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Structured and Labeled Audiovisual Data Collection to Improve AI Training
Structured and Labeled Audiovisual Data Collection to Improve AI Training
The development of Artificial Intelligence (AI) largely depends on the quality and quantity of the data it is trained with. Currently, most of this data comes from the internet and human interactions with AI trainers. However, to achieve a deeper understanding of the world, AI needs structured, labeled audiovisual data with detailed metadata.
This process involves collecting images and videos with detailed descriptions, contextual tags, and specific annotations about visual elements, actions, and spatial relationships. Additionally, it can include graphics, diagrams, and other types of visual content accompanied by explanations that enable AI to develop a richer and more precise understanding of the real world.
To ensure data quality, the certification of human AI agents is proposed. These certified individuals would be responsible for providing and verifying data under rigorous standards of accuracy, diversity, and representativity. This certification system would guarantee data reliability and enable scalable and regulated data collection.
This approach has similarities with Teslaβs autonomous driving systems, which use a combination of cameras, sensors, and real-time processing to interpret the environment and make driving decisions. Teslaβs AI is trained with data from millions of miles driven, using images and object detections processed in real-time to generate appropriate responses.
However, the proposed structured data collection approach differs in several key aspects:
While autonomous cars process images and data in real-time for immediate decision-making, human data collection focuses on prior training to enhance AIβs capabilities in multiple areas, from visual recognition to semantic and contextual interpretation.
Tesla collects data without direct human intervention in annotating each real-time event. In contrast, this approach includes human verification and detailed structuring before the data is used for AI training.
Autonomous driving focuses on mobility, whereas structured data collection for AI can be applied to various fields, including image recognition, education, medical assistance, and accessibility.
One key application of this proposal is assistance for blind people through advanced AI capable of interpreting real-time images and transmitting information to the user via sound and haptic feedback.
The ideal device for this integration would be a system based on Metaβs Ray-Ban glasses, equipped with cameras, sensors, and advanced visual AI. This system could:
Capture real-time images and process them with an AI trained with structured and labeled data.
Interpret what it sees and provide a natural language audio description for the blind user to understand their environment.
Use haptic feedback to alert about obstacles, directions, or relevant environmental information.
Dynamically adapt to different situations, such as recognizing faces, reading signs, or detecting potential dangers.
This system would take accessibility to a new level, allowing blind individuals to have a more detailed and precise perception of the world around them, combining artificial vision, real-time processing, and multimodal interaction
Structured audiovisual data collection is a fundamental piece for AI advancement in multiple areas. By integrating this proposal with existing technologies like Metaβs Ray-Ban glasses, revolutionary devices can be developed, allowing blind people to "see" their environment through a combination of visual AI, audio, and haptics. This approach not only enhances accessibility but also represents a significant leap in human-machine interaction, opening new possibilities for technological development and social inclusion.
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