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Discovering Oxygen: A Brief History

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Because there are three different dead guys who regularly vie for credit for discovering oxygen, we’ve staged a little friendly competition to establish which of these great men deserves the title of the O-master. In evaluating the contenders, we’ll look at when they isolated oxygen and how their experiments furthered our understanding of the element. In addition to bragging rights, the winner takes home one zillion liters of oxygen.

Contender 1: Carl Wilhelm Scheele

Nationality: Swedish
Occupation: Apothecary

Biggest Accomplishment: In 1772, he was the first person to figure out a way – actually a couple of ways - to isolate oxygen. He discovered that mercuric oxide, silver carbonate, magnesium nitrate, and potassium nitrate all gave off the same gas when heated. Scheele dubbed the mystery element “fire air” because he noticed that it produced sparks when it came into contact with charcoal dust.

Other Biggest Accomplishment: Discovered chlorine

Biggest Shortcoming:

Bad timing. Scheele didn’t publish his discovery until 1777, in a treatise called Chemical Observations and Experiments on Air and Fire. By that time, Joseph Priestley had already written a paper describing his findings and published the comprehensive Experiments and Observations on Air. Lavoisier had also successfully isolated the gas. Because Scheele waited so long to get the word out, his groundbreaking experiment was often overlooked by other scientists, earning him the nickname “Hard Luck Scheele.”

Contender 2: Joseph Priestley

Nationality: British

Occupation: Radical Unitarian Minister

Biggest accomplishment: In 1771, Priestley noticed that a mouse in a sealed jar would eventually collapse. He then tried slipping a sprig of mint inside and realized the plant magically revived his subject. Realizing that plants did something to freshen up the air, he wrote to his friend Benjamin Franklin, saying he hoped his discovery would stop people from cutting down so many trees.

Priestley didn’t actually isolate this mystery gas until August 1, 1774, when he heated some mercuric oxide powder and discovered that it gave off a gas that could reignite a glowing ember. He collected large amounts of the gas and tried breathing it himself. After a few puffs, Priestley was hooked. He declared, “My breast felt peculiarly light and easy for some time afterward.”

Other Biggest Accomplishment: Invented seltzer water

Biggest Shortcoming: Priestley just wouldn’t let go of phlogiston theory – a crackpot hypothesis that argued combustion was fueled by an invisible substance called phlogiston. Priestley believed that his mystery gas supported combustion because it was pure and could absorb phlogiston released by burning substances. That’s why he was pushing to name oxygen “dephlogisticated air.”

Contender 3: Antoine Laurent Lavoisier

Nationality: French

Occupation: Tax farmer/Commissioner of the Royal Gunpowder and Saltpeter Administration

Biggest Accomplishment: Lavoisier debunked phlogiston theory. Up until then, scientists couldn’t explain why tin gained weight when it was burned; if it was releasing phlogiston, it should lose weight. Lavoisier realized that there was no way phlogiston could have a negative mass and set out to prove that combustion was caused by something else. He heated Mercury until calx formed, then he heated the calx until it gave off a clear gas. Lavoisier realized combustion resulted from a chemical reaction with this gas – not some flammable mystery element called phlogiston. He dubbed the gas “oxygen” – a name that referred to its ability to create acids.

Other Biggest Accomplishment: Helped establish this thing called the metric system, which some people supposedly use.

Biggest Shortcoming: Lavoisier might have been the one to name oxygen, and for that, we’re grateful (nobody would be caught dead in a dephlogisticated air bar). However, he was not the first to isolate the gas or recognize its unique properties. His methods weren’t even original. In fact, Lavoisier had been in contact with both Priestley and Scheele and borrowed from their experiments.

And the O-Master Is...

We’re giving this one to Joseph Priestley. Although he gets points for publishing first, his real breakthrough was his realization that plants gave off oxygen. This discovery enabled future scientists to understand cellular respiration and photosynthesis – both of which are absolutely essential to life on Earth. We’re also giving Priestley points for recognizing the commercial potential of oxygen when he anticipated that the pure air could be a hit at parties. Sure enough, over 200 years later, oxygen bars have become a thing!

So next time you take a breath (hopefully soon), think of Joseph Priestley and his iconic experiment, which took place exactly 238 years ago today.

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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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Land Cover CCI, ESA
Afternoon Map
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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