6
0
Abstraction Learning
attributed to: Fei Deng, Jinsheng Ren, Feng Chen
There has been a gap between artificial intelligence and human intelligence.
In this paper, we identify three key elements forming human intelligence, and
suggest that abstraction learning combines these elements and is thus a way to
bridge the gap. Prior researches in artificial intelligence either specify
abstraction by human experts, or take abstraction as a qualitative explanation
for the model. This paper aims to learn abstraction directly. We tackle three
main challenges: representation, objective function, and learning algorithm.
Specifically, we propose a partition structure that contains pre-allocated
abstraction neurons; we formulate abstraction learning as a constrained
optimization problem, which integrates abstraction properties; we develop a
network evolution algorithm to solve this problem. This complete framework is
named ONE (Optimization via Network Evolution). In our experiments on MNIST,
ONE shows elementary human-like intelligence, including low energy consumption,
knowledge sharing, and lifelong learning.
0
Vulnerabilities & Strengths