Cognitive architecture design is the process of creating a structure that models how human thinking and reasoning work. It involves building systems that can process information, learn from experience, and make decisions in ways similar to people. These designs are used in artificial intelligence and robotics to help machines solve problems and interact more naturally…
Category: Artificial Intelligence
Neural Network Generalization
Neural network generalisation refers to the ability of a neural network to perform well on new, unseen data after being trained on a specific set of examples. It shows how well the network has learned patterns and rules, rather than simply memorising the training data. Good generalisation means the model can make accurate predictions in…
Behavioral Threat Analytics
Behavioural threat analytics is a method used to detect and assess potential security threats by analysing patterns in user or system behaviour. It involves monitoring actions and comparing them to typical behaviour to spot unusual activities that could indicate a risk, such as fraud or cyberattacks. This approach helps organisations identify threats early, often before…
Incentive Alignment Mechanisms
Incentive alignment mechanisms are systems or rules designed to ensure that the interests of different people or groups working together are in harmony. They help make sure that everyone involved has a reason to work towards the same goal, reducing conflicts and encouraging cooperation. These mechanisms are often used in organisations, businesses, and collaborative projects…
Sparse Activation Maps
Sparse activation maps are patterns in neural networks where only a small number of neurons or units are active at any given time. This means that for a given input, most of the activations are zero or close to zero, and only a few are significantly active. Sparse activation helps make models more efficient by…
Efficient Attention Mechanisms
Efficient attention mechanisms are methods used in artificial intelligence to make the attention process faster and use less computer memory. Traditional attention methods can become slow or require too much memory when handling long sequences of data, such as long texts or audio. Efficient attention techniques solve this by simplifying calculations or using clever tricks,…
Dynamic Inference Paths
Dynamic inference paths refer to the ability of a system, often an artificial intelligence or machine learning model, to choose different routes or strategies for making decisions based on the specific input it receives. Instead of always following a fixed set of steps, the system adapts its reasoning process in real time to best address…
Model Distillation Frameworks
Model distillation frameworks are tools or libraries that help make large, complex machine learning models smaller and more efficient by transferring their knowledge to simpler models. This process keeps much of the original model’s accuracy while reducing the size and computational needs. These frameworks automate and simplify the steps needed to train, evaluate, and deploy…
Inference Latency Reduction
Inference latency reduction refers to techniques and strategies used to decrease the time it takes for a computer model, such as artificial intelligence or machine learning systems, to produce results after receiving input. This is important because lower latency means faster responses, which is especially valuable in applications where real-time or near-instant feedback is needed….
Neural Network Quantization
Neural network quantisation is a technique that reduces the amount of memory and computing power needed by a neural network. It works by representing the numbers used in the network, such as weights and activations, with lower-precision values instead of the usual 32-bit floating-point numbers. This makes the neural network smaller and faster, while often…