We are a Barcelona AI LAB startup solving General Intelligence.

On top of our patent-pending computational model of biological cognition we have implemented a new game-changing algorithm to solve General Intelligence, upscaling from motor-perception towards figurative processing.

General Intelligence will ignite a new era of truly intelligent AIs and robots. We want to lead it with our algorithm to ensure it is for the best of all humankind and our wellbeing.

Our purpose

We pursue one of humanity’s unsolved Grand Challenges: solving general intelligence by understanding how the mind works and replicating it into truly intelligent machines.

These machines will be digital cognitive systems able to solve problems in the same way biological intelligences do: learn and acquire any knowledge to gain understanding and being able to reason to make smart predictions about the future in order to take intelligent decisions.

Current AI algorithms are unable to genuinely understand and reason and are far from being intelligent and thus many problems remain unsolved. And most solved problems require brute-force to be solved (huge amounts of training data and computing power). The resulting AI solutions are narrow, so it is very unlikely to reuse them or their learnt knowledge to solve other AI problems.

Solving General Intelligence will open a new era of Cognitive Computing where computers won’t need to be programmed but educated, of truly intelligent AI machines capable of genuine understanding and reasoning. We want to lead this new era of AI ensuring our algorithm is available to everyone. Thus we are not only focused on the validation and demonstration of our General Intelligence algorithm, but we are also developing the most usable platforms so anyone can seamlessly use our algorithm to build their own intelligent AI, digitize their expert knowledge and solve cognitively any of their AI problems.

Our computational model of the mind

We have faced the challenge of solving General Intelligence from a deep computational perspective, but always inspired by neuron-based intelligent beings.

We have understood Cognition as the information processing model and computational mechanisms underlying all phenomena of abstraction and inference present in all neuron-based species of the evolution timeline.

Without involving neural networks nor symbolic approaches, we have successfully modeled the living mind and its cognitive computational workings and mechanisms with a novel and unique bio-plausible, patent-pending and still unpublished bottom-up computational model of the mind: the Computational Fractal Cognition Model (CFCM).

Fractal model of cognition
High cognition
Low cognition

This model is built on top of very essential and novel approaches but aligned with specific reputed computational theories, models and authors.

As a basic premise, we have postulated that Cognition operates under nature’s fractal computation paradigm: a fundamental cognition primitive that adaptively distributes throughout self-organized hierarchical structures that dynamically and emergently upscale the primitive computational mechanisms.

Our computational model of the mind upscales initially in a foundational Computational Theory of Cognition (CTC) that models low-cognition sensorimotor processing using spatio-transitional conditionable cognitive architectures based on perception/actuation computational mechanisms and effects. Afterwards it upscales into a Computational Theory of Mind (CTM) that also solves high-cognition qualia-based phenomena using episodic and attentional abstract processing computational cognitive mechanisms and their higher cognition and intelligence behaviors.

Once these cognitive architectures are embodied and connected to any set of domains and conditioning signals they produce machines that continuously learn, abstract and infer from their connected domains.

This learning process, that can be indistinctly unsupervised or by behavioral education or a mix of both, fills these machines with unified cognitive ontologies of knowledge that model the domains finding its relevant semantics.

The resulting educated machines can make predictions and produce intelligent behaviours according to this acquired knowledge.

Our high-cognition systems implement all necessary cognitive mechanisms to be equivalent to general intelligence machines, that provided with the appropriate education and knowledge will even express human-like intelligence capabilities.

Our master algorithm

We have implemented our cognition model into a working AI Master Algorithm,
We have implemented our cognition model
into a working AI Master Algorithm,
Synthetic Cognition

We have implemented our computational cognition model into a unique and working AI Master Algorithm: Synthetic Cognition. We have postulated and are successfully validating and demonstrating that it is the first General Intelligence Master Algorithm. Using our algorithm any low-cognition or high-cognition architecture can be seamlessly embodied into AI machines to solve cognitively AI problems, no matter the required cognitive capabilities. Machines that can be embedded with goals through conditioning signals and can unsupervisedly or by education acquire knowledge from the embodied domains, and once this model is rich enough it will make smart predictions or take intelligent decisions and behaviors.

Synthetic Cognition has other unique characteristics that will be game changing and will very quickly spread it as the new dominant standard algorithm of a new AI era.

Traceable & interpretable

It is not a black-box algorithm. Its unified knowledge cognitive ontologies are traceable and its predictions interpretable

Truly cognitive

Being it truly cognitive it will require much less data and much less computational power to learn and predict properly

Valuable knowledge

As it has high generalization capability it will allow real valuable and reusable knowledge digitalization


It is universal as it is able to work simultaneously and indistinctly with different data and signal types and without any prior and/or hardcoded knowledge

Our progress

The Avatar Cognition team is following a 2 year bottom-up roadmap from cognition synthesis to general intelligence.

Using Synthetic Cognition algorithm we are designing and embodying different cognitive architectures upscaling from low-cognition into high-cognition, in order to demonstrate that our computational model of mind really solves the whole General Intelligence problem.

Low-cognition roadmap stage is already solved. Synthetic Cognition is working and already solving diverse relevant current AI problems (prediction, perception, reactive autonomous agents…).

In the following months we will complete our roadmap by also upscaling Synthetic Cognition into currently unsolved high-cognition mechanisms, enabling genuine attentional abstract thinking general intelligence machines (reasoning, decision making, autonomous behavioral robots, communication and language…).

The availability of working General Intelligence machines will open a new roadmap of knowledge transfer and machine education of high-cognition embodiments, many of them imitating human-level cognitive capabilities.

More on our Technology

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Find more information on our blog



Enric Guinovart

Co-founder, co-CEO and Chief Scientist

Inventor of our computational model of the mind. From an out-of-the-box-perspective, he has been passionately devoted for 25 years to the computational  understanding, analysis and modelling of nature’s cognitive processing model.

Pere Mayol

Co-founder and co-CEO in charge of business and operations

Passionate about cognition, AI and sci-tech innovation. He pursues to change AI for good with our algorithm, and ensure that this happens also for best of all humankind.

Board members and advisors

Eduard Alarcón

UPC BarcelonaTech Telecom School EE Professor. AI PhD and researcher in several topics including AI-cognitive Future Internet. Worldwide Vice President Technical Activities IEEE and journal editor-in-chief on Emerging and Selected Topics in Circuits and Systems.

Griselda Garde

Nearly 20 year experienced investor, board member and advisor in deeptech start-ups with special focus in bio-tech and health-tech.

Josep Lluís Sanfeliu

Nearly 20 year experienced investor, board member and advisor in deeptech start-ups with special focus in bio-tech and health-tech.

Pere Vallès

Nearly 20 year experienced investor, board member and advisor in deeptech start-ups with special focus in bio-tech and health-tech.

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