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#432190 In the Future, There Will Be No Limit to ...

New planets found in distant corners of the galaxy. Climate models that may improve our understanding of sea level rise. The emergence of new antimalarial drugs. These scientific advances and discoveries have been in the news in recent months.

While representing wildly divergent disciplines, from astronomy to biotechnology, they all have one thing in common: Artificial intelligence played a key role in their scientific discovery.

One of the more recent and famous examples came out of NASA at the end of 2017. The US space agency had announced an eighth planet discovered in the Kepler-90 system. Scientists had trained a neural network—a computer with a “brain” modeled on the human mind—to re-examine data from Kepler, a space-borne telescope with a four-year mission to seek out new life and new civilizations. Or, more precisely, to find habitable planets where life might just exist.

The researchers trained the artificial neural network on a set of 15,000 previously vetted signals until it could identify true planets and false positives 96 percent of the time. It then went to work on weaker signals from nearly 700 star systems with known planets.

The machine detected Kepler 90i—a hot, rocky planet that orbits its sun about every two Earth weeks—through a nearly imperceptible change in brightness captured when a planet passes a star. It also found a sixth Earth-sized planet in the Kepler-80 system.

AI Handles Big Data
The application of AI to science is being driven by three great advances in technology, according to Ross King from the Manchester Institute of Biotechnology at the University of Manchester, leader of a team that developed an artificially intelligent “scientist” called Eve.

Those three advances include much faster computers, big datasets, and improved AI methods, King said. “These advances increasingly give AI superhuman reasoning abilities,” he told Singularity Hub by email.

AI systems can flawlessly remember vast numbers of facts and extract information effortlessly from millions of scientific papers, not to mention exhibit flawless logical reasoning and near-optimal probabilistic reasoning, King says.

AI systems also beat humans when it comes to dealing with huge, diverse amounts of data.

That’s partly what attracted a team of glaciologists to turn to machine learning to untangle the factors involved in how heat from Earth’s interior might influence the ice sheet that blankets Greenland.

Algorithms juggled 22 geologic variables—such as bedrock topography, crustal thickness, magnetic anomalies, rock types, and proximity to features like trenches, ridges, young rifts, and volcanoes—to predict geothermal heat flux under the ice sheet throughout Greenland.

The machine learning model, for example, predicts elevated heat flux upstream of Jakobshavn Glacier, the fastest-moving glacier in the world.

“The major advantage is that we can incorporate so many different types of data,” explains Leigh Stearns, associate professor of geology at Kansas University, whose research takes her to the polar regions to understand how and why Earth’s great ice sheets are changing, questions directly related to future sea level rise.

“All of the other models just rely on one parameter to determine heat flux, but the [machine learning] approach incorporates all of them,” Stearns told Singularity Hub in an email. “Interestingly, we found that there is not just one parameter…that determines the heat flux, but a combination of many factors.”

The research was published last month in Geophysical Research Letters.

Stearns says her team hopes to apply high-powered machine learning to characterize glacier behavior over both short and long-term timescales, thanks to the large amounts of data that she and others have collected over the last 20 years.

Emergence of Robot Scientists
While Stearns sees machine learning as another tool to augment her research, King believes artificial intelligence can play a much bigger role in scientific discoveries in the future.

“I am interested in developing AI systems that autonomously do science—robot scientists,” he said. Such systems, King explained, would automatically originate hypotheses to explain observations, devise experiments to test those hypotheses, physically run the experiments using laboratory robotics, and even interpret the results. The conclusions would then influence the next cycle of hypotheses and experiments.

His AI scientist Eve recently helped researchers discover that triclosan, an ingredient commonly found in toothpaste, could be used as an antimalarial drug against certain strains that have developed a resistance to other common drug therapies. The research was published in the journal Scientific Reports.

Automation using artificial intelligence for drug discovery has become a growing area of research, as the machines can work orders of magnitude faster than any human. AI is also being applied in related areas, such as synthetic biology for the rapid design and manufacture of microorganisms for industrial uses.

King argues that machines are better suited to unravel the complexities of biological systems, with even the most “simple” organisms are host to thousands of genes, proteins, and small molecules that interact in complicated ways.

“Robot scientists and semi-automated AI tools are essential for the future of biology, as there are simply not enough human biologists to do the necessary work,” he said.

Creating Shockwaves in Science
The use of machine learning, neural networks, and other AI methods can often get better results in a fraction of the time it would normally take to crunch data.

For instance, scientists at the National Center for Supercomputing Applications, located at the University of Illinois at Urbana-Champaign, have a deep learning system for the rapid detection and characterization of gravitational waves. Gravitational waves are disturbances in spacetime, emanating from big, high-energy cosmic events, such as the massive explosion of a star known as a supernova. The “Holy Grail” of this type of research is to detect gravitational waves from the Big Bang.

Dubbed Deep Filtering, the method allows real-time processing of data from LIGO, a gravitational wave observatory comprised of two enormous laser interferometers located thousands of miles apart in California and Louisiana. The research was published in Physics Letters B. You can watch a trippy visualization of the results below.

In a more down-to-earth example, scientists published a paper last month in Science Advances on the development of a neural network called ConvNetQuake to detect and locate minor earthquakes from ground motion measurements called seismograms.

ConvNetQuake uncovered 17 times more earthquakes than traditional methods. Scientists say the new method is particularly useful in monitoring small-scale seismic activity, which has become more frequent, possibly due to fracking activities that involve injecting wastewater deep underground. You can learn more about ConvNetQuake in this video:

King says he believes that in the long term there will be no limit to what AI can accomplish in science. He and his team, including Eve, are currently working on developing cancer therapies under a grant from DARPA.

“Robot scientists are getting smarter and smarter; human scientists are not,” he says. “Indeed, there is arguably a case that human scientists are less good. I don’t see any scientist alive today of the stature of a Newton or Einstein—despite the vast number of living scientists. The Physics Nobel [laureate] Frank Wilczek is on record as saying (10 years ago) that in 100 years’ time the best physicist will be a machine. I agree.”

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#431424 A ‘Google Maps’ for the Mouse Brain ...

Ask any neuroscientist to draw you a neuron, and it’ll probably look something like a star with two tails: one stubby with extensive tree-like branches, the other willowy, lengthy and dotted with spindly spikes.
While a decent abstraction, this cartoonish image hides the uncomfortable truth that scientists still don’t know much about what many neurons actually look like, not to mention the extent of their connections.
But without untangling the jumbled mess of neural wires that zigzag across the brain, scientists are stumped in trying to answer one of the most fundamental mysteries of the brain: how individual neuronal threads carry and assemble information, which forms the basis of our thoughts, memories, consciousness, and self.
What if there was a way to virtually trace and explore the brain’s serpentine fibers, much like the way Google Maps allows us to navigate the concrete tangles of our cities’ highways?
Thanks to an interdisciplinary team at Janelia Research Campus, we’re on our way. Meet MouseLight, the most extensive map of the mouse brain ever attempted. The ongoing project has an ambitious goal: reconstructing thousands—if not more—of the mouse’s 70 million neurons into a 3D map. (You can play with it here!)
With map in hand, neuroscientists around the world can begin to answer how neural circuits are organized in the brain, and how information flows from one neuron to another across brain regions and hemispheres.
The first release, presented Monday at the Society for Neuroscience Annual Conference in Washington, DC, contains information about the shape and sizes of 300 neurons.
And that’s just the beginning.
“MouseLight’s new dataset is the largest of its kind,” says Dr. Wyatt Korff, director of project teams. “It’s going to change the textbook view of neurons.”

Brain Atlas
MouseLight is hardly the first rodent brain atlasing project.
The Mouse Brain Connectivity Atlas at the Allen Institute for Brain Science in Seattle tracks neuron activity across small circuits in an effort to trace a mouse’s connectome—a complete atlas of how the firing of one neuron links to the next.
MICrONS (Machine Intelligence from Cortical Networks), the $100 million government-funded “moonshot” hopes to distill brain computation into algorithms for more powerful artificial intelligence. Its first step? Brain mapping.
What makes MouseLight stand out is its scope and level of detail.
MICrONS, for example, is focused on dissecting a cubic millimeter of the mouse visual processing center. In contrast, MouseLight involves tracing individual neurons across the entire brain.
And while connectomics outlines the major connections between brain regions, the birds-eye view entirely misses the intricacies of each individual neuron. This is where MouseLight steps in.
Slice and Dice
With a width only a fraction of a human hair, neuron projections are hard to capture in their native state. Tug or squeeze the brain too hard, and the long, delicate branches distort or even shred into bits.
In fact, previous attempts at trying to reconstruct neurons at this level of detail topped out at just a dozen, stymied by technological hiccups and sky-high costs.
A few years ago, the MouseLight team set out to automate the entire process, with a few time-saving tweaks. Here’s how it works.
After injecting a mouse with a virus that causes a handful of neurons to produce a green-glowing protein, the team treated the brain with a sugar alcohol solution. This step “clears” the brain, transforming the beige-colored organ to translucent, making it easier for light to penetrate and boosting the signal-to-background noise ratio. The brain is then glued onto a small pedestal and ready for imaging.
Building upon an established method called “two-photon microscopy,” the team then tweaked several parameters to reduce imaging time from days (or weeks) down to a fraction of that. Endearingly known as “2P” by the experts, this type of laser microscope zaps the tissue with just enough photos to light up a single plane without damaging the tissue—sharper plane, better focus, crisper image.
After taking an image, the setup activates its vibrating razor and shaves off the imaged section of the brain—a waspy slice about 200 micrometers thick. The process is repeated until the whole brain is imaged.
This setup increased imaging speed by 16 to 48 times faster than conventional microscopy, writes team leader Dr. Jayaram Chandrashekar, who published a version of the method early last year in eLife.
The resulting images strikingly highlight every crook and cranny of a neuronal branch, popping out against a pitch-black background. But pretty pictures come at a hefty data cost: each image takes up a whopping 20 terabytes of data—roughly the storage space of 4,000 DVDs, or 10,000 hours of movies.
Stitching individual images back into 3D is an image-processing nightmare. The MouseLight team used a combination of computational power and human prowess to complete this final step.
The reconstructed images are handed off to a mighty team of seven trained neuron trackers. With the help of tracing algorithms developed in-house and a keen eye, each member can track roughly a neuron a day—significantly less time than the week or so previously needed.
A Numbers Game
Even with just 300 fully reconstructed neurons, MouseLight has already revealed new secrets of the brain.
While it’s widely accepted that axons, the neurons’ outgoing projection, can span the entire length of the brain, these extra-long connections were considered relatively rare. (In fact, one previously discovered “giant neuron” was thought to link to consciousness because of its expansive connections).
Images captured from two-photon microscopy show an axon and dendrites protruding from a neuron’s cell body (sphere in center). Image Credit: Janelia Research Center, MouseLight project team
MouseLight blows that theory out of the water.
The data clearly shows that “giant neurons” are far more common than previously thought. For example, four neurons normally associated with taste had wiry branches that stretched all the way into brain areas that control movement and process touch.
“We knew that different regions of the brain talked to each other, but seeing it in 3D is different,” says Dr. Eve Marder at Brandeis University.
“The results are so stunning because they give you a really clear view of how the whole brain is connected.”
With a tested and true system in place, the team is now aiming to add 700 neurons to their collection within a year.
But appearance is only part of the story.
We can’t tell everything about a person simply by how they look. Neurons are the same: scientists can only infer so much about a neuron’s function by looking at their shape and positions. The team also hopes to profile the gene expression patterns of each neuron, which could provide more hints to their roles in the brain.
MouseLight essentially dissects the neural infrastructure that allows information traffic to flow through the brain. These anatomical highways are just the foundation. Just like Google Maps, roads form only the critical first layer of the map. Street view, traffic information and other add-ons come later for a complete look at cities in flux.
The same will happen for understanding our ever-changing brain.
Image Credit: Janelia Research Campus, MouseLight project team Continue reading

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#431392 What AI Can Now Do Is Remarkable—But ...

Major websites all over the world use a system called CAPTCHA to verify that someone is indeed a human and not a bot when entering data or signing into an account. CAPTCHA stands for the “Completely Automated Public Turing test to tell Computers and Humans Apart.” The squiggly letters and numbers, often posted against photographs or textured backgrounds, have been a good way to foil hackers. They are annoying but effective.
The days of CAPTCHA as a viable line of defense may, however, be numbered.
Researchers at Vicarious, a Californian artificial intelligence firm funded by Amazon founder Jeff Bezos and Facebook’s Mark Zuckerberg, have just published a paper documenting how they were able to defeat CAPTCHA using new artificial intelligence techniques. Whereas today’s most advanced artificial intelligence (AI) technologies use neural networks that require massive amounts of data to learn from, sometimes millions of examples, the researchers said their system needed just five training steps to crack Google’s reCAPTCHA technology. With this, they achieved a 67 percent success rate per character—reasonably close to the human accuracy rate of 87 percent. In answering PayPal and Yahoo CAPTCHAs, the system achieved an accuracy rate of greater than 50 percent.
The CAPTCHA breakthrough came hard on the heels of another major milestone from Google’s DeepMind team, the people who built the world’s best Go-playing system. DeepMind built a new artificial-intelligence system called AlphaGo Zero that taught itself to play the game at a world-beating level with minimal training data, mainly using trial and error—in a fashion similar to how humans learn.
Both playing Go and deciphering CAPTCHAs are clear examples of what we call narrow AI, which is different from artificial general intelligence (AGI)—the stuff of science fiction. Remember R2-D2 of Star Wars, Ava from Ex Machina, and Samantha from Her? They could do many things and learned everything they needed on their own.
Narrow AI technologies are systems that can only perform one specific type of task. For example, if you asked AlphaGo Zero to learn to play Monopoly, it could not, even though that is a far less sophisticated game than Go. If you asked the CAPTCHA cracker to learn to understand a spoken phrase, it would not even know where to start.
To date, though, even narrow AI has been difficult to build and perfect. To perform very elementary tasks such as determining whether an image is of a cat or a dog, the system requires the development of a model that details exactly what is being analyzed and massive amounts of data with labeled examples of both. The examples are used to train the AI systems, which are modeled on the neural networks in the brain, in which the connections between layers of neurons are adjusted based on what is observed. To put it simply, you tell an AI system exactly what to learn, and the more data you give it, the more accurate it becomes.
The methods that Vicarious and Google used were different; they allowed the systems to learn on their own, albeit in a narrow field. By making their own assumptions about what the training model should be and trying different permutations until they got the right results, they were able to teach themselves how to read the letters in a CAPTCHA or to play a game.
This blurs the line between narrow AI and AGI and has broader implications in robotics and virtually any other field in which machine learning in complex environments may be relevant.
Beyond visual recognition, the Vicarious breakthrough and AlphaGo Zero success are encouraging scientists to think about how AIs can learn to do things from scratch. And this brings us one step closer to coexisting with classes of AIs and robots that can learn to perform new tasks that are slight variants on their previous tasks—and ultimately the AGI of science fiction.
So R2-D2 may be here sooner than we expected.
This article was originally published by The Washington Post. Read the original article here.
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#430988 The Week’s Awesome Stories From Around ...

Lab-Grown Food Startup Memphis Meats Raises $17 Million From DFJ, Cargill, Bill Gates, OthersPaul Sawers | Venture Beat “Meat grown in a laboratory is the future, if certain sustainable food advocates have their way, and one startup just raised a bucketload of cash from major investors to make this goal a reality….Leading the $17 million series A round was venture capital (VC) firm DFJ, backer of Skype, Tesla, SpaceX, Tumblr, Foursquare, Baidu, and Box.”
Blossom: A Handmade Approach to Social Robotics From Cornell and GoogleEvan Ackerman | IEEE Spectrum “Blossom’s overall aesthetic is, in some ways, a response to the way that the design of home robots (and personal technology) has been trending recently. We’re surrounding ourselves with sterility embodied in metal and plastic, perhaps because of a perception that tech should be flawless. And I suppose when it comes to my phone or my computer, sterile flawlessness is good.”
Mercedes’ Outrageously Swoopy Concept Says Nein to the Pod-Car FutureAlex Davies | WIRED “The swooping concept car, unveiled last weekend at the Pebble Beach Concoursd’Elegance, rejects all notions of practicality. It measures nearly 18.7 feet long and 6.9 feet wide, yet offers just two seats…Each wheel gets its own electric motor that draws power from the battery that comprises the car’s underbody. All told, they generate 750 horsepower, and the car will go 200 miles between charges.”
Amazon’s TenMarks Releases a New Curriculum for Educators That Teaches Kids Writing Using Digital Assistants, Text Messaging and MoreSarah Perez | TechCrunch“Now, the business is offering an online curriculum for teachers designed to help students learn how to be better writers. The program includes a writing coach that leverages natural language processing, a variety of resources for teachers, and something called “bursts,” which are short writing prompts kids will be familiar with because of their use of mobile apps.”
What We Can Learn From Immersing Mice, Fruit Flies, and Zebrafish in VRAlessandra Potenza | The Verge “The VR system, called FreemoVR, pretty much resembles a holodeck from the TV show Star Trek. It’s an arena surrounded by computer screens that immerses the animals in a virtual world. Researchers tested the system on mice, fruit flies, and zebrafish, and found that the animals reacted to the virtual objects and environments as they would to real ones.” Continue reading

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#430734 Why XPRIZE Is Asking Writers to Take Us ...

In a world of accelerating change, educating the public about the implications of technological advancements is extremely important. We can continue to write informative articles and speculate about the kind of future that lies ahead. Or instead, we can take readers on an immersive journey by using science fiction to paint vivid images of the future for society.
The XPRIZE Foundation recently announced a science fiction storytelling competition. In recent years, the organization has backed and launched a range of competitions to propel innovation in science and technology. These have been aimed at a variety of challenges, such as transforming the lives of low-literacy adults, tackling climate change, and creating water from thin air.
Their sci-fi writing competition asks participants to envision a groundbreaking future for humanity. The initiative, in partnership with Japanese airline ANA, features 22 sci-fi stories from noteworthy authors that are now live on the website. Each of these stories is from the perspective of a different passenger on a plane that travels 20 years into the future through a wormhole. Contestants will compete to tell the story of the passenger in Seat 14C.
In addition to the competition, XPRIZE has brought together a science fiction advisory council to work with the organization and imagine what the future will look like. According to Peter Diamandis, founder and executive chairman, “As the future becomes harder and harder to predict, we look forward to engaging some of the world’s most visionary storytellers to help us imagine what’s just beyond the horizon and chart a path toward a future of abundance.”
The Importance of Science Fiction
Why is an organization like XPRIZE placing just as much importance on fiction as it does on reality? As Isaac Asimov has pointed out, “Modern science fiction is the only form of literature that consistently considers the nature of the changes that face us.” While the rest of the world reports on a new invention, sci-fi authors examine how these advancements affect the human condition.
True science fiction is distinguished from pure fantasy in that everything that happens is within the bounds of the physical laws of the universe. We’ve already seen how sci-fi can inspire generations and shape the future. 3D printers, wearable technology, and smartphones were first seen in Star Trek. Targeted advertising and air touch technology was first seen in Philip K. Dick’s 1958 story “The Minority Report.” Tanning beds, robot vacuums, and flatscreen TVs were seen in The Jetsons. The internet and a world of global instant communication was predicted by Arthur C. Clarke in his work long before it became reality.
Sci-fi shows like Black Mirror or Star Trek aren’t just entertainment. They allow us to imagine and explore the influence of technology on humanity. For instance, how will artificial intelligence impact human relationships? How will social media affect privacy? What if we encounter alien life? Good sci-fi stories take us on journeys that force us to think critically about the societal impacts of technological advancements.
As sci-fi author Yaasha Moriah points out, the genre is universal because “it tackles hard questions about human nature, morality, and the evolution of society, all through the narrative of speculation about the future. If we continue to do A, will it necessarily lead to problems B and C? What implicit lessons are being taught when we insist on a particular policy? When we elevate the importance of one thing over another—say, security over privacy—what could be the potential benefits and dangers of that mentality? That’s why science fiction has such an enduring appeal. We want to explore deep questions, without being preached at. We want to see the principles in action, and observe their results.”
An Extension of STEAM Education
At its core, this genre is a harmonious symbiosis between two distinct disciplines: science and literature. It is an extension of STEAM education, an educational approach that combines science, technology, engineering, the arts, and mathematics. Story-telling with science fiction allows us to use the arts in order to educate and engage the public about scientific advancements and its implications.
According to the National Science Foundation, research on art-based learning of STEM, including the use of narrative writing, works “beyond expectation.” It has been shown to have a powerful impact on creative thinking, collaborative behavior and application skills.
What does it feel like to travel through a wormhole? What are some ethical challenges of AI? How could we terraform Mars? For decades, science fiction writers and producers have answered these questions through the art of storytelling.
What better way to engage more people with science and technology than through sparking their imaginations? The method makes academic subject areas many traditionally perceived as boring or dry far more inspiring and engaging.
A Form of Time Travel
XPRIZE’s competition theme of traveling 20 years into the future through a wormhole is an appropriate beacon for the genre. In many ways, sci-fi is a precautionary form of time travel. Before we put a certain technology, scientific invention, or policy to use, we can envision and explore what our world would be like if we were to do so.
Sci-fi lets us explore different scenarios for the future of humanity before deciding which ones are more desirable. Some of these scenarios may be radically beyond our comfort zone. Yet when we’re faced with the seemingly impossible, we must remind ourselves that if something is within the domain of the physical laws of the universe, then it’s absolutely possible.
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