Mind Matters News and Analysis on Natural and Artificial Intelligence

TagDeep Learning

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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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Lazy Engineers Treat AI as Magic!

When software engineers mostly use shared code, they save time but risk losing understanding

Building from scratch is different. Knowing when to use a tool and why and knowing the limitations of each tool separates the craftsperson from the novice.

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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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Gary Smith: The AI Delusion

“When I Nod My Head, Hit It!” And Other Commands that Confuse AI.

Pablo Picasso said “Computers Are Useless. They Can Only Give You Answers.”  Picasso didn’t go far enough. The answers that computers give must themselves be questioned. This is especially true of AI. Questioning AI is the topic today on Mind Matters. Show Notes 01:27 | Introduction to Gary Smith 02:40 | The AI Delusion 04:50 | Stocks and Data 07:00 Read More ›

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Robotic Accounting Department

The future of number crunching

Technology has almost entirely replaced the travel agent as well as many brick and mortar stores. But high tech tools like bots are replacing employment in, of all places, accounting. Show Notes 01:30 | Introduction; Jeremiah Marks 02:00 | Forbes and AI accounting prime time 03:00 | What is Mint? 04:48 | Natural language recognition in accounting 06:15 | What Read More ›

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Artificial Intelligence Is Actually Superficial Intelligence

The confusing ways the word “intelligence” is used belie the differences between human intelligence and machine sophistication

Words often have more meaning than we hear at first. Consider colors. We associate green with verdant, healthy life and red with prohibition and danger. But these inferences are not embedded in the basic meaning of “red” or “green.” They are cultural accretions we attach to words that enable the richness of language. That, by the way, is one reason why legal documents and technical papers are so difficult to read. The terms used are stripped clean of such baggage, requiring additional words to fill the gaps. The word “intelligent” is like that. Saying that a computer, or a program, is intelligent can lead us down a rabbit hole of extra meaning. An honest researcher merely means the computer has Read More ›

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Does AI Art Spell the End of the Artist’s Way of Life?

An AI-produced painting sold at auction for $432,500. But is it a trend or just a novelty?

Rather than announce that human artists are now doomed, software engineer Ben Dixon interviewed a number of them and came away with a rather different picture, that “AI-generated art will improve, but artistic creativity will remain a human discipline.”

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Researchers: Deep Learning Vision Is Very Different from Human Vision

Mistaking a teapot shape for a golf ball, due to surface features, is one striking example from a recent open-access paper
The networks did “a poor job of identifying such items as a butterfly, an airplane and a banana,” according to the researchers. The explanation they propose is that “Humans see the entire object, while the artificial intelligence networks identify fragments of the object.” Read More ›
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Can the Air Force Create Thinking Planes?

Smart drones? They are working on general artificial intelligence (GAI)
Eric Holloway: The likely way this will turn out is they'll realize human-in-the-loop is unavoidable for any useful system, so it'll spin off into something like the existing field of human computation. Read More ›
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The “superintelligent AI myth”

The problem that even the skeptical Deep Learning researcher left out
I largely agree with what François Chollet said last year as to why there will be no explosion of general artificial intelligence. But when he challenged the fear of an AI-driven “intelligence explosion,” he, perhaps unwittingly, said more than he meant. Read More ›
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Software pioneer says general superhuman artificial intelligence is very unlikely

The concept, he argues, shows a lack of understanding of the nature of intelligence
François Chollet, author of Keras, for the Python deep learning language, cites the No Free Lunch theorem as one of the reasons. Read More ›
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Deep Learning won’t solve AI

AlphaGo pioneer: We need “another dozen or half-a-dozen breakthroughs”
Hassabis: "AlphaGo doesn't understand language but we would like them to build up to this symbolic level of reasoning — maths, language, and logic. Read More ›