The future of intelligent computing

Today's mathematical and statistical tools are often insufficient to model the dynamics of real world phenomena. Consequently, even conceptually simple tasks can require overly elaborate equations and methodologies that are difficult to implement yet sorely lacking in power, robustness, and intelligence.

The most powerful and robust computational systems ever known operate on an entirely different paradigm. Genetic regulatory networks, biochemical pathways, ecosystems, financial markets, and of course mammalian brains all consist of elaborate interconnected networks of large numbers of simple elements. Such systems can not be emulated by a mere series of equations.

Any intelligent system, artificial or otherwise, will be “neural” at least insofar as it will consist fundamentally of elementary components, with hierarchical complexes of successively more intricate, dynamic, and adaptive structures emergent from coordinated interactions among the elementary components. Neuroblast's intelligent algorithms are made up of such components, generically called neurons.

At Neuroblast, computational sophistication emerges not from complex calculations but rather from relatively simple calculations within complex feedback networks — just like in biological brains. This kind of neural processing enables data analysis capabilities not possible with traditional mathematical or statistical techniques. Neuroblast's neural algorithms are far more powerful, robust, and adaptive. In other words, intelligent.