Seeking universal laws governing both biological and artificial intelligence
An oft-quoted trope to argue for ignoring biology in the design of AI systems involves the comparison of planes to birds. After all, if we wish to create artificial machines that propel humans into the air, it now seems ridiculous to mimic biological ingredients like feathers and flapping wings in order to invent flying machines. However, a closer inspection of this idea reveals much more nuance. The general problem of flight involves solving two fundamental problems: (1) the generation of thrust in order to move forward, and (2) the generation of lift so that we do not fall out of the sky. Birds and planes do indeed solve the problem of thrust very differently; birds flap their wings and planes use jet engines. However, they solve the problem of lift in exactly the same way, by using a curved wing shape that generates higher air pressure below and lower air pressure above. Thus gliding birds and planes operate very similarly.
Indeed, we know that there are general physical laws of aerodynamics governing the motion of different shapes through air that yield computable methods for predicting generated forces like lift and thrust. Moreover, any solution to the problem of flight, no matter whether biological artificial, must obey the laws of aerodynamics. While there may be different viable solutions to the problem of flight under aerodynamic constraints, such solutions may share common properties (i.e., methods for generating lift), while simultaneously differing in other properties (i.e., methods for generating thrust). And finally, while on the subject of flight, there may yet be further engineering inspiration to be gleaned from the biological control laws implemented by the lowly fruit fly. Such flies are capable of rapid aerial maneuvers that far outstrip the capabilities of the world’s most sophisticated fighter jets.
More generally, in our study of the physical world, we are used to the notion that there exist principles or laws governing its behavior. For example, just as aerodynamics governs the motion of flying objects, general relativity governs the curvature of space and time, and quantum mechanics governs the evolution of the nanoworld. We believe that there may also exist general principles, or laws that govern how intelligent behavior can emerge from the cooperative activity of large interconnected networks of neurons. These laws could connect and unify the related disciplines of neuroscience, psychology, cognitive science and AI, and their elucidation would also require help from (as well as contribute to the development of) analytic and computational fields like physics, mathematics and statistics. Indeed the author of this post has used techniques from dynamical systems theory [25–28], statistical mechanics [29–33], Riemannian geometry [34], random matrix theory [13,35], and free probability theory [36] to obtain conceptual insights into the operation of biological and artificial networks alike. However, to elucidate general laws and design principles governing the emergence of intelligence from nonlinear distributed circuits will require much further work, including the development of new concepts, analysis methods, and engineering capabilities. Ultimately, just like the story of birds, planes and aerodynamics, there may be diverse solutions to the problem of creating intelligent machines, with some components shared between biological and artificial solutions, while others may differ. By seeking general laws of intelligence, we could more efficiently understand and traverse this solution space.
|
|
|
Creating a nurturing academic environment at Stanford through the human-centered AI initiative
Discovering potential laws of emergent intelligence applicable to both biological and artificial systems alike, and building new types of AI inspired by neuroscience and psychology, requires the concerted effort of many investigators working together: computer scientists and engineers in pursuit of better AI systems, neuroscientists, psychologists and cognitive scientists probing the properties of the brain and mind, and mathematicians, physicists, statisticians, and other theorists seeking to formalize our combined knowledge and discover general laws and principles. In essence we need to create a new community of researchers traversing these disparate disciplines and freely exchanging ideas, insulated from the pressure of generating short term research results prevalent under both government grant funding mechanisms and industry funding models. We also need to train a next generation of students and thought leaders who are cognizant of techniques and knowledge across many fields, putting together in some sense parts of the computer scientist, neurobiologist, psychologist, and mathematical theorist in the same brain.
|
|
|
These are the goals of one focus area of our newly formed human-centered AI initiative: generating new AI systems inspired by human intelligence. Our initiative will work closely with existing centers and institutes of excellence related to this mission, including the Stanford Artificial Intelligence Lab, the Wu Tsai Neuroscience Institute, the Center for Mind Brain Computation and Technology, the Stanford Institute for Theoretical Physics and the Information Systems Lab, among many others. Moreover we will draw from the expertise of leading Stanford academic departments and programs, including (but not limited to) computer science, electrical engineering, neuroscience, psychology, linguistics, philosophy, education, mathematics, physics, and statistics. By creating and nurturing a new community of interdisciplinary scholars, HAI at Stanford aims to catalyze the intertwined quest for understanding biological intelligence and creating artificial intelligence, which may well be one of the most exciting intellectual activities of this century and beyond.
Дата добавления: 2019-02-26; просмотров: 159; Мы поможем в написании вашей работы! |
Мы поможем в написании ваших работ!