Mind Matters News and Analysis on Natural and Artificial Intelligence

CategoryMachine Learning

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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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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 ›

Composite image of image of data
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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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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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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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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Colorful toothpicks or pick-up sticks

How Can We Measure Meaningful Information?

Neither randomness nor order alone creates meaning. So how can we identify communications?
Dropping a handful of toothpicks on the table seems to produce a different sort of pattern than spelling out a word with toothpicks. Surprisingly, this intuitive distinction is harder to make in math and the sciences. Algorithmic specified complexity (ASC) enables us to distinguish them. Read More ›
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6: AI Can Even Exploit Loopholes in the Code!

AI adopts a solution in an allowed set, maybe not the one you expected
One example the programmers offered of this type of gaming the system was a walking digital robot that moved more quickly by somersaulting than by using a normal walking gait. Read More ›
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7: Computers can develop creative solutions on their own!

AI help, not hype, with Robert J. Marks: Programmers may be surprised by which solution, from a range they built in, comes out on top
Sometimes the results are unexpected and even surprising. But they follow directly from the program doing exactly what the programmer programmed it to do. It’s all program, no creativity. Read More ›
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A host of blue squid

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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Google Search: Its Secret of Success Revealed

The secret is not the Big Data pile. No, Google found a way to harness YOUR wants and needs

Google is one of the most widely misunderstood success stories of our time. Many of us equate Google with “Big Data,” that is, amassing huge quantities of data and then finding useful statistical patterns. But is that how it succeeded? In Life after Google: The Fall of Big Data and the Rise of the Blockchain Economy, George Gilder criticizes Google primarily on two fronts: First, it is a “walled garden,” a great platform, but inherently isolated and closed. That is a point worth exploring, but not the focus here. The second point, the one I want to touch on, is that Big Data’s day has come and gone. Because Google is a Big Data company, its brightest days are behind it. Read More ›

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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 ›
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No, Twitter Is Not the New Awful

It’s the Old Awful back for more. It’s the Town Without Pity we all tried to get away from
We need to decide: Is Twitter the telephone company (a communications platform), the newspaper (a publisher), or interconnected private gossip klatsches where anyone can say whatever they want, whatever ensues? Read More ›
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If Computers Thought Like Fruit Flies, They Could Do More

But even with more sophisticated buzz, there remain "non-computable" things that a computer cannot be programmed to think

Recently, researchers discovered that fruit flies use a filter similar to a computer algorithm to assess the odors that help them find fruit, only the flies’ tools are more sophisticated: When a fly smells an odor, the fly needs to quickly figure out if it has smelled the odor before, to determine if the odor is new and something it should pay attention to,” says Saket Navlakha, an assistant professor in Salk’s Integrative Biology Laboratory. “In computer science, this is an important task called novelty detection. Computers use a Bloom filter for that, Navlakha, an integrative biologist, explains: When a search engine such as Google crawls the Web, it needs to know whether a website it comes across has previously Read More ›

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Can Big Data Help Make Your Book a Best Seller?

It’s more likely to help you picture your odds more clearly and clarify your goals
What does Barabási’s Big Data tell us that we couldn’t just guess? Well, for one thing, that there is a “universal sales curve” which means that a book’s only chance of making the list is shortly after publication. Read More ›
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Potted plants from above

That Plant Is Not a Cyborg

Or a robot. The MIT researcher's underlying idea is a good one but let’s not “plant” mistaken ideas
If plants could move around freely, they would move into the most beneficial lighting arrangement. They compensate for their rootedness by growing in the optimum direction and constantly repositioning their leaves. An MIT researcher has helped out a plant by fitting it with electronic sensors attached to robotic wheels. 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 ›