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Building a Healthy, Ethical and Productive Human-Machine Symbiosis

Nebuli’s core research focuses on building a different kind of human-machine intelligence. One that is safe, responsible, highly intellectual, sustainable and helps people make sense of the world.

Nebuli Human-centred Augmented Intelligence

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:

  • those who believe technology is there to replace humans, as in artificial intelligence, and

  • 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 Better Human-centric AI and Transformation Frameworks

Nebuli Human-machine Symbiosis - light

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.

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:

  • Reasoning
  • Planning
  • Problem-solving
  • Language Production
  • Comprehension
  • Decision-making

Accordingly, we developed the following key stages behind Nebuli’s core Augmented Working Memory models, which are at the heart of our ongoing R&D, as well as 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

Nebuli Frameworks Powered by Augmented Working Memory

Nebuli’s augmented intelligence models generate their Augmented Working Memory from newly given task scenarios based on individual customers’ most needed datasets, which tend to be small.

Being a long-term Augmented Working Memory, Nebuli models also store and utilise other previously acquired “smaller” knowledge from different similar scenarios that might be applicable for the newly given task. Thus, making the process lighter yet more relevant and compelling for specialist applications for teams big and small.

These are the core foundations of Nebuli’s AI and data frameworks, including the Datastack and DeepVUE which power our Innovation solutions and Nano AI.

The Steady and Long-term Knowledge Acquisition Approach

With the principles of the long-term Augmented Working Memory theory, we believe that knowledge building through data acquisition is a long-term and slow process. It is about the utilisation of high-quality data for specific applications, as opposed to randomised web scraping models.

Accordingly, Nebuli models do not demand constant data input and ever-increasing data storage to generate intelligence. Instead, we propose a more passive memory approach by storing only key data elements that intelligence-based systems need for a given task and discarding the rest.

Our founders called these data elements Memory Blocks.

Memory Blocks – Flexible Data Manipulation and Knowledge Building Models

Nebuli knowledge-building models create a Data-Driven World (DDW) for individually targeted data collections as a way of indexing and visualising the critical elements needed for a specific workflow. This DDW is what we describe as a Memory block. The key objectives of each memory block are the following:

  • 1.
    Creation of DDW based on the customer’s data collection.
  • 2.
    Cognitive Search of specific data elements within the DDW.
  • 3.
    Data clustering and segmentation of specific data elements around the customer’s predefined parameters within the DDW.
  • 4.
    Creation maps of DDW (Visualisation) using self-organising map (SOM) models (as shown in the diagram below).
  • 5.
    Create an isolated system with its own database for each DDW.

Below are sample images of Nebuli’s Memory Blocks generated through our work with the University of Leicester’s (UoL) Library. The objective of this project was to visualise the hidden world of the UoL’s internal research papers, to help them facilitate new interdisciplinary and interdepartmental R&D collaborations:

Nebuli Literatur Data Clustering
Nebuli Oncology Data Clustering
Nebuli Proteomics Data Clustering
Nebuli Datastack litrature data clustering.
Nebuli Datastack litrature data clustering.
Nebuli Datastack litrature data clustering.
Nebuli Datastack litrature data clustering.
Nebuli Datastack litrature data clustering.
Nebuli Datastack litrature data clustering.
Nebuli Datastack litrature data clustering.
Nebuli Datastack litrature data clustering.
Nebuli Datastack litrature data clustering.

The above images show 2D and 3D 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 such services as Google Scholar, ResearchGate, Academia.edu and many other tools.

While these tools promote their ability to mine over tens of millions of research papers, this level of data overload was not sufficiently beneficial nor insightful for the UoL. Hence, this scenario was more about the quality and relevancy of the available data, rather than just quantity.

Our ongoing research is centred around helping teams and businesses of all sizes across several industries deploy universally available, responsible, secure, sustainable and personalised augmented intelligence ecosystems.

Nebuli Augmented Intelligence Memory Blocks.
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We Link Human Learning with Machine Learning to Build The Ultimate Intelligence

With Nebuli’s People-first business model, our digital innovations and R&D projects always start by focusing on end users, consumers and evolving human habits.

We connected Nebuli’s Augmented Working Memory Hypothesis with seven core human mindsets that we studied, using 28 identified barriers to change and 56 strategies to influence human behaviours.

This is the foundation of our Human-centric Intelligence frameworks for deep behavioural analysis and psychographics.

Hence, Nebuli is the innovation space where real digital success is achieved by understanding people, their needs, and their aspirations.

Nebuli’s Core Augmented Working Memory Model.

Our Human-centric Intelligence 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.

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We Focus on Quality and Relevant 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.

Nebuli focuses on data quality and explainability over quantity.

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.

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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 Embeddings.

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.

Nebuli federated-learning and explainable AI.

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.

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We Built Our Solutions to Work Together as a Sustainable Innovation Omnichannel

Our Solutions Work Together as Your Innovation Omnichannel Across Markets, Backed by Established Methodology and Executed by Leading Experts and Entrepreneurs

Nebuli's Augmented Intelligence models are industry agnostic, offering a multi-sector convergence approach.
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