These are Matt Taylor's slides from the AI Singapore Meetup on May 17, 2018. Abstract: Today’s wave of AI technology is still being driven by the ANN neuron pioneered decades ago. Hierarchical Temporal Memory (HTM) is a realistic biologically-constrained model of the pyramidal neuron reflecting today’s most recent neocortical research. This talk will describe and visualize core HTM concepts like sparse distributed representations, spatial pooling and temporal memory. Strong AI is a common goal of many computer scientists. So far, machine learning techniques have created amazing results in narrow fields, but haven’t produced something we could all call “intelligent”. Given recent advances in neuroscience research, we know a lot more about how neurons work together now than we did when ANNs were created. We believe systems with a more realistic neuronal model will be more likely to produce Strong AI. Hierarchical Temporal Memory is a theory of intelligence based upon neuroscience research. The neocortex is the seat of intelligence in the brain, and it is structurally homogeneous throughout. This means a common algorithm is processing all your sensory input, no matter which sense. We believe we have discovered some of the foundational algorithms of the neocortex, and we’ve implemented them in software. I’ll show you how they work with detailed dynamic visualizations of Sparse Distributed Representations, Spatial Pooling, and Temporal Memory.

1. The Biological
Path Toward
STRONG AI
Matt Taylor
@rhyolight
Numenta
HTM Community Manager

2. Agenda
• Why Weak AI is not intelligent
• Cortex anatomy lesson
• The power of the pyramidal neuron
• Layers within columns
• HTM Theory details: SDRs, Spatial Pooling, Temporal
Memory
• HTM Sensorimotor Theory details

3. Weak AI
won’t produce
Intelligence

4. what’s missing?

5. Realistic
Neurons

6. Movement

7. Let’s talk about
brains

10. Layers

11. Layers and Columns

12. Layers contain Neurons

13. The
Pyramidal
Neuron

14. The
Pyramidal
Neuron
Inactive State

15. The
Pyramidal
Neuron
Inactive State
Active State

16. The
Pyramidal
Neuron
Inactive State
Active State
Predictive State

17. The
Pyramidal
Neuron

18. Apical
Distal
Proximal
The
Pyramidal
Neuron

19. Layer Integration Zones

20. Layers Compute
Apical
Distal
Proximal
Driver Signal
Modulatory Signals

21. Layer in Software

22. Layer in Software

23. Layer in Software

24. Sparse Distributed Representations
how neurons encode information

25. Encoders
Example:
Date Encoding

26. Spatial Pooling
• proximal feedforward input activates mini-columns
• normalizes sparsity of encoded representation
• maintains semantic meaning of representations

27. Proximal
Driver Signal

31. Distal

32. Temporal Memory
• identifies context of input based upon distal segments
• works within structure of mini-columns
• puts cells into predictive states for next time step

33. Apical
Distal
Proximal
Driver Signal
Modulatory Signals

39. numenta.com/papers/

41. numenta.org
@rhyolight
@numenta