Category Archives: Vision

George Hughes, Buridanus, and the Presumption of Intelligence

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My beloved teacher, the late George Edward Hughes from Victoria University of Wellington, was not only an expert on Modal Logic, he was also a scholar of Buridanus. He loved to talk about how he approached the works of Buridanus. He explained to me once that when he didn’t understand a passage in Buridanus, he reminded himself that Buridanus was a very intelligent man. He would then go back to the passage and reread it. Often, a coherent story emerged. As editors, reviewers, teachers, let’s adopt this way of reading dead authors when evaluating the papers of those who are still alive. Let’s stick to a Presumption of Intelligence. We often read those papers too quickly. We sometimes have to. When we just can’t figure out what a writer is up to, let’s go back and apply the Presumption of Intelligence. The story might fall into place.

The transformation of Amherst College: Everyone of us could make a difference

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The standard job ad for the standard department at a standard American university includes a standard phrase about Affirmative Action.  Amherst College (which is in Amherst, but is a distinct institution from UMass Amherst) is doing more. Their recent job ads for all fields tell prospective applicants that the student population at Amherst College has completely changed during the last ten years, and that any future faculty member will be expected to mentor and teach a broadly diverse student body. Is this a change that only rich private institutions can afford?

Carolyn ("Biddy") Martin, President of Amherst College

Carolyn (“Biddy”) Martin, President of Amherst College

Here is an excerpt from a current ad for two tenure-track positions in Chemistry.

“Amherst College is one of the most diverse liberal arts colleges in the country.  Forty-four percent of our students identify as domestic students of color, and another 10 percent are international, with non-U.S. citizenship; 17 percent are the first members of their families to attend college.  Fifty-one percent of our students are women.  Amherst is committed to providing financial aid that meets 100 percent of every student’s demonstrated need, and 58 percent of our students receive financial aid.  Our expectation is that the successful candidate will excel at teaching and mentoring students who are broadly diverse with regard to race, ethnicity, socioeconomic status, gender, nationality, sexual orientation, and religion.”

And here is an excerpt from their ad for two tenure-track positions in Computer Science:

“Within the last decade, Amherst College has profoundly transformed its student body in terms of socioeconomic status, ethnicity, race, and nationality. Today, nearly one-quarter of Amherst’s students are Pell Grant recipients; 43 percent of our students are domestic students of color; and 10 percent of our students are international students. We seek candidates who will excel at teaching and mentoring students who are broadly diverse with regard to race, ethnicity, socioeconomic status, gender, nationality, sexual orientation, and religion.”

 

Fooling Deep Neural Networks

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A video summary of the paper: Nguyen, Anh, Jason Yosinski, and Jeff Clune. “Deep Neural Networks are Easily Fooled: High Confidence Predictions for Unrecognizable Images.” arXiv preprint arXiv:1412.1897 (2014). The paper is available here.

From MIT Technology Review: “A technique called deep learning has enabled Google and other companies to make breakthroughs in getting computers to understand the content of photos. Now researchers at Cornell University and the University of Wyoming have shown how to make images that fool such software into seeing things that aren’t there. The researchers can create images that appear to a human as scrambled nonsense or simple geometric patterns, but are identified by the software as an everyday object such as a school bus. The trick images offer new insight into the differences between how real brains and the simple simulated neurons used in deep learning process images” (December 24, 2014).

Connections: Learning everything about anything. Also: Google researchers have developed software that can match complex images with simple sentences describing whole scenes, rather than just objects, e.g. “a group of young people playing a game of frisbee.”  

Learning everything about anything?

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Source: Kurzweil Accelerating Intelligence.

Credit: University of Washington

Credit: University of Washington

“Computer scientists from the University of Washington and the Allen Institute for Artificial Intelligence in Seattle have created an automated computer program that they claim teaches everything there is to know about any visual concept. Called Learning Everything about Anything (LEVAN), the program searches millions of books and images on the Web to learn all possible variations of a concept, then displays the results to users as a comprehensive, browsable list of images, helping them explore and understand topics quickly in great detail. You can try it here.”

Intelligent as it may be, LEVAN doesn’t seem to know the difference between a horse eye and an eye horse, between a horse shoe and a shoe horse, or between a horse shed and a shed horse.  

Connections: the discussion of the headedness of noun-noun compounds in my Radcliffe video on Mapping Possibilities. Also: Teon Brooks on representing compounds in the brain.