Large Language Model
Trained on large amounts of data.
A Large Language Model (LLM) is a kind of artificial intelligence trained on vast amounts of text data. It can understand, generate, and predict human language in a way that appears natural. Black Cactus uses tools such as Mara Ki and Galen Ki that utilize LLMs
A Cognitive Large Language Model (LLM) is an advanced AI system trained on extensive datasets to understand, generate, and interpret human language. It operates as a sophisticated prediction engine, forecasting the next word in a sequence to perform tasks like answering questions, writing, summarizing, and translating within Mara and Galen Ki. The term "large" in LLM indicates the presence of billions or trillions of parameters within its deep learning neural network, called Cognitive Ki, enabling it to learn complex language patterns.
Built on a "transformer" architecture, this model features an encoder and a decoder equipped with "self-attention" mechanisms. This enables the model to assess the significance of various words in a sequence, enhancing its understanding of their context and relationships.
Training data
Training data consists of developing models using large datasets, which often include synthetic data that replicate real-world information gathered from various sources. This thorough training helps the model grasp grammar, facts, reasoning, and diverse writing styles effectively.
Tokenization
okenization is an essential first step in Cognitive Ki Large Language Models, where raw text is divided into smaller, manageable units called tokens (words, subwords, characters). This enables the model to interpret and process language numerically. For instance, unstructured text like "Hello world!" is broken down into units such as ["Hello", "world", "!"], which are then transformed into numbers. This process facilitates tasks like text generation and sentiment analysis.
Predictive text generation
Predictive text generation in Mara Ki and Galen Ki, a Cohnative Ki Large Language Model, is an iterative process that produces responses one token at a time—either a fragment or an entire word. Tokens, represented by numerical IDs from prompts, are ranked by likelihood among about 100,000 tokens. Instead of always choosing the most probable token, the model uses strategies like Top-P sampling or temperature adjustments for more natural, varied responses. Each token is added to the sequence, which is then used to predict subsequent tokens until an end marker or max length is reached.
Real-Time Reasoning
Real-Time Reasoning Traces in advanced AI models, such as Mara Ki and Gelen Ki, externalize intermediate steps, making decision-making processes transparent and human-readable. Instead of a black-box answer, the model offers natural-language, brack,eted steps outlining its logic. These traces are crucial for explainable AI helping users understand how conclusions are reached, not just what they are. They assist in debugging, auditing, and compliance by inspecting decision points and failure modes. Chain-of-Thought prompting generates these traces, improving performance on complex tasks by breaking problems into parts. They also serve as educational tools, showing correct methodology or highlighting deviations. The real-time aspect captures traces during operation, enabling monitoring, anomaly detection, and workflow improvement, shifting AI from opaque to transparent.
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.