Nebuli’s Human-centric Augmented Intelligence.
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.
Why Nebuli’s Augmented Intelligence?
In the technology world, there are two camps of thinkers:
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those who believe technology is there to replace humans, as in artificial intelligence, and
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those who believe technology can empower humans and not replace them, focusing on linking human learning with machine learning for ultimate intelligence. This is Nebuli.
Building a Healthy, Ethical and Productive Human-Machine Symbiosis.
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.
Our Unique Human Centric Methodology.
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.
Input
Exploration
Search through resources and datasets to gain information and build initial insights – i.e. the amount of knowledge that one can potentially acquire.
Knowledge
Establishing awareness and understanding of the facts, data, research information, descriptions, or skills acquired through the exploration and learning processes.
Discovery
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.
Self-Exploration
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.
Cognition
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.
Introspection
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.
Improvisation
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.
Output
We Focus on Linking Human Learning with Machine Learning for Ultimate Intelligence.
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.
We Focus on The Quality and Relevancy of The Available Data, Not Just Quantity.
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 and AIQ cited large language models.
You do not always need enormous amounts of data or costly and exhaustive AI software to unlock value from your data.
We apply a Data-Centric AI approach – building AI systems responsibly using quality data. We ensure that the datasets involved clearly convey what the targeted AI model must learn and deliver trustworthy results.
Our approach also enforces strong data ethics and user privacy throughout the process, from planning to delivery.
We Focus on Responsible Large Language Models and Smarter Federated Learning to Protect your Data.
Language has always been the critical factor in human-machine interaction for decades. Hence, we created AIQ as Nebuli’s suite of specialist and fully cited large language models (LLMs), focusing on vertical knowledge built on our Deep Vertical Understanding (DeepVU) framework.
We also apply Federated Learning (FL), a distributed machine learning technique that allows multiple clients with their data and computation resources to collaborate to train AIQ’s models. This is particularly useful in cases involving private or sensitive datasets.
Federated learning is the modern approach to training machine-learning models without anyone seeing or touching your original data.
Combining our expertise in evolving human habits with our suite of specialist and fully cited large language models offers a highly intelligent, personalised and responsible ecosystem for today’s complex, unpredictable, and demanding customer journeys.
Our Solutions Work Together as Your Innovation Omnichannel.
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.
Our vision is to build a deeper, healthier, most-ethical human-machine symbiosis.
We designed our technology to help teams and businesses of all sizes across several industries deploy universally available, responsible, secure, sustainable and personalised augmented intelligence ecosystems.
