We Find Out Why Complex Systems Malfunction.
In 2018 Ontonix introduces the first Complexity Monitoring Chip.
Science, not Opinions.
Excessive Complexity = Risk
Conventional means of risk assessment, management and rating are not suited for a complex and turbulent context. They are subjective and produce results that can be manipulated. There is a pressing need to devise more modern, objective and science-based means of dealing with uncertainty and complexity. This is exactly what we do and what we offer.
Modern software-packed products, such as cars, aircraft, IT systems, critical infrastructures, or the IoT, offer many examples of how complexity can become a nightmare. What makes these systems powerful, also makes them fragile. Complexity is a next generation risk which requires a next generation technology and approach.
We detect anomalies without using machine learning for one very good reason: our clients don’t have the luxury of multiple failures to teach a piece of software to recognize it or to establish the presence of rules.
Correlations play a central role not just in data or risk analysis. However, conventional linear correlations may deliver misleading results.
Ontonix offers an innovative generalized correlation, which takes into account non-linear aspects of data. The method uses brand new next-generation AI technology which transforms data into images, emulating an expert looking at it. The system actually ‘sees’ correlations.
The method has its roots in quantum physics, nonlinear mechanics and biology.
In 2015, Ontonix has been the principal author of the World's first 'Business Complexity Assessment' standard, published in Italy by UNI, 11613. The ISO 22375 standard on business complexity, which follows the UNI standard, has been published in 2018.
In 2018 Ontonix launched the World's first Complexity Monitoring Chip, developed in partnership with SAIC.
Solutions For a Complex World
Traditional technology is unable to deal with the immense complexity of ICT systems or critical infrastructures.
Our solutions have been crafted specifically for:
pinpointing concentrations of vulnerability
extracting new knowledge from data
delivering crisis early warnings
The new science is based on model-free methods, which allow us to concentrate on solving real problems not on building exotic mathematical constructs.
Rapid complexity increases anticipate problems such as shown in the plot below. They provide a formidable early warning signal.
The most recent application of Quantitative Complexity Management is a futuristic Cyber Attack System, CODE, which has the objective of delivering a systemic low-visibility strike, inducing collapse of enemy networks.
Complexity X Uncertainty = Fragility™
The above equation is the Principle of Fragility, which has been coined by Ontonix in 2005. It reveals why in an uncertain context a highly sophisticated and complex business or infrastructure are more exposed, hence more vulnerable. As the uncertainty and turbulence of our World increases, simpler solutions are preferable as they are more resilient.
Over the past few decades new technologies have been accelerating the growth of complexity to levels which are threatening not just the sustainability of our global society but the governability of its critical infrastructures. This is why one cannot design a highly complex system without taking complexity into account from day one.
In order to counter the negative effects of rapid growth of complexity we first need to perform an in-depth analysis of a business, process or infrastructure. We resort to supercomputers to process hundreds of thousands of variables to deliver a truly systemic and holistic reflection of a business and of its state of health. This requires new technology, beyond statistics, neural nets, cluster analysis, Bayesian methods or machine learning. Our tools are based on a radically innovative model-free approach which allows us to solve problems that are beyond the reach of traditional mathematics.
Serious Science Starts When You Begin To Measure
Examples of Complexity Maps, which reveal the structure of complexity and its drivers.