Verbal IQ of a Four-Year Old Achieved by an AI System

Abstract One view of common-sense reasoning ability is that it is the ability to perform those tasks with verbal inputs and outputs that have traditionally been difficult for computer systems, but are easy for fairly young children. We administered the verbal part of the Wechsler Preschool and Primary Scale of Intelligence (WPPSI-III, Third Edition) to the ConceptNet 4 system. The IQ test's questions (e.g., "Why do we shake hands?" or "What do apples and bananas have in common") were translated into ConceptNet 4 inputs using a combination of the simple natural language processing tools that come with ConceptNet together with short Python programs that we wrote. The question-answering primarily used the part of the ConceptNet system that represents the knowledge as a matrix based on spectral methods (AnalogySpace). We found that the system has a Verbal IQ that is average for a four-year-old child, but below average for 5, 6, and 7 yearolds. Large variations from subtest to subtest indicate potential areas of improvement. In particular, results were strongest for the Vocabulary and Similarities subtests, intermediate for the Information subtest, and lowest for the Comprehension and Word Reasoning subtests. Comprehension is the subtest most strongly associated with common sense. Children's verbal IQ tests offer a new, objective, third-party metric for the evaluation and comparison of common-sense AI systems

Efficient Learning of Linear Separators under Bounded Noise

We study the learnability of linear separators in $\Re^d$ in the presence of bounded (a.k.a Massart) noise. This is a realistic generalization of the random classification noise model, where the adversary can flip each example $x$ with probability $\eta(x) \leq \eta$. We provide the first polynomial time algorithm that can learn linear separators to arbitrarily small excess error in this noise model under the uniform distribution over the unit ball in $\Re^d$, for some constant value of $\eta$. While widely studied in the statistical learning theory community in the context of getting faster convergence rates, computationally efficient algorithms in this model had remained elusive. Our work provides the first evidence that one can indeed design algorithms achieving arbitrarily small excess error in polynomial time under this realistic noise model and thus opens up a new and exciting line of research. We additionally provide lower bounds showing that popular algorithms such as hinge loss minimization and averaging cannot lead to arbitrarily small excess error under Massart noise, even under the uniform distribution. Our work instead, makes use of a margin based technique developed in the context of active learning. As a result, our algorithm is also an active learning algorithm with label complexity that is only a logarithmic the desired excess error $\epsilon$

Deploying rural community wireless mesh networks

Inadequate Internet access is widening the digital divide between town and countryside, degrading both social communication and business advancements in rural areas. Wireless mesh networking can provide an excellent framework for delivering broadband services to such areas. With this in mind, Lancaster University deployed a WMN in the rural village of Wray over a three-year period, providing the community with Internet service that exceeds many urban offerings. The project gave researchers a real-world testbed for exploring the technical and social issues entailed in deploying WMNs in the heart of a small community