Glossary
This glossary defines key terms and concepts frequently used in the Project KARL documentation to ensure a common understanding.
| Term | Description |
|---|---|
| Adaptive Learning | A
characteristic of KARL's AI models where they continuously adjust their internal
parameters and behavior based on new, incoming InteractionData from
an individual user. This allows the model to personalize and improve its
performance over time without requiring complete retraining cycles on large,
static datasets. |
| Composable Container (or KarlContainer) | The central
architectural concept in Project KARL, represented by the
KarlContainer interface. It acts as an isolated environment or
"sandbox" for a specific user's AI instance, encapsulating the learning engine,
data storage access, and the orchestration of data flow for learning and
inference. It provides a clear boundary for the AI's operation.
|
Core
Module (:karl-core) |
The
foundational, platform-agnostic Kotlin Multiplatform module in Project KARL. It
defines the main public interfaces (e.g., KarlContainer,
LearningEngine, DataStorage, DataSource)
and core data models (e.g., InteractionData,
Prediction) that form the contract for all KARL implementations and
integrations.
|
DataStorage (Interface)
|
A core KARL
interface defining the contract for how a KarlContainer's learned
state (KarlContainerState) and potentially historical
InteractionData are persistently stored and retrieved locally on
the user's device. Implementations like :karl-room provide concrete
storage mechanisms (e.g., using SQLite).
|
DataSource (Interface)
|
A core KARL
interface that must be implemented by the host application. It is responsible
for observing relevant user actions within the application and feeding
corresponding InteractionData (metadata) into the appropriate
KarlContainer for learning.
|
| Feature Engineering | The process
of transforming raw input data (in KARL's case, InteractionData)
into numerical features suitable for input into a machine learning model. This
is typically handled within a LearningEngine implementation. |
| Incremental Learning (Online Learning) | A type of
machine learning where the model is updated with new data samples individually
or in small batches as they arrive, rather than being retrained from scratch on
an entire dataset. KARL's trainStep() embodies this approach,
allowing for continuous adaptation. |
| Inference (Prediction) | The process
of using a trained AI model to make a prediction, classification, or suggestion
based on new input data or context. In KARL, this is typically invoked via the
KarlContainer.getPrediction() method, which calls the
LearningEngine's predict function.
|
InteractionData (Data Class)
|
The primary data structure used by KARL to represent a single user interaction or event within the host application. It contains metadata such as the interaction type, specific details (as a map), a timestamp, and the user ID. It is designed to capture signals for learning while avoiding sensitive user content. |
KarlContainerState (Data Class)
|
A data
structure representing the serializable state of a LearningEngine's
internal model (e.g., model weights, parameters, version). This state is managed
by the DataStorage implementation to allow the AI's learned
knowledge to be persisted across application sessions. |
KarlInstruction (Sealed Class)
|
A data
structure representing a user-defined or application-defined rule that can
modify or guide the behavior of a KarlContainer, such as filtering
data for learning or adjusting prediction thresholds. |
InstructionParser (Interface)
|
A core KARL
interface defining the contract for components that can parse raw input (e.g., a
user-entered string) into a list of structured KarlInstruction
objects. |
| KMP (Kotlin Multiplatform) | A Kotlin technology that allows developers to share code across different platforms (e.g., JVM, Android, iOS, JavaScript, Native). Project KARL's core is designed with KMP to allow for broad applicability. |
| KSP (Kotlin Symbol Processing) | A powerful and efficient API for developing Kotlin compiler plugins. It is used by libraries like AndroidX Room to generate code at compile time (e.g., for DAOs and database implementations), reducing boilerplate and improving performance. |
LearningEngine (Interface)
|
A core KARL
interface that defines the contract for the actual AI/ML model logic.
Implementations (e.g., :karl-kldl) handle the specifics of model
architecture, training (via trainStep()), and inference (via
predict()).
|
| Local-First / On-Device AI | An AI development paradigm where the primary computation, including model training and inference, occurs directly on the user's end device rather than on remote cloud servers. This is a foundational principle of Project KARL, prioritizing privacy and offline capability. |
| MLP (Multi-Layer Perceptron) | A fundamental type of feedforward artificial neural network consisting of at least three layers of nodes: an input layer, one or more hidden layers, and an output layer. Used as a basic model in KARL's initial KotlinDL implementation. |
| Model State | Refers to the
learned parameters (weights, biases, etc.) of a machine learning model. In KARL,
this is encapsulated within KarlContainerState for persistence.
|
| Personalization | The process of tailoring an application's features, content, or behavior to individual users based on their past interactions, preferences, or characteristics. KARL achieves this through local, adaptive learning. |
Prediction (Data Class)
|
The data structure returned by KARL's inference process. It typically contains the main suggestion, a confidence score, a prediction type, and optional metadata. |
| Privacy-First | A design philosophy where user privacy is a primary consideration and constraint throughout the system architecture and data handling processes. KARL embodies this by keeping data and processing local by default. |
Training (trainStep)
|
In KARL, this
refers to the incremental process where the LearningEngine updates
its internal model based on a new piece of InteractionData,
allowing it to adapt and improve its predictions over time. |
This glossary will be expanded as new terms and concepts are introduced in Project KARL.