A significant challenge in the AI industry is the scarcity of vertical datasets—clear, filtered, and up-to-date information specific to particular sectors. Vertical AI solutions are designed to address the unique challenges and opportunities within specific industries. By leveraging domain expertise and industry-specific data, these applications deliver unparalleled results, transforming business operations.
In the current phase of AI development, data and computing power are the fuel and engine of models. The quality, quantity, and diversity of data directly determine the final outcomes of the models. Tagging the vast amounts of data generated by human society with high-quality labels and using it promptly for model training has become as crucial as competing in computing power.
In addition to the continued high demand for text and image data in mainstream languages, there is a significant and unmet demand for data in other languages, specific content, and audio-visual media in vertical domains. As Jensen Huang mentioned in his conversation with UAE Minister Omar Al Olama, "Sovereign AI brings together your culture, social wisdom, common sense, and history. Your data is unique." This demand for specialized, state-invested data is expected to surge starting in 2024.
Even if you train long enough with enough GPUs, you’ll get similar results with any modern model. It’s not about the model, it’s about the data that it was trained with. The difference between performance is the volume and quality of data, especially human feedback data. You absolutely need it. That will determine your success.
- Ashiqur Rahman, Machine Learning Researcher, Kimberly-Clark
For a long time, there will be a significant demand for large-scale human-labeled data. This is not only because current AI tools and models lack sufficient accuracy and diversity in automatic labeling, but also because of the unique human understanding of the world and the emotional requirements beyond AI capabilities. These aspects are hard to replace in the short term. In the workflow of large models today, Reinforcement Learning from Human Feedback (RLHF) is crucial for model training and development. Therefore, the manual review of human-labeled data will also be irreplaceable by other tools in the near future.
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A survey reveals that 69% of respondent organizations rely on unstructured data such as text, images, and audio-visual media to train models. Among them, 35% consider data quality as the biggest challenge. To address this issue, 55% of organizations use internal labeling teams, 50% employ specialized data labeling services, and 29% utilize crowdsourcing for data labeling. Organizations are expanding the scale of their data labeling efforts, with an increasing demand for specialized, vertical domain labeled data.