When these nodes are wired in a hierarchical topology, they abstract information from noisy lower layers providing higher layers with more consistent values. This is called “deep learning”. GAIuS does this without suffering the problems of artificial neural networks (ANNs) or convolutional neural networks (CNN). GAIuS doesn’t require that you know the number of classifications in your data or environment ahead of time. In fact, your environment can change by needing more classifications. The GAIuS Agent will automatically adapt and learn these new classifications!
GAIuS flourishes on Big Data. The more real-world data your GAIuS agent observes, the better it becomes at its job. GAIuS is an “information processing” engine. It takes raw data, and extracts useful or actionable information from it to discover patterns and make predictions.
Does your data contain noise? Don’t worry. GAIuS can figure out how to separate the signal from that noise. It can even suggest data filtering options to improve the signal-to-noise ratio.
Don’t know if your data contains any useful or actionable information? Again, GAIuS has the solution. It’s modern information processing algorithms will quantify the amount of useful or actionable information. It will also show you the profile of the kind of data that you have, whether it is robust, trivial, or random as well as the key indicators in your data.
The GAIuS framework creates an agent that adapts to its world in real-time. The agent’s modularity allows simple elements to be wired together in different ways to produce complex behavior.
GAIuS agents are created by making a “network topology”. With standardized inputs and outputs, your GAIuS Agent can accept any type of data that your application will encounter in the real-world, including vectors, strings and data from other GAIuS agents! Agents can be networked together to share and process information.
A specific custom GAIuS Agent solution is called a “genie”. The behavior of a genie is controlled by its “DNA” which evolve via genetic algorithms to better suit the genie's environment. Genies mutate and breed with other genies to produce more powerful offspring until a desired behavior is obtained.
GAIuS unique architecture allows modularity and separation of cognitive processing and data processing components. Any solution can be derived from these components, or easily integrated with your own pre-existing solutions in the application layer. Here are some things GAIuS can do:
RECOGNITION & CLASSIFICATIONS
A GAIuS agent can recognize what its seeing. It can be used to “classify” objects based on data.
GAIuS agents can make predictions of future events and their utility (i.e. how good or bad that future is expected to be).
ANOMALIES: MISSING AND EXTRA
GAIuS provides predictions about the future, as well as, the un-observed past. It describes the “present” state (i.e. recognition & classifications), and returns anomalies, i.e what is missing &/or extra in the current observation compared with what it was expecting.
DECISIONS & ACTIONS
Three factors effect your GAIuS Agent’s behavior:
The genome is your GAIuS agent configuration. It consists of various connections and parameters that can be changed for the specific environment or problem the agent faces. Changing the genome changes the agent’s behavior.
Whether your data is streaming live, provided manually, or given as a bulk training session, your GAIuS agent will learn from it. Unlike other techniques, learning isn’t a one-time event for your agent. It will continue learning new data, in real-time. No a priori classifications are needed. No knowledge engineering is needed. And you don’t need to know anything about your data before giving it to an agent. The agent’s behavior adapts to the data it receives.
Feedback from you or its environment.
Your GAIuS agent can be trained to change its behavior through operant conditioning or reinforcement learning. The agent changes its behavior to achieve the goals you or the environment have provided through positive or negative feedback.