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Ze Frank on His Internet Projects

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Ze Frank is best known for his video blog, The Show, which brought a new dose of humor every day from March 17, 2006 through March 17, 2007. But both before and after The Show, Ze was doing fun internet-based experiments -- small games, social investigation, and just straight-out jokes. Here's video of him speaking at Webstock 09, describing some of his favorite projects over the years. The program described his talk as follows, because he failed to come up with his own description on time:

Ze will perform a mime. His silent exercises, which include such classic works as the cage, walking against the wind, the mask maker, and the park, and satires on everything from sculptors to matadors, have been described as works of genius.

Not wanting to disappoint the crowd, he did some mime work, followed by a real talk on actual things. Discussed: how do we do original work on the internet, being interested in rules, going beyond the rules, online flower-making as micro-chat forum, and lots and lots of other interesting internet experiments. If you're interested in what interests Ze Frank when he's not just telling jokes, click on the screenshot below or going here (I would have embedded the video, but embedding was prevented for some reason). A mild word of warning: there is some crude humor and language in this talk.

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science
11-Year-Old Creates a Better Way to Test for Lead in Water
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In the wake of the water crisis in Flint, Michigan, a Colorado middle schooler has invented a better way to test lead levels in water, as The Cut reports.

Gitanjali Rao, an 11-year-old seventh grader in Lone Tree, Colorado just won the 2017 Discovery Education 3M Young Scientist Challenge, taking home $25,000 for the water-quality testing device she invented, called Tethys.

Rao was inspired to create the device after watching Flint's water crisis unfold over the last few years. In 2014, after the city of Flint cut costs by switching water sources used for its tap water and failed to treat it properly, lead levels in the city's water skyrocketed. By 2015, researchers testing the water found that 40 percent of homes in the city had elevated lead levels in their water, and recommended the state declare Flint's water unsafe for drinking or cooking. In December of that year, the city declared a state of emergency. Researchers have found that the lead-poisoned water resulted in a "horrifyingly large" impact on fetal death rates as well as leading to a Legionnaires' disease outbreak that killed 12 people.

A close-up of the Tethys device

Rao's parents are engineers, and she watched them as they tried to test the lead in their own house, experiencing firsthand how complicated it could be. She spotted news of a cutting-edge technology for detecting hazardous substances on MIT's engineering department website (which she checks regularly just to see "if there's anything new," as ABC News reports) then set to work creating Tethys. The device works with carbon nanotube sensors to detect lead levels faster than other current techniques, sending the results to a smartphone app.

As one of 10 finalists for the Young Scientist Challenge, Rao spent the summer working with a 3M scientist to refine her device, then presented the prototype to a panel of judges from 3M and schools across the country.

The contamination crisis in Flint is still ongoing, and Rao's invention could have a significant impact. In March 2017, Flint officials cautioned that it could be as long as two more years until the city's tap water will be safe enough to drink without filtering. The state of Michigan now plans to replace water pipes leading to 18,000 households by 2020. Until then, residents using water filters could use a device like Tethys to make sure the water they're drinking is safe. Rao plans to put most of the $25,000 prize money back into her project with the hopes of making the device commercially available.

[h/t The Cut]

All images by Andy King, courtesy of the Discovery Education 3M Young Scientist Challenge.

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technology
Google's AI Can Make Its Own AI Now
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Artificial intelligence is advanced enough to do some pretty complicated things: read lips, mimic sounds, analyze photographs of food, and even design beer. Unfortunately, even people who have plenty of coding knowledge might not know how to create the kind of algorithm that can perform these tasks. Google wants to bring the ability to harness artificial intelligence to more people, though, and according to WIRED, it's doing that by teaching machine-learning software to make more machine-learning software.

The project is called AutoML, and it's designed to come up with better machine-learning software than humans can. As algorithms become more important in scientific research, healthcare, and other fields outside the direct scope of robotics and math, the number of people who could benefit from using AI has outstripped the number of people who actually know how to set up a useful machine-learning program. Though computers can do a lot, according to Google, human experts are still needed to do things like preprocess the data, set parameters, and analyze the results. These are tasks that even developers may not have experience in.

The idea behind AutoML is that people who aren't hyper-specialists in the machine-learning field will be able to use AutoML to create their own machine-learning algorithms, without having to do as much legwork. It can also limit the amount of menial labor developers have to do, since the software can do the work of training the resulting neural networks, which often involves a lot of trial and error, as WIRED writes.

Aside from giving robots the ability to turn around and make new robots—somewhere, a novelist is plotting out a dystopian sci-fi story around that idea—it could make machine learning more accessible for people who don't work at Google, too. Companies and academic researchers are already trying to deploy AI to calculate calories based on food photos, find the best way to teach kids, and identify health risks in medical patients. Making it easier to create sophisticated machine-learning programs could lead to even more uses.

[h/t WIRED]

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