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Sprint projectNov 2, 2025Kazakhstan

AI Progress Monitoring & Early Warning Systems

Zhanmadi, Alisher, Toretay, Zhan · Team stray team

Submitted to The AI Forecasting Hackathon. Sprint projects are early-stage work by participants, not Apart Research publications.

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Report: AI Progress Monitoring & Early Warning Systems

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This project presents a machine learning system that analyzes historical AI industry data and forecasts future trends using LSTM neural networks. The system tracks 19 AI-related metrics, including model size, compute, costs, and hardware performance, generating 5-year forecasts. A FastAPI backend powers the prediction and visualization pipeline, while a React dashboard displays interactive Plotly charts. The project helps visualize the evolution of AI technology and anticipate future developments.

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Does the project meaningfully advance AI timeline prediction and capability forecasting? Does it clearly connect to measurable indicators of AI progress (compute, benchmarks, economic impacts, automation milestones)? Does it build on or challenge existing forecasting frameworks like biological anchors, scaling laws, or scenario planning? Does it offer novel methodologies, data sources, or empirical insights that could improve forecast accuracy? Is it grounded in observable trends rather than pure speculation?

Does this project inform critical decisions about AI development and preparedness? Does it help identify key uncertainties, decision points, or early warning indicators? How well does the project connect technical metrics to real-world impacts and policy needs? Could the output guide resource allocation, safety research priorities, or regulatory timelines? Does it reduce uncertainty around transformative AI milestones or capability emergence?

Is the project methodologically rigorous, reproducible, and technically sound? Is the forecasting approach well-calibrated with appropriate uncertainty quantification? Are the data sources, assumptions, and limitations clearly documented? Does the project demonstrate sound statistical methodology and honest treatment of model uncertainties? Would the tool, model, or framework be useful for ongoing forecasting efforts, research planning, or policy analysis?

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Cite this project

@misc{zhanmadi2025ai,
  title = {{AI Progress Monitoring \& Early Warning Systems}},
  author = {Zhanmadi and Alisher and Toretay and Zhan},
  year = {2025},
  month = nov,
  note = {Submitted to The AI Forecasting Hackathon, an Apart Research Sprint},
  howpublished = {\url{https://apartresearch.com/sprints/projects/3-ai-progress-monitoring-early-warning-systems-sorry-we-forgot-to-add-code-link-in-previous-submit-ds1e}},
  url = {https://apartresearch.com/sprints/projects/3-ai-progress-monitoring-early-warning-systems-sorry-we-forgot-to-add-code-link-in-previous-submit-ds1e}
}

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