AI Bibliography

Natural intelligence has evolved over billions of years to give us two computing models: the “gene” which encodes the process of “life” in an executable form and a neural network that encodes various processes to interact with the environment in real time. Recent advances in computing performance have increased exponentially, allowing us to implement similar computing models, giving us the framework for today’s software systems. First, symbolic computing outperforms humans in tasks that can be easily described by a list of formal, mathematical rules or a sequence of event-driven actions such as modeling, simulation, business workflows, interaction with devices, etc. Second, the neural network model allows computers to understand the world in terms of a hierarchy of concepts to perform tasks that are easy to do “intuitively”, but are hard to describe formally or a sequence of event-driven actions such as recognizing spoken words or faces.

We examine the role of cognition in symbolic computing and neural networks. We explore the potential for a symbiotic relationship between natural and artificial intelligence to extend the collective consciousness and develop a unified culture that binds the participants to define common goals and execute processes that transcend human and machine capabilities.