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Scaling Up Innovation through Analogy Mining

"Many world-changing breakthroughs in science and technology were enabled by analogical transfer, as ideas from one domain were used to solve a problem in another. Observing water led the Greek philosopher Chrysippus to speculate... ver más

31/12/2025

THE HEBREW UNIVERS...

1M€

Presupuesto del proyecto: 1M€

Líder del proyecto

THE HEBREW UNIVERSITY OF JERUSALEM 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 2019-11-21

ERC-2019-STG: ERC Starting Grant Scope:Objectives

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2 Participantes

THE HEBREW UNIVERSITY OF JER...

1.37M€ | Lider

DEIMOS SPACE

11.51M€ | Participante

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Duración del proyecto: 73 meses Fecha Inicio: 2019-11-21
Fecha Fin: 2025-12-31

Líder del proyecto

THE HEBREW UNIVERSITY OF JERUSALEM No se ha especificado una descripción o un objeto social para esta compañía.

TRL 4-5

Presupuesto del proyecto 1M€

Fecha límite de participación Sin fecha límite de participación.

Descripción del proyecto "Many world-changing breakthroughs in science and technology were enabled by analogical transfer, as ideas from one domain were used to solve a problem in another. Observing water led the Greek philosopher Chrysippus to speculate that sound was a wave phenomenon; an analogy to twisting a cardboard box allowed the Wright brothers to design a steerable aircraft. Despite its value for innovation, very little progress has been made towards automating the process of analogy-finding in real-world settings, and the problem has maintained a longstanding status as a ""holy grail"" in artificial intelligence (AI). The goal of this proposal is to tackle head-on this important problem and develop principled tools for automatically discovering analogies in large, unstructured, natural-language datasets such as patents and scientific papers. Such tools could revolutionize a variety of fields, allowing scientists and inventors to retrieve useful content based on deep structural similarity rather than simple keywords. The explosion of data available online, coupled with novel machine learning and crowdsourcing techniques, creates an unprecedented opportunity to develop novel methods to accelerate innovation and discovery. My approach explores the multiple roles AI and machine learning can play in the analogical innovation pipeline. This research will focus on the three core components of the pipeline -- (1) developing representations and similarity metrics to facilitate comparison between potential analogs, (2) imbuing the algorithms with commonsense knowledge and abstraction capabilities, and (3) guiding the adaptation of the discovered analogies to solve the original problem. For each component, the proposal demonstrates how recent advances suggest effective approaches, and describes our concrete preliminary results and ideas to serve as starting points and indicate the feasibility of this challenging project."

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