Predicting and suggesting research directions with semantic and neural networks with an application

Predicting and suggesting research directions with semantic and neural networks with an application in quantum physics

CQT Online Talks - Series: Quantum Machine Learning Journal Club Talks

Speakers: Mario Krenn, University of Toronto
Abstract: The corpus of scientific literature grows at an ever increasing speed. While this poses a severe challenge for human researchers, computer algorithms with access to a large body of knowledge could help make important contributions to science. In my talk, I will demonstrate the development of a semantic network for quantum physics, denoted SemNet, using 750,000 scientific papers and knowledge from books and Wikipedia. We use it in conjunction with an artificial neural network for predicting future research trends. Individual scientists can use SemNet for suggesting and inspiring personalized, out-of-the-box ideas. Computer-inspired scientific ideas can play a significant role in accelerating scientific progress, and I hope that our work directly contributes to that important goal.

Reference: Mario Krenn, Anton Zeilinger, "Predicting research trends with semantic and neural networks with an application in quantum physics", PNAS 117 (4), 1910-1916 (2020)

[gozzilla]

Now, to put a nail in the coffin of peer-review, let’s use ai to predict the impact of any paper submitted, and let’s accept it based on the prediction! What could possibly go wrong?