skip to main content
US FlagAn official website of the United States government
dot gov icon
Official websites use .gov
A .gov website belongs to an official government organization in the United States.
https lock icon
Secure .gov websites use HTTPS
A lock ( lock ) or https:// means you've safely connected to the .gov website. Share sensitive information only on official, secure websites.


Title: Scaling up analogical innovation with crowds and AI
Analogy—the ability to find and apply deep structural patterns across domains—has been fundamental to human innovation in science and technology. Today there is a growing opportunity to accelerate innovation by moving analogy out of a single person’s mind and distributing it across many information processors, both human and machine. Doing so has the potential to overcome cognitive fixation, scale to large idea repositories, and support complex problems with multiple constraints. Here we lay out a perspective on the future of scalable analogical innovation and first steps using crowds and artificial intelligence (AI) to augment creativity that quantitatively demonstrate the promise of the approach, as well as core challenges critical to realizing this vision.  more » « less
Award ID(s):
1816242
PAR ID:
10084955
Author(s) / Creator(s):
; ; ; ; ; ; ; ;
Publisher / Repository:
Proceedings of the National Academy of Sciences
Date Published:
Journal Name:
Proceedings of the National Academy of Sciences
Volume:
116
Issue:
6
ISSN:
0027-8424
Page Range / eLocation ID:
p. 1870-1877
Format(s):
Medium: X
Sponsoring Org:
National Science Foundation
More Like this
  1. Creativity is typically defined as the generation of novel and useful ideas or artifacts. This generative capacity is crucial to everyday problem solving, technological innovation, scientific discovery, and the arts. A central concern of cognitive scientists is to understand the processes that underlie human creative thinking. We review evidence that one process contributing to human creativity is the ability to generate novel representations of unfamiliar situations by completing a partially-specified relation or an analogy. In particular, cognitive tasks that trigger generation of relational similarities between dissimilar situations—distant analogies—foster a kind of creative mindset. We discuss possible computational mechanisms that might enable relation-driven generation, and hence may contribute to human creativity, and conclude with suggested directions for future research. 
    more » « less
  2. Analogy problems involving multiple ordered relations of the same type create mapping ambiguity, requiring some mechanism for relational integration to achieve mapping accuracy. We address the question of whether the integration of ordered relations depends on their logical form alone, or on semantic representations that differ across relation types. We developed a triplet mapping task that provides a basic paradigm to investigate analogical reasoning with simple relational structures. Experimental results showed that mapping performance differed across orderings based on category, linear order, and causal relations, providing evidence that each transitive relation has its own semantic representation. Hence, human analogical mapping of ordered relations does not depend solely on their formal property of transitivity. Instead, human ability to solve mapping problems by integrating relations relies on the semantics of relation representations. We also compared human performance to the performance of several vector-based computational models of analogy. These models performed above chance but fell short of human performance for some relations, highlighting the need for further model development. 
    more » « less
  3. Freshman engineering students can have a hard time transitioning to college. The freshman year is critical to the students’ academic success; in this year they learn basic skills and establish essential networks with other students, faculty, and resources. How can we help these freshman engineering students in this transition? We propose that freshman students can learn from the engineering design innovation process and apply it by analogy to the design of their academic pathways. There are multiple similarities between product innovation (i.e., technology) and the continuous academic challenges faced by the student. Engineers as designers and innovators have a vast and rich repository of techniques, tools, and approaches to develop new technologies, and a parallelism can be drawn between the design and innovation of a technology (e.g., redesign of a kitchen appliance), and the “design” of the students’ academic career pathways. During the Spring 2023 semester pilot, students in Intro to Mechanical Engineering (Course A) worked in teams in a 6-week product innovation project to redesign a simple kitchen appliance. Students learned basic concepts of the design process (e.g., creative exploration of solutions, decision making, multi objective evaluation, etc.). These same students concurrently took Course B (Learning Frameworks) where they worked on a 6-week project to define their career pathways. Both projects, product innovation and career pathways, followed the Challenge Based Instruction (CBI) approach. Periodically, participant students were shown how to use the lessons from product innovation by analogy and reflection in their career pathways project. The objective is for students to learn about the engineering design process and to apply it to their academic challenges by analogy. This prepares students with meta skills to help solve future problems in their academic path, and at each iteration, the students transform themselves, hence the use of the term self-transformation (also referred as “self-innovation”). Data collected from pre and post surveys will be presented to measure self-efficacy in engineering design, grit, motivation to learn, and STEM identity. Participant interviews provide a qualitative insight into the intervention. This project is funded by NSF award 2225247. 
    more » « less
  4. Computational models of verbal analogy and relational similarity judgments can employ different types of vector representations of word meanings (embeddings) generated by machine-learning algorithms. An important question is whether human-like relational processing depends on explicit representations of relations (i.e., representations separable from those of the concepts being related), or whether implicit relation representations suffice. Earlier machine-learning models produced static embeddings for individual words, identical across all contexts. However, more recent Large Language Models (LLMs), which use transformer architectures applied to much larger training corpora, are able to produce contextualized embeddings that have the potential to capture implicit knowledge of semantic relations. Here we compare multiple models based on different types of embeddings to human data concerning judgments of relational similarity and solutions of verbal analogy problems. For two datasets, a model that learns explicit representations of relations, Bayesian Analogy with Relational Transformations (BART), captured human performance more successfully than either a model using static embeddings (Word2vec) or models using contextualized embeddings created by LLMs (BERT, RoBERTa, and GPT-2). These findings support the proposal that human thinking depends on representations that separate relations from the concepts they relate. 
    more » « less
  5. Who creates the most innovative open-source software projects? And what fate do these projects tend to have? Building on a long history of research to understand innovation in business and other domains, as well as recent advances towards modeling innovation in scientific research from the science of science field, in this paper we adopt the analogy of innovation as emerging from the novel recombination of existing bits of knowledge. As such, we consider as innovative the software projects that recombine existing software libraries in novel ways, i.e., those built on top of atypical combinations of packages as extracted from import statements. We then report on a large-scale quantitative study of innovation in the Python open-source software ecosystem. Our results show that higher levels of innovativeness are statistically associated with higher GitHub star counts, i.e., novelty begets popularity. At the same time, we find that controlling for project size, the more innovative projects tend to involve smaller teams of contributors, as well as be at higher risk of becoming abandoned in the long term. We conclude that innovation and open source sustainability are closely related and, to some extent, antagonistic. 
    more » « less