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The Late Movies: Top 10 Antiques Roadshow Valuations

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The antiques world was rocked (okay, more like gently bumped) when a recent episode of Antiques Roadshow estimated a woman's collection of carved Chinese jade objects at $710,000 to $1.07 million. Either figure would make it the highest-appraised item in Antiques Roadshow history. Unfortunately, when the woman actually sold the items at auction, she only got $494,615. Why the discrepancy? It's complicated -- read about it here if you want the blow-by-blow from real antiques experts. Meanwhile, below I have collected the Top 10 Antiques Roadshow valuations (actually 11 items, as there's a tie for second place), courtesy of YouTube user Ultranothing. The jade still comes in first as I'm counting by valuation, despite its later actual sale value being lower than predicted.

1st Place: Jade Collection

$710,000 to $1.07 million.

Tie for 2nd Place: Clyfford Still Painting

$500,000 "insurance value"; a conservative estimate.

Tie for 2nd Place: Navajo Ute First Phase Blanket

$350,000 - $500,000.

3rd Place: 1790's Card Table

Purchased for $25, valued at $200,000 - $300,000, and apparently sold in 1998 for $541,500.

4th Place: Art Deco Jewelry Collection

$257,000.

5th Place: Patek Philippe Split Chronograph Watch

$250,000.

6th Place: Painting of Ships Possibly by James E. Buttersworth

$200,000 - $500,000 if restored and painter's identity verified. It turned out not to be a Buttersworth, but still sold for $288,000 (it was by Antonio Jacobsen).

7th Place: 6th-9th Century Chinese Marble Sculpture

$120,000 - $180,000 at auction, $150,000 - $250,000 for insurance.

8th Place: 1840's Solid Gold Sword

$200,000 and up.

9th Place: Charles Schulz Cartoon Collection

$150,000 - $200,000 at auction.

10th Place: Jasper Cropsey Painting

$150,000.

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iStock // Ekaterina Minaeva
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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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Name the Author Based on the Character
May 23, 2017
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