Intelligent Offer Optimisation is the use of data analysis and artificial intelligence to determine the most effective deals, discounts, or promotions to present to customers. By analysing customer behaviour, preferences, and market trends, systems can automatically adjust offers to increase the likelihood of a sale. This process helps businesses maximise their revenue while giving customers…
Category: Model Optimisation Techniques
Smart Performance Analysis
Smart performance analysis refers to using advanced tools and data-driven methods to assess how well something or someone is performing. This can involve collecting information from sensors, software, or manual observation, and then using technology like artificial intelligence or specialised software to identify patterns, strengths, and areas for improvement. The aim is to make better…
In-Memory Processing
In-memory processing refers to storing and handling data directly in a computer’s main memory (RAM) rather than on slower storage devices like hard drives. This allows computers to access and analyse information much more quickly, making data processing tasks significantly faster. It is widely used in applications that require real-time results or need to process…
AI Accelerator Chips
AI accelerator chips are specialised computer processors designed to handle artificial intelligence tasks much faster and more efficiently than regular computer chips. These chips are built to process large amounts of data and run complex calculations needed for AI, such as recognising images or understanding language. They are often used in data centres, smartphones, and…
Tiny Machine Learning
Tiny Machine Learning, often called TinyML, is the practice of running machine learning models on very small, low-power devices such as sensors or microcontrollers. These devices typically have limited memory and processing power, so the machine learning models must be small and efficient. TinyML enables smart features like voice recognition, gesture detection, or anomaly detection…
Spiking Neuron Models
Spiking neuron models are mathematical frameworks used to describe how real biological neurons send information using electrical pulses called spikes. Unlike traditional artificial neurons, which use continuous values, spiking models represent brain activity more accurately by mimicking the timing and frequency of these spikes. They help scientists and engineers study brain function and build more…
Quantum Annealing Applications
Quantum annealing is a computational method that uses quantum mechanics to find solutions to complex optimisation problems. It is designed to quickly search through many possible solutions and identify the most efficient one, often much faster than traditional computers can. Quantum annealing is particularly useful for problems where there are many variables and possible combinations…
Quantum Circuit Optimisation
Quantum circuit optimisation is the process of improving quantum circuits so they use fewer resources, such as operations or time, while still giving correct results. This can involve reducing the number of quantum gates, making the circuit shorter, or arranging operations to suit a specific quantum computer. Efficient circuits are important because quantum hardware is…
Data Science Model Drift Remediation
Data science model drift remediation refers to the process of identifying and correcting changes in a model’s performance over time. Model drift happens when the data a model sees in the real world differs from the data it was trained on, causing predictions to become less accurate. Remediation involves steps such as monitoring, diagnosing causes,…
Curriculum Learning in RL
Curriculum Learning in Reinforcement Learning (RL) is a technique where an agent is trained on simpler tasks before progressing to more complex ones. This approach helps the agent build up its abilities gradually, making it easier to learn difficult behaviours. By starting with easy scenarios and increasing difficulty over time, the agent can learn more…