Jamie Condliffe, Google’s AI Guru Says That Great Artificial Intelligence Must Build on Neuroscience, MIT Technology Review, July 20, 2017.
Currently, most AI systems are based on layers of mathematics that are only loosely inspired by the way the human brain works. But different types of machine learning, such as speech recognition or identifying objects in an image, require different mathematical structures, and the resulting algorithms are only able to perform very specific tasks.Building AI that can perform general tasks, rather than niche ones, is a long-held desire in the world of machine learning. But the truth is that expanding those specialized algorithms to something more versatile remains an incredibly difficult problem, in part because human traits like inquisitiveness, imagination, and memory don’t exist or are only in their infancy in the world of AI.In a paper published today in the journal Neuron, Hassabis and three coauthors argue that only by better understanding human intelligence can we hope to push the boundaries of what artificial intellects can achieve.First, they say, better understanding of how the brain works will allow us to create new structures and algorithms for electronic intelligence. Second, lessons learned from building and testing cutting-edge AIs could help us better define what intelligence really is.
Here's the Neuron article:
Demis Hassabis, Dharshan Kumaran, Christopher Summerfield, Matthew Botvinick, Neuroscience-Inspired Artificial Intelligence, Neuron, Volume 95, Issue 2, p245–258, 19 July 2017.Abstract: The fields of neuroscience and artificial intelligence (AI) have a long and intertwined history. In more recent times, however, communication and collaboration between the two fields has become less commonplace. In this article, we argue that better understanding biological brains could play a vital role in building intelligent machines. We survey historical interactions between the AI and neuroscience fields and emphasize current advances in AI that have been inspired by the study of neural computation in humans and other animals. We conclude by highlighting shared themes that may be key for advancing future research in both fields.
Conclusions: In this perspective, we have reviewed some of the many ways in which neuroscience has made fundamental contributions to advancing AI research, and argued for its increasingly important relevance. In strategizing for the future exchange between the two fields, it is important to appreciate that the past contributions of neuroscience to AI have rarely involved a simple transfer of full-fledged solutions that could be directly re-implemented in machines. Rather, neuroscience has typically been useful in a subtler way, stimulating algorithmic-level questions about facets of animal learning and intelligence of interest to AI researchers and providing initial leads toward relevant mechanisms. As such, our view is that leveraging insights gained from neuroscience research will expedite progress in AI research, and this will be most effective if AI researchers actively initiate collaborations with neuroscientists to highlight key questions that could be addressed by empirical work.
The successful transfer of insights gained from neuroscience to the development of AI algorithms is critically dependent on the interaction between researchers working in both these fields, with insights often developing through a continual handing back and forth of ideas between fields. In the future, we hope that greater collaboration between researchers in neuroscience and AI, and the identification of a common language between the two fields (Marblestone et al., 2016), will permit a virtuous circle whereby research is accelerated through shared theoretical insights and common empirical advances. We believe that the quest to develop AI will ultimately also lead to a better understanding of our own minds and thought processes. Distilling intelligence into an algorithmic construct and comparing it to the human brain might yield insights into some of the deepest and the most enduring mysteries of the mind, such as the nature of creativity, dreams, and perhaps one day, even consciousness.