Mind Matters News and Analysis on Natural and Artificial Intelligence

TagGary Smith

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Can a Big Data Program Be Society’s Crystal Ball?

Can a program with enough data on all of us predict our future?

Even fans admit that, if the program works, bad actors can use it just as easily as New Scientist’s virtuecrats.

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The Surprising Importance of a Stock’s Name

Business media have picked up on Gary Smith’s research on the importance of stock ticker names, like WOOF or BABY

The fabled Economist also chimed in, sniffing that Smith’s team’s result was “too farfetched for a sitcom”… but didn’t dispute it.

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Group of kids walk over high log in the forest finding balance with hands one after another view from bellow

If Computers Are Intelligent, Climbing a Tree Is Flying

That, says Edward Feser, is the take-home message from Gary Smith’s book, The AI Delusion

The book’s message is that “the real danger of artificial intelligence is that it will remain dumber than we are,” but we will think it is smarter.

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The Top Ten AI Hype Stories of 2018, Updated

You can segue to each in the podcast and read the accompanying Mind Matters News story, as well as key updates
2019 has seen some remarkable revelations about Google, DeepMind, Watson, Sophia, and other AI faves. Check them out here! Read More ›
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Why Was IBM Watson a Flop in Medicine?

Robert J. Marks and Gary S. Smith discuss how the AI couldn’t identify which information in the tsunami of medical literature actually MATTERED

Last year, the IBM Health Initiative laid off a number of people, seemingly due to market disillusionment with the product.

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Why an AI pioneer thinks Watson is a “fraud”

The famous Jeopardy contest in 2011 worked around the fact that Watson could not grasp the meaning of anything

Gary N. Smith explains that a computer’s inability to understand what “it” means in a sentence is because it doesn’t understand what any of the words in the sentence mean.

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Can AI Combat Misleading Medical Research?

No, because AI doesn’t address the “Texas Sharpshooter Fallacies” that produce the bad data

In this week’s podcast, Pomona College’s Gary Smith, author of The AI Delusion, talks with Walter Bradley Center director Robert J. Marks about why so many bad research papers are accepted in the science establishment and in media.

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The Texas Sharpshooter Fallacies

Gary Smith discusses his book, the AI Delusion, and how the pressure to publish or perish corrupts research

Bob Marks and Gary Smith offer a range of startling examples of how the pressure to publish drives a lack of rigor — and sometimes honesty — in analyzing and presenting experimental data. The result is a never ending parade of headlines in health and medicine that are unwarranted and often reversed or impossible to replicate. Shownotes 01:00 | Data Read More ›

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elephant mask beautiful young hipster woman in the city

AI Is No Match for Ambiguity

Many simple sentences confuse AI but not humans

Because computers lack common sense, they cannot interpret statements that assume a background of general knowledge, as the Winograd Schema challenges show.

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The US 2016 Election: Why Big Data Failed

Economics professor Gary Smith sheds light on the surprise result

Some of the stuff that made a difference, they couldn’t put in a computer. Some people say, if you can’t measure it, it doesn’t count but sometimes the things that count can’t be measured.

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camera record on crane in production on studio and light to stage for entertainment industry

1: IBM’s Watson Is NOT Our New Computer Overlord

AI help, not hype: It won at Jeopardy (with specially chosen “softball” questions) but is not the hoped-for aid to cancer specialists
One problem that has dogged Watson has nothing to do with AI or medicine. The journalism around the introduction of projects like Watson is long on the Gee Whiz! An Electronic Brain! It Won at Jeopardy! And it is short on systematic inquiry as to outcomes versus goals. Read More ›
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Data graph on monitor showing correlation

Study Shows Eating Raisins Causes Plantar Warts

Sure. Because, if you torture a Big Data enough, it will confess to anything
Enormous data sets compiled by Big Data methods have a higher probability of meaningless correlations than smaller ones compiled by traditional methods. More than ever, common sense is needed. And common sense only comes from programmers writing their own common sense into the software. Read More ›