Category: Artificial Intelligence

Martingale Tests for Model Misspecification in Bayesian Sequence Prediction

May 9, 2026

Using sequential hypothesis testing techniques to check the modelling assumptions of Bayesian mixture estimators is a promising way of getting value out of combining the Bayesian and frequentist approaches to probability. Here’s a paper to show how that can be done for Context Tree Weighting and related methods. Paper Abstract: Universal Bayesian sequence predictors like … More Martingale Tests for Model Misspecification in Bayesian Sequence Prediction

Notes on Conformal Prediction and Testing

February 8, 2026

All my life I have been searching for simple and effective methods for constructing prediction intervals for different AI/ML models. I don’t know why I never encountered Conformal Prediction until recently, but I suppose it is better late than never. Conformal prediction is (arguably) the most elegant and practical technique for improving the robustness in … More Notes on Conformal Prediction and Testing

AI Risk Assessment via Threat Modelling

January 24, 2026

Threat modelling is now considered a best practice in comprehensive technical approaches to dealing with AI safety issues [S+25]. Threat modeling [S14] is a structured, proactive process used to identify potential threats and vulnerabilities in a system. While the traditional focus is on cyber-security and privacy issues, threat modelling has been extended for AI systems … More AI Risk Assessment via Threat Modelling

AI Governance vs AI Assurance

January 6, 2026

AI governance and AI assurance are sometimes conflated in conversations but they are not the same thing. In simple terms, In more details, the AI Governance Framework for an organisation is the strategic blueprint that defines the organisation’s overarching approach to developing, deploying, and managing AI systems. Its purpose is to define the roles, responsibilities, … More AI Governance vs AI Assurance

Thoughts on Prompt Injection Attacks

October 26, 2025

Like many difficult cyber security problems, prompt-injection attacks is likely to become an ongoing issue that shifts and turns with the continual discovery of new attacks and new defences going forward. Instead of responding in natural language given a prompt, the best current defence I know involves always generating code, say, in a safe interpreted … More Thoughts on Prompt Injection Attacks

Customising the Australian Government’s AI Fundamentals Training Course

October 6, 2025

To support public-service agencies in the implementation of their own responsible use of AI policies, the Australian Government’s Digital Transformation Agency (DTA) has made publicly available its AI Fundamentals training course in the form of a SCORM package, a commonly used technical standard for putting together content for Learning Management Systems (LMS). The DTA training … More Customising the Australian Government’s AI Fundamentals Training Course

Secure and Ephemeral AI Workloads in Data Mesh Environments

June 3, 2025

A colleague and I have just released on arXiv a paper titled “Enabling Secure and Ephemeral AI Workloads in Data Mesh Environments”. The key innovation is in pushing the now well-established idea of minimal immutable data structures up and down the software infrastructure stack a bit further than what others have done, resulting in a … More Secure and Ephemeral AI Workloads in Data Mesh Environments

Update on Social Cost of Multi-Agent Reinforcement Learning Paper

April 24, 2025

I recently released on arXiv a new version of the paper The Problem of Social Cost in Multi-Agent General Reinforcement Learning: Survey and Synthesis, which can be found at https://arxiv.org/abs/2412.02091 The new version has These are all non-trivial extensions of the paper that build on recent new results in different fields and they are worth … More Update on Social Cost of Multi-Agent Reinforcement Learning Paper