aiQ Cognitive Technologies…
aiQ Cognitive Technologies ('aiQ') emerged from a research programme launched in 2010 at the University of Johannesburg (South Africa) ('UJ')
Modelled on Google’s ‘Moonshot Factory’ (Google ‘X'), the programme sought to anticipate the evolution of future computer technologies over the next decade and beyond…
While the programme exposed several emerging technologies, research was concentrated on the development of Artificial Intelligence (AI) with a specific focus on advancing a more specialised AI technology - Neuromorphic Computing
Professional
References
Equipped with their command of Neuromorphic Computing capabilities, aiQ engineers have successfully been able to assist with the digital transformation of complex business processes and the development of enterprise technology solutions for a diverse range of different business and other services entities…
Proprietary
Technology Developments
Using the extraordinary power of Neuromorphic Computing, research scientists from aiQ have successfully been able to engineer two groundbreaking new technologies…
A neuromorphic biometric technology which accurately simulates human cognition - how the human mind/brain naturally and instinctively proceeds to recognise a person
IOxT
IOxT is a wholly-owned subsidiary of aiQ Cognitive Technologies (‘aiQ’), providing all technical support and development services to aiQ
The technology and development services of IOxT can be contracted to assist with the digital transformation of businesses, organisations, government entities…
Evolution Of AI
Since it first became the subject of scientific research in 1956, Artificial Intelligence (AI) has advanced through several distinct phases of development, with new and improved developments emerging...
Neuromorphic Computing
Neuromorphic computing represents an approach to computing that seeks to simulate the structure and function of the human brain's neural networks
Unlike traditional von Neumann architecture which separates memory and processing units, neuromorphic computing aims to integrate memory and processing within individual units, similar to the way neurons are interconnected in the brain
Key features of neuromorphic can briefly be summarised as follows…
Spiking Neural Networks (SNNs)
Neuromorphic computing systems are often based on spiking neural networks, which model the behaviour of biological neurons by representing information as spikes or pulses of activity These networks are able to perform large computations concurrently synchronously
Parallel Processing
Neuromorphic computing architectures enable massively parallel processing, allowing multiple computations to be performed simultaneously across interconnected neurons or processing units This parallelism enables efficient processing of complex data and real-time analysis of sensory inputs
Low Power Consumption
Neuromorphic computing systems are designed to be energy-efficient, drawing inspiration from the brain's ability to perform complex computations using minimal power By leveraging sparsity and event-driven processing, neuromorphic chips can achieve high computational performance while consuming significantly less energy than traditional computing architectures
Adaptive Learning & Plasticity
Neuromorphic computing systems incorporate principles of adaptive learning and synaptic plasticity, enabling them to learn from and adapt to their environment These systems can self-organise, learn from experience, and continuously improve their performance over time, making them well-suited for tasks such as pattern recognition, classification, and sensor data processing
Overall, neuromorphic computing represents a promising approach to computing that seeks to overcome the limitations of traditional architectures and unlock new capabilities for processing and understanding complex data
While still in the early stages of development, neuromorphic computing holds potential for revolutionising the way we design and deploy intelligent systems in the future
Practical Application
Technology Developments
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