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[19 Sep 2015 | No Comment | 37 views]

Computers have never been good at answering the type of verbal reasoning questions found in IQ tests. Now a deep learning machine unveiled in China is changing that.Verbal reasoning

Just over 100 years ago, the German psychologist William Stern introduced the intelligence quotient test as a way of evaluating human intelligence. Since then, IQ tests have become a standard feature of modern life and are used to determine children’s suitability for schools and adults’ ability to perform jobs.

These tests usually contain three categories of questions: logic questions such as patterns in sequences of images, mathematical questions such as finding patterns in sequences of numbers and verbal reasoning questions, which are based around analogies, classifications, as well as synonyms and antonyms.

It is this last category that has interested Huazheng Wang and pals at the University of Science and Technology of China and Bin Gao and buddies at Microsoft Research in Beijing. Computers have never been good at these. Pose a verbal reasoning question to a natural language processing machine and its performance will be poor, much worse than the average human ability.Today, that changes thanks to Huazheng and pals who have built a deep learning machine that outperforms the average human ability to answer verbal reasoning questions for the first time.


Human performance on these tests tends to correlate with educational background. So people with a high school education tend to do least well, while those with a bachelor’s degree do better and those with a doctorate perform best. “Our model can reach the intelligence level between the people with the bachelor degrees and those with the master degrees,” say Huazheng and co.

That’s fascinating work that reveals the potential of deep learning techniques. Huazheng and co are clearly optimistic about future developments. “With appropriate uses of the deep learning technologies, we could be a further step closer to the true human intelligence.”

Deep learning techniques are currently sweeping through computer science like wildfire and the revolution they are creating is still in its early stages. There’s no telling where this revolution will take us but one thing is for sure: William Stern would be amazed.

Ref: arxiv.org/abs/1505.07909 : Solving Verbal Comprehension Questions in IQ Test by Knowledge-Powered Word Embedding


General News »

[18 May 2013 | No Comment | 246 views]

More than 150 researchers and 75 scientific groups issued a declaration on Thursday against the widespread use of journal “impact factors,” blaming the practice for dangerous distortions in financing and hiring in science.

The impact factor “has a number of well-documented deficiencies as a tool for research assessment,” the scientists said in the letter, which had been in preparation since a conference led by publishers and grant-writing agencies last year in San Francisco.

Those deficiencies include the ability of publishers to manipulate the calculations, and the way the metrics encourage university hiring and promotion decisions, as well as grant agencies’ award distributions, that can lack an in-depth understanding of scientific work.

“There is certainly a need for fair and objective methods to evaluate science and scientists, no doubt about that,” said Stefano Bertuzzi, executive director of the American Society for Cell Biology, which organized the campaign. “But that need does not change the fact that the journal impact factor does not measure what it’s supposed to measure when it is applied to evaluations of scientists’ work.”

For all those who signed the letter, however, the effect may be overshadowed by those who did not, including some of the world’s leading publishers and representatives of leading research universities. They include the Nature Publishing Group and Elsevier, two of the most dominant scientific publishers, and the Association of American Universities, which represents top-ranked research institutions.

The editor in chief of Nature, Philip Campbell, said he and other editors of the company’s journals have regularly published editorials critical of excesses in the use of journal impact factors, especially in rating researchers.

“But the draft statement contained many specific elements, some of which were too sweeping for me or my colleagues to sign up to,” said Mr. Campbell. Among the 18 recommendations in the letter, journals were asked to “greatly reduce emphasis on the journal impact factor as a promotional tool.”

A spokesman for the AAU, Barry Toiv, said he had no comment on the matter.

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Interesting »

[16 May 2013 | No Comment | 123 views]

Professor Gui-Qiang G. Chen presents in his inaugural lecture several examples to illustrate the origins, developments, and roles of partial differential equations in our changing world.

While calculus is a mathematical theory concerned with change, differential equations are the mathematician’s foremost aid for describing change. In the simplest case, a process depends on one variable alone, for example time. More complex phenomena depend on several variables – perhaps time and, in addition, one, two or three space variables. Such processes require the use of partial differential equations. The behaviour of every material object in nature, with timescales ranging from picoseconds to millennia and length scales ranging from sub-atomic to astronomical, can be modeled by nonlinear partial differential equations or by equations with similar features. The roles of partial differential equations within mathematics and in the other sciences become increasingly significant. The mathematical theory of partial differential equations has a long history. In the recent decades, the subject has experienced a vigorous growth, and research is marching on at a brisk pace.

University of Oxford Podcasts – Audio and Video Lectures


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