Mind Matters News and Analysis on Natural and Artificial Intelligence

TagMachine Learning

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AI Is Not a Simple Fix for Plagiarism

The internet speeded up a perennial problem without changing it

If imitation is the sincerest form of flattery, plagiarism amounts to passing ourselves off as experts without tears. It’s not realistic to expect software to detect all of the subtleties.

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The Numbers Don’t Speak for Themselves

The patterns uncovered by machine learning may reflect a larger reality or just a bias in gathering data

Because Machine Learning is opaque—even experts cannot clearly explain how a system arrived at a conclusion—we treat it as magic. Therefore, we should mistrust the systems until proven innocent (and correct).

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Composite image of image of data

Part 1: Navigating the Machine Learning Landscape

To choose the right type of machine learning model for your project, you need to answer a few specific questions
Most machine learning systems fall into three main categories—supervised learning, unsupervised learning, and reinforcement learning. The choice of system depends first on which category of machine learning best addresses your situation. Read More ›
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Will the Free Market Help or Hurt Us in an AI-Empowered World?

We may need new institutions, such as insurance against job obsolescence
If humans are free to experiment with new institutions, I believe we will find an excellent solution. However, there is a great danger that those who benefit from the status quo will use their influence to prevent the adoption of new institutions. 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.” Read More ›
Amoebae move and feed by using pseudopods, which are bulges of cytoplasm formed by the coordinated action of actin microfilaments pushing out the plasma membrane that surrounds the cell.
Amoebae move and feed by using pseudopods, which are bulges of cytoplasm formed by the coordinated action of actin microfilaments pushing out the plasma membrane that surrounds the cell.

Is an Amoeba Smarter Than Your Computer?

Hype aside, the microbe’s math skills ace the Traveling Salesman problem and may help with cybersecurity
When we hear hype about machines that will soon out-think people, we might put it in perspective by recalling that we still struggle to build a machine that can out-think amoebas looking for crumbs. Read More ›
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It’s 2019: Begin the AI Hype Cycle Again!

Media seemingly can’t help portraying today’s high-tech world as a remake of I, Robot (2004), starring you and me.
I have a problem with the possible outcomes when people who don’t know the difference between technology fact and fiction make important decisions based on information from journalists who write as if every computer is a potential personality like HAL from Space Odyssey 2001. Read More ›
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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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AI: Think About Ethics Before Trouble Arises

A machine learning specialist reflects on Micah 6:8 as a guide to developing ethics for the rapidly growing profession
To love mercy sometimes means to give up efficiency. It could mean losing a few points of model accuracy by refusing to take into account features that invade privacy or are proxies for race, leading to discriminatory model behavior. But that’s OK. The merciful are willing to give up some of their rights and advantages so they can help others.   Read More ›
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Can an Algorithm Be Racist?

No, the machine has no opinion. It processes vast tracts of data. And, as a result, the troubling hidden roots of some data are exposed
It’s tempting to assume that a villain lurks behind such a scene when the exact opposite is the problem: A system dominated by machines is all calculations, not thoughts, intentions, or choices. If the input is wrong, so is the output. Read More ›
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2: AI Can Write Novels and Screenplays Better than the Pros!

AI help, not hype: Software can automatically generate word sequences based on material fed in from existing scripts. But with what result?

“AI rites reel gud!” Seriously, the idea is not new. Back in the 1940s, George Orwell (1903–1950) thought that a machine could write popular novels so long as no creative thinking was involved. Thus, in his 1984 police state world, one of the central characters has a job minding a machine that mass produces them. In the 1960s, some film experiments were done along these lines, using Westerns (cowboy stories). At the time, there were masses of formula-based film material to work with in this popular genre. But what does the product look and sound like? In 2016, Ars Technica was proud to sponsor “the first AI-written sci-fi script:” As explained in The Guardian, a recurrent neural network “was fed the Read More ›

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4: Making AI Look More Human Makes It More Human-like!

AI help, not hype, with Robert J. Marks: Technicians can do a lot these days with automated lip-syncs and smiles but what’s behind them?
This summer, some were simply agog over “Sophia, the First Robot Citizen” (“unsettling as it is awe-inspiring”) Read More ›
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9: Will That Army Robot Squid Ever Be “Self-Aware”?

AI help, not hype: What would it take for a robot to be self-aware?
The thrill of fear invites the reader to accept a metaphorical claim as a literal fact. Read More ›
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Revelers await the New Year's countdown amidst lights

This Year’s Top Ten AI Exaggerations, Hyperbole, and Failures: Part I

To end the year, here is our Top Ten Exaggerations, Hyperbole, and Failures list of hype news in artificial intelligence.

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Be Choosy About What You Automate!

Having automated many processes, I can assure you that that is the First Rule of Automation
The worst trap that people who are pursuing automation fall into is the desire to automate everything. That’s usually a road to disaster. Automation is supposed to save time and money, but it can wind up costing you both if you don't carefully consider what you automate. Read More ›
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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 ›
Little boy playing with robot toy at home.

Consumers Were Not Buying Robots as Friends This Year

The market for drudgery busters remains strong. For dogs and pals, not so much
Some consumer robotics will surely find a place under the Christmas tree. The robotic vacuum cleaner market is healthy and expected to grow, in a world where the demand for vacuum cleaners is growing anyway, doubtless due to more urban lifestyles. Read More ›
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Quantity vs Quality: Can AI Help Scientists Produce Better Papers?

What happens when scientists simply can't read all their peers' papers and still find time for original research?
Quantity is definitely a solved problem. STM, the “voice of scholarly publishing” estimated in 2015 that roughly 2.5 million science papers are published each year. Some are, admittedly, in predatory or fake journals. But over 2800 journals are assumed to be genuine. Read More ›
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AI, it turns out, can solve any problem

As long as we are not too persnickety about what we consider a solution
The machine that knows what we mean instead of what we say is still in the concept stage. Meanwhile, Deep Mind researcher Victoria Krakovna keeps a running list of ways that generate "a solution that literally satisfies the stated objective but fails to solve the problem according to the human designer’s intent.” 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 ›