Although large language models (LLMs) like GPT-4 made their big splash as chat bots, they might soon be doing a lot more: taking real-world actions. Research teams are using LLMs to play video games, make transactions on the internet, and even help drive autonomous cars. But language models weren’t initially designed to act. We believe that empirically understanding their emergent action reasoning abilities is crucial for these approaches to be safe, corrigible, and beneficial. Here, we describe and motivate our approach to this research problem, as taken in our Moral Learning project – in which we use theories of human cognition to identify potential ways in which LLMs might solve action decision problems. We focus in particular on how LLMs, vs humans, might combine multiple morally relevant properties of naturalistic scenarios to derive their action choices.

In the heart of Taiwan's technological and democratic evolution, Talk to the City is making significant strides. Deger Turan, the project lead, visited Taiwan in October 2023, engaging with key figures such as the Minister of Digital Affairs, Audrey Tang, and influential members of the Taiwanese open-source community. Their discussions focused on how Talk to the City could be integrated into Taiwanese deliberations, aiming to enhance public participation through advanced data analysis of deliberation. This integration builds on the existing vTaiwan project, which has used Polis to elicit and aggregate the opinions of large numbers of Taiwanese citizens, by analyzing free-form text responses of respondents' opinions.

At the AI Objectives Institute, we have been exploring how modern AI technologies could assist under-resourced communities. In this post, we are sharing our learning from a recent case study, where we used our AI-powered analysis platform, Talk to the City, to help formerly incarcerated individuals raise awareness of the challenges that they face when reintegrating into society.

This post explores how maybe using AI bots built with OpenAI's Custom GPT to make consultations more interactive. These AI agents could help people better understand their views and communicate them effectively during consultations, enabling more accurate input cost-effectively, though not as a replacement human facilitators.

Humans routinely model what the incentives of other humans may be. There is however a natural limit to how much modeling and analysis a single person or a small team can do and this is where we see an opportunity to leverage LLMs.