David Rees School of Mathematics and Statistics, University of Sydney Ghost in the machine: From turbulence, faces and ducks to solar magnetic fields Wednesday 24th, March 14:05-15:55pm, Carslaw Building Room 359. In a recent paper in Science (Vol 293, p 2051, 2001), Mjolsness and DeCoste at the US Jet Propulsion Laboratory emphasized the importance of machine learning (e.g. principal component analysis - PCA, neural networks - NNs, and support vector machines - SVMs), for the future of science. A key driver for such a marriage between science and information technology is the overwhelming flood of digital data facing the scientist of the 21st century. Indeed one can argue that information technology cannot develop in a vacuum, but must be wedded to specific applications. Over the last decade, while at CSIRO, I was involved in a number of projects applying machine learning to signal and image processing, often using PCA. Thus much of this talk is a ghost story, the ghosts being the PCA eigenvectors. The story starts in the early 1990s in CSIRO's Synergetics project which focused on the nexus between pattern formation and pattern recognition in complex dynamical systems. In particular, Sirovich's application of PCA to the control of turbulence and to low dimensional representation of human faces inspired the development of SQIS (System for Quick Image Search), a system for detection and recognition of faces in real time video. SQIS is now a mature technology that is being commercialised. In the mid 1990s Nayar's group at Columbia University showed how PCA could be used in robotics, e.g. to estimate the pose of an object for pick and place by a robotic arm. My favourite object is Nayar?s toy duck because it was the duck that triggered the idea of using PCA for fast solution of inverse problems such as the estimation of vector magnetic fields in the solar atmosphere from measurements of polarization spectra. The Paris Observatory and the US High Altitude Observatory now use PCA routinely because (1) it is at least two orders of magnitude faster than traditional data fitting by least squares, (2) it gives extra physical insight, and (3) it enables the solution of otherwise intractable problems such as weak magnetic field measurements in solar prominences where the quantum theory of line formation is a frightening mess. I will conclude with a summary of the latest developments aimed at real-time inversion of polarimetric data. Recently HAO has implemented a new inversion method based on NNs. This is much faster than PCA and looks like it will be the method of choice for on-board data processing on a satellite to be launched in 2007. But I?m still holding out hopes for another method developed and patented by CSIRO. This is called Multiple Support Vector Regression, a generic inversion method, which still needs considerable research - an ideal topic for a PhD student in the School of Mathematics and Statistics.