Azure AI: The future key to reliable AI?

The Azure AI platform has introduced security and anti-hallucination features to prevent such problems.
These include:

• Data model checking: Azure AI checks AI models and their results to ensure they are consistent and reliable.
  This means that before a model is deployed, it undergoes a thorough evaluation to ensure its reliability and stable performance in different environments.
  The verification of data models ensures that the AI tools provided by the platform are reliable and reduce the chances of hallucinations.
• Reducing bias: Azure AI aims to reduce the potential bias in model training data that can cause hallucinations.
  Bias can lead to the AI model making incorrect inferences or producing inaccurate results, especially in situations where the training data is not diverse or representative.
  The platform provides tools and methods to analyse and process training data to reduce bias, which will help improve the reliability of AI and reduce the risk of hallucinations.
• Countering adversarial attacks: Azure AI protects models from subtle attacks where bad actors manipulate model results by modifying input data.
  With adversarial attack prevention, the platform identifies and blocks attempts to manipulate AI models and their results with corrupted input data.
  This helps ensure that AI models perform as expected and produce reliable results, even in situations where intentional attempts are made to disrupt them.