Reinforcement learning meets iterated game theory meets theory of mind| The Dan MacKinlay stable of variably-well-consider’d enterprises
Figure 1 The open-source AI scene has been kicking goals. People have wrestled models, datasets, and all the fixings away from the big-wigs. The final boss of that game is the access to expensive compute. Training a foundation model from scratch takes a warehouse full of GPUs that costs more than a small nation’s GDP. It’s been the one thing keeping AI development firmly in the hands of a few tech giants with cash to burn. Until now, maybe. So, the citizen science equivalent for the NN a...| The Dan MacKinlay stable of variably-well-consider’d enterprises
Coarse-graining empowerment| The Dan MacKinlay stable of variably-well-consider’d enterprises
Disentangled representation learning| The Dan MacKinlay stable of variably-well-consider’d enterprises
Figure 1 An interesting inverse design question: how should I design a system to optimise for truthfulness? Brief summary here. @Frongillo2024Recent: This note provides a survey for the Economics and Computation community of some recent trends in the field of information elicitation. At its core, the field concerns the design of incentives for strategic agents to provide accurate and truthful information. Such incentives are formalized as proper scoring rules, and turn out to be the same obj...| The Dan MacKinlay stable of variably-well-consider’d enterprises
Figure 1 Learning agents in a multi-agent system which account for and/or exploit the fact that other agents are learning too. This is one way of formalising the idea of theory of mind. Learning with theory of mind works out nicely for reinforcement learning, in e.g. opponent shaping, and may be an important tool for understanding AI agency and AI alignment, as well as aligning more general human systems. Other interesting things might arise from a good theory of other-aware learning, such ...| The Dan MacKinlay stable of variably-well-consider’d enterprises