Source URL: https://slashdot.org/story/25/10/02/191224/ai-has-already-run-out-of-training-data-goldmans-data-chief-says?utm_source=rss1.0mainlinkanon&utm_medium=feed
Source: Slashdot
Title: AI Has Already Run Out of Training Data, Goldman’s Data Chief Says
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AI Summary and Description: Yes
Summary: The text discusses a critical perspective on the current state of AI training data, highlighting the limitations developers face as they build new AI systems. It mentions the use of synthetic data and proprietary datasets as potential solutions, while emphasizing the importance of understanding data within its business context.
Detailed Description: The insights shared by Neema Raphael, Goldman Sachs’ chief data officer, reveal significant implications for professionals engaged in AI development and deployment. Key points include:
– **Training Data Shortage**: Raphael asserts that the AI field is running out of quality training data, which directly impacts how developers approach building AI systems.
– **Synthetic Data Use**: To combat the lack of fresh data, developers are leveraging synthetic data. This type of data, generated by machines, offers theoretically unlimited supply, but raises concerns regarding the quality of the output.
– **Impact of Proprietary Datasets**: The mention of proprietary datasets indicates that corporate data can enhance the value of AI tools, provided it is understood and normalized effectively in its business context.
– **Business Context**: Understanding the context of the data is crucial. Without this, the utility of AI solutions can be limited, underscoring the need for alignment between data attributes and business objectives.
– **Optimism in Data Utilization**: Despite concerns about data shortages, Raphael remains optimistic about the potential for deriving more insights from existing data sources, suggesting that there is still untapped potential within current datasets.
This dialogue is relevant not just to data scientists and AI developers but also to security and compliance professionals who must navigate the complexities of data governance, data privacy, and regulatory compliance as they operate within the realm of AI-driven technologies. Understanding the limits and capabilities of available data is essential for developing secure and compliant AI frameworks moving forward.