UMNAI
Hybrid Intelligence: combining neural learning and symbolic reasoning to make AI decisions understandable, inspectable, and useful.
AI RESEARCHER / FOUNDER / INVESTOR
I build AI that can explain its decisions.
Then I turn it into things people can use.
25+ years working on AI. Co-founder of UMNAI. Working at the intersection of neural learning, symbolic reasoning, and real-world decisions.

Hybrid Intelligence: combining neural learning and symbolic reasoning to make AI decisions understandable, inspectable, and useful.
Taking AI beyond prediction. Connecting models, explanations, counterfactuals, and simulation to the decisions people actually need to make.
A career turning technical ideas into businesses across AI, transportation, entertainment, and fintech. Supporting founders who build things that matter.
“The purpose of an explanation is not simply to persuade someone to accept an answer. It is to help them work out whether they should.”
“I do not want AI that hides uncertainty to look intelligent. I want AI that helps us manage uncertainty intelligently.”
“The best AI should not have the last word. It should help us ask a better next question.”
Build a scalable, repeatable business around contrarian ideas. Question the assumptions. Prove the idea. Make it work at scale.
I built my first neural network in 1996 and have been working on AI for over 25 years. I started selling software as a teenager. What began small became a career building technology companies, researching AI, and backing other entrepreneurs.
My academic path took me from computer science and computational linguistics in Malta to a PhD in AI at the University of Sheffield. I’m interested in the whole journey: from an idea on paper to a system that works outside the lab.
Member of IEEE, AAAI, ACM, and ACL.
Developed mathematical formulas for sequence alignment, with applications in DNA analysis and natural language processing.
Built early applications of licence plate recognition for congestion charging, dynamic pricing, automated parking, and road safety.
Developed recommendation algorithms and data mining methods for large text datasets and commercial transactions across retail, entertainment, and fintech.
Pioneered early commercial uses of AI in entertainment businesses, applying learning and personalisation to products people used every day.
I explore how AI and human creativity can work together, collaborating with artists through UMA, the Universal Machine Artist.
My philanthropic and creative research arm, the Creative Science and Arts Institute Foundation (CSAI), brings together AI, science, and the arts to explore new ways of creating and thinking.
Computer science, computational linguistics, and competitive programming.
Nine startups and four exits. Technical depth with commercial experience.
AI that learns, reasons, and makes its decisions understandable.
AI research, company building, investment, or a thoughtful conversation about what comes next.