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Found 17 papers, 7 papers with code
GATE: An Integrated Assessment Model for AI Automation
We present the Growth and AI Transition Endogenous model (GATE), a dynamic integrated assessment model that simulates the economic effects of AI automation.
International AI Safety Report
The first International AI Safety Report comprehensively synthesizes the current evidence on the capabilities, risks, and safety of advanced AI systems.
FrontierMath: A Benchmark for Evaluating Advanced Mathematical Reasoning in AI
We introduce FrontierMath, a benchmark of hundreds of original, exceptionally challenging mathematics problems crafted and vetted by expert mathematicians.
Ranked #2 on
Mathematical Reasoning
on FrontierMath
International Scientific Report on the Safety of Advanced AI (Interim Report)
This is the interim publication of the first International Scientific Report on the Safety of Advanced AI.
Estimating Idea Production: A Methodological Survey
Accurately modeling the production of new ideas is crucial for innovation theory and endogenous growth models.
Assessing Human Judgment Forecasts in the Rapid Spread of the Mpox Outbreak: Insights and Challenges for Pandemic Preparedness
In May 2022, mpox (formerly monkeypox) spread to non-endemic countries rapidly.
Chinchilla Scaling: A replication attempt
Hoffmann et al. (2022) propose three methods for estimating a compute-optimal scaling law.
Algorithmic progress in language models
We investigate the rate at which algorithms for pre-training language models have improved since the advent of deep learning.
The Compute Divide in Machine Learning: A Threat to Academic Contribution and Scrutiny?
We provide a data-driven survey of the role of the compute divide in shaping machine learning research.
Who is leading in AI? An analysis of industry AI research
The data reveals a diverse ecosystem of companies steering AI progress, though US labs such as Google, OpenAI and Meta lead across critical metrics.
Explosive growth from AI automation: A review of the arguments
Key questions remain about the intensity of regulatory responses to AI, physical bottlenecks in production, the economic value of superhuman abilities, and the rate at which AI automation could occur.
Economic impacts of AI-augmented R&D
Since its emergence around 2010, deep learning has rapidly become the most important technique in Artificial Intelligence (AI), producing an array of scientific firsts in areas as diverse as protein folding, drug discovery, integrated chip design, and weather prediction.
Algorithmic progress in computer vision
Using Shapley values to attribute performance improvements, we find that algorithmic improvements have been roughly as important as the scaling of compute for progress computer vision.
Will we run out of data? Limits of LLM scaling based on human-generated data
We investigate the potential constraints on LLM scaling posed by the availability of public human-generated text data.
Machine Learning Model Sizes and the Parameter Gap
From 1950 to 2018, model size in language models increased steadily by seven orders of magnitude.
Compute Trends Across Three Eras of Machine Learning
Since the advent of Deep Learning in the early 2010s, the scaling of training compute has accelerated, doubling approximately every 6 months.
Toward Trustworthy AI Development: Mechanisms for Supporting Verifiable Claims
With the recent wave of progress in artificial intelligence (AI) has come a growing awareness of the large-scale impacts of AI systems, and recognition that existing regulations and norms in industry and academia are insufficient to ensure responsible AI development.
Computers and Society
