This Guy Invented Shoes That Grow Five Sizes In Five Years For Kids In Developing Countries 05-02

 Shoes That Grow Five Sizes In Five Years For Kids In Developing Countries

“I had no idea how important shoes were,” founder Kenton Lee told BuzzFeed News.

Kenton Lee was working at an orphanage in Kenya when he noticed a little girl with the ends of her shoes cut off and her toes sticking out. It was then that he came up with the idea for The Shoe That Grows.

“For years the idea of these growing shoes wouldn’t leave my mind,” he told BuzzFeed News.

The first step was starting Because International with a few friends in 2006, a nonprofit devoted to “working with and helping those in extreme poverty,” their site says.

Kenton Lee

Proof of Concept

 

Lee and his team at first tried to give the idea to companies like Nike, Crocs, and Toms, to no avail. Eventually they found a “shoe development company” called Proof of Concept who agreed to help them with the design.

The shoe is made out of a high quality soft leather on top, and extremely durable rubber soles similar material to a tire, Lee said. They expand through a simple system of buckles, snaps, and pegs.

Proof of Concept

Because International

 

The shoes are predicted to last a minimum of five years, and expand five sizes in that time. The small size will fit preschoolers through fifth graders, while the large will fit fifth through ninth graders.

Via theshoethatgrows.org

“I had no idea how important shoes were before I went to Kenya,” Lee said. “But kids, especially in urban areas, can get infections from cuts and scrapes on their feet from going barefoot, and contract diseases that cause them to miss school.”

The 30-year-old, who started a church in Idaho with his wife, said he wanted to put these kids in the best possible position to succeed in their lives.

“If I can provide a kid with protection so they stay healthy and keep going to school, I’ll have done my part.”

Because International / Via becauseinternational.org

The shoes cost $10 a pair, and each pair goes into a “duffle bag” that can fit 50 pairs of shoes. Once one organization’s duffle bag is full, Because International ships it to the organization that flies with them to one of seven countries.

Donors can either buy shoes to distribute themselves, or buy a pair of shoes and choose one of five American nonprofit organizations to distribute them to orphanages and churches around the world.

Because International

Because International

 

So far about 2,500 children across seven countries are wearing the shoes, including in Ghana, Haiti, Peru, Colombia, and Kenya.

“We have about 500 left of our first order, currently being stored in a room in my house where my son sometimes chews on them,” said Lee, referring to his 11-month-old son (he also has another one the way). He said they have an order of 3,000 more pairs coming in July for people to donate.

The Shoe That Grows / Via theshoethatgrows.org

“We considered making even larger ones for teenagers,” Lee added, “but we were told that they didn’t want to wear ‘charity shoes,’ they wanted to wear something cooler.”

He said he’s now being flooded with requests, mostly from Americans, to make adult-sized versions.

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The Perilous World of Machine Learning for Fun and Profit 05-01

The Perilous World of Machine Learning for Fun and Profit: Pipeline Jungles and Hidden Feedback Loops

                                                                        George prototyping a machine learning model.

I haven't written a blog post in ages. And while I don't want to give anything away, the main reason I haven't been writing is that I've been too busy doing my day job at MailChimp. The data science team has been working closely with others at the company to do some fun things in the coming year.

That said, I got inspired to write a quick post by this excellent short paper out of Google,  "Machine Learning: The High Interest Credit Card of Technical Debt."

Anyone who plans on building production mathematical modeling systems for a living needs to keep a copy of that paper close.

And while I don't want to recap the whole paper here, I want to highlight some pieces of it that hit close to home.

Pipeline Jungles

Picture

George prototyping a machine learning model.

There was a time as a boy when my favorite book was George's Marvelous Medicine by Roald Dahl. The book is full of all that mischief and malice that makes Dahl books so much fun. 

In the book, George wanders around his house finding chemicals to mix up into a brown soup to give to his grandmother in place of her normal medicine. And reading this bit of felony grand-matricide as a child always made me smile.

Prototyping a new machine learning model is like George's quest for toxic chemicals. It's a chance for the data scientist to root around their company looking for data sources and engineering features that help predict an outcome.

A little bit of these log files. A dash of Google Analytics data. Some of Marge-from-Accounting's spreadsheet.

POOF! We have a marvelous model.

How fun it is to regale others with tales of how you found that a combination of reddit upvotes, the lunar calendar, and the number of times your yoga instructor says FODMAPs is actually somewhat predictive!

But now it's the job of some poor sucker dev to take your prototype model, which pulls from innumerable sources (hell, you probably scraped Twitter too just for good measure), and turn it into a production system.

All of a sudden there's a "pipeline jungle," a jumbled up stream of data sources and glue code for feature engineering and combination, to create something programmatically and reliably in production that you only had to create once manually in your George's-Marvelous-Medicine-revelry.

It's easy in the research and design phase of a machine learning project to over-engineer the product. Too many data sources, too many exotic and brittle features, and as a corollary, too complex a model. One trap the paper points out is leaving in low powered features in your prototype model, because well, they help a little, and they're not hurting anyone right? 

What's the value of those features versus the cost of leaving them in? That's extra code to maintain, maybe an extra source to pull from. And as the Google paper notes, the world changes, data changes, and every model feature is a potential risk for breaking everything.

Remember, the tech press (and vendors) would have you build a deep learning model that's fed scraped data from the internet's butthole, but it's important to exercise a little self-control. As the authors of the technical debt paper put it, "Research solutions that provide a tiny accuracy benefit at the cost of massive increases in system complexity are rarely wise practice." Preach.

Who's going to own this model and care for it and love it and feed it and put a band-aid on its cuts when its decision thresholds start to drift? Since it's going to cost money in terms of manpower and tied up resources to maintain this model, what is the worth of this model to the business? If it's not that important of a model (and by important, I'm usually talking top line revenue), then maybe a logistic regression with a few interactions will do you nicely.

Humans are Feedback Loop Machines

Picture

The graveyard at Haworth

In Haworth England, they used to bury bodies at the top of a hill above the town. When someone died, they got carted up to the overcrowded graveyard and then their choleraic juices would seep into the water supply and infect those down below, creating more bodies for the graveyard.

Haworth had a particularly nasty feedback loop.

Machine learning models suck up all sorts of nasty dead body water too.

At MailChimp, if I know that a user is going to be a spammer in the future, I can shut them down now using a machine learning model and a swift kick to the user's derriere.

But that future I'm changing will someday, maybe next week, maybe next year, be the machine learning system's present day.

And any attempt to train on present day data, data which has now been polluted by the business's model-driven actions (dead spammers buried at the top of the hill), is fraught with peril. It's a feedback loop. All of a sudden, maybe I don't have any spammers to train my ML model on, because I've shut them all down. And now my newly trained model thinks spamming is more unlikely than I know it to be.

Of course, such feedback loops can be mitigated in many ways. Holdout sets for example.

But we can only mitigate a feedback loop if we know about its existence, and we as humans are awesome at generating feedback loops and terrible at recognizing them. 

Think about time-travel in fiction. Once you have a time machine (and make no mistake, a well-suited ML model is pretty close to a forward-leaping time machine when it comes to things like sales and marketing), it's easy to jump through time and monkey with events, but it's hard to anticipate all the consequences of those changes and how they might alter your future training data.

And yet when the outputs of ML models are put in the hands of others to act on, you can bet that the future (and the future pool of training data with it) will be altered. That's the point! I don't predict spammers to do nothing about them! Predictions are meant to be acted upon.

And so, when the police predict that a community is full of criminals and then they start harassing that community, what do you think is going to happen? The future training data gets affected by the police's "special attention." Predictive modeling feeds back into systematic discrimination.

But we shouldn't expect cops to understand that they're burying their dead at the top of the hill.

This is one of my fears with the pedestrianization of data science techniques. As we put predictive models more and more in the hands of the layperson, have we considered that we might cut anyone out of the loop who even understands or cares about their misuse?

Get Integrated, Stay Alert

The technical debt paper makes this astute observation, "It’s worth noting that glue code and pipeline jungles are symptomatic of integration issues that may have a root cause in overly separated 'research' and 'engineering' roles."

This is absolutely true. When data scientists treat production implementation as a black box they shove their prototypes through and when engineers treat ML packages as black boxes they shove data pipelines through, problems abound.

Mathematical modelers need to stay close to engineers when building production data systems. Both need to keep each other in mind and keep the business in mind. The goal is not to use deep learning. The goal is not to program in Go. The goal is to create a system for the business that lives on. And in that context, accuracy, maintainability, sturdiness...they all hold equal weight.

So as a data scientist keep your stats buds close and your colleagues from other teams (engineers, MBAs, legal, ....) closer with the goal of getting work done together. It's the only way your models will survive past prototype.

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How the Nepal Earthquake Happened? 04-29

How the Nepal Earthquake Happened?

A little before noon Saturday in Nepal, a chunk of rock about 9 miles below the earth’s surface shifted, unleashing a shock wave—described as being as powerful as the explosion of more than 20 thermonuclear weapons—that ripped through the Katmandu Valley.

In geological terms, the tremor occurred like clockwork, 81 years after the region’s last earthquake of such a magnitude, in 1934.

Records dating to 1255 indicate the region—known as the Indus-Yarlung suture zone—experiences a magnitude-8 earthquake approximately every 75 years, according to a report by Nepal’s National Society for Earthquake Technology.

‘Earthquakes dissipate energy, like lifting the lid off a pot of boiling water. But it builds back up after you put the lid back on.’

—Lung S. Chan, geophysicist at the University of Hong Kong

The reason is the regular movement of the fault line that runs along Nepal’s southern border, where the Indian subcontinent collided with the Eurasia plate 40 million to 50 million years ago.

“The collision between India and Eurasia is a showcase for geology,” said Lung S. Chan, a geophysicist at the University of Hong Kong. The so-called India plate is pushing its way north toward Asia at a rate of about 5 centimeters, or 2 inches, a year, he said. “Geologically speaking, that’s very fast.”

As the plates push against each other, friction generates stress and energy that builds until the crust ruptures, said Dr. Chan, who compared the quake to a thermonuclear weapons explosion. In the case of Saturday’s quake, the plate jumped forward about 2 meters, or 6.5 feet, said Hongfeng Yang, an earthquake expert at the Chinese University of Hong Kong.Saturday’s quake was also relatively shallow, according to the U.S. Geological Survey. Such quakes tend to cause more damage and more aftershocks than those that occur deeper below the earth’s surface.

After an earthquake, the plates resume moving and the clock resets. “Earthquakes dissipate energy, like lifting the lid off a pot of boiling water,” said Dr. Chan. “But it builds back up after you put the lid back on.”

Nepal is prone to destructive earthquakes, not only because of the massive forces involved in the tectonic collision, but also because of the type of fault line the country sits on. Normal faults create space when the ground cracks and separates. Nepal lies on a so-called thrust fault, where one tectonic plate forces itself on top of another.

The most visible result of this is the Himalayan mountain range. The fault runs along the 1,400-mile range, and the constant collision of the India and Eurasia plates pushes up the height of the peaks by about a centimeter each year.

Despite the seeming regularity of severe earthquakes in Nepal, it isn’t possible to predict when one will happen. Historic records and modern measurements of tectonic plate movement show that if the pressure builds in the region in a way that is “generally consistent and homogenous,” the region should expect a severe earthquake every four to five decades, Dr. Yang said.

The complexity of the forces applying pressure at the fault means scientists are incapable of predicting more than an average number of earthquakes that a region will experience in a century, experts say.

Still, earthquakes in Nepal are more predictable than most, because of the regular movement of the plates. Scientists aren’t sure why this is.

The earth’s tectonics plates are constantly in motion. Some faults release built-up stress in the form of earthquakes. Others release that energy quietly. “Some areas, like Nepal, release energy as a large earthquake, once in a while,” said Dr. Chan. “These regions all have different natures for reasons geologists don’t really know.”

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Wearable Device Helps Vision Impaired Avoid Collision 04-29

Wearable Device Helps Vision Impaired Avoid Collision

People who have lost some of their peripheral vision, such as those with retinitis pigmentosa, glaucoma, or brain injury that causes half visual field loss, often face mobility challenges and increased likelihood of falls and collisions. As therapeutic vision restoration treatments are still in their infancy, rehabilitation approaches using assistive technologies are often times viable alternatives for addressing mobility challenges related to vision loss.

Researchers from Massachusetts Eye and Ear, Schepens Eye Research Institute used an obstacle course to evaluate a wearable collision warning device they developed for patients with peripheral vision loss. They found the device may help patients with a wide range of vision loss avoid collisions with high-level obstacles. Their findings are featured on Investigative Ophthalmology and Visual Science (IOVS).

“We developed this pocket-sized collision warning device, which can predict impending collisions based on time to collision rather than proximity. It gives warnings only when the users approach to obstacles, not when users stand close to objects and not when moving objects just pass by.

 So, the auditory collision warnings given by the device are simple and intuitively understandable. We tested the device in a density obstacle course to evaluate its effect on collision avoidance in people with peripheral vision loss. To show its beneficial effect, we compared the patients’ mobility performance with the device and without it. 

Just demonstrating the device can give warning for obstacles in walking would not prove the device is useful. We have to compare with a baseline, which is walking without the device in this case.” said the senior author Gang Luo, Ph.D., Associate Scientist at Mass. Eye and Ear/Schepens, and Assistant Professor of Ophthalmology at Harvard Medical School.

Gang Luo, Ph.D., Associate Scientist at Mass. Eye and Ear/Schepens, and Assistant Professor of Ophthalmology at Harvard Medical School, adjusts the wearable device that his team created to help those who are visually impaired avoid collision while walking. Image credit: Peter Mallen, Mass. Eye and Ear.

Twenty five patients with tunnel vision or hemianopia completed the obstacle course study and the number of collisions and walking speed were measured.

Compared to walking without the device, collisions were reduced significantly by about 37% with the device and walking speed barely changed. No patient had more collisions when using the device than when not using it.

This video shows a pilot testing of a portable collision device developed for visually impaired. A normally sighted person was wearing a goggle with a small opening, which gave him only 10-deg field of view. A warning device in his pocket gave auditory warnings when imminent collision risk was detected.

“We are excited about the device’s potential value for helping visually impaired and completely blind people walk around safely. Our next job is to test its usefulness in patients’ daily lives in a clinical trial study.” Dr. Luo said.

This video demonstrates an end-user prototype of collision warning device we developed for visually impaired and blind people.

This study is entitled Evaluation of a portable collision warning device for patients with peripheral vision loss in an obstacle course. Other authors are Shrinivas Pundlik and Matteo Tomasi.

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Bluetooth Disc Plays Your Digital Music Like a Vinyl Record 04-29

Bluetooth Disc Plays Your Digital Music Like a Vinyl Record

JESSE ENGLAND HAS an evocative way of describing record players: He considers them “altars” for music. “There’s no contemporary media environment that I know of in the past 50 years that requires that amount of reverence and that amount of care,” he says.

It’s hard to argue with him. Compared to Spotify and iTunes, playing vinyl is an elaborate process: You have to flip through your collection, select a record and carefully remove it from the sleeve. If you’re really obsessive, you’ll give it a wipe with record brush before placing it on the turntable and delicately lowering the stylus. Listening is similarly involved: You have to be careful not to bump the table, and you’ve got to get up every 15 to 20 minutes or so and flip the disc. 

“Universal Record,” England’s latest project, revives that ritual for the modern age. It’s a vibrating plastic disc that lets you play music from any digital source, via Bluetooth, on any record player. It’s a clever hack, but it’s even more interesting as a piece of media archaeology, focusing our attention not on the sound quality of vinyl but on the experience of using it.

England, who lives in Pittsburgh, Pennsylvania, and holds a MFA degree from Carnegie Mellon University, is fond of examining media through unlikely technological mash-ups. With “Sincerity Machine,” he modified a vintage typewriter to print text in Comic Sans. He used a laser engraving machine to cut letterforms in acrylic and glued them onto the typewriter’s strikes. Universal Record was even more straightforward: Inside the chunky plastic disc is a transducer—essentially “a speaker without a cone,” England says—which receives audio via Bluetooth. The vibrations from the transducer are picked up by the stylus. Thus, through vibration, the digital music is made analog.

The point isn’t simply to fetishize vinyl. “I am critical of unbridled and unchecked nostalgia as a marker of credibility,” England says. Rather, it’s about exposing folks to different ways of seeing and listening. The project encourages us to consider both what’s lost and gained as we shed old forms of media. I doubt anyone would think to describe iTunes as an “altar” to music (other than a sacrificial one, perhaps).

You don't have to burn e-books; you just need to get the right person to type a command, and all the copies disappear.

England thinks there’s additional value in reacquainting people with vinyl—a more permanent form of media whose embedded values are increasingly rare in our dematerialized, digital world. “I am concerned about the trend of all media and all information, by extension, only being available through a networked source,” England says. “I think the experience of listening to music today is exponentially richer and more fulfilling in terms of access and the sheer amount of content, but with that there’s an associated volatility.”

With services like Netflix and Spotify, entertainment is built on the ever-shifting tectonics of licensing agreements. For music listeners, it’s easy to imagine rare material falling through those cracks. But the problems are even more profound when you consider societies in which the free flow of information isn’t a given. Networked media is dangerously susceptible to censorship and suppression. You don’t have to burn e-books; you just need to get the right person to type a command, and all the copies disappear. “I’m concerned with people in any part of the world being comfortable with media being redefined from something that you have into something that you have access to,” England says.

Vinyl records, England says, have a built-in reliability. “You can play them back with the most basic of playback tools. You can just take a needle and a paper cone and you’d be able to recall the sound.” And how is the Universal Record’s sound quality? “All in all, it’s listenable,” England says. “But it really exists for the concept.”

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