|Photo: Will Knight|
Like any proud father, Gary Marcus is only too happy to talk about the latest achievements of his two-year-old son. More unusually, he believes that the way his toddler learns and reasons may hold the key to making machines much more intelligent.
Sitting in the boardroom of a bustling Manhattan startup incubator, Marcus, a 45-year-old professor of psychology at New York University and the founder of a new company called Geometric Intelligence, describes an example of his boy’s ingenuity. From the backseat of the car, his son had seen a sign showing the number 11, and because he knew that other double-digit numbers had names like “thirty-three” and “seventy-seven,” he asked his father if the number on the sign was “onety-one.”
“He had inferred that there is a rule about how you put your numbers together,” Marcus explains with a smile. “Now, he had overgeneralized it, and he made a mistake, but it was a very sophisticated mistake.”
|Photo: Gary Marcus|
Marcus has a very different perspective from many of the computer scientists and mathematicians now at the forefront of artificial intelligence. He has spent decades studying the way the human mind works and how children learn new skills such as language and musicality. This has led him to believe that if researchers want to create truly sophisticated artificial intelligence—something that readily learns about the world—they must take cues from the way toddlers pick up new concepts and generalize. And that’s one of the big inspirations for his new company, which he’s running while on a year’s leave from NYU. With its radical approach to machine learning, Geometric Intelligence aims to create algorithms for use in an AI that can learn in new and better ways...
Marcus, who was born in Baltimore, became fascinated by the mind in high school after reading The Mind’s I (PDF), a collection of essays on consciousness edited by the cognitive scientist Douglas Hofstadter and the philosopher Daniel Dennett, as well as Hofstadter’s metaphorical book on minds and machines, Gödel, Escher, Bach. Around the same time, he wrote a computer program designed to translate Latin into English. The difficulty of the task made him realize that re-creating intelligence in machines would surely require a much greater understanding of the phenomena at work inside the human mind.
Marcus’s Latin-to-English program wasn’t particularly practical, but it helped convince Hampshire College to let him embark on an undergraduate degree a couple of years early. Students at the small liberal-arts school in Amherst, Massachusetts, are encouraged to design their own degree programs. Marcus devoted himself to studying the puzzle of human cognition.
The mid-1980s were an interesting time for the field of AI. It was becoming split between those who sought to produce intelligent machines by copying the basic biology of the brain and those who aimed to mimic higher cognitive functions using conventional computers and software. Early work in AI was based on the latter approach, using programming languages built to handle logic and symbolic representation. Birds are the classic example. The fact that birds can fly could be encoded as one piece of knowledge. Then, if a computer were told that a starling was a bird, it would deduce that starlings must be able to fly. Several big projects were launched with the aim of encoding human knowledge in vast databases, in hopes that some sort of complex intelligence might eventually emerge...
|The Algebraic Mind: Integrating Connectionism and |
Cognitive Science (Learning, Development,
and Conceptual Change)
Marcus’s work with children, in fact, led him to an important conclusion. In a 2001 book called The Algebraic Mind, he argued that the developing human mind learns both from examples and by generating rules from what it has learned. In other words, the brain uses something like a deep-learning system for certain tasks, but it also stores and manipulates rules about how the world works so that it can draw useful conclusions from just a few experiences.
Source: MIT Technology Review