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The (New) New Einsteins: Nathalie Cabrol

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In the current issue of mental_floss magazine, Erik Vance profiled nine "New Einsteins"—visionaries who are discovering how to grow organs, peer into black holes, levitate food, cure plagues, and let blind men see. This week, Mr. Vance will be anointing five additional New Einsteins here on mentalfloss.com, one per day. Today, it's Nathalie Cabrol's turn.


Who She Is: Nathalie Cabrol, astrobiologist and principal investigator with the SETI Institute


What She Did: Cabrol's work covers a number of different aspects of the search for extra-terrestrial intelligence (often called SETI). First, she looks for life in one of the harshest environments on the planet, the Atacama Desert in Northern Chile. The idea is that understanding plants and animals in an environment that might go decades without rain (until 1971, some parts might have gone rainless since 1570) tells us something about what might survive on Mars. The Atacama Desert is so harsh that if the same Viking landers that couldn't find life on Mars in the mid-1970s were to land there, they would say the same thing about Earth.


A few years ago, she climbed the nearby volcano, Licancabur. At the 20,000-foot summit, she descended into the cauldron and dove into the crater lake. It was the unofficial record for the highest female dive. Yet even there, where temperatures get down to -30 degrees, she found tiny living organisms.

Recently she has been focused on helping the Mars rovers. She has helped run several experiments with one version (named Zo), where researchers follow it along and see if it can detect life in the desert.

Why You Should Start Idolizing Her Immediately:

Cabrol is an alien hunter who climbs desert mountains and SCUBA dives the world's highest lakes for a living. And she plays with robots. She's like a Michael Crichton book that met a Jerry Bruckheimer film and decided to guest star on an episode of The X-Files.

More than that, her work is actually a crucial link between theory and practice of SETI. For the brief time that Mars had a working atmosphere, water on its surface, and was moderately hospitable to life, it probably looked a lot like the Atacama Desert "“ heavy UV radiation, not much oxygen, kind of cold. Scientists who believe Mars has life must assume that something managed to form, evolve, and make the jump from water to land before the water dried up.

Science increasingly tells us this may be possible. On our own planet life exists at the bottom of the ocean, in boiling sulfur pits, and deep in the Earth's crust. And in the Atacama. It's not hard to imagine some of the microbes that she found at the top of Licancabur surviving under the surface of Mars "“ with a few adaptations. But we are going to have to find them, and that may harder than just digging a few scoops of slushy dirt looking for water. We will need to find the right place to land and know just what to look for. By doing ecological maps of Mars-on-Earth, Cabrol gives us a rough blueprint for what to look for when we see the real thing.

Previous (New) New Einsteins: Marin Soljačić , Roland Fryer

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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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