Original image

How Did You Know Margaret Cunniff and Leah Alpert?

Original image

We had a lot of fun giving away daily neatorama prizes this month! We hope you enjoyed the new aspect of the game. We're taking all your feedback into consideration for the next hunt, so thanks for being active on the Facebook page. Head over there to find out if you're a random winner, as we'll be pulling those later today. Meantime, congrats to our first place winners, two Freshman from MIT who got all the correct answers in first. Let's meet them now...

For the final challenge, we were both on our computers next to each other with our solutions printed out. I mainly wrote the email and found the blog post, and Margaret got most of the clues and told me them. When we realized we posted first on the blog, we screamed for a while and then realized there was a whole room of people watching TV in the lounge next door :)


I'm a freshman at MIT from Needham, Massachusetts. Margaret and I are roommates, and she's the one who introduced me to mental_floss. We began puzzle-solving together at MIT's annual Mystery Hunt in January this year. We've been doing HDYK together for the last few months and we're excited that we won just in time for summer! At MIT, I'm majoring in Computer Science. I love painting, ice hockey, softball, crossword puzzles, and ice cream.


I'm a freshman at MIT studying Brain and Cognitive Sciences. I enjoy horseback riding, bands that play their own instruments, Arrested Development, being a subpar ice hockey goalie, and making self-deprecating comments. I work for The Tech, MIT's newspaper, which has improved my skills at finding obscure information online.

Final Answer


Day 1

Level 1:

The people are (clockwise from top):

* Charlie Brown
* Jack Black
* Seth Green
* Ben Affleck
* Betty White

They all have colors as their last names except Ben Affleck. We recognized all of the people and looked up Ben Affleck's younger brother's name to get the answer.

Answer: Casey

Level 2:

To solve this, we found a graphic with pictures and labels of flags of the world. We started with the Colombian flag because we figured the uneven stripe distribution would be distinct. As we identified colors, we were able to recognize more flags. We looked for flags that could fit with the puzzle and were able to identify all of them.


1. White
2. Blue
3. Red
4. Black
5. Yellow
6. Green

Flags (from left to right):

* Russia
* Estonia
* Poland
* Ukraine
* Bulgaria
* Lithuania
* Indonesia
* Colombia
* Austria
* Netherlands (also the flag of Luxembourg)

Answer: The first letters of the country names spell REPUBLICAN.

Your browser may not support display of this image.

Level 3:

When we first approached this problem, we started by trying to fit squares into the corners because there were fewer options for which one could go in the corners. However, we realized this didn't help us figure out where the rest of the boxes went. We decided to start with the middle square. Once we placed a middle square, this restricted where all of the other squares went, so it was relatively easy to figure out if that middle square would lead to a solution. Our fourth try worked and led to the solution.Your browser may not support display of this image.

Answer: The color of the specified square was red.

Level 4:

We copied the two graphics into Photoshop and overlaid them to easily see which color each letter went with. From there, we were able to unscramble the words.

Orange: Surfer

Yellow: Bullet

Green: Lining

Blue: Dollar

Purple: Screen

Day 2

Level 1:

These are the letters and numbers on a car's automatic transmission.

P "“ R "“ N "“ D "“ 2 "“ L

Answer: ND

Level 2:

The movies are:

* Cars
* Austin Powers in Goldmember
* Greased Lightning
* Herbie Goes to Monte Carlo
* Chitty Chitty Bang Bang

We recognized Cars and Chitty Chitty Bang Bang. We recognized the car from Herbie Goes to Monte Carlo as the Love Bug, and we looked up all of the movie posters in that series to see which movie it was. To get Goldmember, we looked up "Danger is my middle name" and recognized the Austin Powers logo in the bottom right corner, which pointed us to the Austin Powers series. Based on the text in the upper right, we figured out that the movie is Goldmember. We were able to find Greased Lightning from a clue on the facebook page that said it "cleans where others can't."

Answer: Austin Powers in Goldmember is the only movie that is not about cars, and the character's first name is Austin.

Level 3:

The books are:

1. Mansfield Park
2. Pride and Prejudice
3. Emma
4. Persuasion
5. Sense and Sensibility

We got these by recognizing Pride and Prejudice and Sense and Sensibility. Once we had those two, we looked up the names of other Jane Austen books and found that all of the hidden books were by Jane Austen.

Day 3

Level 1:

Words (clockwise from between 1 and 2):

* Grand Slam
* Racket
* Birdie
* Pin
* Strike

The sports are:

1. Baseball/softball
2. Tennis
3. Badminton
4. Golf
5. Bowling

We started with the three letter word because we figured there were fewer options. The three letter sports words we thought of were net, bat, and pin. When we tried "pin", we filled in as many letters as we knew and were able to think of "strike" next. As we got more words, we filled in the rest of the letters and were able to figure out the words based on the letters we knew and possible sport associations.

Answer: The 8 ball represents M

Level 2:

1. Squash (Squash)
2. Rugby (Rug + Bee)
3. Ice Hockey (Eye + Sock + Key)
4. Bocce (Baa + Cheeto "“ Toe)
5. Cricket (Crib "“ (Bicycle "“ Icicle) + Kit)

First we got squash. Next, we were able to recognize Rugby from Rug and Bee. We love ice hockey, so that one came to mind soon after. The next two took longer, but once we remembered the name "Cheeto" we got Bocce. Cricket was the last to fall into place. We knew that it must be bicycle "“ icicle, but we didn't know how a car fit in until our friend told us that it was the car from Knight Rider, which is named Kit.

Day 4


1. Prokofiev
2. Bernstein
3. Montagues

We knew "We Didn't Start the Fire" was on the album Storm Front by Billy Joel, which was the first of the album covers. Since the lyrics of that song are primarily an extensive list of people and events, we decided to cross reference the lyrics of the song with people involved in the "Romeo and Juliet" ballet. We found out that Sergei Prokofiev composed the "Romeo and Juliet" that was conducted by Riccardo Muti. This gave us link 1. We also knew that "It's the End of the World As We Know It (And I Feel Fine)" by R.E.M. is on the album Document, which was another of the album covers. We knew the lyrics to that song similarly had multiple references to historical people. We looked at lyrics of the song and found that it mentions Leonard Bernstein, who composed the music for "West Side Story". For the final link, we knew that "West Side Story" is an adaptation of "Romeo and Juliet". The equivalent of the Jets from "West Side Story" are the Montagues from "Romeo and Juliet", giving us the final link.

Day 5



Original image
iStock // Ekaterina Minaeva
Man Buys Two Metric Tons of LEGO Bricks; Sorts Them Via Machine Learning
Original image
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!

Original image
200 Health Experts Call for Ban on Two Antibacterial Chemicals
Original image

In September 2016, the U.S. Food and Drug Administration (FDA) issued a ban on antibacterial soap and body wash. But a large collective of scientists and medical professionals says the agency should have done more to stop the spread of harmful chemicals into our bodies and environment, most notably the antimicrobials triclosan and triclocarban. They published their recommendations in the journal Environmental Health Perspectives.

The 2016 report from the FDA concluded that 19 of the most commonly used antimicrobial ingredients are no more effective than ordinary soap and water, and forbade their use in soap and body wash.

"Customers may think added antimicrobials are a way to reduce infections, but in most products there is no evidence that they do," Ted Schettler, science director of the Science and Environmental Health Network, said in a statement.

Studies have shown that these chemicals may actually do more harm than good. They don't keep us from getting sick, but they can contribute to the development of antibiotic-resistant bacteria, also known as superbugs. Triclosan and triclocarban can also damage our hormones and immune systems.

And while they may no longer be appearing on our bathroom sinks or shower shelves, they're still all around us. They've leached into the environment from years of use. They're also still being added to a staggering array of consumer products, as companies create "antibacterial" clothing, toys, yoga mats, paint, food storage containers, electronics, doorknobs, and countertops.

The authors of the new consensus statement say it's time for that to stop.

"We must develop better alternatives and prevent unneeded exposures to antimicrobial chemicals," Rolf Haden of the University of Arizona said in the statement. Haden researches where mass-produced chemicals wind up in the environment.

The statement notes that many manufacturers have simply replaced the banned chemicals with others. "I was happy that the FDA finally acted to remove these chemicals from soaps," said Arlene Blum, executive director of the Green Science Policy Institute. "But I was dismayed to discover at my local drugstore that most products now contain substitutes that may be worse."

Blum, Haden, Schettler, and their colleagues "urge scientists, governments, chemical and product manufacturers, purchasing organizations, retailers, and consumers" to avoid antimicrobial chemicals outside of medical settings. "Where antimicrobials are necessary," they write, we should "use safer alternatives that are not persistent and pose no risk to humans or ecosystems."

They recommend that manufacturers label any products containing antimicrobial chemicals so that consumers can avoid them, and they call for further research into the impacts of these compounds on us and our planet.