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UNION

Financiado

Cerrado

Unsupervised Perception

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, inc... ver más

31/05/2027

UOXF

2M€

Presupuesto del proyecto: 2M€

Líder del proyecto

THE CHANCELLOR MASTERS AND SCHOLARS OF THE UN... No se ha especificado una descripción o un objeto social para esta compañía.

TRL 4-5

Fecha límite participación Sin fecha límite de participación.

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Financiación concedida El organismo H2020 notifico la concesión del proyecto el día 2021-12-07

ERC-2020-COG: ERC CONSOLIDATOR GRANTS Scope:Objectives

I+D

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Información adicional privada

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2 Participantes

UOXF

2.31M€ | Lider

GALOL

767.28K€ | Participante

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Duración del proyecto: 65 meses Fecha Inicio: 2021-12-07
Fecha Fin: 2027-05-31

Líder del proyecto

THE CHANCELLOR MASTERS AND SCHOLARS OF THE UN... No se ha especificado una descripción o un objeto social para esta compañía.

TRL 4-5

Presupuesto del proyecto 2M€

Fecha límite de participación Sin fecha límite de participación.

Descripción del proyecto 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 tasks with at most lightweight manual assistance.

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