David Gamez

Dr. David Gamez is a Senior Lecturer in Computer Science at Middlesex University, UK, affiliated with the Faculty of Science and Technology. He specializes in interdisciplinary research at the intersection of philosophy, artificial intelligence, neuroscience, and psychology. His current focus is developing a universal measure of intelligence based on prediction, aiming to unify the assessment of human, animal, and AI intelligence. He also leads the module 'Web Applications and Databases' and supervises AI-related projects, including generative AI.

Education: PhD in Computer Science from the University of Essex (2008), supported by the CRONOS project. Previously held a JTF Turing Research Fellowship (2012-2015) at the Sackler Centre for Consciousness Science and worked at Imperial College London (2010-2012) on modular neural simulation. His research has explored spiking neural networks, agent systems, and the philosophy of consciousness, resulting in publications across journals like Journal of Artificial Intelligence and Consciousness and conference proceedings.

Research Interests: Machine consciousness, intelligence measurement, predictive processing, cognitive architectures, and ethical implications of AI. His work bridges theoretical frameworks with practical implementations, including experiments on predictive intelligence via web interfaces (https://www.davidgamez.eu/pi). He co-edits the Journal of Artificial Intelligence and Consciousness and organized AAAI workshops on consciousness topics.

Advising & Grants: Supervises student projects on AI and generative systems. His research has been supported by EPSRC grants, including work on neural simulators like NeMo and SpikeStream. Past projects include developing a conscious robot within the CRONOS initiative and analyzing computational correlates of consciousness.

Labs/Teams: Previously collaborated on the Sackler Centre’s consciousness studies, Imperial College’s neural simulation projects, and the iCub robot sensory interface. His current work involves interdisciplinary teams exploring predictive intelligence metrics and AI safety implications.