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From Wikimedia Commons: Picture of the Pitch Drop Experiment from University of Queensland featuring the current (2007) custodian, John Mainstone (picture taken in 1990), two years into the life of the 8th drop.

On what is quite likely the most boring webcam ever, you can stare in rapt dumb awe as a drop of Australian pitch (a petroleum product used in waterproofing, among other things) very, very, very slowly drips out of a funnel. How slowly? Well, only eight drops of pitch have fallen since the experiment began in 1927. However, according to this table, the 8th drop fell on November 28, 2000 (12.3 years after the previous drop in 1988), so we're due for another drop any time in the next, oh, year or so. Or not. You see, the pitch drops are quite variable because the experiment is subject to changing room temperatures, and the installation of air conditioning in 1988 in the experiment's home building has really slowed things down.

This pitch funnel currently holds the Guinness World Record for the longest continuously-running experiment. There are two other notable experiments that almost make the grade -- the 1840 Oxford Electric Bell and 1864 Beverly Clock -- they haven't been running continuously, but nearly so. Better luck next time, Nineteenth Century scientists!

From The University of Queensland's page on the experiment:

The first Professor of Physics at the University of Queensland, Professor Thomas Parnell, began an experiment in 1927 to illustrate that everyday materials can exhibit quite surprising properties. The experiment demonstrates the fluidity and high viscosity of pitch, a derivative of tar once used for waterproofing boats. At room temperature pitch feels solid - even brittle - and can easily be shattered with a blow from a hammer. It's quite amazing then, to see that pitch at room temperature is actually fluid!

In 1927 Professor Parnell heated a sample of pitch and poured it into a glass funnel with a sealed stem. Three years were allowed for the pitch to settle, and in 1930 the sealed stem was cut. From that date on the pitch has slowly dripped out of the funnel - so slowly that now, 80 years later, the ninth drop is only just forming.

If that isn't exciting enough for you, go watch grass grow in real time (warning: plays sound). (Update, 10:30am Pacific: Mr. Grass has added red lawn chairs to the lawn; things are heating up!)

(Story via DVICE; Image courtesy of John Mainstone at The University of Queensland.)

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Google's AI Can Make Its Own AI Now
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iStock

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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Land Cover CCI, ESA
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European Space Agency Releases First High-Res Land Cover Map of Africa
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Land Cover CCI, ESA

This isn’t just any image of Africa. It represents the first of its kind: a high-resolution map of the different types of land cover that are found on the continent, released by The European Space Agency, as Travel + Leisure reports.

Land cover maps depict the different physical materials that cover the Earth, whether that material is vegetation, wetlands, concrete, or sand. They can be used to track the growth of cities, assess flooding, keep tabs on environmental issues like deforestation or desertification, and more.

The newly released land cover map of Africa shows the continent at an extremely detailed resolution. Each pixel represents just 65.6 feet (20 meters) on the ground. It’s designed to help researchers model the extent of climate change across Africa, study biodiversity and natural resources, and see how land use is changing, among other applications.

Developed as part of the Climate Change Initiative (CCI) Land Cover project, the space agency gathered a full year’s worth of data from its Sentinel-2A satellite to create the map. In total, the image is made from 90 terabytes of data—180,000 images—taken between December 2015 and December 2016.

The map is so large and detailed that the space agency created its own online viewer for it. You can dive further into the image here.

And keep watch: A better map might be close at hand. In March, the ESA launched the Sentinal-2B satellite, which it says will make a global map at a 32.8 feet-per-pixel (10 meters) resolution possible.

[h/t Travel + Leisure]

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