[brendano]

Has Numenta done any of the empirical evaluations that are standard in the mainstream applied machine learning communities? For example, if they're doing image classification, one of the standard shared tasks from the field of vision research. When you ask what research supports Numenta's approach, they always point to the Hawkins book. It's a nice story, but there's quite a history of nice stories in AI that haven't always been ready from prime time.

[MrQuincle]

Good point. There are so many benchmarks that can be performed. POMDP (Partial Observable Markov Decision Processes) literature has a lot of benchmarks (bandits, etc.). Reinforcement literature has many. There are many standard problems in nonlinear control theory solved, not only the inverted pendulum. It is common knowledge that an algorithm that performs well on task A, will be outperformed on task B. What are the tasks B in Hawkins book? In what do these specific type of recurrent networks excel, and in what do they sink as a brick?