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An analysis of Vals' measured autonomous-R&D trend methodology projects that frontier-level AI researchers will be reached by July 2027, coinciding exactly with the timeline previously projected by the AI 2027 scenario report.
The paper investigates when forecasting agents should employ different behaviors like reasoning or retrieval, and introduces ReliabilityRoute, a method that routes agent mechanisms based on reliability features, demonstrating that optimal behavior is source-dependent and more reasoning isn't always better.
This article evaluates the accuracy of AI progress forecasts, revealing that experts tend to underestimate progress on benchmarks but have mixed success with adoption and diffusion predictions, while noting limitations and plans for future analysis.
Mantic's AI outperformed all human participants in a major forecasting tournament covering political, economic, and cultural events, highlighting a significant capability without requiring AGI.
The paper proposes EPA-CarbonNet, a six-layer transformer architecture for carbon credit price prediction that integrates market data and policy text, but tests on S&P carbon index data show mixed results with a random walk outperforming on some metrics while directional accuracy is promising.
A $25,000 contest was held to uncover where AI forecasting systems disagree about future predictions, attracting superforecasters and hedge fund quants to probe for wedge questions.
This paper introduces Tianmu-TC, a physics-constrained generative AI framework for global tropical cyclone forecasting that outperforms traditional systems in reliability and computational efficiency.
This article updates AI Futures' timelines forecasts for Automated Coder, introducing coding uplift and revenue as new methods to refine predictions and improve confidence in AI development timelines.
An article discussing why AI forecasting is difficult, emphasizing that capability gains often fail to translate into end-to-end impact due to social, institutional, and tacit bottlenecks, using radiology as a case study.
The article examines the growing threat of weather data manipulation, exacerbated by prediction markets and AI forecasting, and highlights a real-world incident where a Paris weather station was tampered with to influence betting outcomes.
Raven-Agent is the first autonomous trading agent for prediction markets, featuring an explicit belief-to-trade layer. It achieves positive returns on a controlled replay, bridging the gap between calibrated forecasts and profitable trading.
Scientists report a 20% improvement in hurricane forecasts using AI, but remain cautious about the technology's ability to handle unprecedented storms as climate change intensifies hurricanes.