The core foundation of our human-centric Augmented Intelligence models involves investigating the true meaning and the working of human intelligence, going far beyond merely developing mathematical models.
We created Nebuli to help our customers explore new, exciting and transformative solutions that empower their people and communities around the world.
It’s not about AI vs humans. It’s humans plus AI. This is Augmented Intelligence.
Simon Jack – Co-founder & Chief Design Officer – Nebuli.
From our founders’ experience with AI and biomedical science since the late 1990s, they developed their Augmented Working Memory Hypothesis, which compares conventional AI systems with the human brain’s short and long-term memory mechanisms.
We identified and developed the key stages behind Nebuli’s core Working Memory models, which are at the heart of our products, services and behavioural frameworks available to all customers.
Search through resources and datasets to gain information and build initial insights – i.e. the amount of knowledge that one can potentially acquire.
Establishing awareness and understanding of the facts, data, research information, descriptions, or skills acquired through the exploration and learning processes.
Detecting something new, such as new trends, events, anomalies, actions, or ideas, providing new reasoning to explain the knowledge gathered through such detections and building initial “quick” decisions.
Retention of the new information and knowledge discovery over time for the purpose of influencing future actions and decisions. This forms the building blocks for Working Memory by examining decisions, ideas, thoughts, data, behaviors, feelings and motivations and asking why.
Formation of Working Memory – i.e. detailed evaluation, reasoning, data processing, and deeper analysis – using the stored knowledge for problem solving, decision making and generation of new ideas.
Self-examination of own reliability, ensuring overall consistency – using a set of internal test scores related to the number of random errors recorded within a database to generate better and smarter cognition.
Using applied and/or collaborative improvisation methods to establish true personalization and creativity to accommodate specific needs and interests of oneself, generating new data and ideas for exploration.
We connected our Augmented Working Memory Hypothesis with seven core human mindsets we studied, using 28 identified barriers to change and 56 strategies to influence human behaviours – the foundation of our BehaviorLink framework.
BehaviorLink offers expertise in emerging consumer trends and evolving human habits, psychometrics and psychographics methodologies to help your team explore new, exciting and transformative smart hyper-personalisation architectures.
Our Augmented Working Memory Hypothesis is predicated on the principle of generating a maximum level of long-term intelligence output from a minimal input of usable information, powered by our Datastack framework.
You do not always need enormous amounts of data or expensive and exhaustive AI software to unlock value from your data.
Our models also enforce strong data ethics and privacy throughout the process, from planning to delivery.
Federated learning is the modern approach to training machine-learning models without anyone seeing or touching your original data.
Typically, data-driven applications gather and crunch your datasets in one place, which is unsustainable and very expensive to manage over time.
Our Nanobot framework, combined with the Datastack, provides explainable AI that turns your existing systems into robotic coworkers and offers a decentralised approach to unlocking your data to feed new AI applications.
Nebuli’s evolving frameworks and R&D are the foundation of the founders’ vision for a favourable, healthier, and ethical human-machine symbiosis across markets.
Our transition toward a decentralised approach allows for more collaborative learning on the edge, for both machines and end users.
More critically, your data remains locked in your mobile devices, laptops, or private servers, while the algorithms perform their operations.
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