Nebuli never stores copies of your datasets uploaded to its system.
Instead, it builds compressed “memory blocks” in its internally-generated language that is virtually impossible for a human to interpret or decrypt.
Using their medical point of view of intelligence, rather than mathematical, Teacha and Tim architected Nebuli’s intelligence and data storage model around the neuropsychological theory of Working Memory to achieve the following principle function:
Minimal Data Input Must Generate Maximum Intelligence Output.
The Working Memory is responsible for “flexible” manipulation of crucial information that is available for specific processing and is critical for many domains of cognition, including:
We discuss the detail of this theory and why we believe that artificial intelligence and augmented intelligence models must involve understanding human intelligence, not just maths.
The founders identified key stages needed for Nebuli to generate human-like Working Memory, which central to our company’s ongoing research and development.
Nebuli generates its Augmented Working Memory from newly given task scenarios based on your team’s most needed datasets, which tend to be small.
Being a long-term Augmented Working Memory, Nebuli also stores and utilises other previously acquired “smaller” knowledge from diverse, yet closely related scenarios to your project that might be applicable for newly given tasks. Thus, making the process lighter yet more relevant and compelling for specialist applications for teams big and small.
You do not need enormous amounts of data or expensive and exhaustive AI software for Nebuli to work.
Like the human brain, Nebuli does not demand constant data input and ever-increasing data storage to generate intelligence.
Instead, Nebuli applies a more passive memory approach by storing only key data elements that it needs for a given task and discards the rest.
These data units are the Memory Blocks.
Hence, our Augmented Working Memory Theory is predicated on the principle of generating a maximum level of long-term intelligence output from a minimal input of your useable information only.
Nebuli constructs a Data-Driven World (DDW) for each customer or team based on their available datasets as a way of indexing and visualising the critical elements needed for their workflow.
Each DDW unit that Nebuli generates is a Memory Block. The key objectives of each Memory Block are the following:
Below are sample images of Nebuli’s Memory Blocks generated through our work with the University of Leicester’s (UoL) Library. The aim here is to visualise the hidden world of the UoL’s internal research papers, to help them facilitate new interdisciplinary and interdepartmental R&D collaborations:
The above images show SOM-based visualisation of segmented datasets according to specific parameters set by the UoL library team. Where the dots mostly condense is where the most relevant interdisciplinary opportunities are likely to be found.
In this scenario, Nebuli only needed to utilise 13,000 research papers to generate the most insightful opportunities for the UoL, which was not otherwise possible with the currently available online services that claim to offer research discovery with artificial intelligence.
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