The problem of intelligence –the nature of it, how the brain generates it and how it could be replicated in machines – is arguably one of the deepest and important problems in science today. Philosophers have studied intelligence for centuries, but it is only in the last several decades that key developments in a broad range of science and engineering fields have opened up a thriving "intelligence research" enterprise, making questions such as these approachable: How does the mind process sensory information to produce intelligent behavior – and how can we design intelligent computer algorithms that behave similarly? What is the structure and form of human knowledge – how is it stored, represented and organized? How do human minds arise through processes of evolution, development and learning, and what are their roots in genetics? How can we build more intelligent synthetic hardware, at the diverse scales of robots, micro- and nano-devices, or networks? How does collective intelligence arise in social and economic systems? Are there common principles of learning, prediction, decision or planning that span these diverse scales and systems?
Many of us at MIT believe that the time has come for a new, fresh attack on these problems. The launching off point will be a new integration of the fields of cognitive science, which studies the mind, neuroscience, which studies the brain, and computer science and artificial intelligence, which develop intelligent hardware and software. These fields grew up together in the 1950’s but drifted apart as each became more specialized. In the 21st century they are re-converging, driven by new tools that allow studies of the brain and mind to inform the design of intelligent artifacts and vice versa – a virtuous loop from science to engineering and from engineering to science. These tools include radical increases in computing power, the availability of massive multimodal datasets, and the development of unifying mathematical frameworks for learning, inference and decision that support a common understanding of intelligence in minds, brains and machines. Around the core of these fields, the true scope of the enterprise driven by these new tools is highly interdisciplinary. We envision the mind-brain-computation core as one pillar of a broad initiative spanning many departments and all schools of the Institute. Other complementary pillars will include collective intelligence, with a core built on the social sciences, economics, management, media, and computer science; and physical or molecular intelligence, with a core built on biology and bioengineering, electrical engineering and computing, materials science, chemistry and physics. As a global leader in so many of these fields, MIT is uniquely positioned to lead this synthesis.
To fully exploit MIT’s unique potential we propose an Intelligence Initiative (I²). I² would draw as broadly as possible from faculty across the Institute whose work connects to the topic of intelligence – intelligence in humans or animals, in machines or molecules, in cultural or collective settings. A central goal of I² is to encourage and enable more integrative approaches than do conventional funding sources and institutional structures. For example, intelligence develops in a child from the interaction of genetic priors with learning from the environment, and integrates the growth of knowledge across vision, language, motor and social understanding, yet conventional research rarely focus on these interactions. Likewise, the study of collective and individual intelligence should be much more tightly coupled: understanding when the collective “whole” will be more or less intelligent than the sum of its parts must be grounded in knowledge of the capacities of individual minds; at the same time, the most important societal effects of individual cognition may emerge only in collective settings like financial markets, public health, political conflict, or energy and natural resource decisions. Thus, the “I²” label could also stand for a program of integrative intelligence research.
Beyond being a great intellectual mission, understanding the origins of intelligence, building more intelligent artifacts and systems, and improving mechanisms for collective decisions will be critical to the future prosperity, education, health, and security of the US and the world at large. The transformative power of internet resources like Google, Wikipedia and YouTube prompts us to ask, “How much more could we do if search engines really understood the language and intent behind a user’s questions, or the scene unfolding in a video, the way another human does?” The recent financial crisis forces us to examine where and when our intelligence failed – in the minds of individual traders, executives, or investors? In the mechanisms of our economy or our legal and regulatory system? On what timescale? – and to consider how our financial markets and aspects of our economy could be engineered in more intelligent ways. The National Academy of Engineering recently announced 14 Grand Challenges for the 21st century. Fully half of these challenges represent potential projects for I². Four are at the core of the mind-brain-computation interface: reverse engineer the brain, advance personalized learning, enhance virtual reality, and engineer the tools of scientific discovery. Three others highlight the application of computational, collective and physical intelligence to problems of global importance: advance health informatics, engineer better medicines, secure cyberspace.