Lilian Weng's recent article, "Why We Think", provides an excellent overview of the current state-of-the-art in AI reasoning. Intriguingly, and perhaps unsurprisingly, the methods employed to enable AI to "think" closely mirror human cognitive processes. These parallels are summarized in the table below.
While forecasting how AI will impact my career in Consumer Lending Credit Risk Management (Article Link), I explored the timeline forecast methodology used in AI 2027. Prerequisites Time Horizon The concept of time horizon, proposed by the Model Evaluation & Threat Research (METR) team, states that the "50%-task-completion
Recently, I've been exploring the theory of computation. With the rapid advancement of artificial intelligence—essentially a vast collection of algorithms and computational instructions designed to process inputs and generate outputs—I find myself increasingly curious about the fundamental capabilities and limitations of computation itself. Concepts such as
Background Recently, I have been exploring AI 2027 a detailed month-by-month forecast of artificial intelligence advancements through 2027. This resource offers a comprehensive framework outlining how AI is expected to evolve over the next three years. Inspired by this approach, I aim to conduct a similar thought experiment focused specifically
In the offline cellphone-loan business, we have observed that default risk increases with the premium features of the device used during loan application. For example, borrowers applying with phones that have larger storage capacities tend to show higher default rates—counter to the intuitive expectation that “better” devices signal lower