Mental Models 4 Life

In the last 10 years or so, there has been a paradigm shift in how we train AI models.

In a recent TED talk titled One Thing to Teach in the Age of AI, innovation practitioner Bryan Cassady shared a story about an exam created by one of his colleagues.

If AI really turns out to be normal technology, an increasingly persuasive case Arvind Narayanan and Sayash Kapoor have continued to advance, what happens to the competitive dynamics and industry structures for the organisations operating in that space?

A significantly expanded version of a paper I shared earlier on Privacy Technologies for Financial Intelligence is now published in the Journal of Economic Criminology’s Special Issue on Economic Crime in a Changing World: Emerging Threats and Strategic R…

We have substantially revised the factored conditional filtering paper to frame it more generally as a solution for belief acquisition in AI agents.

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.

All my life I have been searching for simple and effective methods for constructing prediction intervals for different AI/ML models.

Threat modelling is now considered a best practice in comprehensive technical approaches to dealing with AI safety issues [S+25].

AI governance and AI assurance are sometimes conflated in conversations but they are not the same thing.

I read a couple of really good books on China’s economic system this year that I can recommend for those who want to know the country better (from a distance).

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.

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 pa…

Differential Privacy (DP) is one of the most widely adopted formal model of privacy protection but its semantics, especially in the presence of correlated data and in the adversarial interactive setting, is still not broadly understood among data science…

Cyber security and data security are closely related concepts that operate at different levels and provide different safeguards.

Modern competitive analysis started in the 1930’s with the introduction of the Structural-Conduct-Performance (SCP) paradigm (shown in Figure 1 below) that was used to analyse the causal dynamics of Industrial Organisation, the field of study concerned to…

A colleague and I have just released on arXiv a paper titled “Enabling Secure and Ephemeral AI Workloads in Data Mesh Environments”.

Of the many questions we wish to answer using LLMs, it can be useful to distinguish between puzzles and mysteries.

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 a more comprehensive descrip…

I wrote the following comments very quickly on LinkedIn about 10 days ago in the middle of the DeepSeek frenzy, and the post turned out to be my second most-read post ever and people continues to look at it.

There’s been a lot written about Fourier Transforms over the years.

Fairness in Machine Learning is a topic that I have been wanting to better understand for a little while now, and this blog post summarises what I learned from reading the Fairness and Machine Learning (FML) book by Solon Barocas, Moritz Hardt and Arvind…

While I have worked on aspects of AI safety for quite a few years now, in particular privacy technologies and confidential computing, I am a late convert on the importance of Artificial General Intelligence (AGI) safety research and did not take the probl…

With NVIDIA seemingly steaming ahead in their latest quarterly result, Apple Intelligence receiving a lukewarm response from users, Wall Street increasingly worried about the return-on-investment from the hyperscalers’ massive capital investments, stories…

As is well-known by now, the universal AI agent AIXI is made up of two key components: Solomonoff Induction for universal sequential prediction, and expectimax search for planning.

Have you ever wondered why the natural exponential function shows up so frequently in mathematical inequalities?

A key claim of differential privacy in [DR14] is that it provides “automatic neutralization of linkage attacks, including all those attempted with all past, present, and future datasets and other forms and sources of auxiliary information”.

This blog post Sizing up the New Axis by Noah Smith that compares the combined economic strength of China and Russia against that of US and its key allies provides interesting insights into the robustness of the industrial base and manufacturing supply ch…

It took a little while to write, but hopefully the following survey paper by Yang Li, Thilina Ranbaduge and yours truly can help demystify financial intelligence and privacy technologies for practitioners and technologists alike.

Differential Privacy (DP) is, by now, the most widely adopted formal model of privacy protection used in industry [L23] and government [ABS22] but my sense is that its “semantics”, especially in the presence of correlated data and in the adversarial inter…

We have just released another paper on arXiv: https://arxiv.org/abs/2406.17649 Here’s the abstract: We consider the problem of privacy protection in Reinforcement Learning (RL) algorithms that operate over population processes, a practical but understudied

The Bretagnolle-Huber Inequality provides a bound on the total variation distance between two probability distributions in terms of their Kullback-Leibler divergence, and it is better than Pinsker’s Inequality when the KL divergence is larger than two, and

I have been incredibly frustrated with the lack of quality and content in many responsible AI (RAI) conversations.

I have been thinking about data security issues, in particular database-reconstruction attacks.

Regular users of arXiv.org may have noticed that on every paper’s page, under the Related Papers tab, one can now find the paper’s Influence Flower, which is a nice way to visualise citation influences among academic entities, including papers, authors, i…

My phd student just got a new paper accepted at the upcoming AAAI Conference on Artificial Intelligence.

My phd student has just released a paper on 3D Object-Centric Learning on arXiv.

There are many different definitions of Artificial Intelligence in the literature, all are suggestive and insightful.

About 5 years ago, Tania Churchill and I assembled a team of researchers and engineers across AUSTRAC and ANU to work on privacy technologies for detecting criminal activities across the financial system, funded by the Fintel Alliance Expansion budget mea…

My colleagues Mike Purcell, Kelvin Yang Li and I have a new paper on differentially private set intersection cardinality algorithm accepted at this year’s Uncertainty in Artificial Intelligence conference.

Here is my attempt to map out the major classes of algorithms in Machine Learning, organised around the associated induction principles and learning theory.

I realised recently that I don’t have a good working definition of what Data Products are or should be.

I was recently asked to speak about my leadership brand, a topic I hadn’t seriously thought about ever, until now.

In this post on the last day of the year, I thought I will share a map of mathematical structures that are useful for thinking about knowledge representation and reasoning (KRR) issues in Artificial Intelligence and Machine Learning.

Artificial Intelligence as a well-defined mathematical problem was solved a number of years ago through the formulation of the AIXI agent by Prof Marcus Hutter — see https://theconversation.com/to-create-a-super-intelligent-machine-start-with-an-equation-…

A few of us have been working with homomorphic encryption for a number of years now, but we never found a paper / book that covers all the foundational mathematical material in one place.

A few colleagues and I have just completed a new research paper titled Factored Conditional Filtering: Tracking States and Estimating Parameters in High-Dimensional Spaces.

Cyclotomic polynomials are widely used in the construction of lattice cryptography and homomorphic encryption schemes based on the Ring Learning With Error problem.

Data matching and entity resolution is a common first step in data preparation and there is a thousand academic papers written on the subject in the literature.

I read the book Divining a Digital Future: Mess and Mythology in Ubiquitous Computing by Genevieve Bell and Paul Dourish almost cover to cover last year and I have to say that finally gave me some understanding of the research agenda being pursued at the…

A few colleagues and I have just put on arXiv a new survey paper on Private Graph Data Release, which took us nearly 9 months to write.