I find the similarities between modern chatbots and the babble/prune model more appropriate. For example, the recent MILA chatbot uses several response models to generate candidate responses based on the dialogue history, and then a response selection policy to select which of the responses to return.
More generally, the concept of seperate algorithms for action proposal and action evaluation is quite widespread in modern deep learning. For example, you can think of AlphaGo’s policy network as serving the action proposal/babble role, while the MCTS procedure serves does action evaluation/pruning. (More generally, you can see this with any sort of game tree search algorithm that uses a heuristic to expand promising nodes.) Or, with some stretching, you can think of actor-critic based reinforcement learning algorithms as being composed of babble/prune parts.
GANs fall into the Babble/Prune model mainly insofar as there are two parts, one serving as action proposal and the other serving as action evaluation; beyond this high level; the fit feels very forced. I think that from modern deep learning, both the MILA chatbot and AlphaGo’s MCTS procedure are much better fits to the babble/prune model than GANs.
I find the similarities between modern chatbots and the babble/prune model more appropriate. For example, the recent MILA chatbot uses several response models to generate candidate responses based on the dialogue history, and then a response selection policy to select which of the responses to return.
More generally, the concept of seperate algorithms for action proposal and action evaluation is quite widespread in modern deep learning. For example, you can think of AlphaGo’s policy network as serving the action proposal/babble role, while the MCTS procedure serves does action evaluation/pruning. (More generally, you can see this with any sort of game tree search algorithm that uses a heuristic to expand promising nodes.) Or, with some stretching, you can think of actor-critic based reinforcement learning algorithms as being composed of babble/prune parts.
GANs fall into the Babble/Prune model mainly insofar as there are two parts, one serving as action proposal and the other serving as action evaluation; beyond this high level; the fit feels very forced. I think that from modern deep learning, both the MILA chatbot and AlphaGo’s MCTS procedure are much better fits to the babble/prune model than GANs.