more misc notes on the biological path to strong ai
agree that this is looking like the best path to Strong AI. don’t agree that the biological is the only potential path.
the biological path is only the best path while we share fundamental constraints with the brain.
SDRs are a great example of evolution solving a problem working against limiting constraints. In the the case of SDRs the constraints are those of computing power, storage space and access times in the brain.
The reliance on SDRs is likely linked to one of the bugs in our intelligence - the bias toward equivalence (the “IS A” bias). A hypothetical predictive system implemented without SDRs or with less dependence on SDRs might also be an intelligence that suffers less from the is-a bias.
Video Description
Today’s (2017) 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.