Mind Matters News and Analysis on Natural and Artificial Intelligence

CategoryMachine Learning

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Slaughterbots: How far is too far?

And how will we know if we have crossed a line?
A greater focus should be on restoring the foundations of our nation over building superweapons. And the key foundation is all human beings' right to life. Read More ›
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Karl Marx’s Eerie AI Prediction

He felt that capitalism would fall when machines replaced human labor
Because Marx held that the value of goods resided in the labor required to produce them, if goods were produced by automatons, without human labor, the economy would fall apart and capitalism would fail. Read More ›
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Imagining life after Google

Reviewers of George Gilder's new book weigh in
If we have simply taken the big software, hardware, and social media companies who dominate our lives for granted, the reactions from the business world to Life after Google: The Fall of Big Data and the Rise of the Blockchain Economy should give us a lot to think about. Read More ›
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Robogeddon!! Pause.

Wait. This just in: AI is NOT killing all our jobs
Jay Richards, author of The Human Advantage: The Future of American Work in an Age of Smart Machines,sees it as more of a retooling than a meltdown. But retooling does mean change, work, cost, and risk. Read More ›
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Ethics for an information society

Because machines can’t learn to solve their own ethical problems
AI (machine learning) was probably faster and cheaper but the whole point of the system was supposed to be justice which, whatever the explanation, proved too difficult to calculate… Read More ›
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AI tools for mass manipulation?

Machine learning can unleash a perfect storm of malice, experts warn
In 2017, a group of 26 AI researchers got together at Oxford and created a report which offers a number of examples of malicious technologies of the near future. Read More ›
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Sometimes the ‘bots turn out to be humans

That “lifelike” effect was easier to come by than some might think
Companies sometimes pretend to be using AI or machine learning when they are actually using human employees for various reasons. One reason is that they have promised potential investors more high tech than they can deliver. Sometimes, as we learned recently at The Guardian, it gets a bit sticky... Read More ›
Biometric facial recognition on smartphone. Unlock smartphone as it scans his face.

Will AI liberate or enslave developing countries?

Perhaps that depends on who gets there first with the technology
Karl D. Stephan: Zimbabwe, an African country well-known for its human-rights abuses, has received advanced Chinese AI technology from a startup company in exchange for letting the firm have access to the country’s facial-recognition database. So China is helping the government of Zimbabwe to keep tabs on its citizens as well. Read More ›
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Who creates information in a market?

Do exchange-traded funds (ETFs)' algorithms make personally gathering information obsolete?
Algorithmic strategies can only be as good as the information that goes into them.  Ignoring how the information is created causes us to misunderstand the dynamics of value creation.  Algorithms can leverage information, they can’t create it. Read More ›
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Why machines can’t think as we do

As philosopher Michael Polanyi noted, much that we know is hard to codify or automate
Human life is full of these challenges. Some knowledge simply cannot be conveyed—or understood or accepted—in a propositional form. For example, a nurse counselor may see clearly that her elderly post-operative patient would thrive better in a retirement home. But she cannot just tell him so. Read More ›
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Why can’t machines learn simple tasks?

They can learn to play chess more easily than to walk
If specifically human intelligence is related to consciousness, the robotics engineers might best leave consciousness out of their goals for their products and focus on more tangible ones. Read More ›
Bitcoin gold coin. Cryptocurrency concept.

A wallet you can’t feel?

Will Bitcoin change the rituals around money?
It’s tempting to assume that cryptocurrencies like Bitcoin will succeed because social media did. But digital doesn’t mean magic. Cryptocurrencies will work if the needs met are more significant to most people than the problems created are. Read More ›
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Better medicine through machine learning?

Data can be a dump or a gold mine
The biggest problem today isn’t the sheer mass of data so much as the difficulty of determining what it is worth. The answer lies, unfortunately, in the undone studies and the unreported events. Machine learning will be a much greater help when those problems are addressed. Read More ›
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AI Is Not (Yet) an Intelligent Cause

So-called “white hat” hackers who test the security of AI have found it surprisingly easy to fool.
Hutson describes one test last year where a computer scientist at UC Berkeley subtly altered a stop sign with stickers. It fooled an autonomous vehicle’s image recognition system into “thinking” it was a 45 mph speed limit sign. Humans could immediately recognize the stop sign, but the car did not. Autonomous car makers wonder, could hackers turn them into terror weapons? Read More ›
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The driverless car: A bubble soon to burst?

Author says journalists too gullible about high tech
Why do we constantly hear that driverless, autonomous vehicles will soon be sharing the road with us? Wolmar blames “gullible journalists who fail to look beyond the extravagant claims of the press releases pouring out of tech companies and auto manufacturers, hailing the imminence of major developments that never seem to materialise.” Read More ›
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GIGO alert: AI can be racist and sexist, researchers complain

Can the bias problem be addressed? Yes, but usually after someone gets upset about a specific instance.

From James Zou and Londa Ziebinger at Nature: When Google Translate converts news articles written in Spanish into English, phrases referring to women often become ‘he said’ or ‘he wrote’. Software designed to warn people using Nikon cameras when the person they are photographing seems to be blinking tends to interpret Asians as always blinking. Word embedding, a popular algorithm used to process and analyse large amounts of natural-language data, characterizes European American names as pleasant and African American ones as unpleasant. Now where, we wonder, would a mathematical formula have learned that? Maybe it was listening to the wrong instructions back when it was just a tiny bit? Seriously, machine learning, we are told, depends on  absorbing datasets of Read More ›