Marcus joined Pinker’s lab at MIT at 19, and Pinker remembers him as a precocious student. “I assigned to him a project analyzing a simple yes-no hypothesis on a small data set of the recorded speech from three children,” he said in an e-mail. “A few days later he had performed an exhaustive analysis on the speech of 25 children which tested a half-dozen hypotheses and became the basis for a major research monograph.”
As a graduate student, Marcus gathered further evidence to support Pinker’s ideas about learning and added insights of his own. He pioneered the computerized analysis of large quantities of cognitive research data, studying thousands of recordings of children’s speech to find instances where they made errors like “breaked” and “goed” instead of “broke” and “went.” This seemed to confirm that children grasp the rules of grammar and then apply them to new words, while learning the exceptions to these rules by rote.
On the basis of this research, Marcus began questioning the connectionist belief that intelligence would essentially emerge from larger neural networks, and he started focusing on the limitations and quirks of deep learning. A deep-learning system could be trained to recognize particular species of birds in images or video clips, and to tell the difference between ones that can fly and ones that can’t. But it would need to see millions of sample images in order to do this, and it wouldn’t know anything about why a bird isn’t able to fly.
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.
This doesn’t exactly mean that Geometric Intelligence is trying to mimic the way things happen in the brain. “In an ideal world, we would know how kids do it,” Marcus says. “We would know what brain circuits are involved, the computations they are doing. But the neuroscience remains a mystery.” Rather, he hints that the company is using a grab bag of techniques, including ones “compatible” with deep learning, to try to re-create human learning.
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As a graduate student, Marcus gathered further evidence to support Pinker’s ideas about learning and added insights of his own. He pioneered the computerized analysis of large quantities of cognitive research data, studying thousands of recordings of children’s speech to find instances where they made errors like “breaked” and “goed” instead of “broke” and “went.” This seemed to confirm that children grasp the rules of grammar and then apply them to new words, while learning the exceptions to these rules by rote.
On the basis of this research, Marcus began questioning the connectionist belief that intelligence would essentially emerge from larger neural networks, and he started focusing on the limitations and quirks of deep learning. A deep-learning system could be trained to recognize particular species of birds in images or video clips, and to tell the difference between ones that can fly and ones that can’t. But it would need to see millions of sample images in order to do this, and it wouldn’t know anything about why a bird isn’t able to fly.
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.
This doesn’t exactly mean that Geometric Intelligence is trying to mimic the way things happen in the brain. “In an ideal world, we would know how kids do it,” Marcus says. “We would know what brain circuits are involved, the computations they are doing. But the neuroscience remains a mystery.” Rather, he hints that the company is using a grab bag of techniques, including ones “compatible” with deep learning, to try to re-create human learning.
- More Here
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