hmt cbp

 

--- HUMAN MACHINE TEAMING ---

Integration Strategy

To enhance the capabilities of  IBM Federal DHS (CBP) with Equitus.ai KGNN, Equitus Video Sentinel, WatsonX, Granite, and Bee Agents, the following integration strategy can be implemented:

1. Data Unification with Equitus.ai KGNN

   - Purpose: KGNN (Knowledge Graph Neural Network) can unify and process CBP's vast datasets, breaking down silos and enabling actionable intelligence.

   - Integration with WatsonX:

     - Use Watson X's AI tools to preprocess raw data for KGNN ingestion.

     - Deploy KGNN to structure data into semantically rich formats, enabling retrieval-augmented generation (RAG) pipelines for real-time decision-making[2][8].

   - CBP Use Case:

     - Support border operations by analyzing and correlating data from various sources, such as surveillance systems, customs records, and threat databases[4].   

 2. Enhanced Surveillance with Equitus Video Sentinel

   - Purpose: Video Sentinel provides advanced video analytics, including real-time object detection, anomaly recognition, and behavior analysis.

   - Integration with Granite Models:

     - Combine Sentinel’s video analytics with Granite’s language models to generate detailed summaries of surveillance footage.

     - Use WatsonX Orchestrate to automate alerts and responses based on Sentinel’s insights[2][5].

   - CBP Use Case:

     - Deploy Video Sentinel at border checkpoints to monitor live feeds for suspicious activities, contraband detection, or unauthorized crossings[4][5].

 3. Autonomous Operations with Bee Agent Framework

   - Purpose: Bee Agents enable modular workflows that automate specific tasks using AI models.

   - Integration with WatsonX and Equitus Systems:

     - Develop Bee Agents to interface with KGNN and Video Sentinel outputs, automating threat prioritization and resource allocation.

     - Use WatsonX APIs to integrate Bee Agents into CBP's operational workflows for real-time decision-making[3][6].

   - CBP Use Case:

     - Automate routine tasks like license plate recognition or cargo inspection, reducing cognitive load on officers while improving efficiency[4].

 4. Ethical AI Deployment

   - Ensure compliance with CBP’s "Responsible AI" principles by leveraging Watson X’s transparency features to explain AI decisions.

   - Integrate ethical oversight mechanisms into all AI workflows to respect human dignity and rights while maintaining operational effectiveness[4][7].

 5. Scalable Edge Computing

   - Deploy Equitus solutions on IBM Power10 servers equipped with Matrix Math Accelerators (MMA) for efficient edge computing without GPUs.

   - This setup ensures low-latency processing for real-time surveillance and data analysis at border checkpoints or remote locations[2][5].

 6. Training and Trust Building

   - Conduct training programs for CBP officers to familiarize them with AI tools like KGNN, Video Sentinel, and Bee Agents.

   - Build trust in Human-Machine Teaming by demonstrating the reliability and accuracy of these systems in simulated environments[1][4].

By integrating these technologies, IBM Federal DHS (CBP) can significantly enhance its ability to process vast datasets, monitor borders more effectively, and automate routine tasks while adhering to ethical AI standards.

Citations:

[1] https://www.pnnl.gov/sites/default/files/media/file/PNNL-SA-158996_Human-Machine_Teaming.pdf

[2] https://nas01.tallpaul.net/wordpress/2024/10/equitus-ai/

[3] https://suedbroecker.net/2024/10/23/an-example-of-how-use-the-bee-agent-framework-v0-0-33-with-watsonx-ai/

[4] https://www.cbp.gov/newsroom/spotlights/artificial-intelligence-harness-key-insights-cbp

[5] https://equitus.ai

[6] https://community.ibm.com/community/user/watsonx/blogs/armand-ruiz-gabernet/2024/12/01/meet-the-bee-stack-everything-you-need-to-run-agen

[7] https://www.dhs.gov/ai/using-ai-to-secure-the-homeland

[8] https://equitus.ai/2024/05/equitus-ai-shines-at-sof-week-2024-empowering-defense-and-commercial-organizations-with-advanced-ai-solutions/