Welcome to Part 2 of our Decoding AI: A Government Perspective learning series. In the previous module, we embarked on a fascinating journey into the world of generative AI. As we progress, our aim is to delve deeper into the ways AI can shape the future of federal processes and governance. Today, we will shed light on the indispensable role of learning patterns and anomalies, and their significance in the world of AI. – Miri Rodriguez, Siddhartha Chaturvedi

How does AI think? Artificial intelligence finds patterns in a way similar to human cognition. In essence, AI learns from data by guessing the chances and changing its models, based on the many patterns it sees. Where AI truly stands out is in its unparalleled capacity to manage vast datasets, generating insights from the patterns it discerns. Concurrently, its prowess in anomaly detection functions as a vigilant guardian, alerting us to unforeseen challenges or looming threats by identifying deviations from established patterns, in record time.

Anomaly detection builds on AI’s ability to look at multiple patterns and decipher what’s the same, and what’s different, and highlighting divergence when it detects something out of the ordinary.

Federal Use Cases: Healthcare and Documentation

Let’s look at some real-life examples of how generative AI can help find anomalies for actionable solutions in government:

1. Biothreat Prevention with Proactive Monitoring: We all know that climate change has posed multifaceted challenges, some of which are less overt but all of which could have sweeping implications. Take the realm of public health: Mosquito-borne diseases, for example, could see a surge in the near future with changing climatic conditions. Microsoft Premonition, a biological intelligent monitoring system, can help detect anomalies in mosquito species distribution and movement, and act as an early warning system. Such innovative approach could be invaluable for agencies like the Centers for Disease Control and Prevention (CDC) and the Department of Health and Human Services (HHS) to proactively address biothreats, ensuring rapid response mechanisms are in place before potential outbreaks.
2. Data Integrity in Documentation: As government agencies embrace the digital age, ensuring the authenticity and integrity of documents becomes paramount. For agencies like the National Archives and Records Administration (NARA) that manage vast amounts of documentation, AI offers transformative solutions. Microsoft Azure Data services can assist in streamlining document categorization, but it can also trace data lineages, safeguard data integrity, and forecast storage requirements based on the anticipated volume of incoming records. The underlying principle is clear: by bolstering the trustworthiness of our digital records, we fortify the very foundations of government transparency and accountability.
3. Financial Integrity through multi-agency collaboration: Whether it is in tax collection, procurement, or entitlement programs, financial fraud is an intricate issue that government agencies face. In this domain, detecting anomalies is paramount to uncovering deceitful patterns and potentially saving billions. Consider an advanced AI system, such as Dynamics 365 Fraud Protection that spots inconsistencies in financial transactions to help safeguard individual accounts. Such triggers could initiate a multi-agency collaboration between entities like the Internal Revenue Service (IRS), the General Services Administration (GSA), and the Department of the Treasury. In real-time, they could share insights, consolidate data, and create a holistic view of the financial landscape to pinpoint and combat fraud more effectively. This not only protecting the nation’s financial health but also in reinforcing public trust in government financial operations.

Finding the right tool for the right job

The saying, “using a bulldozer to crack a nut” encapsulates the importance of task-appropriate tools, especially when it comes to AI. Detecting anomalies requires tools that ensure reproducibility and auditability. While generative AI is still grappling with some nuances today, dedicated solutions like AI anomaly detection are designed to help it excel. Once an anomaly is detected, generative AI can further delve into documentation, workflow data, and relevant manuals. This aids in root cause analysis, offering not just an understanding of the anomaly, but also potential paths forward. The synthesis of these AI functionalities enables a swift transition from “detection to action.”

The Promise of Integrated AI Solutions

Consider an instance where anomaly detection picks up a deviation in a complex system. Instead of triggering alarms that merely state the existence of a problem, the system integrates with generative AI. The generative AI system uses cognitive search to dive into available documentation, cross-references with known issues and, using its vast internal knowledge base, translates the error into comprehensible language, replete with potential mitigation paths.

Such integrations do not just streamline processes; they empower decision-makers with clarity and actionable insights.

An example to highlight this is Azure Anomaly Detector, which offers univariate anomaly detection, focusing on singular variables like costs, and multivariate anomaly detection, which investigates correlations across multiple variables. Such services, when paired with generative AI’s capabilities in understanding context, pave the way for a future where anomalies are not just detected but are also comprehended and addressed efficiently.

Ready for Action

In this era of AI, the right synergy between tools can mean the difference between proactive problem-solving and reactive troubleshooting.

Making it real:

• Step 1: Set up your data sources and anomaly detection models
  • Prepare your data for Azure services using Azure Data Factory or Azure Data Lake Storage and choose an anomaly detection model using Azure Anomaly Detector Service or Azure Machine Learning.
  • Read more: What is Anomaly Detector? – Azure AI services | Microsoft Learn
• Step 2: Create a flow to trigger the generative AI system
  • Create a Power Automate flow that runs when an anomaly is detected by your model, and calls the generative AI system using Azure OpenAI Service, giving the anomaly details as input.
  • Read more: Power Automate on Microsoft Learn | Microsoft Learn
• Step 3: Configure the Generative AI system
  • Configure the generative AI system using Azure Machine Learning Studio, like Azure OpenAI Service, and specify the output format and language of the system.
  • Read more: Use the text generation model in Power Automate (preview) | Microsoft Learn

In our upcoming module, we will explore how you can harness your data, use semantic search, and integrate with large language models and generative AI to translate intricate data into “human speak.”

The journey of melding data with cognition is poised to redefine how all of us interact with technology, and we are thrilled to guide you through it.

Stay tuned, and as always, your insights and feedback remain invaluable to our ongoing conversation. If you have any questions, please don’t hesitate to reach out to us, and register here for the next installment in our series in which we will explore how you can harness your data, use semantic search, and integrate with large language models and generative AI to translate intricate data into “human speak.”