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  Forecasting the future of artificial intelligence with machine learning-based link prediction in an exponentially growing knowledge network

Krenn, M., Buffoni, L., Coutinho, B., Eppel, S., Foster, J. G., Gritsevskiy, A., et al. (2023). Forecasting the future of artificial intelligence with machine learning-based link prediction in an exponentially growing knowledge network. Nature Machine Intelligence, 10.1038/s42256-023-00735-0. doi:10.1038/s42256-023-00735-0.

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Item Permalink: https://hdl.handle.net/21.11116/0000-000B-32B8-D Version Permalink: https://hdl.handle.net/21.11116/0000-000D-DAF0-E
Genre: Journal Article

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 Creators:
Krenn, Mario1, Author
Buffoni, Lorenzo, Author
Coutinho, Bruno, Author
Eppel, Sagi, Author
Foster, Jacob Gates, Author
Gritsevskiy, Andrew, Author
Lee, Harlin, Author
Lu, Yichao, Author
Moutinho, Joao P., Author
Sanjabi, Nima, Author
Sonthalia, Rishi, Author
Tran, Ngoc Mai, Author
Valente, Francisco, Author
Xie, Yangxinyu, Author
Yu, Rose, Author
Kopp, Michael, Author
Affiliations:
1Krenn Research Group, Marquardt Division, Max Planck Institute for the Science of Light, Max Planck Society, Staudtstraße 2, 91058 Erlangen, DE, ou_3345237              

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Free keywords: Computer Science, Artificial Intelligence, cs.AI,Computer Science, Learning, cs.LG
 Abstract: A tool that could suggest new personalized research directions and ideas by taking insights from the scientific literature could profoundly accelerate the progress of science. A field that might benefit from such an approach is artificial intelligence (AI) research, where the number of scientific publications has been growing exponentially over recent years, making it challenging for human researchers to keep track of the progress. Here we use AI techniques to predict the future research directions of AI itself. We introduce a graph-based benchmark based on real-world data—the Science4Cast benchmark, which aims to predict the future state of an evolving semantic network of AI. For that, we use more than 143,000 research papers and build up a knowledge network with more than 64,000 concept nodes. We then present ten diverse methods to tackle this task, ranging from pure statistical to pure learning methods. Surprisingly, the most powerful methods use a carefully curated set of network features, rather than an end-to-end AI approach. These results indicate a great potential that can be unleashed for purely ML approaches without human knowledge. Ultimately, better predictions of new future research directions will be a crucial component of more advanced research suggestion tools.

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 Dates: 2023-10-16
 Publication Status: Published online
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 Identifiers: DOI: 10.1038/s42256-023-00735-0
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Title: Nature Machine Intelligence
Source Genre: Journal
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Publ. Info: London : Springer Nature Publishing
Pages: - Volume / Issue: - Sequence Number: 10.1038/s42256-023-00735-0 Start / End Page: - Identifier: ISSN: 2522-5839
CoNE: https://pure.mpg.de/cone/journals/resource/2522-5839