We're at a crossroads in human civilization. As a species, we're facing problems with exponentially increasing complexity, while the number of those problems is growing faster than we can identify. The low-hanging fruit has all been picked. What's left are difficult problems that require greater knowledge gained over multiple lifetimes. To survive, to thrive, we need help.

We must create adaptive, intelligent machine solutions that can distill lifetimes worth of information and hone it to address each problem. But in order to work with them, we must understand exactly how they function. The current trends in AI/ML are useful for solving some of today's problems but are woefully lacking when life, limb, or liberty is at stake. The inherent complexity of these solutions creates a black box barrier to assuring effective and trusted solutions that reduce the number of problems humanity is facing. Instead, they add to them. We need to scientifically understand and explain the nature of information and how it is transformed within electronic minds from data to become knowledge, wisdom, and behavior. We must be able to deterministically predict the actions of these intelligent machines. And when things go wrong, we must be able to guarantee the fixes. Our future will be determined by our next steps in machine intelligence.

As has always been the case throughout the sciences, abstracting complexities away to produce simpler concepts leads to greater clarity and understanding. It is a false premise that an artificial mind’s implementation must be as complicated and difficult to understand as the natural mind. At Intelligent Artifacts, we've built a framework with understandable modular components that make the data-to-behavior path transparent. We’ve cast this into a unique platform designed to forge that path to the future of machine intelligence technology. Our Cognitive Processor provides the required common modular foundation. It can be reused on any data set, providing solutions for a broader class of problems than was previously achievable. Data manipulatives networked with Cognitive Processors provide solution breadth, regardless of problem complexity. Our ThinkFlux module provides a reasoning layer on top. Each solution integrates cleanly with the others and multiple solutions applied to the same data sets provide robustness. With this framework, we’ve enabled a 'plug-and-play' building block approach to machine intelligence.

We believe that the path to a world with intelligent machines requires trust and we believe trust begins with understanding. To trust in machine intelligence, we must be able to verify and validate the solution. We must be able to explain and interpret the results and how they were generated. We must be able to trace answers back to the raw data that produced them. And we must be able to edit responses to mitigate disasters. And this must be repeatable every time, deterministically, regardless of the environment.