Hyperdimensional computing (HDC) is a powerful algorithmic framework at the intersection of symbolic and neural network Artificial Intelligence. In particular, HDC has received significant attention as a suitable candidate for low-resource machine learning tasks, exemplified by wearable Internet of Things. To solve classification tasks, HDC transforms input data to a high-dimensional space and uses simple component-wise vector operations to create, train, and operate the classification model. While the classical centroid model has been often used in HDC, iterative updating of centroids with wrongly classified samples improves the classification performance. In this paper, using a large and variable collection of 121 UCI datasets, we explore how confidence-driven training of centroids formed from HDC representations further improves the classification accuracy.

Neuromorphic Computing surpasses conventional von Neumann architectures in terms of energy efficiency, parallelisation, scalability, and stochasticity. Given the inherent structure of neurons and synapses, neuromorphic computers can be directly implemented as spiking neural networks. Despite these advantages, neuromorphic computing applications are hitherto limited to benchmark datasets and empirical demonstrations. This is primarily due to the lack of a unifying computing framework that designates a middle-layer abstraction between the actual neuromorphic computing and the required application functionality. Drawing on the distributed vector representation of symbolic and numerical data structures and robust dual interface with diverse operational primitives, Vector Symbolic Architectures (VSA) have been positioned as a suitable candidate to address this middle-layer void. In this paper, we explore the potential of VSA as an intermediary abstraction layer to advance practical neuromorphic computing applications. We introduce a novel vectorised framework that efficiently processes parallel streams of spiking data by combining and computing them through VSA for real-time downstream learning tasks, leveraging spike latency encoding. Our implementation utilises containerised methods within Lava, an open-source framework for neuromorphic computing.

The explicit kernel transformation of input data vectors to their distributed high-dimensional representations has recently been receiving increasing attention in the field of hyperdimensional computing (HDC). The main argument is that such representations endow simpler last-leg classification models, often referred to as HDC classifiers. HDC models have obvious advantages over resource-intensive deep learning models for use cases requiring fast, energy-efficient computations both for model training and deploying. Recent approaches to training HDC classifiers have primarily focused on various methods for selecting individual learning rates for incorrectly classified samples. In contrast to these methods, we propose an alternative strategy where the decision to learn is based on a margin applied to the classifier scores. This approach ensures that even correctly classified samples within the specified margin are utilized in training the model. This leads to improved test performances while maintaining a basic learning rule with a fixed (unit) learning rate. We propose and empirically evaluate two such strategies, incorporating either an additive or multiplicative margin, on the standard subset of the UCI collection, consisting of 121 datasets. Our approach demonstrates superior mean accuracy compared to other HDC classifiers with iterative error-correcting training.

Reservoir Computing with Cellular Automata (ReCA) is a relatively novel and promising approach. It consists of 3 steps: encoding the problem into the CA, the CA iterations step, and a simple classifying step. This paper demonstrates that the ReCA concept is effective even in arguably the simplest implementation of a ReCA system. However, we also report a failed attempt on the UCR Time Series Classification Archive where ReCA seems to work, but only because of the encoding scheme, not the CA. This highlights the need for ablation testing, i.e., comparing internally without sub-parts of one model, but also raises an open question on what kind of tasks ReCA is best suited for. 

We propose a new nature- and neuro-science-inspired algorithm for spatiotemporal learning and prediction based on sequential recall and vector symbolic architecture. A key novelty is the learning of spatial and temporal patterns as decoupled concepts where the temporal pattern sequences are constructed using the learned spatial patterns as an alphabet of elements. The decoupling, motivated by cognitive neuroscience research, provides the flexibility for fast and adaptive learning with dynamic changes to data and concept drift and as such is better suited for real-time learning and prediction. The algorithm further addresses several key computational requirements for predicting the next occurrences based on real-life spatiotemporal data, which have been found to be challenging with current state-of-the-art algorithms. Firstly, spatial and temporal patterns are detected using unsupervised learning from unlabeled data streams in changing environments; secondly, vector symbolic architecture (VSA) is used to manage variable-length sequences; and thirdly, hyper dimensional (HD) computing-based associative memory is used to facilitate the continuous prediction of the next occurrences in sequential patterns. The algorithm has been empirically evaluated using two benchmark and three time-series datasets to demonstrate its advantages compared to the state-of-the-art in spatiotemporal unsupervised sequence learning where the proposed ST-SOM algorithm is able to achieve 45% error reduction compared to HTM algorithm.

Large Language Models (LLMs) are leading the Generative Artificial Intelligence transformation in natural language understanding. Beyond language understanding, LLMs have demonstrated capabilities in reasoning tasks, including commonsense, logical, and mathematical reasoning. However, their proficiency in causal understanding has been limited due to the complex nature of causal reasoning. Several recent studies have discussed the role of external causal models for improved causal understanding. Building on the success of Retrieval-Augmented Generation (RAG) for factual reasoning in LLMs, this paper introduces a novel approach that utilizes Causal Graphs as external sources for establishing causal relationships between complex vectors. This method is empirically evaluated using two benchmark datasets across the metrics of Context Relevance, Answer Relevance, and Grounding, in its ability to retrieve relevant context with causal alignment. The retrieval effectiveness is further compared with traditional RAG methods that are based on semantic proximity.

Direct policy learning (DPL) is a widely used approach in imitation learning for time-efficient and effective convergence when training mobile robots. However, using DPL in real-world applications is not sufficiently explored due to the inherent challenges of mobilizing direct human expertise and the difficulty of measuring comparative performance. Furthermore, autonomous systems are often resource-constrained, thereby limiting the potential application and implementation of highly effective deep learning models. In this work, we present a lightweight DPL-based approach to train mobile robots in navigational tasks. We integrated a safety policy alongside the navigational policy to safeguard the robot and the environment. The approach was evaluated in simulations and real-world settings and compared with recent work in this space. The results of these experiments and the efficient transfer from simulations to real-world settings demonstrate that our approach has improved performance compared to its hardware-intensive counterparts. We show that using the proposed methodology, the training agent achieves closer performance to the expert within the first 15 training iterations in simulation and real-world settings.

The United Nations Sustainable Development Goal 11 aims to make cities and human settlements inclusive, safe, resilient and sustainable. Smart cities have been studied extensively as an overarching framework to address the needs of increasing urbanisation and the targets of SDG 11. Digital twins and artificial intelligence are foundational technologies that enable the rapid prototyping, development and deployment of systems and solutions within this overarching framework of smart cities. In this paper, we present a novel AI approach for hypervector approximation of complex manifolds in high-dimensional datasets and data streams such as those encountered in smart city settings. This approach is based on hypervectors, few-shot learning and a learning rule based on single-vector operation that collectively maintain low computational complexity. Starting with high-level clusters generated by the K-means algorithm, the approach interrogates these clusters with the Hyperseed algorithm that approximates the complex manifold into fine-grained local variations that can be tracked for anomalies and temporal changes. The approach is empirically evaluated in the smart city setting of a multi-campus tertiary education institution where diverse sensors, buildings and people movement data streams are collected, analysed and processed for insights and decisions.

The vector superposition operation plays a central role in Hyperdimensional Computing (HDC), enabling compositionality of hypervectors without expanding the dimensionality, unlike concatenation. However, a problem arises when the quantity of superimposed vectors surpasses a certain threshold, which is determined by the hypervector’s information capacity relative to its dimensionality. Beyond this point, cross-talk noise incrementally obscures the distinctiveness of individual hypervectors and information is lost. To solve this challenge, we introduce a novel method for weighting individual hypervectors within the superposition, ensuring that only those hypervectors crucial for a given task are prioritized. The weights are learned end-to-end using the backpropagation algorithm in a neural network. Our method is characterized by two key features: (1) The resultant weighting model is exceptionally compact, as the number of trainable weights is equal to the total number of hypervectors in the superposition; (2) The model offers enhanced explainability due to the compositional nature of its encoding. These features collectively contribute to the efficiency and effectiveness of our proposed classification approach using hyperdimensional computing. We illustrate our approach through the multi-channel time series classification task. In this framework, each channel is encoded as a hypervector-descriptor, and those are subsequently composed into a single hypervector via superposition. This superimposed vector forms the basis for training the classification model based on the neural network. Applying our approach of weighted superposition on this task improved the classification performance compared to standard superposition or concatenation of feature vectors, especially for larger numbers of channels.