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

arXiv: https://arxiv.org/abs/2210.00881 · HTML: https://mlsanity.com/paper/2210.00881/

## Key facts

- Cited by 36 papers in the map (data through 14 Oct 2026).
- Builds on 34 of its 101 references that are in the map.
- First posted on arXiv 23 Sep 2022.

## Builds on (most cited first, 30 of 34)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Learning Transferable Visual Models From Natural Language Supervision](https://mlsanity.com/paper/2103.00020/) (2021)
- [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020)
- [PaLM: Scaling Language Modeling with Pathways](https://mlsanity.com/paper/2204.02311/) (2022)
- [Large Language Models are Zero-Shot Reasoners](https://mlsanity.com/paper/2205.11916/) (2022)
- [node2vec: Scalable Feature Learning for Networks](https://mlsanity.com/paper/1607.00653/) (2016)
- [Scaling Language Models: Methods, Analysis & Insights from Training Gopher](https://mlsanity.com/paper/2112.11446/) (2021)
- [Using DeepSpeed and Megatron to Train Megatron-Turing NLG 530B, A Large-Scale Generative Language Model](https://mlsanity.com/paper/2201.11990/) (2022)
- [Deep Learning for Anomaly Detection: A Review](https://mlsanity.com/paper/2007.02500/) (2020)
- [Abstraction and Analogy-Making in Artificial Intelligence](https://mlsanity.com/paper/2102.10717/) (2021)
- [On the Paradox of Learning to Reason from Data](https://mlsanity.com/paper/2205.11502/) (2022)
- [CLOOB: Modern Hopfield Networks with InfoLOOB Outperform CLIP](https://mlsanity.com/paper/2110.11316/) (2021)
- [Growth rates of modern science: A latent piecewise growth curve approach to model publication numbers from established and new literature databases](https://mlsanity.com/paper/2012.07675/) (2020)
- [On scientific understanding with artificial intelligence](https://mlsanity.com/paper/2204.01467/) (2022)
- [The physics of higher-order interactions in complex systems](https://mlsanity.com/paper/2110.06023/) (2021)
- [Scale-free networks are rare](https://mlsanity.com/paper/1801.03400/) (2018)
- [PaperRobot: Incremental Draft Generation of Scientific Ideas](https://mlsanity.com/paper/1905.07870/) (2019)
- [Limits of End-to-End Learning](https://mlsanity.com/paper/1704.08305/) (2017)
- [Stacking Models for Nearly Optimal Link Prediction in Complex Networks](https://mlsanity.com/paper/1909.07578/) (2019)
- [Tradition and Innovation in Scientists' Research Strategies](https://mlsanity.com/paper/1302.6906/) (2013)
- [Predicting Research Trends with Semantic and Neural Networks with an application in Quantum Physics](https://mlsanity.com/paper/1906.06843/) (2019)
- [The CSO Classifier: Ontology-Driven Detection of Research Topics in Scholarly Articles](https://mlsanity.com/paper/2104.00948/) (2021)
- [Progresses and Challenges in Link Prediction](https://mlsanity.com/paper/2102.11472/) (2021)
- [Quantifying patterns of research interest evolution](https://mlsanity.com/paper/1709.03319/) (2017)
- [Link prediction via linear optimization](https://mlsanity.com/paper/1804.00124/) (2018)
- [Automated Deep Learning Analysis of Angiography Video Sequences for Coronary Artery Disease](https://mlsanity.com/paper/2101.12505/) (2021)
- [Predicting Research Trends in Artificial Intelligence with Gradient Boosting Decision Trees and Time-aware Graph Neural Networks](https://mlsanity.com/paper/2201.05743/) (2022)
- [Memristor Hardware-Friendly Reinforcement Learning](https://mlsanity.com/paper/2001.06930/) (2020)
- [The Utility of General Domain Transfer Learning for Medical Language Tasks](https://mlsanity.com/paper/2002.06670/) (2020)
- [Demographic Influences on Contemporary Art with Unsupervised Style Embeddings](https://mlsanity.com/paper/2009.14545/) (2020)

## Built on by (most cited first, 30 of 36)

- [Forecasting high-impact research topics via machine learning on evolving knowledge graphs](https://mlsanity.com/paper/2402.08640/) (2024)
- [Generating Interesting Scientific Ideas using Knowledge Graphs and LLMs: Evaluations with 100 Research Group Leaders](https://mlsanity.com/paper/2405.17044/) (2024)
- [Shallow Synthesis of Knowledge in GPT-Generated Texts: A Case Study in Automatic Related Work Composition](https://mlsanity.com/paper/2402.12255/) (2024)
- [Activation Space Selectable Kolmogorov-Arnold Networks](https://mlsanity.com/paper/2408.08338/) (2024)
- [Toward Reliable Scientific Hypothesis Generation: Evaluating Truthfulness and Hallucination in Large Language Models](https://mlsanity.com/paper/2505.14599/) (2025)
- [Predicting New Research Directions in Materials Science using Large Language Models and Concept Graphs](https://mlsanity.com/paper/2506.16824/) (2025)
- [Towards autonomous quantum physics research using LLM agents with access to intelligent tools](https://mlsanity.com/paper/2511.11752/) (2025)
- [Position: Embracing Negative Results in Machine Learning](https://mlsanity.com/paper/2406.03980/) (2024)
- [CHIMERA: A Knowledge Base of Scientific Idea Recombinations for Research Analysis and Ideation](https://mlsanity.com/paper/2505.20779/) (2025)
- [Towards a Science Exocortex](https://mlsanity.com/paper/2406.17809/) (2024)
- [Discovering emergent connections in quantum physics research via dynamic word embeddings](https://mlsanity.com/paper/2411.06577/) (2024)
- [An Exploratory Study of ML Sketches and Visual Code Assistants](https://mlsanity.com/paper/2412.13386/) (2024)
- [Decision-Making with Deliberation: Meta-reviewing as a Document-grounded Dialogue](https://mlsanity.com/paper/2508.05283/) (2025)
- [Conversational Exploration of Literature Landscape with LitChat](https://mlsanity.com/paper/2505.23789/) (2025)
- [FOS: A Large-Scale Temporal Graph Benchmark for Scientific Interdisciplinary Link Prediction](https://mlsanity.com/paper/2511.18631/) (2025)
- [PaperTrail: A Claim-Evidence Interface for Grounding Provenance in LLM-based Scholarly Q&A](https://mlsanity.com/paper/2602.21045/) (2026)
- [An Efficient Two-stage Gradient Boosting Framework for Short-term Traffic State Estimation](https://mlsanity.com/paper/2302.10400/) (2023)
- [SPACE-IDEAS: A Dataset for Salient Information Detection in Space Innovation](https://mlsanity.com/paper/2403.16941/) (2024)
- [Towards Graph Prompt Learning: A Survey and Beyond](https://mlsanity.com/paper/2408.14520/) (2024)
- [Structural-Temporal Coupling Anomaly Detection with Dynamic Graph Transformer](https://mlsanity.com/paper/2505.08330/) (2025)
- [Ask, Retrieve, Summarize: A Modular Pipeline for Scientific Literature Summarization](https://mlsanity.com/paper/2505.16349/) (2025)
- [HypoChainer: A Collaborative System Combining LLMs and Knowledge Graphs for Hypothesis-Driven Scientific Discovery](https://mlsanity.com/paper/2507.17209/) (2025)
- [Evidence-Driven Decision Support for AI Model Selection in Research Software Engineering](https://mlsanity.com/paper/2512.11984/) (2025)
- [An Expert Schema for Evaluating Large Language Model Errors in Scholarly Question-Answering Systems](https://mlsanity.com/paper/2602.21059/) (2026)
- [A Retrieval-Augmented Generation Framework for Academic Literature Navigation in Data Science](https://mlsanity.com/paper/2412.15404/) (2024)
- [Density, asymmetry and citation dynamics in scientific literature](https://mlsanity.com/paper/2506.23366/) (2025)
- [Monitoring Transformative Technological Convergence Through LLM-Extracted Semantic Entity Triple Graphs](https://mlsanity.com/paper/2510.25370/) (2025)
- [Big Tech-Funded AI Papers Have Higher Citation Impact, Greater Insularity, and Larger Recency Bias](https://mlsanity.com/paper/2512.05714/) (2025)
- [Evidence-Grounded Frontier Mapping and Agentic Hypothesis Generation in Nanomedicine](https://mlsanity.com/paper/2605.18144/) (2026)
- [Forecasting Conceptual Diffusion in Science: The Case of Quantum Computing](https://mlsanity.com/paper/2606.03919/) (2026)

## Abstract (arXiv)

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.