Source URL: https://www.theregister.com/2025/10/03/ai_training_requires_more_data/
Source: The Register
Title: AI devs close to scraping bottom of data barrel
Feedly Summary: Analysts at Goldman Sachs Global Institute say training is starting to hit its limits, enterprise info troves may be last hope
Those spiffy AI systems that tech companies keep promising require mountains of training data, but high-quality sources may have already run out—unless enterprises can unlock the information trapped behind their firewalls, according to Goldman Sachs…
AI Summary and Description: Yes
Summary: The text discusses insights from Goldman Sachs Global Institute regarding the challenges in training AI systems due to a depletion of high-quality data sources. It suggests that enterprises may need to leverage their internal data repositories to fuel AI training efforts.
Detailed Description: The insights presented by analysts at Goldman Sachs highlight a critical issue in the realm of AI development and information security. As AI technologies continue to advance, the need for robust training data becomes increasingly vital. The emphasis on enterprise data indicates potential intersections with various categories, particularly in Information Security and AI Security. Key points include:
– **Training Data Limitations**: The article points out that AI systems rely heavily on extensive amounts of quality training data, but suggests that the supply of such data may be dwindling.
– **Enterprise Data Utilization**: It presents the notion that internal business information, often stored securely behind firewalls, represents a significant opportunity for organizations to boost their AI training processes.
– **Security Implications**: Unlocking and accessing this data necessitates careful attention to security protocols, compliance requirements, and potential privacy issues, as enterprises must navigate safeguarding sensitive information while leveraging it for AI development.
– **Industry Perspective**: The mention of Goldman Sachs provides a perspective grounded in financial analytics, which is relevant for organizations considering the economic and strategic investments in AI technologies.
These insights are particularly relevant for professionals involved in AI, cloud computing, infrastructure security, and data privacy, as they underline the balance between leveraging enterprise information for innovation and maintaining stringent security practices.