Earlier this summer, a tourism advocate named Durita Dahl Andreassen made a plea to Google. In a blog post, Andreassen explained how she’d spent the last few months using sheep equipped with 360-degree cameras to capture images of the Faroe Islands — an isolated North Atlantic archipelago that had yet to be documented by Google Street View. “My sheep are great for capturing the tracks and trails of the Faroe Islands,” wrote Andreassen, who works for the Faroe Islands’ tourism board, “but in order to cover the big sweeping Faroese roads and the whole of the breathtaking landscape, we need Google to come and map them.”

“Even sheep can contribute to Street View”

Andreassen’s homespun-version of Google Street View — which she dubbed “Sheep View” — made its rounds on the internet in July. Now Google has answered her call. Last week, Google Maps sent a team to the Faroe Islands, bringing 360-degree cameras and a Street View Trekker — a 40-pound backpack equipped with 15 cameras — along with them. The equipment is on loan to Andreassen and the Faroe Islanders, and residents and tourists can now borrow cameras from the Islands’ tourism office to document the rest of the Faroe Islands for the world to see.

“The Faroe Islands have shown us that even sheep can contribute to Street View,” wrote Google Maps in a blog post, going on to explain how anyone can apply to borrow one of its 360-degree cameras through the Street View camera loan program.

The Sheep View project has drawn others to map the Faroe Islands

Meanwhile, Andreassen and her sheep haven’t been the only ones photomapping the Faroe Islands. Peter Neubauer, co-founder of the crowdsourcing photo map service Mapillary, recently took a trip to the Faroe Islands with his son where they set out to map as much of the islands as they could in five days. Neubauer wrote on the Mapillary blog that he and his son decided to travel to Faroe after reading about Andreassen’s Sheep View project.

For five days they drove across the islands, a camera rig snapping photos from the top of their car, filling in the missing pieces of the Faroese landscape. And just like that, the streets of the Faroe Islands are now visible to anyone with internet access.

Human brains and machine brains are different. Human brains are really good at critical analysis. Machine brains are really good at working with lots of data. Human brains are power efficient. Machine brains need 100 million times more power than human brains to perform similar cognitive tasks.

That’s why researchers and companies have been interested in chips with structures that mimic the human brain, also known as “neuromorphic computing.” IBM’s TrueNorth is one of those chips. It has 4,096 computer cores that support about a million digital brain cells and 256 million connections. Information travels over those connections like it does across human synapses.

On Thursday, Eric Ryu, a vice president of research at the Samsung Advanced Institute of Technology, showed off how TrueNorth could help a computer be better at recognizing hand gestures while using one-tenth of the power used by typical phones. Samsung isn’t the first to use the chip, although the company does manufacture it. The Lawerence Livermore National Lab has been using it for cyber security research. The US Air Force has been using it to detect unusual events in videos and to build smarter autonomous drones.

Samsung has built TrueNorth into its Dynamic Vision Sensor, which uses the chip to recognize images at 2,000 frames per second. That kind of speed is really good for generating 3D maps, driving autonomously, and controlling computers with gestures.

Venture Beat shot a quick video of Ryu on stage at IBM’s research laboratory in Almaden, California, demonstrating how these hand gestures could be used to control a TV.

“It recognized hand waves, finger waves, closed fists and finger pinches from about 10 feet away,” wrote CNET.

Seems like machine learning-optimized hardware is here to stay. Let’s just hope the tech industry stops thinking we want to use it to make ridiculous hand gestures.

Mark Johnson wants to beat the United States Department of Agriculture at its own game: predicting yields of America’s crops. The USDA puts boots on the ground, deploying hundreds of workers to survey thousands of farms a month ahead of the October corn harvest, America’s biggest crop. Johnson’s startup, Descartes Labs, has just 20 employees, and they never leave the office in Los Alamos, New Mexico. Instead, Descartes relies on 4 petabytes of satellite imaging data and a machine learning algorithm to figure out how healthy the corn crop is from space.

Corn yield prediction is big business in the US. Billions of dollars are at stake along the ag supply chain each year as corn starts to come out of the ground in August. Grain elevator operators, ethanol producers, commodities traders, hedge funds, insurance companies, and even the farmers growing the corn will all look to the USDA’s August crop report being released August 12th to try and understand how the supply side of the corn market will behave.

Descartes says it can consistently out-predict the USDA’s corn estimates

Descartes, which launched in 2014, began releasing corn yield estimates ahead of the USDA’s August crop report last year. Johnson says its model has consistently out-predicted the USDA’s estimates at the national level at every point in the growing season. It beat the accuracy of the USDA’s 2015 August predictions by a percentage point, according to numbers provided by Descartes. Now, Johnson says their algorithms have gotten even more precise, with a 2.5 percent average margin of error when run through historical backtests.

Johnson argues that the depth and frequency of data his company is able to analyze is a game-changer in crop prediction. “What’s great about our techniques is that traditionally you have to talk to tons of farmers in the US to get a USDA-style number,” Johnson tells The Verge. “With machine learning techniques, with us, we look at tons of pixels from satellites, and that tells us what’s growing.”

shrinking sensors and cheap cloud computing

It’s a familiar narrative at the moment — companies across industries are tapping into large data sets accumulated by the proliferation of shrinking sensors and processing them using cheap cloud computing services from companies like Google and Amazon. The Weather Company, for example, just announced its hyper-local weather forecaster called Deep Thunder, which uses machine learning to crunch through historical weather reports to predict future conditions.

But big data and machine learning are just one side of the equation with Descartes.

The rise in popularity of nanosatellites — small satellites roughly the size of a shoebox — over the past five years has opened up a broad realm of possibilities for Descartes and startups like it.

In the past, if a company wanted satellite imagery data, it would turn to US government-run satellite programs like Landsat or MODIS, which image the entire globe at 20- to 30-meter resolutions roughly once a week. Now new nanosatellite constellations, like the one run by satellite imaging startup Planet, are taking snapshots of the entire globe at 3- to 5-meter resolutions every day.

Image of Le Mars, Iowa taken by Planet’s satellite constellation.

The amount of imaging data being collected right now is enormous. “One way to think about it,” says Johnson, “is that it took Landsat over 40 years to collect under a petabyte of data with 7 satellites. Planet will easily produce over a petabyte a year.”

Companies have been increasingly using this data to analyze global trends. Satellite imagery provider DigitalGlobe helped Facebook create a map of 2 billion disconnected people across the world earlier this year. Orbital Insights tracks industrial development in China and monitors the parking lots of over 50 US retailers from space to gain insight into store traffic.

Johnson and his partners chose to track agriculture for a few reasons. First, food scarcity and global climate change are pressing issues. Second, year’s-worth of data sets already existed from images taken by Landsat and MODIS that could be used to train their machine learning models. Third, corn grows slowly, and farmers can benefit from observing it with extra-spectral bands like infrared, which both older and newer generations of imaging satellites record.

Measuring chlorophyll from space

Finally, and most importantly, Johnson says, it was a hard problem. “It’s not like a satellite looking at all the Walmart parking lots and picking out the cars,” says Johnson. “That’s a problem of automation; it’s something a human could do. What we do is an automation of what humans can’t do.”

Descartes uses spectral information, not visible to the human eye to measure chlorophyll levels. “There are well-established methods for getting a proxy for crop health from chlorophyll levels,” says Josh Alban, Planet’s vice president of business development. Planet, which is formal partners with Descartes, provides it with data to calculate yield estimates and helps it build custom products for corporate clients. Johnson wouldn’t provide the names of any of his company’s clients, saying that people on the supply side of the ag business are very tight-lipped about where they get they get data. Sources familiar with the industry weren’t surprised.

Descartes’ crop identification algorithm layered on top of Planet’s satellite image.

Descartes says it analyzes satellite data of every single farm in the US on a daily basis (provided there is no cloud cover) and updates its corn yield prediction every two days. The USDA only updates its forecasts once a month. While the USDA provides country and statewide predictions, Descartes delivers both in addition to county-level predictions, which the USDA only provides at the end of the season, when those numbers matter less.

Damien Lepoutre, founder of Geosys, a global crop and analytics company that has operated for nearly three decades, says the ability to deliver local estimates is paramount. “One thing I’ve seen over the past 35 years is the complexities of agriculture,” Lepoutre said. “Agriculture is always local. You don’t have the two same soils in different places. Every time is a bit different.”

The hard part is building your neural network

Geosys also analyzes spectral satellite data, as do other large players in the industry. But Lepoutre thinks recent advances in technology, including the specific focus on machine learning, give startups a better chance at surviving than they had in the past.

It all comes down to a company’s model, says Chris Curran, chief technologist at PricewaterhouseCoopers. “The hard part is the training of your algorithm, the building of your neural network to actually create value out of your data.”

“With better satellites, better access to data, and more powerful algorithms, our models will continue to get better,” Johnson says. And as Descartes accomplishes that, Johnson says they’ll begin moving on to other crops (Descartes has already begun tracking soybeans) and other regions such as Brazil and Argentina, the Black Sea region, China, and the EU.

After that, Johnson says his company has ambitions to better understand the planet as a living organism. Descartes, he says, aims to “understand our natural resources, understanding how those resources move around, and then how we as humans change the planet.”

Today, the International Olympic Committee said individual sports federations will be responsible for barring Russian athletes from this summer’s games in Rio. The ruling comes just 12 days before the start of the games, and the call is now left to the 28 federations that oversee individual sports at the Olympics.

The decision follows a report released earlier this week that confirmed a state-supported Russian doping program. While the IOC Executive Board has deferred the decision to the individual federations, they have taken a firm stance on the participation of Russian athletes. Stating that each athlete bears some collective responsibility for their country’s actions, the board ruled that the nation’s athletes will be barred from each of the 28 sports, unless certain criteria are met:

• The International federations must apply the World Anti-Doping code and its principles to all eligible Russian athletes.
• Russian athletes must have a negative doping test: the mere absence of a positive test is not sufficient.
• The record of each Russian athlete must be examined individually to determine the reliability of their testing history.
• All Russian athletes must be checked against the Independent Person (IP) Report released by the World Anti-Doping Organization" on Monday. Any athletes who are implicated will be barred."
• International Federations will also apply their rules when it comes to sanctioning National Federations.

The IOC was under pressure from over a dozen national anti-doping organizations to issue a blanket ban on Russian athletes, The New York Times reported. And on July 22nd, Russia was banned from competing in the Paralympics after 35 doping violations were found among Russian Paralympians; these violations had been hidden by the Russian sports ministry, the Times reported.

In its statement, the IOC noted that the IP Report didn’t point directly at the Russian Olympics Committee as an institution, but made the determination that all Russian athletes looking to attend the games were “considered to be affected by a system subverting and manipulating the anti-doping system.” Because the IP report hadn’t comprehensively examined the data due to the amount of time available to its authors, the IOC decided that its results should not be restricted to the 20 sports highlighted in the findings.

“Under these exceptional circumstances,” the IOC said in the statement, “Russian athletes in any of the 28 Olympic summer sports have to assume the consequences of what amounts to a collective responsibility in order to protect the credibility of the Olympic competitions, and the “presumption of innocence” cannot be applied to them.”

The IP report released by WADA on Monday confirmed previous allegations Russia was running an organized doping regime. An investigation, conducted by Canadian sports lawyer Richard McLaren, found that Russia had launched the scheme as early as 2011, following the country’s poor performance at the 2010 Winter Olympics. The report implicated Russia’s anti-doping organization, ministry of sport, and secret service, and detailed how Russia manipulated its athlete’s urine samples across “virtually all sports.”

The IOC had previously said it would factor a decision made by the Court of Arbitration of Sports on Thursday into its decision. The court, which is the highest arbitrator of international sports issues, did not grant appeals to 68 Russian track and field athletes who had been banned earlier this year by the sport’s international federation from competing in this summer’s games.

The McLaren report was the latest investigation into the doping practices of Russian athletes. WADA released its first statistics on doping violations in June 2015, which showed Russia had 225 doping violations in 2013, the most out of any country. Track and field had the highest number of violations, and a report WADA published in November was focused on doping in that sport. It recommended the International Association of Athletics Federations (which oversees track and field) ban Russia from its events. The federation issued a suspension four days later, and upheld its decision last month after Russia failed to meet the strict criteria for the ban to be lifted.

Today, agtech startup Indigo launched a probiotic-covered seed called Indigo Cotton, which promises to reduce how much water a cotton plant needs by harnessing the power of microbes. Indigo also closed a $100 million Series C funding round and announced it will launch its next microbe-enhanced seed Indigo Wheat later this year.

When we first heard about Indigo in February, it was promising to increase crop yields by replenishing the microbes that decades of industrial farming have stripped out of our most common crops. The Indigo team studied hundreds of plant species and employed machine learning to figure out which microbes found in heritage plants were missing from modern agricultural staples. Based on this, they developed a microbe seed coating and have claimed that in over four plantings, coated seeds yielded 10 percent more crop than uncoated seeds.

There’s a real difference in root mass

Indigo Cotton is the startup’s first commercial product and the company said it’s already been planted across 50,000 acres in Texas and four surrounding states. Indigo CEO David Perry told The Verge that he’s expecting around the same 10 percent yield increase seen in earlier trial plantings — assuming normal climate conditions — when the crops are harvested this fall.

“We’re in these fields all the time and we’re able to make comparisons,” Perry says, referring to the prerelease planting. “You can see a real difference in stem diameters and root mass, which is an especially good predictor for a plant’s ability to manage water stress.”

The microbes covering Indigo Cotton are specifically chosen to help make the plants more resilient to that stress, Perry explained. Indigo plans to develop seed coatings that address issues like low nutrient stress, high salience stress, and threats like insect infestations. But Perry said they chose to tackle water first because agricultural water use is such a massive issue. Farmers currently consume 70 percent of the planet’s fresh water. And studies show that if current trends continue, there will be a 50 percent jump in agricultural water usage by 2050. At the same time, global water consumption is already dangerously high.

A unique pricing model

Current agricultural technologies like GMOs and agrochemicals don’t really address water use and water scarcity either. “There aren’t any chemicals you spray on a plant to make it perform better when it doesn’t have enough water,” says Perry. Meanwhile, cotton is traditionally grown in drier areas of the planet, like the American South, which can have an impact come harvest time.

Tyler McClendon, president of Oxbow Agriculture, which operates 40,000 acres of farmland in Arkansas, told The Verge he believes Indigo Cotton has great potential to increase crop yields and address water scarcity. This past spring he partnered with Indigo and planted 1,000 acres of the microbe-enhanced seed.

“We ultimately feel that Indigo’s ideas are good not only for the farmer, but for the bigger problem of feeding the world,” says McClendon, “and as a business we would say that’s our mission, to feed the world.” While cotton can’t feed anyone, Indigo’s forthcoming product Indigo Wheat grows into a crop that can. Indigo is also developing solutions for corn and soybeans.

Beyond technology, McClendon was also attracted by the startup’s unique pricing model. Perry said Indigo will charge a minimum for its seeds in exchange for a 33 percent share of any additional value that Indigo’s crop creates for a farm. This means that Indigo makes a profit only when their seeds perform well, and it allows farmers to attempt to increase their yield using Indigo’s product without taking on too much risk — something that McClendon said farmers are very wary to do.

Indigo Cotton will prove itself during the harvest this fall

Indigo’s product and profit model have certainly attracted a lot of interest. This latest funding round was led by the $54 billion Alaska Permanent Fund, which pledged to match its investment in future financings. Perry said that the $100 million raised makes this round the largest private equity financing in the agricultural technology sector ever. That puts Indigo right in front of Climate Corp, which raised $50 million in 2012 and was subsequently bought by Monsanto for $930 million in 2013. Indigo will be using the cash to scale their operations in preparation for the launch of Indigo Wheat later this year.

Around the same time, Indigo Cotton will be proving itself during the fall harvest.

“The plants look very healthy,” McClendon says, referring to his cotton crop at the moment. “But I would be the first to tell you that one of the key tenets of our business is we need facts and data. And I don’t really care what the plants look like, I care whether they produce more crop at the end of the year.”