Scaling Up Innovation through Analogy Mining

Project Information

SIAM

Grant agreement ID: 852686

DOI

10.3030/852686

EC signature date 21 November 2019

Start date 1 January 2020

End date 31 December 2026

Funded under

• EXCELLENT SCIENCE - European Research Council (ERC)

Total cost

€ 1 373 057,00

EU contribution

€ 1 373 057,00

Coordinated by

THE HEBREW UNIVERSITY OF JERUSALEM
Israel

Periodic Reporting for period 3 - SIAM (Scaling Up Innovation through Analogy Mining)

Reporting period: 2023-01-01 to 2024-06-30

Summary of the context and overall objectives of the project

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.

The goal of this project 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.

This project explores the multiple roles AI and machine learning can play in the analogical innovation pipeline. We 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

(3) Guiding people in adapting the discovered analogies to solve the original problem

Work performed from the beginning of the project to the end of the period covered by the report and main results achieved so far

We have made substantial initial progress along several fronts:

* We have developed new methods to extract structure from texts and augment it with commonsense facts.

* We have explored new domains to test our ideas on. We have extended our work to natural-language texts describing processes, to biomimicry (bio-inspired design), and to images.

* We have suggested new approaches to improve commonsense abilities of natural-language understanding algorithms, based on recent advances in cognitive psychology.

* We have identified a set of clues that could potentially be useful for helping people in the adaptation phase, and devised and coded an experiment to test their effectiveness.

* We improved on our initial prototype analogical search engine to create a working interactive version that could be used by researchers in real time. This necessitated major improvements in scalability, accuracy, and enabling new ways for researchers to filter and explore the results. Under realistic usage with PhD-level researchers we validated that our system was able to spur more creative ideas than baselines.

Progress beyond the state of the art and expected potential impact (including the socio-economic impact and the wider societal implications of the project so far)

We expect to continue and make progress towards our goal, ultimately resulting in a prototype inspiration engine that can

(1) Identify analogies between distant domains based on abstract similarity,

(2) Take advantage of commonsense knowledge not appearing in the text,

(3) Highlight the commonalities and differences between the source and target in a way that will help the user quickly adapt the solution to the problem at hand.

We will conduct a series of ideation studies, testing the engine’s ability to improve users’ creativity and innovation.

From the same session

From a journal cover mockup (analogy live mouse:computer mouse)

From the same session