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Found 9 papers, 3 papers with code
Explaining data using causal Bayesian networks
I introduce Causal Bayesian Networks as a formalism for representing and explaining probabilistic causal relations, review the state of the art on learning Causal Bayesian Networks and suggest and illustrate a research avenue for studying pairwise identification of causal relations inspired by graphical causality criteria.
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.
Power Law Trends in Speedrunning and Machine Learning
Using a random effects model, we improve on this baseline for relative mean square error made on predicting out-of-sample world record improvements as the comparison metric at a $p < 10^{-5}$ significance level.
Will we run out of data? An analysis of the limits of scaling datasets in Machine Learning
We analyze the growth of dataset sizes used in machine learning for natural language processing and computer vision, and extrapolate these using two methods; using the historical growth rate and estimating the compute-optimal dataset size for future predicted compute budgets.
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.
Finding, Scoring and Explaining Arguments in Bayesian Networks
We propose a new approach to explain Bayesian Networks.
Forecasting timelines of quantum computing
We consider how to forecast progress in the domain of quantum computing.
Quantum Physics Emerging Technologies
Implications of Quantum Computing for Artificial Intelligence alignment research
We explain some key features of quantum computing via three heuristics and apply them to argue that a deep understanding of quantum computing is unlikely to be helpful to address current bottlenecks in Artificial Intelligence Alignment.
