Tag Archives: DARPA
#435308 Brain-Machine Interfaces Are Getting ...
Elon Musk grabbed a lot of attention with his July 16 announcement that his company Neuralink plans to implant electrodes into the brains of people with paralysis by next year. Their first goal is to create assistive technology to help people who can’t move or are unable to communicate.
If you haven’t been paying attention, brain-machine interfaces (BMIs) that allow people to control robotic arms with their thoughts might sound like science fiction. But science and engineering efforts have already turned it into reality.
In a few research labs around the world, scientists and physicians have been implanting devices into the brains of people who have lost the ability to control their arms or hands for over a decade. In our own research group at the University of Pittsburgh, we’ve enabled people with paralyzed arms and hands to control robotic arms that allow them to grasp and move objects with relative ease. They can even experience touch-like sensations from their own hand when the robot grasps objects.
At its core, a BMI is pretty straightforward. In your brain, microscopic cells called neurons are sending signals back and forth to each other all the time. Everything you think, do and feel as you interact with the world around you is the result of the activity of these 80 billion or so neurons.
If you implant a tiny wire very close to one of these neurons, you can record the electrical activity it generates and send it to a computer. Record enough of these signals from the right area of the brain and it becomes possible to control computers, robots, or anything else you might want, simply by thinking about moving. But doing this comes with tremendous technical challenges, especially if you want to record from hundreds or thousands of neurons.
What Neuralink Is Bringing to the Table
Elon Musk founded Neuralink in 2017, aiming to address these challenges and raise the bar for implanted neural interfaces.
Perhaps the most impressive aspect of Neuralink’s system is the breadth and depth of their approach. Building a BMI is inherently interdisciplinary, requiring expertise in electrode design and microfabrication, implantable materials, surgical methods, electronics, packaging, neuroscience, algorithms, medicine, regulatory issues, and more. Neuralink has created a team that spans most, if not all, of these areas.
With all of this expertise, Neuralink is undoubtedly moving the field forward, and improving their technology rapidly. Individually, many of the components of their system represent significant progress along predictable paths. For example, their electrodes, that they call threads, are very small and flexible; many researchers have tried to harness those properties to minimize the chance the brain’s immune response would reject the electrodes after insertion. Neuralink has also developed high-performance miniature electronics, another focus area for labs working on BMIs.
Often overlooked in academic settings, however, is how an entire system would be efficiently implanted in a brain.
Neuralink’s BMI requires brain surgery. This is because implanted electrodes that are in intimate contact with neurons will always outperform non-invasive electrodes where neurons are far away from the electrodes sitting outside the skull. So, a critical question becomes how to minimize the surgical challenges around getting the device into a brain.
Maybe the most impressive aspect of Neuralink’s announcement was that they created a 3,000-electrode neural interface where electrodes could be implanted at a rate of between 30 and 200 per minute. Each thread of electrodes is implanted by a sophisticated surgical robot that essentially acts like a sewing machine. This all happens while specifically avoiding blood vessels that blanket the surface of the brain. The robotics and imaging that enable this feat, with tight integration to the entire device, is striking.
Neuralink has thought through the challenge of developing a clinically viable BMI from beginning to end in a way that few groups have done, though they acknowledge that many challenges remain as they work towards getting this technology into human patients in the clinic.
Figuring Out What More Electrodes Gets You
The quest for implantable devices with thousands of electrodes is not only the domain of private companies. DARPA, the NIH BRAIN Initiative, and international consortiums are working on neurotechnologies for recording and stimulating in the brain with goals of tens of thousands of electrodes. But what might scientists do with the information from 1,000, 3,000, or maybe even 100,000 neurons?
At some level, devices with more electrodes might not actually be necessary to have a meaningful impact in people’s lives. Effective control of computers for access and communication, of robotic limbs to grasp and move objects as well as of paralyzed muscles is already happening—in people. And it has been for a number of years.
Since the 1990s, the Utah Array, which has just 100 electrodes and is manufactured by Blackrock Microsystems, has been a critical device in neuroscience and clinical research. This electrode array is FDA-cleared for temporary neural recording. Several research groups, including our own, have implanted Utah Arrays in people that lasted multiple years.
Currently, the biggest constraints are related to connectors, electronics, and system-level engineering, not the implanted electrode itself—although increasing the electrodes’ lifespan to more than five years would represent a significant advance. As those technical capabilities improve, it might turn out that the ability to accurately control computers and robots is limited more by scientists’ understanding of what the neurons are saying—that is, the neural code—than by the number of electrodes on the device.
Even the most capable implanted system, and maybe the most capable devices researchers can reasonably imagine, might fall short of the goal of actually augmenting skilled human performance. Nevertheless, Neuralink’s goal of creating better BMIs has the potential to improve the lives of people who can’t move or are unable to communicate. Right now, Musk’s vision of using BMIs to meld physical brains and intelligence with artificial ones is no more than a dream.
So, what does the future look like for Neuralink and other groups creating implantable BMIs? Devices with more electrodes that last longer and are connected to smaller and more powerful wireless electronics are essential. Better devices themselves, however, are insufficient. Continued public and private investment in companies and academic research labs, as well as innovative ways for these groups to work together to share technologies and data, will be necessary to truly advance scientists’ understanding of the brain and deliver on the promise of BMIs to improve peoples’ lives.
While researchers need to keep the future societal implications of advanced neurotechnologies in mind—there’s an essential role for ethicists and regulation—BMIs could be truly transformative as they help more people overcome limitations caused by injury or disease in the brain and body.
This article is republished from The Conversation under a Creative Commons license. Read the original article.
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#435098 Coming of Age in the Age of AI: The ...
The first generation to grow up entirely in the 21st century will never remember a time before smartphones or smart assistants. They will likely be the first children to ride in self-driving cars, as well as the first whose healthcare and education could be increasingly turned over to artificially intelligent machines.
Futurists, demographers, and marketers have yet to agree on the specifics of what defines the next wave of humanity to follow Generation Z. That hasn’t stopped some, like Australian futurist Mark McCrindle, from coining the term Generation Alpha, denoting a sort of reboot of society in a fully-realized digital age.
“In the past, the individual had no power, really,” McCrindle told Business Insider. “Now, the individual has great control of their lives through being able to leverage this world. Technology, in a sense, transformed the expectations of our interactions.”
No doubt technology may impart Marvel superhero-like powers to Generation Alpha that even tech-savvy Millennials never envisioned over cups of chai latte. But the powers of machine learning, computer vision, and other disciplines under the broad category of artificial intelligence will shape this yet unformed generation more definitively than any before it.
What will it be like to come of age in the Age of AI?
The AI Doctor Will See You Now
Perhaps no other industry is adopting and using AI as much as healthcare. The term “artificial intelligence” appears in nearly 90,000 publications from biomedical literature and research on the PubMed database.
AI is already transforming healthcare and longevity research. Machines are helping to design drugs faster and detect disease earlier. And AI may soon influence not only how we diagnose and treat illness in children, but perhaps how we choose which children will be born in the first place.
A study published earlier this month in NPJ Digital Medicine by scientists from Weill Cornell Medicine used 12,000 photos of human embryos taken five days after fertilization to train an AI algorithm on how to tell which in vitro fertilized embryo had the best chance of a successful pregnancy based on its quality.
Investigators assigned each embryo a grade based on various aspects of its appearance. A statistical analysis then correlated that grade with the probability of success. The algorithm, dubbed Stork, was able to classify the quality of a new set of images with 97 percent accuracy.
“Our algorithm will help embryologists maximize the chances that their patients will have a single healthy pregnancy,” said Dr. Olivier Elemento, director of the Caryl and Israel Englander Institute for Precision Medicine at Weill Cornell Medicine, in a press release. “The IVF procedure will remain the same, but we’ll be able to improve outcomes by harnessing the power of artificial intelligence.”
Other medical researchers see potential in applying AI to detect possible developmental issues in newborns. Scientists in Europe, working with a Finnish AI startup that creates seizure monitoring technology, have developed a technique for detecting movement patterns that might indicate conditions like cerebral palsy.
Published last month in the journal Acta Pediatrica, the study relied on an algorithm to extract the movements from a newborn, turning it into a simplified “stick figure” that medical experts could use to more easily detect clinically relevant data.
The researchers are continuing to improve the datasets, including using 3D video recordings, and are now developing an AI-based method for determining if a child’s motor maturity aligns with its true age. Meanwhile, a study published in February in Nature Medicine discussed the potential of using AI to diagnose pediatric disease.
AI Gets Classy
After being weaned on algorithms, Generation Alpha will hit the books—about machine learning.
China is famously trying to win the proverbial AI arms race by spending billions on new technologies, with one Chinese city alone pledging nearly $16 billion to build a smart economy based on artificial intelligence.
To reach dominance by its stated goal of 2030, Chinese cities are also incorporating AI education into their school curriculum. Last year, China published its first high school textbook on AI, according to the South China Morning Post. More than 40 schools are participating in a pilot program that involves SenseTime, one of the country’s biggest AI companies.
In the US, where it seems every child has access to their own AI assistant, researchers are just beginning to understand how the ubiquity of intelligent machines will influence the ways children learn and interact with their highly digitized environments.
Sandra Chang-Kredl, associate professor of the department of education at Concordia University, told The Globe and Mail that AI could have detrimental effects on learning creativity or emotional connectedness.
Similar concerns inspired Stefania Druga, a member of the Personal Robots group at the MIT Media Lab (and former Education Teaching Fellow at SU), to study interactions between children and artificial intelligence devices in order to encourage positive interactions.
Toward that goal, Druga created Cognimates, a platform that enables children to program and customize their own smart devices such as Alexa or even a smart, functional robot. The kids can also use Cognimates to train their own AI models or even build a machine learning version of Rock Paper Scissors that gets better over time.
“I believe it’s important to also introduce young people to the concepts of AI and machine learning through hands-on projects so they can make more informed and critical use of these technologies,” Druga wrote in a Medium blog post.
Druga is also the founder of Hackidemia, an international organization that sponsors workshops and labs around the world to introduce kids to emerging technologies at an early age.
“I think we are in an arms race in education with the advancement of technology, and we need to start thinking about AI literacy before patterns of behaviors for children and their families settle in place,” she wrote.
AI Goes Back to School
It also turns out that AI has as much to learn from kids. More and more researchers are interested in understanding how children grasp basic concepts that still elude the most advanced machine minds.
For example, developmental psychologist Alison Gopnik has written and lectured extensively about how studying the minds of children can provide computer scientists clues on how to improve machine learning techniques.
In an interview on Vox, she described that while DeepMind’s AlpahZero was trained to be a chessmaster, it struggles with even the simplest changes in the rules, such as allowing the bishop to move horizontally instead of vertically.
“A human chess player, even a kid, will immediately understand how to transfer that new rule to their playing of the game,” she noted. “Flexibility and generalization are something that even human one-year-olds can do but that the best machine learning systems have a much harder time with.”
Last year, the federal defense agency DARPA announced a new program aimed at improving AI by teaching it “common sense.” One of the chief strategies is to develop systems for “teaching machines through experience, mimicking the way babies grow to understand the world.”
Such an approach is also the basis of a new AI program at MIT called the MIT Quest for Intelligence.
The research leverages cognitive science to understand human intelligence, according to an article on the project in MIT Technology Review, such as exploring how young children visualize the world using their own innate 3D models.
“Children’s play is really serious business,” said Josh Tenenbaum, who leads the Computational Cognitive Science lab at MIT and his head of the new program. “They’re experiments. And that’s what makes humans the smartest learners in the known universe.”
In a world increasingly driven by smart technologies, it’s good to know the next generation will be able to keep up.
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#434827 AI and Robotics Are Transforming ...
During the past 50 years, the frequency of recorded natural disasters has surged nearly five-fold.
In this blog, I’ll be exploring how converging exponential technologies (AI, robotics, drones, sensors, networks) are transforming the future of disaster relief—how we can prevent them in the first place and get help to victims during that first golden hour wherein immediate relief can save lives.
Here are the three areas of greatest impact:
AI, predictive mapping, and the power of the crowd
Next-gen robotics and swarm solutions
Aerial drones and immediate aid supply
Let’s dive in!
Artificial Intelligence and Predictive Mapping
When it comes to immediate and high-precision emergency response, data is gold.
Already, the meteoric rise of space-based networks, stratosphere-hovering balloons, and 5G telecommunications infrastructure is in the process of connecting every last individual on the planet.
Aside from democratizing the world’s information, however, this upsurge in connectivity will soon grant anyone the ability to broadcast detailed geo-tagged data, particularly those most vulnerable to natural disasters.
Armed with the power of data broadcasting and the force of the crowd, disaster victims now play a vital role in emergency response, turning a historically one-way blind rescue operation into a two-way dialogue between connected crowds and smart response systems.
With a skyrocketing abundance of data, however, comes a new paradigm: one in which we no longer face a scarcity of answers. Instead, it will be the quality of our questions that matters most.
This is where AI comes in: our mining mechanism.
In the case of emergency response, what if we could strategically map an almost endless amount of incoming data points? Or predict the dynamics of a flood and identify a tsunami’s most vulnerable targets before it even strikes? Or even amplify critical signals to trigger automatic aid by surveillance drones and immediately alert crowdsourced volunteers?
Already, a number of key players are leveraging AI, crowdsourced intelligence, and cutting-edge visualizations to optimize crisis response and multiply relief speeds.
Take One Concern, for instance. Born out of Stanford under the mentorship of leading AI expert Andrew Ng, One Concern leverages AI through analytical disaster assessment and calculated damage estimates.
Partnering with the cities of Los Angeles, San Francisco, and numerous cities in San Mateo County, the platform assigns verified, unique ‘digital fingerprints’ to every element in a city. Building robust models of each system, One Concern’s AI platform can then monitor site-specific impacts of not only climate change but each individual natural disaster, from sweeping thermal shifts to seismic movement.
This data, combined with that of city infrastructure and former disasters, are then used to predict future damage under a range of disaster scenarios, informing prevention methods and structures in need of reinforcement.
Within just four years, One Concern can now make precise predictions with an 85 percent accuracy rate in under 15 minutes.
And as IoT-connected devices and intelligent hardware continue to boom, a blooming trillion-sensor economy will only serve to amplify AI’s predictive capacity, offering us immediate, preventive strategies long before disaster strikes.
Beyond natural disasters, however, crowdsourced intelligence, predictive crisis mapping, and AI-powered responses are just as formidable a triage in humanitarian disasters.
One extraordinary story is that of Ushahidi. When violence broke out after the 2007 Kenyan elections, one local blogger proposed a simple yet powerful question to the web: “Any techies out there willing to do a mashup of where the violence and destruction is occurring and put it on a map?”
Within days, four ‘techies’ heeded the call, building a platform that crowdsourced first-hand reports via SMS, mined the web for answers, and—with over 40,000 verified reports—sent alerts back to locals on the ground and viewers across the world.
Today, Ushahidi has been used in over 150 countries, reaching a total of 20 million people across 100,000+ deployments. Now an open-source crisis-mapping software, its V3 (or “Ushahidi in the Cloud”) is accessible to anyone, mining millions of Tweets, hundreds of thousands of news articles, and geo-tagged, time-stamped data from countless sources.
Aggregating one of the longest-running crisis maps to date, Ushahidi’s Syria Tracker has proved invaluable in the crowdsourcing of witness reports. Providing real-time geographic visualizations of all verified data, Syria Tracker has enabled civilians to report everything from missing people and relief supply needs to civilian casualties and disease outbreaks— all while evading the government’s cell network, keeping identities private, and verifying reports prior to publication.
As mobile connectivity and abundant sensors converge with AI-mined crowd intelligence, real-time awareness will only multiply in speed and scale.
Imagining the Future….
Within the next 10 years, spatial web technology might even allow us to tap into mesh networks.
As I’ve explored in a previous blog on the implications of the spatial web, while traditional networks rely on a limited set of wired access points (or wireless hotspots), a wireless mesh network can connect entire cities via hundreds of dispersed nodes that communicate with each other and share a network connection non-hierarchically.
In short, this means that individual mobile users can together establish a local mesh network using nothing but the computing power in their own devices.
Take this a step further, and a local population of strangers could collectively broadcast countless 360-degree feeds across a local mesh network.
Imagine a scenario in which armed attacks break out across disjointed urban districts, each cluster of eye witnesses and at-risk civilians broadcasting an aggregate of 360-degree videos, all fed through photogrammetry AIs that build out a live hologram in real time, giving family members and first responders complete information.
Or take a coastal community in the throes of torrential rainfall and failing infrastructure. Now empowered by a collective live feed, verification of data reports takes a matter of seconds, and richly-layered data informs first responders and AI platforms with unbelievable accuracy and specificity of relief needs.
By linking all the right technological pieces, we might even see the rise of automated drone deliveries. Imagine: crowdsourced intelligence is first cross-referenced with sensor data and verified algorithmically. AI is then leveraged to determine the specific needs and degree of urgency at ultra-precise coordinates. Within minutes, once approved by personnel, swarm robots rush to collect the requisite supplies, equipping size-appropriate drones with the right aid for rapid-fire delivery.
This brings us to a second critical convergence: robots and drones.
While cutting-edge drone technology revolutionizes the way we deliver aid, new breakthroughs in AI-geared robotics are paving the way for superhuman emergency responses in some of today’s most dangerous environments.
Let’s explore a few of the most disruptive examples to reach the testing phase.
First up….
Autonomous Robots and Swarm Solutions
As hardware advancements converge with exploding AI capabilities, disaster relief robots are graduating from assistance roles to fully autonomous responders at a breakneck pace.
Born out of MIT’s Biomimetic Robotics Lab, the Cheetah III is but one of many robots that may form our first line of defense in everything from earthquake search-and-rescue missions to high-risk ops in dangerous radiation zones.
Now capable of running at 6.4 meters per second, Cheetah III can even leap up to a height of 60 centimeters, autonomously determining how to avoid obstacles and jump over hurdles as they arise.
Initially designed to perform spectral inspection tasks in hazardous settings (think: nuclear plants or chemical factories), the Cheetah’s various iterations have focused on increasing its payload capacity, range of motion, and even a gripping function with enhanced dexterity.
Cheetah III and future versions are aimed at saving lives in almost any environment.
And the Cheetah III is not alone. Just this February, Tokyo’s Electric Power Company (TEPCO) has put one of its own robots to the test. For the first time since Japan’s devastating 2011 tsunami, which led to three nuclear meltdowns in the nation’s Fukushima nuclear power plant, a robot has successfully examined the reactor’s fuel.
Broadcasting the process with its built-in camera, the robot was able to retrieve small chunks of radioactive fuel at five of the six test sites, offering tremendous promise for long-term plans to clean up the still-deadly interior.
Also out of Japan, Mitsubishi Heavy Industries (MHi) is even using robots to fight fires with full autonomy. In a remarkable new feat, MHi’s Water Cannon Bot can now put out blazes in difficult-to-access or highly dangerous fire sites.
Delivering foam or water at 4,000 liters per minute and 1 megapascal (MPa) of pressure, the Cannon Bot and its accompanying Hose Extension Bot even form part of a greater AI-geared system to conduct reconnaissance and surveillance on larger transport vehicles.
As wildfires grow ever more untameable, high-volume production of such bots could prove a true lifesaver. Paired with predictive AI forest fire mapping and autonomous hauling vehicles, not only will solutions like MHi’s Cannon Bot save numerous lives, but avoid population displacement and paralyzing damage to our natural environment before disaster has the chance to spread.
But even in cases where emergency shelter is needed, groundbreaking (literally) robotics solutions are fast to the rescue.
After multiple iterations by Fastbrick Robotics, the Hadrian X end-to-end bricklaying robot can now autonomously build a fully livable, 180-square-meter home in under three days. Using a laser-guided robotic attachment, the all-in-one brick-loaded truck simply drives to a construction site and directs blocks through its robotic arm in accordance with a 3D model.
Meeting verified building standards, Hadrian and similar solutions hold massive promise in the long-term, deployable across post-conflict refugee sites and regions recovering from natural catastrophes.
But what if we need to build emergency shelters from local soil at hand? Marking an extraordinary convergence between robotics and 3D printing, the Institute for Advanced Architecture of Catalonia (IAAC) is already working on a solution.
In a major feat for low-cost construction in remote zones, IAAC has found a way to convert almost any soil into a building material with three times the tensile strength of industrial clay. Offering myriad benefits, including natural insulation, low GHG emissions, fire protection, air circulation, and thermal mediation, IAAC’s new 3D printed native soil can build houses on-site for as little as $1,000.
But while cutting-edge robotics unlock extraordinary new frontiers for low-cost, large-scale emergency construction, novel hardware and computing breakthroughs are also enabling robotic scale at the other extreme of the spectrum.
Again, inspired by biological phenomena, robotics specialists across the US have begun to pilot tiny robotic prototypes for locating trapped individuals and assessing infrastructural damage.
Take RoboBees, tiny Harvard-developed bots that use electrostatic adhesion to ‘perch’ on walls and even ceilings, evaluating structural damage in the aftermath of an earthquake.
Or Carnegie Mellon’s prototyped Snakebot, capable of navigating through entry points that would otherwise be completely inaccessible to human responders. Driven by AI, the Snakebot can maneuver through even the most densely-packed rubble to locate survivors, using cameras and microphones for communication.
But when it comes to fast-paced reconnaissance in inaccessible regions, miniature robot swarms have good company.
Next-Generation Drones for Instantaneous Relief Supplies
Particularly in the case of wildfires and conflict zones, autonomous drone technology is fundamentally revolutionizing the way we identify survivors in need and automate relief supply.
Not only are drones enabling high-resolution imagery for real-time mapping and damage assessment, but preliminary research shows that UAVs far outpace ground-based rescue teams in locating isolated survivors.
As presented by a team of electrical engineers from the University of Science and Technology of China, drones could even build out a mobile wireless broadband network in record time using a “drone-assisted multi-hop device-to-device” program.
And as shown during Houston’s Hurricane Harvey, drones can provide scores of predictive intel on everything from future flooding to damage estimates.
Among multiple others, a team led by Texas A&M computer science professor and director of the university’s Center for Robot-Assisted Search and Rescue Dr. Robin Murphy flew a total of 119 drone missions over the city, from small-scale quadcopters to military-grade unmanned planes. Not only were these critical for monitoring levee infrastructure, but also for identifying those left behind by human rescue teams.
But beyond surveillance, UAVs have begun to provide lifesaving supplies across some of the most remote regions of the globe. One of the most inspiring examples to date is Zipline.
Created in 2014, Zipline has completed 12,352 life-saving drone deliveries to date. While drones are designed, tested, and assembled in California, Zipline primarily operates in Rwanda and Tanzania, hiring local operators and providing over 11 million people with instant access to medical supplies.
Providing everything from vaccines and HIV medications to blood and IV tubes, Zipline’s drones far outpace ground-based supply transport, in many instances providing life-critical blood cells, plasma, and platelets in under an hour.
But drone technology is even beginning to transcend the limited scale of medical supplies and food.
Now developing its drones under contracts with DARPA and the US Marine Corps, Logistic Gliders, Inc. has built autonomously-navigating drones capable of carrying 1,800 pounds of cargo over unprecedented long distances.
Built from plywood, Logistic’s gliders are projected to cost as little as a few hundred dollars each, making them perfect candidates for high-volume remote aid deliveries, whether navigated by a pilot or self-flown in accordance with real-time disaster zone mapping.
As hardware continues to advance, autonomous drone technology coupled with real-time mapping algorithms pose no end of abundant opportunities for aid supply, disaster monitoring, and richly layered intel previously unimaginable for humanitarian relief.
Concluding Thoughts
Perhaps one of the most consequential and impactful applications of converging technologies is their transformation of disaster relief methods.
While AI-driven intel platforms crowdsource firsthand experiential data from those on the ground, mobile connectivity and drone-supplied networks are granting newfound narrative power to those most in need.
And as a wave of new hardware advancements gives rise to robotic responders, swarm technology, and aerial drones, we are fast approaching an age of instantaneous and efficiently-distributed responses in the midst of conflict and natural catastrophes alike.
Empowered by these new tools, what might we create when everyone on the planet has the same access to relief supplies and immediate resources? In a new age of prevention and fast recovery, what futures can you envision?
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