Update refs to latest retail AI suite

retail-ai-suite/README.md

@@ -8,8 +8,8 @@ Key use cases include:
 
 | Sample Application | Definitions | User Docs       |
 |:-------------------|:------------|:----------------|
-| [Automated Self-Checkout](https://github.com/intel-retail/automated-self-checkout/releases/tag/v3.6.3) | Product recognition (detection, classification, and tracking), full pipeline workflow (product, weight, text, and barcode), and age verification. | [Link](https://intel-retail.github.io/documentation/use-cases/automated-self-checkout/automated-self-checkout.html) |
-| [Loss Prevention](https://github.com/intel-retail/loss-prevention/releases/tag/v4.3.2)| Fake scans, items in basket, multi-product identification, product switching, shopper behavior (obscuring/hiding an item), and event video summation. | [Link](https://intel-retail.github.io/documentation/use-cases/loss-prevention/loss-prevention.html) |
-| [Order Accuracy](https://github.com/intel-retail/order-accuracy/releases/tag/v1.2.2)| An easily repeatable process for generating performance numbers across multi modalities on Intel’s heterogenous compute. The tool helps determine compute requirements for scaling retail edge AI workloads. |  [Link](https://intel-retail.github.io/documentation/use-cases/order-accuracy/order-accuracy.html) |
+| **Automated Self-Checkout** | Product recognition (detection, classification, and tracking), full pipeline workflow (product, weight, text, and barcode), and age verification. | [Link](https://intel-retail.github.io/documentation/use-cases/automated-self-checkout/automated-self-checkout.html) |
+| **Loss Prevention** | Fake scans, items in basket, multi-product identification, product switching, shopper behavior (obscuring/hiding an item), and event video summation. | [Link](https://intel-retail.github.io/documentation/use-cases/loss-prevention/loss-prevention.html) |
+| **Order Accuracy** | An easily repeatable process for generating performance numbers across multi modalities on Intel’s heterogenous compute. The tool helps determine compute requirements for scaling retail edge AI workloads. |  [Link](https://intel-retail.github.io/documentation/use-cases/order-accuracy/order-accuracy.html) |
 
 The Retail AI Suite is built with modularity and extensibility in mind. It is not meant to be used as a series of reference applications, but rather as code to understand the hardware requirements for embedding AI workloads into retail applications. The pipelines provide clear guidance on how to use Intel’s hardware-optimized software stacks while primarily focusing on enabling partners to determine the hardware for scale deployments. There are many more retail use cases under consideration. Additional details and documentation are available [here](https://github.com/intel-retail).