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I'm Tom hope from the Hebrew University of Jerusalem and the Allen Institute for AI, presenting our work on scaling creative inspiration with fine grained functional aspects of ideas.
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Take a look around your home, and you'll see some commonplace appliances and products whose history is anything but commonplace. For example, the microwave oven uses technology originating from the World War Two radar.
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The Teflon nonstick pan is based on chemicals first used in the atom bomb Manhattan Project.
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And cordless power tools and even your bed's mattress have their origins in space exploration.
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In fact, NASA actively engages in finding opportunities for repurposing of technologies.
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NASA has generated over 2000 spin offs, commercial products, and services that are used throughout daily life.
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These are just a few examples of a fundamental pattern of human creativity, where innovators repurpose existing mechanisms for new alternative uses by detecting abstract structural connections across problems and across distant domains.
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This raises a question that lies at the center of our work. How can we scale the process of innovation by harnessing many millions of technologies and ideas available in online repositories of ideas, in product descriptions, patents and scientific publications, to discover new uses and new solutions to problems?
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A main hurdle is that the way we currently interact with these repositories of ideas via standard search engines, is very limited, and does not come close to tapping into the potential for augmenting and scaling human ingenuity.
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In order to support creative innovation interactions, we need to go beyond our standard presentation of ideas in the form of unstructured textual descriptions, and move toward a structure that enables automatically finding interesting matches between technologies and distant domains, traversing multiple levels of abstraction around a focal problem, breaking out of fixation on the details of a specific problem by exploring the design space and viewing novel perspectives on problems and solutions. Our goal in this paper is to obtain a representation of ideas that can bring us closer to this vision.
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We do this by focusing on a representation inspired by research on cognitive processes of creativity that show the central role played by functional aspects of ideas, namely, purposes and mechanisms. We use this representation to enable new interactions with idea repositories, powered by two key capabilities.
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First, as we'll see in a moment, our representation can support finding inspirations for repurposing opportunities, finding products with similar mechanisms, but distant purposes.
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Secondly, this representation can support discovering novel perspectives around problems to avoid fixation on certain problem aspects. Let's briefly show how we do this and see some examples.
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We first want to automatically extract fine grained purposes and mechanisms from raw product descriptions, so that we can use them for our applications. We begin by collecting crowdsourced annotations from real world product descriptions, like this example of a technological pillow that has the purposes of monitoring and supporting sleep, achieved with all kinds of mechanisms such as sensors, and a built in alarm.
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We then take these crowdsourced annotations and use them as training data to train a natural language processing model, so that we can extract purposes and mechanisms automatically from any incoming product description in a corpus of products.
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We can now represent ideas in terms of vector embeddings of these extracted purposes and mechanisms, so that for any given description of a product, we have a set of purpose embeddings and a set of mechanism embeddings.
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Using this representation, we built a search engine that allows users to search for alternative atypical uses of certain mechanisms, like finding products that use the mechanism of light, for a purpose that does not mainly involve light. Take a moment to think about how you would do this on Google, without our representation of purposes and mechanisms.
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With our search system, the query is natural. And we retrieve for example, a UV light sanitizer, where the purpose of light is now cleaning.
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Furthermore, by automatically extracting purposes and mechanisms from across many products in our corpus, we build a network of purposes, which are seen here in pink, and mechanisms which are seen in green. We call this network the functional concept graph. And traversing this graph can allow us to find inspirations for problems and solutions that we potentially would not have otherwise considered.
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For example, say you are an engineer or designer, developing smartphone chargers, and you're looking for inspirations.
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You could traverse this automatically constructed graph, and land in areas you had not considered before, inspiring you to explore mechanisms, for example of solar power and mechanical stored energy for smartphone charging.
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So we have these two core capabilities, how do we evaluate them?
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First, we measure retrieval accuracy for those queries that involve purposes and mechanisms by collecting human relevance labels for search results.
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And using standard information retrieval metrics, we find our approach to yield much higher search result accuracy, than retrieval baselines.
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Secondly, for our main result, we evaluate utility for creative innovation, using our representation to surface novel inspirations for creative problem solving.
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And in an ideation study, we find that our approach based on traversing the functional concept graph was able to dramatically increase the proportion of inspirations found both useful and novel for creative problem solving in comparison to strong baselines.
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That's it. I invite you to read our paper and to reach out for more information