Analogy Mining for Specific Design Needs

Gilon, Karni; Chan, Joel; Ng, Felicia; Liifshitz-Assaf, Hila; Kittur, Aniket; Shahaf, Dafna

doi:10.1145/3173574.3173695

Cited by 31 publications

(24 citation statements)

References 24 publications

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“…Building on our AI approach to augmenting analogical innovation, in ref. 51 we introduced a system in which a designer can specify a focus for a given product description and then abstract that focus beyond its surface features in a targeted manner by specifying the key properties of the relations and entities involved that are crucial for understanding the core relational structure.…”

Section: Complexitymentioning

confidence: 99%

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Scaling up analogical innovation with crowds and AI

Kittur

Hope

et al. 2019

Proc. Natl. Acad. Sci. U.S.A.

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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.

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Section: Complexitymentioning

confidence: 99%

“…The system then finds analogies (Bottom) for the focus-abstracted query, supporting an analogical search for complex problems with multiple aspects and levels of abstraction. Republished with permission of Association for Computing Machinery, from ref 51…”

mentioning

confidence: 99%

Scaling up analogical innovation with crowds and AI

Kittur

Hope

et al. 2019

Proc. Natl. Acad. Sci. U.S.A.

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“…In the latter, lexical relationships between concepts are modelled, for instance, 'flower' is a 'plant' and a 'plant' is an 'organism. ' Other research uses knowledge graphs for enriching ideas semantically [23]. A knowledge graph organizes various topically different real-world entities, called concepts, with their relationships in a graph.…”

Section: Use Case: Collaborative Ideationmentioning

confidence: 99%

“…Enabling the annotation of concepts in idea texts would have multiple advantages. The concepts could be used, for example, as an input for a faceted search tool (e.g., finding all ideas that talk about kinds of plants), they enable aggregation and cluster visualization of the solution space [63], and they could potentially enable effective inspiration mechanisms such as analogical transfer [23] (via a 'has-usage' relationship between concepts).…”

Section: Use Case: Collaborative Ideationmentioning

confidence: 99%

Discovering the Sweet Spot of Human-Computer Configurations

Mackeprang

Müller-Birn

Stauss

2019

Proc. ACM Hum.-Comput. Interact.

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Interactive intelligent systems, i.e., interactive systems that employ AI technologies, are currently present in many parts of our social, public and political life. An issue reoccurring often in the development of these systems is the question regarding the level of appropriate human and computer contributions. Engineers and designers lack a way of systematically defining and delimiting possible options for designing such systems in terms of levels of automation. In this paper, we propose, apply and reflect on a method for human-computer configuration design. It supports the systematic investigation of the design space for developing an interactive intelligent system. We illustrate our method with a use case in the context of collaborative ideation. Here, we developed a tool for information extraction from idea content. A challenge was to find the right level of algorithmic support, whereby the quality of the information extraction should be as high as possible, but, at the same time, the human effort should be low. Such contradicting goals are often an issue in system development; thus, our method proposed helped us to conceptualize and explore the design space. Based on a critical reflection on our method application, we want to offer a complementary perspective to the value-centered design of interactive intelligent systems. Our overarching goal is to contribute to the design of so-called hybrid systems where humans and computers are partners. intelligent systems are AI 1 -supported systems, with which people interact when selecting songs, reading news or searching for products. Such systems should be examined by considering both the human and the system. Jameson and Riedl call this a 'binocular view' of interactive intelligent systems because the system's design includes algorithm design with interaction design, on the one hand, and a combined evaluation of a system's performance and human behavior, on the other hand [33]. However, existing research provides little guidance on how we should design interactive intelligent systems. Should a task be carried out by a human or a computer? What is an appropriate level of interaction vs. integration? Is human labor more preferable than automated action? How can we make an informed decision about allocating the task to either one or the other? How can we evaluate our decision? A proposal of a method for elaborating this spectrum is still missing.The authors have experienced this issue in the context of information extraction from ideas in the research area of collaborative ideation. In addition to complete manual approaches for making sense of an idea's content [4], algorithmic approaches were proposed which describe the content of the ideas statistically (e.g., [6]). However, both perspectives (manual vs. automatic) have their limitations; thus, the question is whether a 'sweet spot' that emphasizes the advantages of both perspectives exists and how such a 'sweet spot' can be identified. A 'sweet spot' defines a compromise between the often contradictory evaluative...

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“…However, advanced creativity support tools are rarely, if ever, deployed in early stage design [33]. The hurdle is to make creative contributions without distracting design thinking [15,17,21]. This is a challenge for non-interactive approaches in machine-learning, or for any approach assuming pre-defined objectives, which may yield irrelevant proposals [46].…”

Section: Introductionmentioning

confidence: 99%

May AI?

Koch

Lucero

Hegemann

et al. 2019

Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems

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Design ideation is a prime creative activity in design. However, it is challenging to support computationally due to its quickly evolving and exploratory nature. The paper presents cooperative contextual bandits (CCB) as a machine-learning method for interactive ideation support. A CCB can learn to propose domain-relevant contributions and adapt their exploration/exploitation strategy. We developed a CCB for an interactive design ideation tool that 1) suggests inspirational and situationally relevant materials ("may AI?"); 2) explores and exploits inspirational materials with the designer; and 3) explains its suggestions to aid reflection. The application case of digital mood board design is presented, wherein visual inspirational materials are collected and curated in collages. In a controlled study, 14 of 16 professional designers preferred the CCB-augmented tool. The CCB approach holds promise for ideation activities wherein adaptive and steerable support is welcome but designers must retain full outcome control. CCS CONCEPTS • Human-centered computing → Interactive systems and tools; • Computing methodologies → Machine learning; • Applied computing → Arts and humanities.

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Analogy Mining for Specific Design Needs

Cited by 31 publications

References 24 publications

Scaling up analogical innovation with crowds and AI

Scaling up analogical innovation with crowds and AI

Discovering the Sweet Spot of Human-Computer Configurations

May AI?

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