Google just published the paper “Scaling Robot Learning with Semantically Imagined Experience” showing how to use generated images like Imagen to generate Training data for their robot system. This allows the robot to have a more diverse data set and therefore be more robust and able to solve unseen tasks. We saw similar approaches using simulations for cars, but this is the first time that generative models were used.

Also from google, we got a new paper where they present their advancements in quantum error correction. By scaling to larger numbers of Qubits and combining them to logical Qubits they can reduce the quantum error rate significantly. This opens up a clear path to better quantum computers by just scaling them up.