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Humans Feasted on Horses and Sloths in Argentina 14,000 Years Ago

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The stone artifacts found in the same stratigraphic level as the animal bones: (a) side scraper, quartzite; (b) retouched flake, quartzite; (c) retouched flake, quartzite; (d-e) scrapers made on coastal rounded cobbles; (f) bipolar cobble.


If you head to Buenos Aires, you’re inevitably going to be urged to eat a steak. Beef is a big deal there today. If you showed up in the area 14,000 years ago, you’d be treated to much stranger culinary delights—like meat from elephant-sized ground sloths, American horses, and other extinct megafauna.

Archaeologists report today in the journal PLOS One that they’ve discovered buried leftovers of ancient feasts in the grassy plains south of Buenos Aires. The bones not only offer a snapshot of the diet of early Argentinians, but they might also help scientists reconstruct the bigger picture of how the first humans migrated through South America.

Over the past 30 years, thousands of bones have been unearthed at a site called Arroyo Seco 2, in the Pampas region of Argentina, on the southern tip of South America. When the first humans arrived there, the place would have been a lakeside, treeless landscape with big animals to hunt (or scavenge) for food.

(A) Geographic location of the AS2 site. (B) Digital Elevation Model (DEM) of the knoll and location of the excavation units. (C) Photograph of central excavation units and trench. Image credit: Politis et al. in PLOS One

"The Arroyo Seco 2 site must have had unique landscape characteristics, because people kept returning there for thousands of years,” study co-author Daniel Rafuse, of the Universidad Nacional del Centro de la Provincia de Buenos Aires, tells mental_floss.

The oldest human bones from the Arroyo Seco 2 site are from about 8000 years ago; people were camping out at the site even earlier, but they didn’t leave any burials behind. Instead, they left their mark on the animal bones.

Archaeologists at Arroyo Seco 2 have found bones from animals that are still around today, like rodents and guanacos (the wild ancestors of llamas). They’ve also found remains from extinct animals such as giant ground sloths, Volkswagen Beetle–sized armadillo relatives called glypotodons, and toxodons, which were strange hoofed beasts that sort of looked like a cross between a rhino and a hippo.

Some of these animal bones had clear signs of butchering by humans, such as characteristic fractures and marks left by stone tools, the researchers said. The oldest example is a 14,064-year-old leg bone of a now-extinct horse species (Equus neogeus) that looks like it was cracked while still fresh by a human-made hammerstone.

Cut bones from an extinct horse found at the site. Image credit: Politis et al. in PLOS One


The horse bones found at the site are highlighted in red. Image credit: Politis et al. in PLOS One

A human presence in this part of Argentina 14,000 years ago has wider implications for scientists studying how humans spread through the Americas.

It was long thought that the first Americans were the Clovis people, a culture of hunters and gatherers who start showing up in the archaeological record about 13,000 years ago; the culture is named after the site near Clovis, New Mexico where fluted stone points were first found in the 1920s. But then in the late 1970s, archaeologist Tom Dillehay, of Vanderbilt University, started digging at the Monte Verde site in southern Chile. He made the then-controversial discovery that humans occupied that area by 14,500 years ago. (In another paper published last year, Dillehay pushed that date back even further, to 18,500 years ago.)

Over the last few decades, more pre-Clovis sites have been found in the Americas. And while the long-held belief among scholars used to be that the New World was first colonized by settlers who crossed the Bering Strait via land bridge and then spread south, other lines of evidence have complicated our understanding of this migration route, including the recent finding that an ice-free corridor across the Bering land bridge only became viable for humans to cross about 12,600 years ago. Rafuse said the findings fit into a newer model that suggests the Americas were first populated 17,000 to 16,000 years ago, as the last ice age was ending.

Dillehay, who was not involved in the new study, tells mental_floss that the findings from Arroyo Seco 2 are valuable in documenting yet another human presence in southern South America at least 14,000 years ago.

“The data are trustworthy, and the work is very well done,” Dillehay says.

University of Oklahoma archaeologist Bonnie Pitblado, who studies the early settling of the Americas with a focus on the Rocky Mountains, says we’re likely only beginning to understand the importance of South America in the peopling of the New World. Pitblado, who was not involved in the study, tells mental_floss “There are simply too many sites with evidence for pre-Clovis occupation of South America to reasonably deny that the continent played a crucial role in the peopling process."

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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
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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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