aiQ Cognitive Technologies
aiQ Cognitive Technologies
aiQ Cognitive Technologies

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

An anonymous neuromorphic identity technology that is able to identify someone without having to know anything about the someone

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...

2010

PROCEDURAL

The first generation of AI technologies adopted a strictly rules-based approach to emulate classical (‘if/then’-type) logic to draw 'reasoned' conclusions within a very specific, narrowly-defined problem domain

2010

PROCEDURAL

The first generation of AI technologies adopted a strictly rules-based approach to emulate classical (‘if/then’-type) logic to draw 'reasoned' conclusions within a very specific, narrowly-defined problem domain

2010

PROCEDURAL

The first generation of AI technologies adopted a strictly rules-based approach to emulate classical (‘if/then’-type) logic to draw 'reasoned' conclusions within a very specific, narrowly-defined problem domain

2018

NEURAL

The current generation of AI technologies uses deep-learning networks in order to ‘interpret’ data sources (eg the content of a digital image) - this approach suffers from the fact that examples that fall outside the range of a particular dataset which the neural net has been trained for, cause the process to fail

2018

NEURAL

The current generation of AI technologies uses deep-learning networks in order to ‘interpret’ data sources (eg the content of a digital image) - this approach suffers from the fact that examples that fall outside the range of a particular dataset which the neural net has been trained for, cause the process to fail

2018

NEURAL

The current generation of AI technologies uses deep-learning networks in order to ‘interpret’ data sources (eg the content of a digital image) - this approach suffers from the fact that examples that fall outside the range of a particular dataset which the neural net has been trained for, cause the process to fail

2024

NEUROMORPHIC

The future generation of AI technologies promises to advance AI technologies well beyond current capabilities Properly referred to as ‘Neuromorphic Computing’, the technology properly simulates the neural processes of the human mind/brain to achieve a certain measure of 'cognitive interpretation' and 'autonomous adaptation'

2024

NEUROMORPHIC

The future generation of AI technologies promises to advance AI technologies well beyond current capabilities Properly referred to as ‘Neuromorphic Computing’, the technology properly simulates the neural processes of the human mind/brain to achieve a certain measure of 'cognitive interpretation' and 'autonomous adaptation'

2030

QUANTUM

Quantum computing has the potential to significantly enhance various aspects of AI by offering new computational capabilities and solving complex problems more efficiently - faster optimisation algorithms, improved machine learning models, enhanced simulation modelling and more efficient data harvesting and analysis However, it will take time for quantum computing technologies to mature and become practical for widespread use in AI applications - researchers at aiQ continue to explore the possibilities and challenges of integrating quantum computing with AI

2030

QUANTUM

Quantum computing has the potential to significantly enhance various aspects of AI by offering new computational capabilities and solving complex problems more efficiently - faster optimisation algorithms, improved machine learning models, enhanced simulation modelling and more efficient data harvesting and analysis However, it will take time for quantum computing technologies to mature and become practical for widespread use in AI applications - researchers at aiQ continue to explore the possibilities and challenges of integrating quantum computing with AI

2030

QUANTUM

Quantum computing has the potential to significantly enhance various aspects of AI by offering new computational capabilities and solving complex problems more efficiently - faster optimisation algorithms, improved machine learning models, enhanced simulation modelling and more efficient data harvesting and analysis However, it will take time for quantum computing technologies to mature and become practical for widespread use in AI applications - researchers at aiQ continue to explore the possibilities and challenges of integrating quantum computing with AI

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

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

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

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

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

Healthcare

Medical Diagnosis, Drug Discovery & Personalised Medicine

AI systems are able to analyse medical imaging data (e.g., X-rays, MRIs) and patient records to assist radiologists and physicians to diagnose diseases and conditions AI algorithms are able to analyse large datasets to identify potential drug candidates, predict their efficacy, and optimise drug design AI-powered predictive analytics can help healthcare providers tailor treatment plans and interventions to individual patients based on their genetic makeup, medical history, and lifestyle factors

Healthcare

Medical Diagnosis, Drug Discovery & Personalised Medicine

AI systems are able to analyse medical imaging data (e.g., X-rays, MRIs) and patient records to assist radiologists and physicians to diagnose diseases and conditions AI algorithms are able to analyse large datasets to identify potential drug candidates, predict their efficacy, and optimise drug design AI-powered predictive analytics can help healthcare providers tailor treatment plans and interventions to individual patients based on their genetic makeup, medical history, and lifestyle factors

Healthcare

Medical Diagnosis, Drug Discovery & Personalised Medicine

AI systems are able to analyse medical imaging data (e.g., X-rays, MRIs) and patient records to assist radiologists and physicians to diagnose diseases and conditions AI algorithms are able to analyse large datasets to identify potential drug candidates, predict their efficacy, and optimise drug design AI-powered predictive analytics can help healthcare providers tailor treatment plans and interventions to individual patients based on their genetic makeup, medical history, and lifestyle factors

Finance

Algorithmic Trading, Fraud Detection & Risk Assessment

AI-driven trading algorithms analyse market data in real-time to identify trends, patterns, and opportunities for investment AI systems can detect suspicious transactions, anomalies, and patterns indicative of fraudulent activity in financial transactions and networks AI models analyse financial data and market trends to assess credit risk, predict loan defaults, and optimise investment portfolios

Finance

Algorithmic Trading, Fraud Detection & Risk Assessment

AI-driven trading algorithms analyse market data in real-time to identify trends, patterns, and opportunities for investment AI systems can detect suspicious transactions, anomalies, and patterns indicative of fraudulent activity in financial transactions and networks AI models analyse financial data and market trends to assess credit risk, predict loan defaults, and optimise investment portfolios

Finance

Algorithmic Trading, Fraud Detection & Risk Assessment

AI-driven trading algorithms analyse market data in real-time to identify trends, patterns, and opportunities for investment AI systems can detect suspicious transactions, anomalies, and patterns indicative of fraudulent activity in financial transactions and networks AI models analyse financial data and market trends to assess credit risk, predict loan defaults, and optimise investment portfolios

Autonomous Vehicles

Self-Driving Vehicles & Autonomous Drones

AI algorithms enable vehicles to perceive their environment, navigate roads, interpret traffic signals, and make real-time decisions to safely and efficiently transport passengers AI-powered drones can perform tasks such as aerial surveillance, package delivery, agriculture monitoring, and search & rescue operations with minimal human intervention

Autonomous Vehicles

Self-Driving Vehicles & Autonomous Drones

AI algorithms enable vehicles to perceive their environment, navigate roads, interpret traffic signals, and make real-time decisions to safely and efficiently transport passengers AI-powered drones can perform tasks such as aerial surveillance, package delivery, agriculture monitoring, and search & rescue operations with minimal human intervention

Autonomous Vehicles

Self-Driving Vehicles & Autonomous Drones

AI algorithms enable vehicles to perceive their environment, navigate roads, interpret traffic signals, and make real-time decisions to safely and efficiently transport passengers AI-powered drones can perform tasks such as aerial surveillance, package delivery, agriculture monitoring, and search & rescue operations with minimal human intervention

Natural Language Processing (NLP)

Language Translation, Chatbots & Virtual Assistants

AI-powered translation systems use deep learning techniques to translate text and speech between different languages with a very high degree of accuracy AI-driven conversational agents can interact with users in natural language, answer questions, provide recommendations, and assist with tasks such as scheduling appointments and making reservations

Natural Language Processing (NLP)

Language Translation, Chatbots & Virtual Assistants

AI-powered translation systems use deep learning techniques to translate text and speech between different languages with a very high degree of accuracy AI-driven conversational agents can interact with users in natural language, answer questions, provide recommendations, and assist with tasks such as scheduling appointments and making reservations

Natural Language Processing (NLP)

Language Translation, Chatbots & Virtual Assistants

AI-powered translation systems use deep learning techniques to translate text and speech between different languages with a very high degree of accuracy AI-driven conversational agents can interact with users in natural language, answer questions, provide recommendations, and assist with tasks such as scheduling appointments and making reservations

Robotics

Industrial Automation & Surgical Robotics

AI-powered robots are used in manufacturing, logistics, and warehousing to perform tasks such as assembly, pick-and-place operations, and material handling AI-enhanced robotic systems assist surgeons in performing minimally invasive surgeries with precision, accuracy, and improved patient outcomes

Robotics

Industrial Automation & Surgical Robotics

AI-powered robots are used in manufacturing, logistics, and warehousing to perform tasks such as assembly, pick-and-place operations, and material handling AI-enhanced robotic systems assist surgeons in performing minimally invasive surgeries with precision, accuracy, and improved patient outcomes

Robotics

Industrial Automation & Surgical Robotics

AI-powered robots are used in manufacturing, logistics, and warehousing to perform tasks such as assembly, pick-and-place operations, and material handling AI-enhanced robotic systems assist surgeons in performing minimally invasive surgeries with precision, accuracy, and improved patient outcomes

Computer Vision

Object/People Recognition & Behavioural Analytics

AI algorithms can detect and classify objects in images and videos, enabling applications such as facial recognition, object tracking, and surveillance

Computer Vision

Object/People Recognition & Behavioural Analytics

AI algorithms can detect and classify objects in images and videos, enabling applications such as facial recognition, object tracking, and surveillance

Computer Vision

Object/People Recognition & Behavioural Analytics

AI algorithms can detect and classify objects in images and videos, enabling applications such as facial recognition, object tracking, and surveillance

Technology Developments

aiDx Neuromorphic Biometric

2018

aiDx Neuromorphic Biometric

2018

aiDx Neuromorphic Biometric

2018

Driver Assistance & Analytics

2019

Driver Assistance & Analytics

2019

Driver Assistance & Analytics

2019

Covid Detection

2019

Covid Detection

2019

Covid Detection

2019

Consumer Behaviour Analytics

2022

Consumer Behaviour Analytics

2022

Consumer Behaviour Analytics

2022

QiD Anonymous Neuromorphic Identity Technology

2024

QiD Anonymous Neuromorphic Identity Technology

2024

QiD Anonymous Neuromorphic Identity Technology

2024

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