Author: Kee Siong Ng

A Simplistic Guide to Using Fairness Criteria in Machine Learning

January 10, 2025

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 Narayanan available at https://fairmlbook.org these past couple of days. (The book … More A Simplistic Guide to Using Fairness Criteria in Machine Learning

The Problem of Social Cost in Multi-Agent Universal Reinforcement Learning

December 5, 2024

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 problem seriously until about 1 year ago. My mindset has now changed completely … More The Problem of Social Cost in Multi-Agent Universal Reinforcement Learning

Winners and Losers in the AI Commercial Landscape

December 3, 2024

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 that CIOs are struggling to find ROI for AI, and news in the last two days that Intel and Samsung are both struggling … More Winners and Losers in the AI Commercial Landscape

Approximating Solomonoff Induction

November 23, 2024

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. There are several proposed and reasonably effective approximations of the Solomonoff Induction component using the factored, binarised Context Tree Weighting algorithm [WST95, VNHUS09] and its generalisation to … More Approximating Solomonoff Induction

Natural Exponential Functions in Inequalities

October 16, 2024

Have you ever wondered why the natural exponential function shows up so frequently in mathematical inequalities? Here’s a graph of the natural exponential function. The constant e has a special place in mathematics, which is beautifully chronicled in Eli Maor’s book [M94]. The definition of e that is most useful and intuitive for our purpose … More Natural Exponential Functions in Inequalities

Dealing with Linkage Attacks using Differential Privacy

September 13, 2024

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 is an important and often repeated claim — see e.g. [N17, Section E] and [PR23] — but the … More Dealing with Linkage Attacks using Differential Privacy

Privacy Technologies for Financial Intelligence

August 20, 2024

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. The focus is on anti-money laundering and counter-terrorism financing, but the opportunity set is much broader. https://arxiv.org/abs/2408.09935 Here’s the abstract of … More Privacy Technologies for Financial Intelligence

What Can Differential Privacy Actually Protect?

July 6, 2024

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 interactive setting, is still not broadly understood in the community, especially among practitioners. In the … More What Can Differential Privacy Actually Protect?

Privacy-Preserving Reinforcement Learning for Population Processes

July 1, 2024

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 setting that includes, for example, the control of epidemics in large populations of dynamically interacting individuals. In this setting, the RL algorithm … More Privacy-Preserving Reinforcement Learning for Population Processes