Deep Learning
Enables systems to learn from data
Cognitive Ki uses Deep Learning, a subset of Machine Learning that enables systems to learn and improve from data. It is the driving force behind Cognitive Ki's artificial intelligence.
Deep learning is a strong subset of machine learning that uses multi-layered neural networks modeled after the human brain. It identifies complex patterns in large datasets, such as text and images, which supports applications like image recognition, natural language processing, and AI systems. This method offers advanced decision-making with minimal human intervention but requires significant data and computational power.
Deep Learning involves the number of hidden layers used to process data. Unlike traditional neural networks, which typically have just one or two hidden layers, deep learning models often contain hundreds or even thousands. This layered structure enables the system to learn hierarchical representations: early layers identify simple patterns like edges and lines, middle layers combine these into shapes such as circles or corners, and the final layers recognize complex objects like faces, cars, or animals.
Neural Networks
Deep learning models utilize artificial neural networks, consisting of interlinked layers of nodes (neurons) that analyze information. These models depend on Artificial Neural Networks (ANNs), which mimic the layered, interconnected structure of the human brain, allowing them to identify complex patterns from large datasets. This capability supports tasks such as image recognition, natural language processing, and predictions. The term "deep" in deep learning refers to the multiple hidden layers in these networks, which help them extract progressively more abstract features from raw data.
Layered Learning
Layered Learning is the core idea behind artificial neural networks, particularly deep learning models. In this framework, data passes through several "layers," with each layer transforming the input into a more abstract representation
Learning from Data
Learning from data is essential in deep learning, where systems independently gain knowledge by identifying complex patterns in extensive datasets instead of using explicitly programmed rules. This approach continues to be a leading focus in AI, advancing from simple outputs to a deeper understanding of context and nuance.
Self-Correction
The network improves its accuracy by iteratively adjusting the connection weights between neurons based on errors. These weights, which indicate connection strengths, are constantly updated to minimize differences between predicted and actual outcomes. This adjustment process is driven by an error or loss function that quantifies mistakes, with backpropagation serving as the primary method to propagate the error backward and update the weights efficiently
Cognitive Ki Learning
Machine Learning
Machine learning, a subset of artificial intelligence (AI), uses algorithms and statistical models to help computers learn from data and improve on specific tasks. Its goal is to develop models that recognize patterns and make predictions or decisions without explicit programming.
Cognitive Ki Natural Language
Large Language Mode
​Natural Language Processing is a field of artificial intelligence that allows computers to understand, interpret, and generate human language. It is utilized in many technologies, including chatbots and real-time translation systems.
Cognitive Ki Swarm intelligence
Swarm Intelligence
Swarm intelligence is an AI approach inspired by decentralized natural systems such as ant colonies and bird flocks. It shows how simple agents following local rules without a leader can produce complex, adaptable, and intelligent behavior.