This article explores the power of causal inference as a method for quantifying the impact of actions and improving decision-making, particularly in comparison to traditional A/B testing. It details how causal inference can provide a deeper understanding of customer behavior by estimating the individual impact of treatments (like marketing campaigns) and addressing the limitations of A/B testing, such as treating customer variability as noise and requiring large sample sizes. The article highlights applications in marketing, product recommendations, and customer retention, emphasizing benefits like customer segmentation, more precise estimates, and real-time learning. Ultimately, it argues that embracing causal inference can lead to more effective testing, optimized customer experiences, and shorter test cycles.
The use of AI tools in the attacks on Iran is collapsing the time required for military decision-making, raising fears that human oversight is being sidelined. The US military reportedly used Anthropic’s Claude AI model to shorten the 'kill chain' during almost 900 strikes on Iranian targets, including one that killed Ayatollah Ali Khamenei.
A new model developed by researchers at MIT and the University of Washington predicts human goals or actions more accurately than previous models. The latent inference budget model identifies patterns in human or machine decision-making and uses this information to forecast behavior.
