The authors, on the basis of brief arguments, have dismissed tensor networks as a viable response to Jackendoff's challenges. However, there are reasons to believe that connectionist approaches descended from tensor networks are actually very well suited to answering Jackendoff's challenges. I rebut their arguments for dismissing tensor networks and briefly compare the approaches.

Identity crime is well known, prevalent, and costly; and credit application fraud is a specific case of identity crime. The existing non-data mining detection systems of business rules and scorecards, and known fraud matching have limitations. To address these limitations and combat identity crime in real-time, this paper proposes a new multi-layered detection system complemented with two additional layers: Communal Detection (CD) and Spike Detection (SD). CD finds real social relationships to reduce the suspicion score, and is tamper-resistant to synthetic social relationships. It is the whitelist-oriented approach on a fixed set of attributes. SD finds spikes in duplicates to increase the suspicion score, and is probe-resistant for attributes. It is the attribute-oriented approach on a variable-size set of attributes. Together, CD and SD can detect more types of attacks, better account for changing legal behaviour, and remove the redundant attributes. Experiments were carried out on CD and SD with several million real credit applications. Results on the data support the hypothesis that successful credit application fraud patterns are sudden and exhibit sharp spikes in duplicates. Although this research is specific to credit application fraud detection, the concept of resilience, together with adaptivity and quality data discussed in the paper, are general to the design, implementation, and evaluation of all detection systems.

Analogy-making is a key function of human cognition. Therefore, the development of computational models of analogy that automatically learn from examples can lead to significant advances in cognitive systems. Analogies require complex, relational representations of learned structures, which is challenging for both symbolic and neurally inspired models. Vector symbolic architectures (VSAs) are a class of connectionist models for the representation and manipulation of compositional structures, which can be used to model analogy. We study a novel VSA network for the analogical mapping of compositional structures, which integrates an associative memory known as sparse distributed memory (SDM). The SDM enables non-commutative binding of compositional structures, which makes it possible to predict novel patterns in sequences. To demonstrate this property we apply the network to a commonly used intelligence test called Raven's Progressive Matrices. We present results of simulation experiments for the Raven's task and calculate the probability of prediction error at 95% confidence level. We find that non-commutative binding requires sparse activation of the SDM and that 10-20% concept-specific activation of neurons is optimal. The optimal dimensionality of the binary distributed representations of the VSA is of the order 10 4 , which is comparable with former results and the average synapse count of neurons in the cerebral cortex.

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This article presents a proof-of-concept validation of the use of Vector Symbolic Architectures as central component of an online learning architectures. It is demonstrated that Vector Symbolic Architectures enable the structured combination of features/relations that have been detected by a perceptual circuitry and allow such relations to be applied to novel structures without requiring the massive training needed for classical neural networks that depend on trainable connections.The system is showcased through the functional imitation of concept learning in honey bees. Data from real-world experiments with honey bees (Avarguès-Weber et al., 2012) are used for benchmarking. It is demonstrated that the proposed pipeline features a similar learning curve and accuracy of generalization to that observed for the living bees. The main claim of this article is that there is a class of simple artificial systems that reproduce the learning behaviors of certain living organisms without requiring the implementation of computationally intensive cognitive architectures. Consequently, it is possible in some cases to implement rather advanced cognitive behavior using simple techniques.