Both the DeepMind and CMU approaches use deep reinforcement learning, popularized by DeepMind's Atari-playing AI. A neural network is fed raw pixel data from a virtual environment and uses rewards, like points in a computer game, to learn by trial and error (see "10 Breakthrough Technologies 2017: Reinforcement Learning"). By running through millions of training scenarios at accelerated speeds, both AI programs learned to associate words with particular objects and characteristics, which let them follow the commands. The millions of training runs required means Domingos is not convinced pure deep reinforcement learning will ever crack the real world.
The way we learn natural languages hasn't really changed for decades. We now have beautiful apps like Duolingo and Spaced Repetition software like Anki, but I'm talking about our fundamental approach. We still follow pre-defined curricula, and do essentially random exercises. Learning isn't personalized, and learning isn't driven by data. And I think there's a big opportunity to change that.