Signed in as:
filler@godaddy.com
Our project portfolio
A growing array of impacts
Consortium membership in the Trusted AI Alliance brings access to an array of translational research topics with a sharp focus on bringing benchtop to boardroom and from insight to impact. Our array of researchers span the top institutions in the world as we seek to deliver on the promise of intelligence to the greatest number of people with the most potential for uplift.
Discover OUR WORK
Sovereign AI
Synchronizing Intelligence
Intelligence Capital
Understanding policy, technology and skills requirements to create nationally-funded or nationally-supported GPT projects, including open source, while simultaneously addressing the brutal demands of AI systems on the energy and water grids.
Intelligence Capital
Synchronizing Intelligence
Intelligence Capital
A new theory of the firm explains the Age of AI. To thrive in the current economic shift, leadership must transition from "innovation theater", characterized by scattershot AI pilots with a 95% failure rate, to a strategic framework that treats Generative AI as a fundamental capital asset. Enterprises must reframe AI from a tool of efficiency into a system of compounding value creation. We are delivering a free webinar on 30 January at 12pm GMT.
Synchronizing Intelligence
Synchronizing Intelligence
Synchronizing Intelligence
Reskilling may not be enough in the age of AI. Workers need to acquire new knowledge at blinding speed and embed new ways of working in the process. How can AI be used to help the AI transition? Is there technology that can synchronize human intelligence and synthetic intelligence?
Scaling Ideas
Digital Wellness Lab (with Boston Childrens Hospital / Harvard Medical School)
Synchronizing Intelligence
Highly interconnected digital networks, new and complex capital models, changing social contracts, shifting structures and missions of academic institutions, all are creating a new and uncertain environment in which to launch new ideas. Many ideas fail and die in the chasms found at every transition point of growth (from benchtop to boardroom, from startup to national scale, from national scale to global span, and from global span to enduring legacy). Yet some ideas end up scaling and enduring the test of time. A toolkit and a platform are need to improve the rates of success and speed by which ideas scale, and a new kind of organisation can deliver them.
Digital Wellness Lab (with Boston Childrens Hospital / Harvard Medical School)
Digital Wellness Lab (with Boston Childrens Hospital / Harvard Medical School)
Digital Wellness Lab (with Boston Childrens Hospital / Harvard Medical School)
The Digital Wellness Lab at Boston Children’s Hospital and Harvard Medical School brings together interdisciplinary research, clinical insight, and cross-sector collaboration to understand how digital technologies shape the mental, social, and emotional well-being of children, adolescents, and families. The Lab examines digital environments as complex ecosystems and focuses on the building blocks of wellbeing, including connection, identity development, creativity, safety, and belonging, across technologies such as artificial intelligence, social gaming, and online communities.
Next Generation Payment Fraud (together with MIT FinTechAI@CSAIL)
Digital Wellness Lab (with Boston Childrens Hospital / Harvard Medical School)
Digital Wellness Lab (with Boston Childrens Hospital / Harvard Medical School)
Predicts the "next generation" of payment fraud strategies that are emerging due to advancements in Generative AI. The team intends to use adversarial agent-based modeling and coevolutionary techniques to anticipate "unknown unknown" threats—new fraud strategies that do not yet exist but could be enabled by new tools. The project aims to produce a proof-of-concept workflow to help the anti-fraud community stay ahead of these evolving threats.
Decentralized Coevolutionary Federated Learning for Synthetic Data Generation (together with MIT Fin
Decentralized Coevolutionary Federated Learning for Synthetic Data Generation (together with MIT Fin
Decentralized Coevolutionary Federated Learning for Synthetic Data Generation (together with MIT Fin
This project aims to enhance a Decentralized Coevolutionary Federated Learning (DCEFL) system to generate secure, privacy-preserving synthetic data. By co-evolving multiple populations of AI models (such as GANs and Autoencoders), the system seeks to provide a flexible and resilient method for sharing useful data insights without exposing sensitive raw information. The goal is to overcome regulatory barriers to information sharing by exchanging robust AI models instead of the data itself.
Red Team
Decentralized Coevolutionary Federated Learning for Synthetic Data Generation (together with MIT Fin
Decentralized Coevolutionary Federated Learning for Synthetic Data Generation (together with MIT Fin
Stress test AI system security with Red Team exercises.
Protecting Against Fraud
Decentralized Coevolutionary Federated Learning for Synthetic Data Generation (together with MIT Fin
Protecting Against Fraud
New approaches to fraud detection, improving accuracy by identifying sophisticated attack patterns that traditional rule-based systems miss.
Model Validation
Model Validation
Protecting Against Fraud
Assurance and compliance with internal and external requirements including EU AI Act.
This website uses cookies.
We use cookies to analyze website traffic and optimize your website experience. By accepting our use of cookies, your data will be aggregated with all other user data.