforms aim to protect their intellectual property and commercial interests. They understand that data is a valuable resource that can be monetized, and thus they limit third-party access to their data.

### Impact on AI Development

Restrictions on data collection have serious consequences for AI development. Developers face challenges in training their models, which can slow progress in the field. This can result in AI becoming less accurate and effective in performing tasks such as natural language processing, image recognition, or autonomous driving.

### Seeking Alternative Data Sources

To overcome this problem, AI developers are forced to seek alternative data sources. One approach is to use synthetic data, which is artificially created to mimic real data. However, synthetic data may not always accurately reproduce all aspects of real data, limiting its usefulness.

Another approach is to collaborate with organizations that can provide access to necessary data based on confidentiality agreements and data-sharing partnerships. This allows developers to access a broader range of data without violating data protection laws.

### Conclusion

The shortage of data for training AI models poses a significant challenge for the advancement of this technology. Restrictions on data collection imposed by online platforms complicate the task for developers and may slow progress in AI. To overcome this issue, it is necessary to find new approaches to data collection and use, ensuring the continued development of AI and its application in various spheres of life.

In the future, more comprehensive regulation may be required to balance the interests of data protection and technological development. Only then can AI continue to evolve and make a significant contribution to our society.