Building a Better Human-centric AI

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

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 a Healthy, Ethical and Productive Human-Machine Symbiosis

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 Human Lab frameworks.

Our Human Lab 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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Explore Human Lab

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.

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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Explore our Data Lab

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.

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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Explore AIQ

Explore DeepVUE

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.

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Innovte with Nebuli

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.

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