Predicting the Future State of AI

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Source arXiv

Machine Learning

Artificial Intelligence

Predicting the Future State of AI

Predicting the Future of AI with AI: High-quality link prediction in an exponentially growing knowledge network

We develop a new graph-based benchmark based on real-world data, the science4cast benchmark, which aims to predict the future state of an evolving semantic network of artificial intelligence (ai).

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 end-to-end approach.

It indicates a great potential that can be unleashed for purely mlapproaches without human knowledge.

Ultimately, better predictions of new future research directions will be a crucial component of more advanced research suggestion tools.

Authors

Mario Krenn, Lorenzo Buffoni, Bruno Coutinho, Sagi Eppel, Jacob Gates Foster, Andrew Gritsevskiy, Harlin Lee, Yichao Lu, Joao P. Moutinho, Nima Sanjabi, Rishi Sonthalia, Ngoc Mai Tran, Francisco Valente, Yangxinyu Xie, Rose Yu, Michael Kopp

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