3 accepted papers
We introduce a conceptual framework and provide considerations for the institutional design of AI incident reporting (IR) systems, i.e., processes for collecting information about safety- and rights-related events caused by general-purpose AI. As general-purpose AI systems are increasingly adopted,
Training advanced AI models requires large investments in computational resources, or compute. Yet, as hardware innovation reduces the price of compute and algorithmic advances make its use more efficient, the cost of training an AI model to a given performance falls over time - a concept we describ…
We investigate the potential constraints on LLM scaling posed by the availability of public human-generated text data. We forecast the growing demand for training data based on current trends and estimate the total stock of public human text data. Our findings indicate that if current LLM developmen…