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10 Trailblazing Scientists About to Change Your Future

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by Eric Furman

1. Erich Jarvis, Neurobiologist

jarvis.jpgWhen Duke professor Erich Jarvis wanted to find the key to human communication, he turned to birds. Strange, but true. Jarvis has been studying songbirds' brains for insight into human linguistics, and his research has led to a startling discovery: Birds use two distinct neural pathways to learn songs—one in the front of the brain and one in the back. Guess what? Humans learn to speak in the same way. Jarvis believes this is an evolutionary clue suggesting that, when we shared an ancestor 300 million years ago, our brains were hardwired for language. Theoretically, once Jarvis and other neuroscientists fully understand this genetic blueprint, they can alter it and, in the process, make it easier to learn new languages and possibly even repair brain damage.

2. Nathan Wolfe, Epidemiologist

Nathan_Wolfe1.jpgInstead of spending his days in a lab, UCLA professor Nathan Wolfe has thrown himself into the heart of the jungle. Trekking right along with hunters in Cameroon, he's attempting to learn how they're exposed to diseases by asking them to donate blood samples (their own and their prey's). Wolfe's method is difficult, but his idea is simple: HIV, Ebola, and other human viruses originated from human-animal contact, so it's possible that these hunters—who come in close contact with their catch—are the ones inadvertently triggering the outbreaks. Wolfe's work will go a long way toward predicting where emerging diseases could occur and stopping the next HIV or Ebola epidemic before it starts.

3. Emily Oster, Economist

emily_oster.jpgA few years ago, as an economics PhD student at Harvard, Emily Oster chose to focus her attention on the AIDS epidemic in Africa. Traditionally, that was the turf of sociologists, anthropologists, and public health officials. But the 26-year-old Oster wasn't afraid to hop the scientific fence and join the other side. She also hasn't been afraid to suggest things we haven't heard before—namely, that treating herpes and other STDs (instead of AIDS) can significantly reduce HIV transmissions. Oster also believes that while the HIV numbers commonly used by the UN, popular press, and researchers are about three times too high, the disease is spreading faster than ever in Africa. By casting her economist's eyes on the issue, Oster has forced the old turf-guarders to reevaluate their approaches to AIDS in Africa and come up with new solutions.

4. Hiroshi Ishiguro, Roboticist

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Most robots look like, well, robots, but Ishiguro's robots look remarkably human. To many people, this is discomforting—creepy even. To Ishiguro, it's essential. As director of Osaka University's Intelligent Robotics Lab, Ishiguro believes robots' main role in our future will be to interact naturally with people—to pitch in as the workforce shrinks or to do necessary, unpleasant tasks. And because Ishiguro contends that people respond better to his humanlike robots (aka, androids) than other machine-like ones, he's taken a no-holds-barred approach to studying cognitive behavior and human activity. In addition to nearly perfecting his silicone molds and metal skeletons, he's figured out how to mimic even the most minute human movements, such as breathing, blinking, and even fidgeting. The result is "android science." The idea is that by using robots that are indistinguishable from humans in scientific experiments, researchers can still elicit natural responses from their subjects but also have more control over the environment. So far, Ishiguro has already learned plenty about his students using the Geminoid HI-1, an android version of himself, which he operates via remote control to teach class.

5. Jeffrey H. Schwartz, Forensic Anthropologist

schwartz_72.jpgJeffrey Schwartz became the first modern man to lay eyes on a young George Washington. Yes, that George Washington. Although he normally works on forensic cases reconstructing faces from bones, Schwartz re-created Washington by working from the outside in. Using only clues from statues, portraits, dentures, and clothing, Schwartz plugged his "evidence" into a three-dimensional computer program, which allowed him to combine and manipulate the clues to arrive at his reproduction. Schwartz created renderings of the founding father at ages 19, 45, and 57, and from the looks of it, George Washington might have been the George Clooney of his day. The lasting ramifications of Schwartz's applications and research will be seen almost immediately, as other forensic anthropologists follow his method to see what distant past heroes (and villains) really looked like.

6. Pardis Sabeti, Biological Anthropologist

dr_sabet.jpgPulling a typical all-nighter in med school, Pardis Sabeti achieved a not-so-typical feat—she confirmed the effects of genetics on the evolution of human diseases. By inputting different DNA sequences into an algorithm she created, Sabeti was able to find genes still linked to their neighbors—suggesting that their success within the gene pool is due to natural selection, not pure chance.

Sabeti now plans on using her algorithm to deconstruct the malaria parasite. By seeing how the parasite has evolved to develop drug resistances, she hopes to detect genetic vulnerabilities in malaria's makeup. If she's successful, future cures will be designed to attack those weaknesses. Meanwhile, Sabeti isn't your typical lab rat. She's the lead singer of the alt-rock band Thousand Days and sounds more than a little like Liz Phair. And did we mention that she's a Rhodes Scholar who just graduated summa cum laude from Harvard Medical School in 2006?

7. Thomas A. Jackson, Aerospace Engineer

Piloting a real-life Luke Skywalker X-wing fighter is every aeronautical engineer's fantasy, and Thomas Jackson is helping make it a reality. A scientist for the U.S. Air Force Research Laboratory, Jackson is setting the direction for the supersonic combustion ramjet—aka, the scramjet. By scooping up oxygen from the atmosphere as it ascends, the scramjet eliminates the need for the heavy liquid oxygen and solid oxidizer used by a typical space shuttle. And once it catches on, it will revolutionize air travel. How does a 2-hour flight from New York to Sydney sound? Or a layover on the Moon? And the best thing is, it'll all happen sooner than you think. In April 2007, NASA successfully test-powered a hydrocarbon-fueled scramjet engine to Mach 5.

8. , Probabilistic Roboticist

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Sebastian Thrun is a Stanford professor who drives a Volkswagen—but not just any Volkswagen. Thrun's Touareg is autonomous, and its name is Stanley. The VW drives itself thanks to state-of-the-art road-finding and obstacle-avoidance software, along with radar systems, video screens, and laser range finders. Like every driver, Stanley makes mistakes, and Thrun programmed him with that in mind. Stanley's decisions are based not on absolutes, but on probabilities, which results in more natural and realistic driver reactions. But Thrun isn't so sure people will immediately hand over the keys to a bunch of Stanleys. It may take up to 30 years, he says, "simply because we don't know how to insure a car where no one is at the wheel."

9. Nima Arkani-Hamed, Particle Physicist and Applied String Theorist

NimaArkani-Hamed.jpgNima Arkani-Hamed thinks big. He has a theory that our universe is one of an infinite number of universes—meaning the largest thing we can wrap our minds around is actually pretty tiny. He didn't pull the "multiverse" out of thin air, though. After becoming a Harvard professor at age 30, Arkani-Hamed first made a name for himself by suggesting that our universe is five-dimensional. Then he moved on to the multiverse, theorizing that our own universe has a hidden feature called "split supersymmetry," which means that half of all particles have partner particles. The theory will be tested soon in Switzerland's brand-new Large Hadron Collider (LHC), and if the LHC finds Arkani-Hamed's partner particles, it could prove that the multiverse is real—and that our place in it is that much smaller.

10. Margaret Turnbull, Astrobiologist

MargaretTurnbull.jpgHunting for aliens isn't necessarily the most respected academic endeavor in the world, but Margaret Turnbull pursued it anyway. More precisely, she set out to catalog the stars most likely to develop intelligent alien civilizations. Turnbull's system was painstakingly tedious. She started with the 120,000 cataloged stars, narrowed down her list to 17,129 (excluding the ones that were too hot, too close together, or too erratic), and then parsed that list down to 100 candidates. Her final criteria? An ideal star would be at least 3 billion years old and have a high iron content (the better to spin off life-yielding planets with).

Turnbull's mind-blowing patience has paid off. In 2015, NASA will be launching its Terrestrial Planet Finder, which will use space telescopes to look for planets beyond our solar system, and it'll start with the stars on Turnbull's short list. In other words, nobody's laughing at Turnbull's search for aliens now.

This article originally appeared in mental_floss magazine. Care to subscribe?

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iStock // Ekaterina Minaeva
technology
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Man Buys Two Metric Tons of LEGO Bricks; Sorts Them Via Machine Learning
May 21, 2017
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iStock // Ekaterina Minaeva

Jacques Mattheij made a small, but awesome, mistake. He went on eBay one evening and bid on a bunch of bulk LEGO brick auctions, then went to sleep. Upon waking, he discovered that he was the high bidder on many, and was now the proud owner of two tons of LEGO bricks. (This is about 4400 pounds.) He wrote, "[L]esson 1: if you win almost all bids you are bidding too high."

Mattheij had noticed that bulk, unsorted bricks sell for something like €10/kilogram, whereas sets are roughly €40/kg and rare parts go for up to €100/kg. Much of the value of the bricks is in their sorting. If he could reduce the entropy of these bins of unsorted bricks, he could make a tidy profit. While many people do this work by hand, the problem is enormous—just the kind of challenge for a computer. Mattheij writes:

There are 38000+ shapes and there are 100+ possible shades of color (you can roughly tell how old someone is by asking them what lego colors they remember from their youth).

In the following months, Mattheij built a proof-of-concept sorting system using, of course, LEGO. He broke the problem down into a series of sub-problems (including "feeding LEGO reliably from a hopper is surprisingly hard," one of those facts of nature that will stymie even the best system design). After tinkering with the prototype at length, he expanded the system to a surprisingly complex system of conveyer belts (powered by a home treadmill), various pieces of cabinetry, and "copious quantities of crazy glue."

Here's a video showing the current system running at low speed:

The key part of the system was running the bricks past a camera paired with a computer running a neural net-based image classifier. That allows the computer (when sufficiently trained on brick images) to recognize bricks and thus categorize them by color, shape, or other parameters. Remember that as bricks pass by, they can be in any orientation, can be dirty, can even be stuck to other pieces. So having a flexible software system is key to recognizing—in a fraction of a second—what a given brick is, in order to sort it out. When a match is found, a jet of compressed air pops the piece off the conveyer belt and into a waiting bin.

After much experimentation, Mattheij rewrote the software (several times in fact) to accomplish a variety of basic tasks. At its core, the system takes images from a webcam and feeds them to a neural network to do the classification. Of course, the neural net needs to be "trained" by showing it lots of images, and telling it what those images represent. Mattheij's breakthrough was allowing the machine to effectively train itself, with guidance: Running pieces through allows the system to take its own photos, make a guess, and build on that guess. As long as Mattheij corrects the incorrect guesses, he ends up with a decent (and self-reinforcing) corpus of training data. As the machine continues running, it can rack up more training, allowing it to recognize a broad variety of pieces on the fly.

Here's another video, focusing on how the pieces move on conveyer belts (running at slow speed so puny humans can follow). You can also see the air jets in action:

In an email interview, Mattheij told Mental Floss that the system currently sorts LEGO bricks into more than 50 categories. It can also be run in a color-sorting mode to bin the parts across 12 color groups. (Thus at present you'd likely do a two-pass sort on the bricks: once for shape, then a separate pass for color.) He continues to refine the system, with a focus on making its recognition abilities faster. At some point down the line, he plans to make the software portion open source. You're on your own as far as building conveyer belts, bins, and so forth.

Check out Mattheij's writeup in two parts for more information. It starts with an overview of the story, followed up with a deep dive on the software. He's also tweeting about the project (among other things). And if you look around a bit, you'll find bulk LEGO brick auctions online—it's definitely a thing!

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Animals
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Scientists Think They Know How Whales Got So Big
May 24, 2017
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iStock

It can be difficult to understand how enormous the blue whale—the largest animal to ever exist—really is. The mammal can measure up to 105 feet long, have a tongue that can weigh as much as an elephant, and have a massive, golf cart–sized heart powering a 200-ton frame. But while the blue whale might currently be the Andre the Giant of the sea, it wasn’t always so imposing.

For the majority of the 30 million years that baleen whales (the blue whale is one) have occupied the Earth, the mammals usually topped off at roughly 30 feet in length. It wasn’t until about 3 million years ago that the clade of whales experienced an evolutionary growth spurt, tripling in size. And scientists haven’t had any concrete idea why, Wired reports.

A study published in the journal Proceedings of the Royal Society B might help change that. Researchers examined fossil records and studied phylogenetic models (evolutionary relationships) among baleen whales, and found some evidence that climate change may have been the catalyst for turning the large animals into behemoths.

As the ice ages wore on and oceans were receiving nutrient-rich runoff, the whales encountered an increasing number of krill—the small, shrimp-like creatures that provided a food source—resulting from upwelling waters. The more they ate, the more they grew, and their bodies adapted over time. Their mouths grew larger and their fat stores increased, helping them to fuel longer migrations to additional food-enriched areas. Today blue whales eat up to four tons of krill every day.

If climate change set the ancestors of the blue whale on the path to its enormous size today, the study invites the question of what it might do to them in the future. Changes in ocean currents or temperature could alter the amount of available nutrients to whales, cutting off their food supply. With demand for whale oil in the 1900s having already dented their numbers, scientists are hoping that further shifts in their oceanic ecosystem won’t relegate them to history.

[h/t Wired]

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