Jeff Hawkins takes the position that only Numenta sees the human brain as a temporal prediction engine based on sparse, hierarchical memory. But actually, RNNs

Jeff Hawkins takes the position that only Numenta sees the human brain as a temporal prediction engine based on sparse, hierarchical memory. But actually, RNNs are great for recognizing and predicting temporal sequences (as we saw in the Karpathy post), RNNs use a sparse representation, and RNNs can be extended with hierarchical memory [1] The big difference is that the neural network crowd are getting some spectacular results, and Numenta, well, maybe they'll be show more progress in the future. Jeff Hawkins is super smart and a good guy, and he might get more done if he acknowledged the commonalities in the approaches rather than having to invent it all separately at Numenta. I really dont mean to be critical. Jeff inspired my own interest in machine intelligence. [1] page 442, "Over the past year, several authors have made different proposals to augment RNNs with a memory module. Proposals include the Neural Turing Machine in which the network is augmented by a ‘tape-like’ memory that the RNN can choose to read from or write to, and memory networks, in which a regular network is augmented by a kind of associative memory. Memory networks have yielded excellent performance on standard question-answering benchmarks. The memory is used to remember the story about which the network is later asked to answer questions."