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Notes on a New Philosophy of Empirical Science

arXiv.org Machine Learning

This book presents a methodology and philosophy of empirical science based on large scale lossless data compression. In this view a theory is scientific if it can be used to build a data compression program, and it is valuable if it can compress a standard benchmark database to a small size, taking into account the length of the compressor itself. This methodology therefore includes an Occam principle as well as a solution to the problem of demarcation. Because of the fundamental difficulty of lossless compression, this type of research must be empirical in nature: compression can only be achieved by discovering and characterizing empirical regularities in the data. Because of this, the philosophy provides a way to reformulate fields such as computer vision and computational linguistics as empirical sciences: the former by attempting to compress databases of natural images, the latter by attempting to compress large text databases. The book argues that the rigor and objectivity of the compression principle should set the stage for systematic progress in these fields. The argument is especially strong in the context of computer vision, which is plagued by chronic problems of evaluation. The book also considers the field of machine learning. Here the traditional approach requires that the models proposed to solve learning problems be extremely simple, in order to avoid overfitting. However, the world may contain intrinsically complex phenomena, which would require complex models to understand. The compression philosophy can justify complex models because of the large quantity of data being modeled (if the target database is 100 Gb, it is easy to justify a 10 Mb model). The complex models and abstractions learned on the basis of the raw data (images, language, etc) can then be reused to solve any specific learning problem, such as face recognition or machine translation.


How Does Face-Recognition Sunglasses Work? Chinese Police Increase Use Of Smart Tech

International Business Times

How much surveillance is too much? That is a question being asked in China after police in the country began using sunglasses equipped with fixed facial recognition cameras in order to help identify potential suspects, reports said Wednesday.



Inside China's dystopian dreams: Artificial intelligence, shame and lots of cameras - Times of India

#artificialintelligence

ZHENGZHOU: In the Chinese city of Zhengzhou, a police officer wearing facial recognition glasses spotted a heroin smuggler at a train station. In Qingdao, a city famous for its German colonial heritage, cameras powered by artificial intelligence helped police snatch two dozen criminal suspects in the midst of a big annual beer festival. In Wuhu, a fugitive murder suspect was identified by a camera as he bought food from a street vendor. With millions of cameras and billions of lines of code, China is building a high-tech authoritarian future. Beijing is embracing technologies like facial recognition and artificial intelligence to identify and track 1.4 billion people. It wants to assemble a vast and unprecedented national surveillance system, with crucial help from its thriving technology industry.