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Infants Can See Image Differences That Adults Cannot, Study Finds

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Babies may be able to see image details that are invisible or imperceptible to adults. According to a recent study [PDF] from Japanese scientists Jiale Yang, So Kanazawa, Masami K. Yamaguchi, and Isamu Motoyoshi, three- and four-month-old infants may view certain images differently because they lack perceptual constancy. That means they can see small image differences that are invisible to adults because of changes in lighting conditions.

For example, when adults see the same objects in different lighting, their brains automatically adjust to those lighting conditions. If your friend steps in front of a blue spotlight, they might appear blue, but you still recognize them. That’s perceptual constancy in action: Your brain recognizes that, though your friend is bathed in blue light, they aren’t suddenly a totally different blue person. Young babies who haven’t yet developed perceptual constancy, meanwhile, are able to see subtle images differences that adults ignore, but may be unable to recognize the same objects in different light.

In the study, 42 babies between three and eight months old looked at images rendered from 3D objects like snails and teapots. Since the babies were pre-verbal, scientists tracked their eye movements to determine how they interacted with the images. According to Scientific American, previous studies have shown that infants spend more time looking at new objects than those they’ve seen before, so the scientists in the study measured infant recognition of different objects by recording how long they looked at each image. They found that infants up to four months old recognized image differences caused by changes in illumination that weren’t visible to adult viewers. At five months old, however, the infants lost that ability, and by seven or eight months, began to develop perceptual constancy.

In the above image, for example, adults generally see snails A and B as the most similar because of their glossy finish. In reality, B and C are most similar with regards to pixel intensity—a characteristic that three- and four-month-old infants recognized.

According to the scientists, “These findings support the notion that acquiring perceptual constancy leads to a loss of sensitivity to variant information, which is negligible for constant surface material perception.”

That is, as we develop, we lose the ability to see certain information, but that loss makes it easier for us to understand the world around us. As Scientific American explains, “The loss of sensitivity to variant information that we all experienced as babies created an unbreachable gap between us and the physical world. At the same time, it served to tune our perception to our environment, allowing us to navigate it efficiently and successfully.”

[h/t: Scientific American]

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Man Buys Two Metric Tons of LEGO Bricks; Sorts Them Via Machine Learning
May 21, 2017
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iStock // Ekaterina Minaeva

Jacques Mattheij made a small, but awesome, mistake. He went on eBay one evening and bid on a bunch of bulk LEGO brick auctions, then went to sleep. Upon waking, he discovered that he was the high bidder on many, and was now the proud owner of two tons of LEGO bricks. (This is about 4400 pounds.) He wrote, "[L]esson 1: if you win almost all bids you are bidding too high."

Mattheij had noticed that bulk, unsorted bricks sell for something like €10/kilogram, whereas sets are roughly €40/kg and rare parts go for up to €100/kg. Much of the value of the bricks is in their sorting. If he could reduce the entropy of these bins of unsorted bricks, he could make a tidy profit. While many people do this work by hand, the problem is enormous—just the kind of challenge for a computer. Mattheij writes:

There are 38000+ shapes and there are 100+ possible shades of color (you can roughly tell how old someone is by asking them what lego colors they remember from their youth).

In the following months, Mattheij built a proof-of-concept sorting system using, of course, LEGO. He broke the problem down into a series of sub-problems (including "feeding LEGO reliably from a hopper is surprisingly hard," one of those facts of nature that will stymie even the best system design). After tinkering with the prototype at length, he expanded the system to a surprisingly complex system of conveyer belts (powered by a home treadmill), various pieces of cabinetry, and "copious quantities of crazy glue."

Here's a video showing the current system running at low speed:

The key part of the system was running the bricks past a camera paired with a computer running a neural net-based image classifier. That allows the computer (when sufficiently trained on brick images) to recognize bricks and thus categorize them by color, shape, or other parameters. Remember that as bricks pass by, they can be in any orientation, can be dirty, can even be stuck to other pieces. So having a flexible software system is key to recognizing—in a fraction of a second—what a given brick is, in order to sort it out. When a match is found, a jet of compressed air pops the piece off the conveyer belt and into a waiting bin.

After much experimentation, Mattheij rewrote the software (several times in fact) to accomplish a variety of basic tasks. At its core, the system takes images from a webcam and feeds them to a neural network to do the classification. Of course, the neural net needs to be "trained" by showing it lots of images, and telling it what those images represent. Mattheij's breakthrough was allowing the machine to effectively train itself, with guidance: Running pieces through allows the system to take its own photos, make a guess, and build on that guess. As long as Mattheij corrects the incorrect guesses, he ends up with a decent (and self-reinforcing) corpus of training data. As the machine continues running, it can rack up more training, allowing it to recognize a broad variety of pieces on the fly.

Here's another video, focusing on how the pieces move on conveyer belts (running at slow speed so puny humans can follow). You can also see the air jets in action:

In an email interview, Mattheij told Mental Floss that the system currently sorts LEGO bricks into more than 50 categories. It can also be run in a color-sorting mode to bin the parts across 12 color groups. (Thus at present you'd likely do a two-pass sort on the bricks: once for shape, then a separate pass for color.) He continues to refine the system, with a focus on making its recognition abilities faster. At some point down the line, he plans to make the software portion open source. You're on your own as far as building conveyer belts, bins, and so forth.

Check out Mattheij's writeup in two parts for more information. It starts with an overview of the story, followed up with a deep dive on the software. He's also tweeting about the project (among other things). And if you look around a bit, you'll find bulk LEGO brick auctions online—it's definitely a thing!

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Opening Ceremony
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These $425 Jeans Can Turn Into Jorts
May 19, 2017
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Opening Ceremony

Modular clothing used to consist of something simple, like a reversible jacket. Today, it’s a $425 pair of detachable jeans.

Apparel retailer Opening Ceremony recently debuted a pair of “2 in 1 Y/Project” trousers that look fairly peculiar. The legs are held to the crotch by a pair of loops, creating a disjointed C-3PO effect. Undo the loops and you can now remove the legs entirely, leaving a pair of jean shorts in their wake. The result goes from this:

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Opening Ceremony

To this:

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Opening Ceremony

The company also offers a slightly different cut with button tabs in black for $460. If these aren’t audacious enough for you, the Y/Project line includes jumpsuits with removable legs and garter-equipped jeans.

[h/t Mashable]

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