There are many different definitions of Artificial Intelligence in the literature, all are suggestive and insightful. However, at the end of the day, I think there is really one simple enough to be understood and formalised rigorously. This is John McCarthy’s original definition of AI from 1955: “the science and engineering of making intelligent machines”. … More A Simple Definition of Artificial Intelligence
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 measure, the Investigative Analytics NPP (led by CSIRO’s Data61), and an ANU Translational Fellowship. The overall … More FinTracer and Friends
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 the abstract:We describe a simple two-party protocol in which each party contributes a set as input. The output of the protocol is an estimate … More Split Count and Share: A Differentially Private Set Intersection Cardinality Algorithm
Here is my attempt to map out the major classes of algorithms in Machine Learning, organised around the associated induction principles and learning theory. The usual caveats apply around this being biased towards my own experience. At the highest level, we can distinguish between the Passive and Active learning settings. In the passive case, the … More A Map of Machine Learning Principles and Algorithms
I realised recently that I don’t have a good working definition of what Data Products are or should be. Sure, a quick googling will surface many generic definitions of Data Products from respected sources like Forbes and McKinsey. They are all usually variations of DJ Patil’s definition: “A data product is a product that facilitates … More What Are Data Products?
I was recently asked to speak about my leadership brand, a topic I hadn’t seriously thought about ever, until now. So what is my brand. Here are a few things that I know: So I think my leadership brand is the rational leader who tries to understand how things work, and who seeks to inspire … More My Leadership Brand
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. It is built on top of the diagram shown in Max Tegmark’s paper Is “the theory of … More A Map of Mathematical Structures for AI
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-20756 for a quick introduction — but a key fundamental issue with the AIXI theory has always been the incomputability of the general solution. In a continuation of … More A Direct Approximation of AIXI using Logical State Abstractions
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. So we decided to write one — well my postdoc Kelvin Yang Li decided to write one and Mike Purcell and I assisted … More A Tutorial Introduction to Lattice-based Cryptography and Homomorphic Encryption
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. The research took over 3 years and I am really excited about the underlying theory and its possible applications. In particular, the paper shows how we can lift’ Bayesian filtering to … More Bayesian Filtering on Structured Environments
Mental Models 4 Life 