We are living in an age obsessed with intelligent systems. All walks of life are being transformed by machine learning, by software platforms that amplify human ability to manipulate mathematics and statistics, by the availability of Massive Open Online Courses, and by unprecedented access to data and collective insights through networks like Wikipedia and Stack Exchange. These innovations are changing both science and business.

A defining feature of complex systems is their ability to encode, store, process, and employ functional information. This feature encompasses the elementary gradient sensing capabilities of single cells, through to the large-scale perceptual and decision-making abilities of large populations of neurons. Complex intelligent systems are fundamentally collective, distributed, error-prone, and hybrid. 

In technological settings, many features associated with intelligent behavior are outsourced to artifacts that serve as a community resource. These include language, mathematics, calculators, modern digital computers, and a growing library of inferential software. Modern intelligent systems are hybrids in which induction, encoding, storage, deduction, and strategy are all shared across organically and culturally evolved intelligent systems, from spider webs to the world wide web. In hybrid systems, the lone organism's reason is amplified through a collective of individuals and intelligent machines. Defense systems, health care systems, and financial markets are all examples of intelligent hybrid systems.  

This research theme seeks to understand unique and species-spanning forms of intelligence — how these forms of intelligence can be measured and compared, and how we should think about the differences and commonalities among natural perceptual, motor, and “analytical” intelligence. In order to intelligently engineer intelligent systems, we must first pursue a rigorous and quantitative understanding of intelligence itself, in all its diversity.