The real world is not in our AI models either.

According to the DIKW model, wisdom arises from knowledge, particularly knowledge derived from experience

What happens when your models degrade over time?

This shift will require an operational model that is more than training, audits and corrective actions

exists because we don't know enough about the system, haven't collected sufficient data, or lack the models There's model uncertainty—the risk that our fundamental assumptions about how systems work are flawed

This is why a feed-forward process is needed using a model-driven control. It is the model that provides predictive capabilities that can help to address the effects of uncertainty

It acts as a virtual model, allowing you to identify potential issues before they snowball into problems

OLD FACTORY MODEL The first category are organizations that are operating under the OLD FACTORY MODEL NEW FACTORY MODEL The second category are organizations that are operating under the NEW FACTORY MODEL This model is based on seeing the supply chain as a network or hub. The goal of the NEW FACTORY MODEL is to continuously add new value for each customer.

These centers feed our most advanced language models, which parse through billions of words describing

components actually requires substantial custom engineering to handle behavioural consistency across model This includes designing model versioning and rollback capabilities, creating human oversight mechanisms

kind of uncertainty, "reducible uncertainty", which we can buy down by improving our knowledge, our models

information systems that help individuals or organizations make choices by providing relevant data and models DSS typically includes software tools, techniques, and models that enable users to access and analyze The system can learn from its past decisions and outcomes and adjust its models and parameters to optimize