Extracting meaning from data

Doctor

Extracting meaning from data is the central business of the information era. ML Market is a European consortium of leading researchers that span a range of areas in information processing, data analysis, statistics and machine learning.

ML Market groups are formed from world-leading research groups within the Pascal European network that also actively engage to find business solutions to challenging real-world problems. ML Market exists to promote the academic and industrial expertise of its researchers and provides a platform to engage and broker industrial contacts.

Case Studies

Letting stones go unturned

Wéiqí, Baduk or simply, Go. Over two thousand years old, this challenging game of strategy and tactics has taught warriors, monks and intellectuals to focus, concentrate and plan. But a program is beginning to change our views of what is possible. In computer competitions this multi-armed bandit has stolen first prize more often than any other. It’s MoGo, or Monte Carlo Go.

Bonaparte Disaster Victim Identification System

Society is increasingly aware of the possibility of a mass disaster. Recent examples are the WTC attacks, the tsunamis, and various airplane crashes. In such an event, the recovery and identification of the remains of the victims is of great importance, both for humanitarian as well as legal reasons. Disaster victim identification (DVI), i.e. the identification of victims of a mass disaster, is greatly facilitated by the advent of modern DNA technology. In forensic laboratories, DNA profiles can be recorded from small samples of body remains which may otherwise be unidentifiable.

What are you looking at?

You’re waiting at the station for your train and you glance at the electronic poster next to you. It notices that you’re looking at it, and from your gaze it works out what you would most like to see. It’s as though it’s reading your mind – but really it’s reading your eyes.

Topics

Interactive Natural Language Processing

KeyboardThe current state of the art in different areas of natural language processing (NLP) is very far from allowing fully automatic high quality results (HQRs), therefore human intervention is required to correct the output of the NLP engines. This applies specifically to NLP fields such as: machine translation and cross-language processing, text recognition, parsing, speech recognition, information retrieval, etc.

Its goal is to produce HQRs through a tight collaboration between a human operator and a NLP system, following an interactive-predictive paradigm. On the other hand, interactivity offers a unique context in which the feedback provided by the human can be used as new training data for adapting the NLP systems to new environments.

Statistical Machine Translation

Half of the EU citizens are not able to hold a conversation in a language other than their mother tongue, let alone to conduct a negotiation, or interpret a law. In a time of wide availability of communication technologies, language barriers are a serious bottleneck to European integration and to economic and cultural exchanges in general. More effective tools to overcome such barriers, in the form of software for machine translation and other cross-lingual textual information access tasks, are in strong demand.

Machine Vision

Machine vision is a branch of engineering that uses computer vision in the context of manufacturing. While the scope of Machine Vision is broad and a comprehensive definition is difficult to distil, a "generally accepted definition of machine vision is '... the analysis of images to extract data for controlling a process or activity.'" Put another way, Machine Vision processes are targeted at "recognizing the actual objects in an image and assigning properties to those objects--understanding what they mean." The commercial applications of Machine Vision include tracking of people in crowds, numberplate recognition, reconstructing the 3D geometry of an environment.