Projects

At the TKAI Lab, our mission is to develop stable systems for Artificial Intelligence applications. Our approach integrates insights from neuroscience, control theory, advanced physics, formal methods, and cognitive science. By combining theoretical foundations with empirical validation, we aim to pioneer stability-driven methodologies. Our focus is on creating systems that demand minimal supervision, leverage smaller datasets, and remain computationally efficient. Above all, we strive to ensure these systems are both explainable and trustworthy, paving the way for reliable and transparent AI solutions.

Neuro-Mimetic Approaches

At TKAI Lab, we are inspired by the remarkable efficiency and adaptability of biological systems. Our work focuses on integrating neuro-mimetic principles into AI to develop stable, adaptive, and efficient systems capable of tackling real-world challenges. By fusing insights from neuroscience, control theory, and cognitive science, we aim to create AI systems that emulate human-like learning and decision-making.

Reinforcement Learning

We explore cutting-edge reinforcement learning techniques that emulate the way biological systems learn through interaction and feedback. Our focus is on developing stable, efficient agents that require minimal data and supervision, while still making optimal decisions in complex environments.

Continual Learning

Taking inspiration from the brain’s ability to learn incrementally, we develop systems that can seamlessly acquire new knowledge without losing previously learned information. This ensures robust adaptability and stability, enabling AI to thrive in dynamic, real-world settings.

Alternative to Backpropagation

Challenging the limitations of traditional backpropagation, we are pioneering alternative training methods inspired by neurobiological and control-theoretic principles. These approaches aim to reduce computational overhead, improve energy efficiency, and enhance system stability while maintaining or exceeding current performance benchmarks.

Predictive Coding

Predictive coding is a cornerstone of our research, drawing from the brain’s ability to minimize prediction errors. This approach enables the design of AI systems that are not only computationally efficient but also adaptive, allowing them to learn and respond in real time with greater precision and stability.

Formal Methods

At TKAI Lab, we recognize that stability and trustworthiness must be built on a solid theoretical foundation. Formal methods form the backbone of our research, enabling us to ensure that our AI systems are reliable, transparent, and verifiable in both theory and practice.

Theory

We develop rigorous mathematical frameworks to analyze and guarantee the stability of AI systems. Our theoretical work bridges the gap between foundational research and real-world applications, ensuring our methods are robust under a variety of conditions.

Empirical

To validate our theoretical models, we conduct comprehensive empirical studies. These experiments test the applicability and scalability of our methods in real-world scenarios, ensuring that our stability-driven AI systems perform reliably under diverse and challenging conditions.

AI + X (Science)

We are committed to harnessing the power of AI to revolutionize other scientific domains. At TKAI Lab, we explore the intersection of AI with physics, biology, cybersecurity, health and cognitive science to address some of the most complex interdisciplinary challenges. Our aim is to create transformative solutions that are explainable, efficient, and trustworthy, pushing the boundaries of both AI and the sciences it touches.