Predicting the Future of AI with AI: High-quality link prediction in an exponentially growing knowledge network
arXiv:2210.00881 · doi:10.1038/s42256-023-00735-0
Abstract
A tool that could suggest new personalized research directions and ideas by taking insights from the scientific literature could significantly 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 the last 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 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 AI. For that, we use more than 100,000 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. It indicates 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.
arXiv comment13 pages, 7 figures. Comments welcome!
References in corpus (22)
- Deep Learning for Anomaly Detection: A Review
- Predicting Missing Links via Local Information
- Powerlaw: a Python package for analysis of heavy-tailed distributions
- The physics of higher-order interactions in complex systems
- Scale-free networks are rare
- Missing and spurious interactions and the reconstruction of complex networks
- On scientific understanding with artificial intelligence
- Using DeepSpeed and Megatron to Train Megatron-Turing NLG 530B, A Large-Scale Generative Language Model
- Quantifying patterns of research interest evolution
- Stacking Models for Nearly Optimal Link Prediction in Complex Networks
- Progresses and Challenges in Link Prediction
- Link prediction via linear optimization
- Predicting Research Trends with Semantic and Neural Networks with an application in Quantum Physics
- The CSO Classifier: Ontology-Driven Detection of Research Topics in Scholarly Articles
- Covering Problems and Core Percolations on Hypergraphs
- PaperRobot: Incremental Draft Generation of Scientific Ideas
- Loss of sustainability in scientific work
- Automated Deep Learning Analysis of Angiography Video Sequences for Coronary Artery Disease
- Predicting Research Trends in Artificial Intelligence with Gradient Boosting Decision Trees and Time-aware Graph Neural Networks
- Improving random walk rankings with feature selection and imputation
- Link Prediction of Artificial Intelligence Concepts using Low Computational Power
- The Utility of General Domain Transfer Learning for Medical Language Tasks