Ivan Vendrov

Yann LeCun recently posted A Path Towards Autonomous Machine Intelligence, a high-level description of the architecture he considers most promising to advance AI capabilities. This post summarizes the architecture and describes some implications for AI safety work if we accept the hypothesis that the first transformative AI will have this architecture. Why is this a hypothesis worth considering? 1. LeCun has a track record of being ahead of mainstream academic research, from working on CNNs in the 90s to advocating for self-supervised learning back in 2014-2016 when supervised learning was ascendant. 2. LeCun runs Meta AI (formerly FAIR) which has enormous resources and influence to advance his research agenda, making it more likely that his proposed architecture will be built at scale. In general I think this is an underrated factor; AI research exhibits a great deal of path dependence, and most plausible paths to AI are not taken primarily because nobody is willing to take a big risk on them. 3. The architecture is dramatically different from the architectures commonly assumed (implicitly) in much AI alignment work, such as model-free deep RL and "GPT-3 but scaled up 10000x". This makes it a good robustness check for plans that are overly architecture-specific. Architecture Overview The Overall Agent At a high level, the proposed architecture is a set of specialized cognitive modules. With the exception of the Actor and the Intrinsic Cost (see below) they are all deep neural networks trained with gradient descent. The high level architecture of LeCun's proposed agent. Arrows indicate dependence; gradients flow backward through the thin arrows. What is this agent doing, exactly? It's meant to be a general architecture for any autonomous AI agent, but LeCun repeatedly emphasizes video inputs and uses self-driving cars as a recurrent example, so the central use case is embodied agents taking actions in the physical world. Other talks I've seen by