Conflict, Paradox, and the Role of Structure in True Intelligence

Novel forms of brain-inspired programming models related to novel computer architecture are required to both understand the mysteries of intelligence as well as break barriers in computational complexity, and computer parallelism. Artificial Intelligence is focused on developing complex programs based on abstract, statistical prediction engines that require large datasets, vast amounts of computational power, and unbounded computation time. By contrast, the brain utilizes relatively few experiences to make decisions in unpredictable, time-constrained situations while utilizing relatively small amounts of physical computational space and power with high degrees of complexity and parallelism. We observe that intelligence requires the accommodation of ambiguity, conflict, and paradox. From a structural perspective, this means the same set of inputs leads to conflicting results that are likely produced in isolated regions of the brain that function independently until an answer must be chosen. We further observe that, unlike computer programs, brains constantly interact with the physical world where external constraints force the selection of the best available response in time-quality trade-offs ranging from fight-or-flight to deep thinking. For example, when intelligent beings are presented with a set of inputs, those inputs can be processed with different levels of thinking, utilizing heterogeneous algorithms to produce answers dependent upon the time available to process them. We introduce the Troop meta-approach, which is a novel meta computer architecture and programming. Experiments demonstrate our approach in modeling conflict when the same set of inputs are heterogeneously processed independently using maze solving and ordered search in real-world environments with unpredictable, random time constraints. Across one hundred trials, on average, the Troop solution solves mazes almost six times faster than the only other solution, which does not accommodate conflict but can always produce a result when required. Two other experiments based on ordered search show that, on average, the Troop solution returns a position that is over twice as accurate as the other solutions which do not accommodate conflict but always produce a result when required. This work lays the foundation for more research in algorithms that utilize time-accuracy trade-offs consistent with our approach.