📋 Tender Overview
Unsupervised Perception
Machine Learning Without Manual Supervision
Today, We Can Benefit From Powerful Search Engines, Medical Devices And Personal Assistants That Employ Machine Learning. However, Machines Learn New Skills Via Manual Supervision. The Eu-Funded Union Project Intends To Develop Machines That Can Understand And Integrate Audiovisual Data With Little Or No Manual Supervision, Paving The Way For Countless New Applications Of Artificial Intelligence. The Project Will Investigate Two Hypotheses: First, That Concepts Reflecting Intrinsic Properties Of The Natural World Can Be Learned Without Manual Supervision While Still Being Interpretable To A Human; Second, That Given This Ability, A Machine Can Gather New Skills Useful To Specific Stakeholders From Few Or No Manually Annotated Examples.
Funded Under: Excellent Science - European Research Council (Erc)
Total Cost: € 2 311 847,00
Eu Contribution: € 2 311 847,00
Coordinated By: The Chancellor, Masters And Scholars Of The University Of Oxford
United Kingdom
Objective
The aim of UNION is to liberate machine learning, enabling everyone to use it productively and creatively instead of being the demesne of experts. Today, machines must be explicitly taught any new skill via manual supervision, incurring a cost justifiable only for applications of general interest. Thus, as laypeople, we can benefit from powerful search engines, medical devices and personal assistants that are designed by means of machine learning, but we cannot easily teach machines to address our particular professional or personal needs. From recognizing illustrations on Greek vases to building catalogues of store products, machine learning could empower millions of individuals, but current technology cannot scale to these micro-tasks. The goal of UNION is thus to develop machines that can learn to understand audio-visual data with little to no manual supervision, opening up artificial intelligence to countless new applications. To this end, UNION will investigate two key hypotheses. The first is that concepts that reflect intrinsic properties of the natural world, such as detachable objects and their 3D geometry, physics and high-level class, can be learned without manual supervision, while still being interpretable to a human. The second hypothesis is that, given this ability, a machine can pick up new skills useful to specific stakeholders from no or just a few manually-annotated examples. These hypotheses will be validated (1) by developing algorithms that can learn without manual supervision, (2) by endowing machines with advanced general-purpose audio-visual analytical skills, and (3) by using the knowledge already acquired to learn new skills very efficiently, from little data and even less manual supervision. This will be delivered as an open-source package that will demonstrate how one can create open-ended audio-visual analysis software that can be taught a large variety of different tas
