Untangling Sequences: Behavior vs. External Causes

Ahmad, Subutai; Hawkins, Jeff

doi:10.1101/190678

Cited by 4 publications

(2 citation statements)

References 58 publications

(70 reference statements)

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“…Sections 5.5-5.7 outline how cortical columns learn models of objects using grid cells, displacement cells, and reference frames. Numenta's simulation results show that individual cortical columns learn hundreds of objects [39,42,44]. Recall that a cortical column learns features of objects.…”

Section: Simulation Resultsmentioning

confidence: 99%

A thousand brains: toward biologically constrained AI

Hole

Ahmad²

2021

SN Appl. Sci.

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This paper reviews the state of artificial intelligence (AI) and the quest to create general AI with human-like cognitive capabilities. Although existing AI methods have produced powerful applications that outperform humans in specific bounded domains, these techniques have fundamental limitations that hinder the creation of general intelligent systems. In parallel, over the last few decades, an explosion of experimental techniques in neuroscience has significantly increased our understanding of the human brain. This review argues that improvements in current AI using mathematical or logical techniques are unlikely to lead to general AI. Instead, the AI community should incorporate neuroscience discoveries about the neocortex, the human brain’s center of intelligence. The article explains the limitations of current AI techniques. It then focuses on the biologically constrained Thousand Brains Theory describing the neocortex’s computational principles. Future AI systems can incorporate these principles to overcome the stated limitations of current systems. Finally, the article concludes that AI researchers and neuroscientists should work together on specified topics to achieve biologically constrained AI with human-like capabilities.

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Section: Simulation Resultsmentioning

confidence: 99%

A thousand brains: toward biologically constrained AI

Hole

Ahmad²

2021

SN Appl. Sci.

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“…Both data types can be merged in an input data stream to HTM because both are converted to a sparse distributed representation (SDR) using encoders. Each time, HTM calculates an anomaly score for a new pattern as it enters [10][11][12][13]. If a received pattern is symmetrical to predict, then the anomaly score is zero.…”

Section: Research Study Motivation Hierarchical Temporalmentioning

confidence: 99%

Anomalous Behavior Detection Framework Using HTM-Based Semantic Folding Technique

Khan

et al. 2021

Computational and Mathematical Methods in Medicine

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Upon the working principles of the human neocortex, the Hierarchical Temporal Memory model has been developed which is a proposed theoretical framework for sequence learning. Both categorical and numerical types of data are handled by HTM. Semantic Folding Theory (SFT) is based on HTM to represent a data stream for processing in the form of sparse distributed representation (SDR). For natural language perception and production, SFT delivers a solid structural background for semantic evidence description to the fundamentals of the semantic foundation during the phase of language learning. Anomalies are the patterns from data streams that do not follow the expected behavior. Any stream of data patterns could have a number of anomaly types. In a data stream, a single pattern or combination of closely related patterns that diverges and deviates from standard, normal, or expected is called a static (spatial) anomaly. A temporal anomaly is a set of unexpected changes between patterns. When a change first appears, this is recorded as an anomaly. If this change looks a number of times, then it is set to a “new normal” and terminated as an anomaly. An HTM system detects the anomaly, and due to continuous learning nature, it quickly learns when they become the new normal. A robust anomalous behavior detection framework using HTM-based SFT for improving decision-making (SDR-ABDF/P2) is a proposed framework or model in this research. The researcher claims that the proposed model would be able to learn the order of several variables continuously in temporal sequences by using an unsupervised learning rule.

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Goal-Directed Procedure Learning

Hammer

Lofthouse²

2018

Artificial General Intelligence

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Untangling Sequences: Behavior vs. External Causes

Cited by 4 publications

References 58 publications

A thousand brains: toward biologically constrained AI

A thousand brains: toward biologically constrained AI

Anomalous Behavior Detection Framework Using HTM-Based Semantic Folding Technique

Goal-Directed Procedure Learning

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