Artificial intelligence performs better than human

Baku, January 25, AZERTAC

The artificial intelligence, “performed better than the average American”, said Northwestern University, where the research was done.

The Raven’s test is the best existing predictor of what psychologists call ‘fluid intelligence, or the general ability to think abstractly, reason, identify patterns, solve problems, and discern relationships,” said psychologist Dr Andrew Lovett, formerly at Northwestern, and now at the US Naval Research Laboratory.

With Northwestern engineer Professor Ken Forbus, Lovett has been attempting to model human thinking.

Their model is built on CogSketch, an artificial intelligence product of Forbus’ laboratory.

“The platform has the ability to solve visual problems and understand sketches in order to give immediate, interactive feedback. CogSketch also incorporates a computational model of analogy, based on psychology professor Dedre Gentner’s structure-mapping theory,” said the University.

On the results of the Raven’s test, Forbus said: “The model performs in the 75th percentile for American adults, making it better than average. Problems that are hard for people are also hard for the model, providing additional evidence that its operation is capturing some important properties of human cognition.”

The ability to use and understand sophisticated relational representations, according to the University, is a key to higher-order cognition. Relational representations connect entities and ideas such as ‘the clock is above the door’ or ‘pressure differences cause water to flow’. These types of comparisons are crucial for making and understanding analogies, which humans use to solve problems, weigh moral dilemmas, and describe the world around them.

“Most artificial intelligence research today concerning vision focuses on recognition, or labelling what is in a scene rather than reasoning about it,” said Forbus. “But recognition is only useful if it supports subsequent reasoning. Our research provides an important step toward understanding visual reasoning more broadly.”


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