# [UOM - Free5 gc]
## 4 AI Applications in B5 gemini
### NDT for Smart and Robust-by-Design AI/ML-Based Applications  
As it is well known, most ML algorithms need large amounts of real data to be trained. One of the major potential applications of an NDT is that it could serve as a platform for data collection that can be later used to train algorithms when the real network does not provide enough data, or it is too complex or expensive to gather. Moreover, there is an increasing need to design realistic virtual network scenarios in which different types of traffic can be generated in a controlled way and ML models can be trained and validated. B5 GEMINI provides these capabilities, allowing the generation and capture of network traffic in a controlled manner to be used for training and testing ML components with realistic data. It should be noted that since the collected traffic is generated in a controlled environment, data labeling, which is required for training and testing supervised ML components, can be conducted without human intervention. This capability facilitates the labeling of data sets in the big data regime that are required to train and validate complex Deep Learning (DL) models. In addition, B5 GEMINI replicates physical network elements and therefore, ML and DL components can be deployed and validated in real-time scenarios. Furthermore, using these approaches, different problems and faulty behaviors that rarely occur during the real network operation can be easily replicated now without negatively affecting the network. In this way, this approach allows one to train ML components for those situations in a more cost-effective and safe manner in order to ensure that they operate accordingly when they occur in real-world situations.  
Moreover, recent applications of General Data Protection Regulation (GDPR) imply new challenges for the design of robust AI systems in the context of automated decision-making systems [30]. In this regard, interpretability is the key metric for providing reliable explanations of results, which is critical to avoid negative consequences of their use that may harm data subjects. To this end, these systems must provide not only a clear and intelligible explanation of their results, but also a way to assess the reliability of the results. At the network level, this interpretation capability must be technically accurate with respect to this domain to provide meaningful insight that can successfully explain the underlying decision-making process of the AI/ML system to network operators.  
On the other hand, ML models are known to be vulnerable to adversarial attacks, which are attacks performed by adding small perturbations to the input data in a way that is unperceivable to humans but can trick a machine learning model into predicting an incorrect label, even with high confidence. To address this problem, ML resilience has emerged as a new research area that introduces mechanisms that make ML models more robust to adversarial attacks. In order to adopt a robustness-by-design approach, both Explainable AI and ML Resilience are two methodologies that should be carefully considered from the early stages of the ML/DL model design and development pipeline. Both are described in detail in Section 6.2.