Over the past twenty years, Ontonix has explored and worked on hundreds of applications of its QCM-powered Artificial Intuition technology, in many cases in industrial projects with large corporations. Artificial Intuition is application independent. However, there are scenarios and conditions in which the technology excells. Here is the list:

Prioritization

Prioritization relies on identifying the relative importance or order of variables, tasks, or goals. It’s about finding out what matters most, especially when under pressure. In complex situations this can be very difficult. In any situation, knowing where to focus saves time and resources. These key locations, or hotspots, are where the actions is.

Monitoring and early anomaly detection

Artificial Intuition is particularly good at spotting the onset of anomalies and malfunctions, providing early precursors and indicating the underlying causes. This can be accomplished in real time, monitoring sensor outputs to provide instantaneous indications of potential problems before they materialize in a threatening manner.

Read article by CISCO.

Autopsy of a collapse

When highly complex systems fail they do so in very many, often unexpected, non intuitive ways. Sometimes the causes of a collapse are never determined even though abundant data may be available for analysis. Complex catastrophic collapses offer no opportunity of any form of training, ruling out any application of AI-related techniques.

The need for explainability

It is known that AI is in many cases a black box that produces an answer but without offering explanation as to how the answer has been arrived at. Artificial Intuition offers 100% explainability because it doesn’t guess the answer, it computes it. It also breaks down the answer into components in a quantitative manner.

Conditions of high uncertainty and variability

Situations in which the dynamics of a particular system or process are dominated by uncertainty can be particularly nasty, especially if the underlying physical phenomena contain discontinuities, bifurcations, non-linearities, clustering, transitions, etc. This makes model building or model training and validation very difficult.

Data scarsity

Many anomalies or malfunctions which affect complex systems – both natural and man made – are often very rare and/or unique. There are simply not enough examples for a Machine Learning approach, no patterns to recognise. And yet, a decision must be made, often on the fly. Artificial Intuition bridges the gap.

A New Class of AI Training Data

A new class of AI training data is obtained by transforming raw data into Complexity Spectra. Complexity Spectra not only condense large quantities of data by orders of magnitude, they also contain additional information which raw data does not. This reduces massively the size of data, it can also reduce enormously the training.