Artificial Intelligence.

Artificial Intelligence has the potential to unleash a range of new applications by automating and simplifying complex tasks that rely on visual, audio and digital streams of information.

However, it faces three major challenges.  It needs to compute faster, recognise temporal data and integrate different data types.

At Cogisen we believe we have found radical breakthroughs.


What is wrong with AI today?

Today’s state-of-the-art AI technology faces three major challenges.  It needs to compute faster, recognise temporal data and integrate different data types.


Cognitive Modelling.

This approach to AI reflects the way the cognitive mind creates models of information.  At Cogisen we have unpacked the underlying mathematics and physics to enable an approach which is not based on brute-force analysis of statistics.


Understanding the human eye.

From a very early age the cognitive mind is able to process information rapidly to enable us to understand and respond to our surroundings.  The brain builds up a library of information, which can be used to cross-reference new or partial information to make accurate decisions.  Indeed, the technology in the human visual cortex is so sophisticated it can often decide about objects and information without requiring input from the cognitive mind.  Cogisen is seeking to replicate the mechanics and efficiency of this process in the Cognitive AI Platform.

Re-mapping data.

In the world around us signals are being emitted constantly with a unique signature for every piece of information we sense.  This offers a more effective way to recognise and interpret information compared to analysing every pixel of an object.  Cogisen has developed patented technology, which makes it possible re-map data to reveal the information relevant to this process.



Extracting information from temporal effects.

When today’s AI technology analyses temporal information, such as streaming video, it must examine every frame to discover a pattern before recognising and interpreting that information.  It requires far more information to make a decision, whereas Cogisen has developed an algorithm which examines only the relevant parts of each frame to discover patterns and recognise information.  The same approach can be applied to most information in motion where decisions need to be made quickly with less training for the AI application.

The Cognitive Modelling Lab.

The lab brings together expert engineers from multiple fields to explore how cognitive modeling can expand the use of AI for recognition and interpretation of information in fields such as mobile and social, the Internet of Things and autonomous vehicles.  Its initial focus is on commercialising the Cognitive AI Platform.  In the future the Lab will examine different applications of the platform with its technology partners to optimise its adoption, as well as undertaking academic research with leading universities on future innovations in cognitive modelling.



Quantum Machine Learning.

“If AI is to achieve the next major breakthrough then we have to move beyond the brute force statistical models in use today and Quantum Information will clearly help to speed up the processing of data to achieve faster responses.  Combined with the Cogisen AI Platform we believe this approach will put us at the cutting edge of research around Quantum Computing and AI innovation."

Christiaan Erik Rijnders, CEO and Founder, Cogisen


Quantum Computing.

We believe the Bayesian approach is not guaranteed to provide the best description of a phenomena. Our quantum probabilistic models give a more insightful framework.  Data can be purposely remapped in a much more structured space, where mathematical models exploited in quantum mechanics can effectively manipulate information.

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