TL;DR I believe that: * There exists a parallel track of AI research which has been largely ignored by the AI safety community. This agenda aims to implement human-like online learning in ML models, and it is now close to maturity. Keywords: Hierarchical Reasoning Model, Energy-based Model, Test time training. * Within 6 months this line of research will produce a small natural-language capable model that will perform at the level of a model like GPT-4, but with improved persistence and effectively no "context limit" since it is constantly learning and updating weights. * Further development of this research will produce models that fulfill most of the criteria we associate with "AGI". Overview Almost all frontier models today share two major features in their training regime: they are trained offline and out of sequence. * By offline, I mean that there is a distinct "pretraining" phase, followed by a separate "post-training", "character/safety training", or "fine-tuning" phase, and then finally a "deployment" phase. New iterations of the models must also go through these stages. In almost all cases, deployed models do not receive weight updates when they run and perform "inference". * By out of sequence, I mean that models receive random samples from the training set, instead of continuous sequences of tokens, characters, images, or audio samples. This is a necessary result of batched SGD, which attempts to both approximate an ideal gradient descent algorithm and also prevent problems like catastrophic forgetting. As a result, models cannot use past inputs to predict future inputs, only learn a general solution to the task they are being trained on. These features of training regimes make sense if you believe the classic function approximation or statistical approximation explanation of machine learning. In this story the model is meant to learn some fixed "target distribution" or "target function" by sampling i.i.d. data points from the training set. T