Current methodologies won’t create the future

Machine intelligence has traditionally been siloed into separate research and development applications. Each application requiring its own sets of algorithms, data and knowledge representations, and specific assumptions. Inevitably, knowledge engineering or modeling creep into the process, eliminating the possibility of a true autonomous intelligence by injecting human intelligence into the solution. Though some successes have been made, such an approach is counter to the goal of developing true Artificial Intelligence. Often, it is guesswork to discover one of many algorithms in a toolset, or a statistical or neural net model that works well on the historic dataset. When one is found that appears to work well, this has never lasted when the solution is applied in the real world. Real world data and the environment it springs from, changes. These static solutions need to be constantly re-worked to keep up.

But, intelligence cannot be manually coded. Instead, an environment must be created that allows for intelligence to emerge from it.

Intelligence is an Artifact

Nature did not set out to create intelligent creatures.  Intelligence is an artifact of configuration (DNA), environment (data and evolution), and processes that drive survival and reproduction.  A race for survival that began with classification and recognition lead to prediction and cogitation.

Rather than focus on any single algorithm or “how” the brain or collections of neurons do what they do, our approach holistically investigates “why” they do what they do. Uncovering the “why” has allowed us to understand the processes from which intelligence emerges. Instead of simulating the mechanics of neural activity, our research replicates the functions of large collections of neurons. This enables it to work – without change – within multiple application and problem domains.

We have created a proprietary system that draws on deep cross-disciplinary insights and discoveries. We have used principles derived from neuroscience, physics, mathematics, computer science and biology to create a framework for machine-generated intelligence. This research has yielded the “Cognitive Processor”. The Cognitive Processor is the fundamental cognitive processing unit of intelligence of our General Artificial Intelligence using Software Framework (GAIuS). Cognitive Processors can be linked in any network topology to further process information. Like the brain, some of these topologies can be hierarchical. Others can be more ambiguous. The connections between Cognitive Processors are analogous to the connections mapped by the Human Connectome Project.

We have built our General Evolving Networked Intelligence Engine Platform (Genie), on top of GAIuS to research these connections. But, Genie pushes the technology even further.

Autonomy requires Freedom

For true machine intelligence, autonomy of both the agent and the construction of the agent is necessary. Otherwise, a human would always be required in creating the agents.  For true machine intelligence, the machines need the freedom to develop without artificial limits set on them by humans.

That’s not to say that we have no control over our genies. There is an existing precedence for how humans adapt other intelligences. Just as domesticated animals have been bred for centuries to work with us and be our companions, genies can also be bred to behave as we wish. Desirous traits can be bred into a breed, and undesirable traits can be eliminated.

Our R&D includes provisions for automatically generating agents known as “genies” and automatically evolving them within their environments. We use genetic algorithms to both produce and breed new generations of genies. The result is a powerful platform that can provide solutions to any data-driven problem, in any problem domain.