Machine Learning Street Talk (MLST)//Alexander Mattick
Why Scaling Prediction Cannot Create Intelligence - Alexander Mattick
Alexander Mattick argues that many modern ML methods are best understood as different ways of doing inference and density decomposition, not as routes to “intelligence” from scaling prediction alone. He is skeptical of energy-based models, broad labels like JEPA/world models, and unconstrained reinforcement learning, and instead emphasizes practical tradeoffs, safety constraints, and whether a method actually gives usable control or sampling efficiency.
