‘BETTER’ AI via Continuous Site-Specific Data and Daily Audits: Overcoming AI’s Biggest Challenges

Introduction: Many AI models struggle due to their reliance on generic data, which often leads to inaccuracies in businesses with specific operational needs.

COM-SUR, the world’s only CCTV video footage auditing and intelligent incident reporting software, solves this challenge. While solving the typical pain points of video surveillance, COM-SUR now embarks upon solving a very big problem in AI - data integrity. It provides the foundation for creating custom AI models by storing years of real-time, site-specific data. This allows AI companies to train models on data that is specific to a client’s operations, enhancing accuracy and adaptability. Importantly, COM-SUR works with any type of camera, not just AI-enabled or smart cameras, making it a versatile solution for organizations utilizing legacy systems.by generating continuous site-specific data. This ensures that custom AI models are built on relevant, fresh data, addressing major issues like data exhaustion, data wall, and synthetic data limitations.

Reinforcement Learning from Human Feedback (RLHF) and Explainable AI (XAI)

COM-SUR is a tool that democratizes AI by generating continuous, site-specific data from real-world surveillance sources such as CCTV cameras, drones, body-worn devices, dashcams, and even mobile phones. It functions as a data foundry, centralizing data collection, processing, storage, and validation from various sources, and transforming raw data into usable formats. Coupled with Reinforcement Learning from Human Feedback (RLHF) and Explainable AI (XAI), COM-SUR creates a continuous feedback loop where human insights refine AI models in real-time. COM-SUR bridges the gap between synthetic and real data, ensuring AI models continuously access the real-world data they need to remain relevant and accurate.

It is accepted that Artificial Intelligence is only as good as the data that feeds it. Today, AI companies face a significant challenge: generic AI models fail to meet the specific needs of individual businesses, and the availability of high-quality, accessible data is rapidly declining, leading to what is called the data wall. Many public datasets that AI relied on are now restricted, paywalled, or blocked, resulting in data exhaustion. Furthermore, these data issues often trigger data cascades—a chain of compounding problems caused by poor data quality, leading to downstream performance degradation in AI models.

RLHF and XAI directly involve human auditors in refining and improving AI models in real time. By integrating human insights into AI decision-making processes, COM-SUR ensures AI systems become more accurate, transparent, and accountable. For instance, RLHF allows AI models to continuously learn from human feedback, enhancing better accuracy and reducing bias. XAI, meanwhile, promotes transparency by making AI decisions understandable, fostering trust and accountability in AI systems.

The Pain Points of AI: Why Data Matters

AI models face several critical challenges today:

• Generic AI Models Lack Specificity: Most AI systems are trained on generic datasets, leading to inaccuracies for businesses with unique requirements, and which therefore need custom models.
• Data Wall and Data Exhaustion: Declining access to public datasets forces companies to rely on restricted or paywalled data, leading to data limitations.
• Synthetic vs. Real Data: While synthetic data offers advantages, it often lacks the unpredictability and richness of real-world data.
• Bias in AI Models: Training AI on static or synthetic datasets without real-world feedback can lead to bias in decision-making processes.
• Managing Massive Data Volumes: Surveillance systems generate large amounts of footage, posing challenges for AI systems to process and analyze efficiently.

How COM-SUR Solves These Challenges

COM-SUR tackles these challenges directly:

• Facilitating Custom AI Models: COM-SUR enables site-specific data collection and long-term storage. This empowers AI models to train on real-world data specific to each business.
• Overcoming the Data Wall: COM-SUR continuously generates fresh, site-specific data from surveillance cameras, ensuring that AI models remain relevant and avoiding data exhaustion.
• Bridging the Synthetic and Real Data Divide: By capturing real-world surveillance footage, COM-SUR offers a richer dataset that complements synthetic data, ensuring a more accurate AI model.
• RLHF and Human Feedback Integration: COM-SUR empowers human auditors to refine AI models making enabling users to audit hours of footage in minutes. This integration enables continuous learning, improving model accuracy while reducing bias.
• Efficient Data Scaling: COM-SUR provides data compression and smart backup solutions, allowing businesses to manage large volumes of data without overwhelming their infrastructure.

The COM-SUR Advantage: Democratizing AI

COM-SUR democratizes AI by allowing organizations of all sizes to generate high-quality, site-specific data without the need for expensive infrastructure or deep technical expertise. Through its continuous data generation, small businesses and non-technical users can implement COM-SUR easily, improving AI responsiveness to real-world conditions while avoiding data cascades where poor-quality data leads to compounding issues.

Conclusion: The Future of AI Lies in Continuous Data Production and Human-Led Insights

As the AI industry faces challenges such as data exhaustion, data cascades, and the limitations of synthetic data, COM-SUR stands out as a vital solution. By generating real-time, site-specific frontier data and enabling continuous human-in-the-loop learning through RLHF, COM-SUR ensures AI models remain contextually aware, responsible, and adaptable.

For any organization in the computer vision space, COM-SUR transforms surveillance video from a static tool into a dynamic resource for AI development. Besides solving the typical pain points of surveillance systems, COM-SUR allows for smarter, scalable, and contextually relevant AI models that can tackle real-world challenges, all while avoiding the negative effects of data cascades.

No wonder then that this new approach by COM-SUR is called ‘BETTER’ AI – NO MORE DATA WALL!

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