SIAM SIGNED
Scaling Up Innovation through Analogy Mining
Total Cost €
0EC-Contrib. €
0Partnership
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0SIAM project word cloud
Explore the words cloud of the SIAM project. It provides you a very rough idea of what is the project "SIAM" about.
Project "SIAM" data sheet
The following table provides information about the project.
| Coordinator |
THE HEBREW UNIVERSITY OF JERUSALEM
Organization address contact info |
| Coordinator Country | Israel [IL] |
| Total cost | 1˙373˙057 € |
| EC max contribution | 1˙373˙057 € (100%) |
| Programme |
1. H2020-EU.1.1. (EXCELLENT SCIENCE - European Research Council (ERC)) |
| Code Call | ERC-2019-STG |
| Funding Scheme | ERC-STG |
| Starting year | 2020 |
| Duration (year-month-day) | from 2020-01-01 to 2024-12-31 |
Partnership
Take a look of project's partnership.
| # | ||||
|---|---|---|---|---|
| 1 | THE HEBREW UNIVERSITY OF JERUSALEM | IL (JERUSALEM) | coordinator | 1˙373˙057.00 |
Map
Project objective
'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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The information about "SIAM" are provided by the European Opendata Portal: CORDIS opendata.