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Mind Matters Reporting on Natural and Artificial Intelligence

TagNeural networks

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An AI Flash From the Past

Artificial intelligence has been in the news for a long time. Robert J. Marks airs one of his older interviews with Jim French on KIRO Radio to show the similarity to today’s reporting on artificial intelligence. Mind Matters News appreciates the permission of Jim French and KIRO Radio in Seattle to rebroadcast this interview. Show Notes 01:08 | Do computers Read More ›

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Bingecast: Is Cheese Consumption Causing Deaths from Tangled Sheets?

Those dealing with data must always remember “If you torture data long enough, it will confess to anything.” The answers that computers give must themselves be questioned. Robert J. Marks and Gary Smith address artificial intelligence, spurious correlations, and data research on Mind Matters. Show Notes 01:34 | Introduction to Gary Smith, the Fletcher Jones Professor of Economics at Pomona Read More ›

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The Unexpected and the Myth of Creative Computers – Part II

Robert J. Marks talks with Larry L. Linenschmidt of the Hill Country Institute about the misattribution of creativity and understanding to computers. This is Part 2 of 2 parts. Other Larry L. Linenschmidt podcasts from the Hill Country Institute are available at HillCountryInstitute.org. We appreciate the permission of the Hill Country Institute to rebroadcast this podcast on Mind Matters. Show Read More ›

Beautiful mandarin duck on the frozen lake in a park

Just a light frost—or AI winter?

It’s nice to be right once in a while—check out the evidence for yourself

About a year ago, I wrote that mounting AI hype would likely give way to yet another AI winter. Now, according to the panelists at “the world’s leading academic AI conference” the temperature is already falling.

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Happy African American guy in VR glasses

Gee-Whiz Tech and AI Reality – Part I

Robert J. Marks talks with Larry L. Linenschmidt of the Hill Country Institute about the nature and limitations of artificial intelligence from a computer science perspective. This is Part 1 of 2 parts. Other Larry L. Linenschmidt podcasts from the Hill Country Institute are available at HillCountryInstitute.org. We appreciate the permission of the Hill Country Institute to rebroadcast this podcast Read More ›

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We Built the Power Big Social Media Have Over Us

Click by click, and the machines learned the patterns. Now we aren’t sure who is in charge

We’re stuck, working for free, training the Web giants’ ML systems to reap benefits for them while enduring (assuming we notice) the downsides.

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Helmeted police officers photographed from behind during a protest

Can AI Predict and Prevent Political Unrest?

The 1996 Democratic Convention tried neural networks but discovered a hidden flaw

The police union’s 1996 objection to fingering specific officers as violence risks without a detailed explanation pinpoints a weakness of neural networks even to this day. The neural network is basically a black box.

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images from a computerized tomography of the brain.
images from a computerized tomography of the brain.

Can Buzzwords About “Neural Networks” Save Materialist Neuroscience?

No. Experiments that support an immaterial consciousness often involve split or massively damaged neural networks

The attribution of abstract thought to the material brain is philosophical and logical nonsense and has been repeatedly discredited by the best neuroscience over the past century.

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AI as the Artful Dodger

Watch what happens when I train a neural network on portraits of 56 famous scientists, starting the process with a right eye
New AI is much more sophisticated but the old and new AI share the property that the final result is nothing more than an interpolation of the training images used to train the AI. Read More ›
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Classified section of a newspaper

Part 2: Navigating the Machine Learning Landscape — Supervised Classifiers

Supervised classifiers can sort items like posts to a discussion group or medical images, using one of many algorithms developed for the purpose
In Part 1 of our series, we looked at machine learning, including supervised learning, unsupervised learning, and reinforcement learning. Now we’re going to dive a little deeper into how supervised learning works. Read More ›