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#435070 5 Breakthroughs Coming Soon in Augmented ...

Convergence is accelerating disruption… everywhere! Exponential technologies are colliding into each other, reinventing products, services, and industries.

In this third installment of my Convergence Catalyzer series, I’ll be synthesizing key insights from my annual entrepreneurs’ mastermind event, Abundance 360. This five-blog series looks at 3D printing, artificial intelligence, VR/AR, energy and transportation, and blockchain.

Today, let’s dive into virtual and augmented reality.

Today’s most prominent tech giants are leaping onto the VR/AR scene, each driving forward new and upcoming product lines. Think: Microsoft’s HoloLens, Facebook’s Oculus, Amazon’s Sumerian, and Google’s Cardboard (Apple plans to release a headset by 2021).

And as plummeting prices meet exponential advancements in VR/AR hardware, this burgeoning disruptor is on its way out of the early adopters’ market and into the majority of consumers’ homes.

My good friend Philip Rosedale is my go-to expert on AR/VR and one of the foremost creators of today’s most cutting-edge virtual worlds. After creating the virtual civilization Second Life in 2013, now populated by almost 1 million active users, Philip went on to co-found High Fidelity, which explores the future of next-generation shared VR.

In just the next five years, he predicts five emerging trends will take hold, together disrupting major players and birthing new ones.

Let’s dive in…

Top 5 Predictions for VR/AR Breakthroughs (2019-2024)
“If you think you kind of understand what’s going on with that tech today, you probably don’t,” says Philip. “We’re still in the middle of landing the airplane of all these new devices.”

(1) Transition from PC-based to standalone mobile VR devices

Historically, VR devices have relied on PC connections, usually involving wires and clunky hardware that restrict a user’s field of motion. However, as VR enters the dematerialization stage, we are about to witness the rapid rise of a standalone and highly mobile VR experience economy.

Oculus Go, the leading standalone mobile VR device on the market, requires only a mobile app for setup and can be transported anywhere with WiFi.

With a consumer audience in mind, the 32GB headset is priced at $200 and shares an app ecosystem with Samsung’s Gear VR. While Google Daydream are also standalone VR devices, they require a docked mobile phone instead of the built-in screen of Oculus Go.

In the AR space, Lenovo’s standalone Microsoft’s HoloLens 2 leads the way in providing tetherless experiences.

Freeing headsets from the constraints of heavy hardware will make VR/AR increasingly interactive and transportable, a seamless add-on whenever, wherever. Within a matter of years, it may be as simple as carrying lightweight VR goggles wherever you go and throwing them on at a moment’s notice.

(2) Wide field-of-view AR displays

Microsoft’s HoloLens 2 leads the AR industry in headset comfort and display quality. The most significant issue with their prior version was the limited rectangular field of view (FOV).

By implementing laser technology to create a microelectromechanical systems (MEMS) display, however, HoloLens 2 can position waveguides in front of users’ eyes, directed by mirrors. Subsequently enlarging images can be accomplished by shifting the angles of these mirrors. Coupled with a 47 pixel per degree resolution, HoloLens 2 has now doubled its predecessor’s FOV. Microsoft anticipates the release of its headset by the end of this year at a $3,500 price point, first targeting businesses and eventually rolling it out to consumers.

Magic Leap provides a similar FOV but with lower resolution than the HoloLens 2. The Meta 2 boasts an even wider 90-degree FOV, but requires a cable attachment. The race to achieve the natural human 120-degree horizontal FOV continues.

“The technology to expand the field of view is going to make those devices much more usable by giving you bigger than a small box to look through,” Rosedale explains.

(3) Mapping of real world to enable persistent AR ‘mirror worlds’

‘Mirror worlds’ are alternative dimensions of reality that can blanket a physical space. While seated in your office, the floor beneath you could dissolve into a calm lake and each desk into a sailboat. In the classroom, mirror worlds would convert pencils into magic wands and tabletops into touch screens.

Pokémon Go provides an introductory glimpse into the mirror world concept and its massive potential to unite people in real action.

To create these mirror worlds, AR headsets must precisely understand the architecture of the surrounding world. Rosedale predicts the scanning accuracy of devices will improve rapidly over the next five years to make these alternate dimensions possible.

(4) 5G mobile devices reduce latency to imperceptible levels

Verizon has already launched 5G networks in Minneapolis and Chicago, compatible with the Moto Z3. Sprint plans to follow with its own 5G launch in May. Samsung, LG, Huawei, and ZTE have all announced upcoming 5G devices.

“5G is rolling out this year and it’s going to materially affect particularly my work, which is making you feel like you’re talking to somebody else directly face to face,” explains Rosedale. “5G is critical because currently the cell devices impose too much delay, so it doesn’t feel real to talk to somebody face to face on these devices.”

To operate seamlessly from anywhere on the planet, standalone VR/AR devices will require a strong 5G network. Enhancing real-time connectivity in VR/AR will transform the communication methods of tomorrow.

(5) Eye-tracking and facial expressions built in for full natural communication

Companies like Pupil Labs and Tobii provide eye tracking hardware add-ons and software to VR/AR headsets. This technology allows for foveated rendering, which renders a given scene in high resolution only in the fovea region, while the peripheral regions appear in lower resolution, conserving processing power.

As seen in the HoloLens 2, eye tracking can also be used to identify users and customize lens widths to provide a comfortable, personalized experience for each individual.

According to Rosedale, “The fundamental opportunity for both VR and AR is to improve human communication.” He points out that current VR/AR headsets miss many of the subtle yet important aspects of communication. Eye movements and microexpressions provide valuable insight into a user’s emotions and desires.

Coupled with emotion-detecting AI software, such as Affectiva, VR/AR devices might soon convey much more richly textured and expressive interactions between any two people, transcending physical boundaries and even language gaps.

Final Thoughts
As these promising trends begin to transform the market, VR/AR will undoubtedly revolutionize our lives… possibly to the point at which our virtual worlds become just as consequential and enriching as our physical world.

A boon for next-gen education, VR/AR will empower youth and adults alike with holistic learning that incorporates social, emotional, and creative components through visceral experiences, storytelling, and simulation. Traveling to another time, manipulating the insides of a cell, or even designing a new city will become daily phenomena of tomorrow’s classrooms.

In real estate, buyers will increasingly make decisions through virtual tours. Corporate offices might evolve into spaces that only exist in ‘mirror worlds’ or grow virtual duplicates for remote workers.

In healthcare, accuracy of diagnosis will skyrocket, while surgeons gain access to digital aids as they conduct life-saving procedures. Or take manufacturing, wherein training and assembly will become exponentially more efficient as visual cues guide complex tasks.

In the mere matter of a decade, VR and AR will unlock limitless applications for new and converging industries. And as virtual worlds converge with AI, 3D printing, computing advancements and beyond, today’s experience economies will explode in scale and scope. Prepare yourself for the exciting disruption ahead!

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#433928 The Surprising Parallels Between ...

The human mind can be a confusing and overwhelming place. Despite incredible leaps in human progress, many of us still struggle to make our peace with our thoughts. The roots of this are complex and multifaceted. To find explanations for the global mental health epidemic, one can tap into neuroscience, psychology, evolutionary biology, or simply observe the meaningless systems that dominate our modern-day world.

This is not only the context of our reality but also that of the critically-acclaimed Netflix series, Maniac. Psychological dark comedy meets science fiction, Maniac is a retro, futuristic, and hallucinatory trip that is filled with hidden symbols. Directed by Cary Joji Fukunaga, the series tells the story of two strangers who decide to participate in the final stage of a “groundbreaking” pharmaceutical trial—one that combines novel pharmaceuticals with artificial intelligence, and promises to make their emotional pain go away.

Naturally, things don’t go according to plan.

From exams used for testing defense mechanisms to techniques such as cognitive behavioral therapy, the narrative infuses genuine psychological science. As perplexing as the series may be to some viewers, many of the tools depicted actually have a strong grounding in current technological advancements.

Catalysts for Alleviating Suffering
In the therapy of Maniac, participants undergo a three-day trial wherein they ingest three pills and appear to connect their consciousness to a superintelligent AI. Each participant is hurled into the traumatic experiences imprinted in their subconscious and forced to cope with them in a series of hallucinatory and dream-like experiences.

Perhaps the most recognizable parallel that can be drawn is with the latest advancements in psychedelic therapy. Psychedelics are a class of drugs that alter the experience of consciousness, and often cause radical changes in perception and cognitive processes.

Through a process known as transient hypofrontality, the executive “over-thinking” parts of our brains get a rest, and deeper areas become more active. This experience, combined with the breakdown of the ego, is often correlated with feelings of timelessness, peacefulness, presence, unity, and above all, transcendence.

Despite being not addictive and extremely difficult to overdose on, regulators looked down on the use of psychedelics for decades and many continue to dismiss them as “party drugs.” But in the last few years, all of this began to change.

Earlier this summer, the FDA granted breakthrough therapy designation to MDMA for the treatment of PTSD, after several phases of successful trails. Similar research has discovered that Psilocybin (also known as magic mushrooms) combined with therapy is far more effective than traditional forms of treatment to treat depression and anxiety. Today, there is a growing and overwhelming body of research that proves that not only are psychedelics such as LSD, MDMA, or Psylicybin effective catalysts to alleviate suffering and enhance the human condition, but they are potentially the most effective tools out there.

It’s important to realize that these substances are not solutions on their own, but rather catalysts for more effective therapy. They can be groundbreaking, but only in the right context and setting.

Brain-Machine Interfaces
In Maniac, the medication-assisted therapy is guided by what appears to be a super-intelligent form of artificial intelligence called the GRTA, nicknamed Gertie. Gertie, who is a “guide” in machine form, accesses the minds of the participants through what appears to be a futuristic brain-scanning technology and curates customized hallucinatory experiences with the goal of accelerating the healing process.

Such a powerful form of brain-scanning technology is not unheard of. Current levels of scanning technology are already allowing us to decipher dreams and connect three human brains, and are only growing exponentially. Though they are nowhere as advanced as Gertie (we have a long way to go before we get to this kind of general AI), we are also seeing early signs of AI therapy bots, chatbots that listen, think, and communicate with users like a therapist would.

The parallels between current advancements in mental health therapy and the methods in Maniac can be startling, and are a testament to how science fiction and the arts can be used to explore the existential implications of technology.

Not Necessarily a Dystopia
While there are many ingenious similarities between the technology in Maniac and the state of mental health therapy, it’s important to recognize the stark differences. Like many other blockbuster science fiction productions, Maniac tells a fundamentally dystopian tale.

The series tells the story of the 73rd iteration of a controversial drug trial, one that has experienced many failures and even led to various participants being braindead. The scientists appear to be evil, secretive, and driven by their own superficial agendas and deep unresolved emotional issues.

In contrast, clinicians and researchers are not only required to file an “investigational new drug application” with the FDA (and get approval) but also update the agency with safety and progress reports throughout the trial.

Furthermore, many of today’s researchers are driven by a strong desire to contribute to the well-being and progress of our species. Even more, the results of decades of research by organizations like MAPS have been exceptionally promising and aligned with positive values. While Maniac is entertaining and thought-provoking, viewers must not forget the positive potential of such advancements in mental health therapy.

Science, technology, and psychology aside, Maniac is a deep commentary on the human condition and the often disorienting states that pain us all. Within any human lifetime, suffering is inevitable. It is the disproportionate, debilitating, and unjust levels of suffering that we ought to tackle as a society. Ultimately, Maniac explores whether advancements in science and technology can help us live not a life devoid of suffering, but one where it is balanced with fulfillment.

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#433852 How Do We Teach Autonomous Cars To Drive ...

Autonomous vehicles can follow the general rules of American roads, recognizing traffic signals and lane markings, noticing crosswalks and other regular features of the streets. But they work only on well-marked roads that are carefully scanned and mapped in advance.

Many paved roads, though, have faded paint, signs obscured behind trees and unusual intersections. In addition, 1.4 million miles of U.S. roads—one-third of the country’s public roadways—are unpaved, with no on-road signals like lane markings or stop-here lines. That doesn’t include miles of private roads, unpaved driveways or off-road trails.

What’s a rule-following autonomous car to do when the rules are unclear or nonexistent? And what are its passengers to do when they discover their vehicle can’t get them where they’re going?

Accounting for the Obscure
Most challenges in developing advanced technologies involve handling infrequent or uncommon situations, or events that require performance beyond a system’s normal capabilities. That’s definitely true for autonomous vehicles. Some on-road examples might be navigating construction zones, encountering a horse and buggy, or seeing graffiti that looks like a stop sign. Off-road, the possibilities include the full variety of the natural world, such as trees down over the road, flooding and large puddles—or even animals blocking the way.

At Mississippi State University’s Center for Advanced Vehicular Systems, we have taken up the challenge of training algorithms to respond to circumstances that almost never happen, are difficult to predict and are complex to create. We seek to put autonomous cars in the hardest possible scenario: driving in an area the car has no prior knowledge of, with no reliable infrastructure like road paint and traffic signs, and in an unknown environment where it’s just as likely to see a cactus as a polar bear.

Our work combines virtual technology and the real world. We create advanced simulations of lifelike outdoor scenes, which we use to train artificial intelligence algorithms to take a camera feed and classify what it sees, labeling trees, sky, open paths and potential obstacles. Then we transfer those algorithms to a purpose-built all-wheel-drive test vehicle and send it out on our dedicated off-road test track, where we can see how our algorithms work and collect more data to feed into our simulations.

Starting Virtual
We have developed a simulator that can create a wide range of realistic outdoor scenes for vehicles to navigate through. The system generates a range of landscapes of different climates, like forests and deserts, and can show how plants, shrubs and trees grow over time. It can also simulate weather changes, sunlight and moonlight, and the accurate locations of 9,000 stars.

The system also simulates the readings of sensors commonly used in autonomous vehicles, such as lidar and cameras. Those virtual sensors collect data that feeds into neural networks as valuable training data.

Simulated desert, meadow and forest environments generated by the Mississippi State University Autonomous Vehicle Simulator. Chris Goodin, Mississippi State University, Author provided.
Building a Test Track
Simulations are only as good as their portrayals of the real world. Mississippi State University has purchased 50 acres of land on which we are developing a test track for off-road autonomous vehicles. The property is excellent for off-road testing, with unusually steep grades for our area of Mississippi—up to 60 percent inclines—and a very diverse population of plants.

We have selected certain natural features of this land that we expect will be particularly challenging for self-driving vehicles, and replicated them exactly in our simulator. That allows us to directly compare results from the simulation and real-life attempts to navigate the actual land. Eventually, we’ll create similar real and virtual pairings of other types of landscapes to improve our vehicle’s capabilities.

A road washout, as seen in real life, left, and in simulation. Chris Goodin, Mississippi State University, Author provided.
Collecting More Data
We have also built a test vehicle, called the Halo Project, which has an electric motor and sensors and computers that can navigate various off-road environments. The Halo Project car has additional sensors to collect detailed data about its actual surroundings, which can help us build virtual environments to run new tests in.

The Halo Project car can collect data about driving and navigating in rugged terrain. Beth Newman Wynn, Mississippi State University, Author provided.
Two of its lidar sensors, for example, are mounted at intersecting angles on the front of the car so their beams sweep across the approaching ground. Together, they can provide information on how rough or smooth the surface is, as well as capturing readings from grass and other plants and items on the ground.

Lidar beams intersect, scanning the ground in front of the vehicle. Chris Goodin, Mississippi State University, Author provided
We’ve seen some exciting early results from our research. For example, we have shown promising preliminary results that machine learning algorithms trained on simulated environments can be useful in the real world. As with most autonomous vehicle research, there is still a long way to go, but our hope is that the technologies we’re developing for extreme cases will also help make autonomous vehicles more functional on today’s roads.

Matthew Doude, Associate Director, Center for Advanced Vehicular Systems; Ph.D. Student in Industrial and Systems Engineering, Mississippi State University; Christopher Goodin, Assistant Research Professor, Center for Advanced Vehicular Systems, Mississippi State University, and Daniel Carruth, Assistant Research Professor and Associate Director for Human Factors and Advanced Vehicle System, Center for Advanced Vehicular Systems, Mississippi State University

This article is republished from The Conversation under a Creative Commons license. Read the original article.

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#433282 The 4 Waves of AI: Who Will Own the ...

Recently, I picked up Kai-Fu Lee’s newest book, AI Superpowers.

Kai-Fu Lee is one of the most plugged-in AI investors on the planet, managing over $2 billion between six funds and over 300 portfolio companies in the US and China.

Drawing from his pioneering work in AI, executive leadership at Microsoft, Apple, and Google (where he served as founding president of Google China), and his founding of VC fund Sinovation Ventures, Lee shares invaluable insights about:

The four factors driving today’s AI ecosystems;
China’s extraordinary inroads in AI implementation;
Where autonomous systems are headed;
How we’ll need to adapt.

With a foothold in both Beijing and Silicon Valley, Lee looks at the power balance between Chinese and US tech behemoths—each turbocharging new applications of deep learning and sweeping up global markets in the process.

In this post, I’ll be discussing Lee’s “Four Waves of AI,” an excellent framework for discussing where AI is today and where it’s going. I’ll also be featuring some of the hottest Chinese tech companies leading the charge, worth watching right now.

I’m super excited that this Tuesday, I’ve scored the opportunity to sit down with Kai-Fu Lee to discuss his book in detail via a webinar.

With Sino-US competition heating up, who will own the future of technology?

Let’s dive in.

The First Wave: Internet AI
In this first stage of AI deployment, we’re dealing primarily with recommendation engines—algorithmic systems that learn from masses of user data to curate online content personalized to each one of us.

Think Amazon’s spot-on product recommendations, or that “Up Next” YouTube video you just have to watch before getting back to work, or Facebook ads that seem to know what you’ll buy before you do.

Powered by the data flowing through our networks, internet AI leverages the fact that users automatically label data as we browse. Clicking versus not clicking; lingering on a web page longer than we did on another; hovering over a Facebook video to see what happens at the end.

These cascades of labeled data build a detailed picture of our personalities, habits, demands, and desires: the perfect recipe for more tailored content to keep us on a given platform.

Currently, Lee estimates that Chinese and American companies stand head-to-head when it comes to deployment of internet AI. But given China’s data advantage, he predicts that Chinese tech giants will have a slight lead (60-40) over their US counterparts in the next five years.

While you’ve most definitely heard of Alibaba and Baidu, you’ve probably never stumbled upon Toutiao.

Starting out as a copycat of America’s wildly popular Buzzfeed, Toutiao reached a valuation of $20 billion by 2017, dwarfing Buzzfeed’s valuation by more than a factor of 10. But with almost 120 million daily active users, Toutiao doesn’t just stop at creating viral content.

Equipped with natural-language processing and computer vision, Toutiao’s AI engines survey a vast network of different sites and contributors, rewriting headlines to optimize for user engagement, and processing each user’s online behavior—clicks, comments, engagement time—to curate individualized news feeds for millions of consumers.

And as users grow more engaged with Toutiao’s content, the company’s algorithms get better and better at recommending content, optimizing headlines, and delivering a truly personalized feed.

It’s this kind of positive feedback loop that fuels today’s AI giants surfing the wave of internet AI.

The Second Wave: Business AI
While internet AI takes advantage of the fact that netizens are constantly labeling data via clicks and other engagement metrics, business AI jumps on the data that traditional companies have already labeled in the past.

Think banks issuing loans and recording repayment rates; hospitals archiving diagnoses, imaging data, and subsequent health outcomes; or courts noting conviction history, recidivism, and flight.

While we humans make predictions based on obvious root causes (strong features), AI algorithms can process thousands of weakly correlated variables (weak features) that may have much more to do with a given outcome than the usual suspects.

By scouting out hidden correlations that escape our linear cause-and-effect logic, business AI leverages labeled data to train algorithms that outperform even the most veteran of experts.

Apply these data-trained AI engines to banking, insurance, and legal sentencing, and you get minimized default rates, optimized premiums, and plummeting recidivism rates.

While Lee confidently places America in the lead (90-10) for business AI, China’s substantial lag in structured industry data could actually work in its favor going forward.

In industries where Chinese startups can leapfrog over legacy systems, China has a major advantage.

Take Chinese app Smart Finance, for instance.

While Americans embraced credit and debit cards in the 1970s, China was still in the throes of its Cultural Revolution, largely missing the bus on this technology.

Fast forward to 2017, and China’s mobile payment spending outnumbered that of Americans’ by a ratio of 50 to 1. Without the competition of deeply entrenched credit cards, mobile payments were an obvious upgrade to China’s cash-heavy economy, embraced by 70 percent of China’s 753 million smartphone users by the end of 2017.

But by leapfrogging over credit cards and into mobile payments, China largely left behind the notion of credit.

And here’s where Smart Finance comes in.

An AI-powered app for microfinance, Smart Finance depends almost exclusively on its algorithms to make millions of microloans. For each potential borrower, the app simply requests access to a portion of the user’s phone data.

On the basis of variables as subtle as your typing speed and battery percentage, Smart Finance can predict with astounding accuracy your likelihood of repaying a $300 loan.

Such deployments of business AI and internet AI are already revolutionizing our industries and individual lifestyles. But still on the horizon lie two even more monumental waves— perception AI and autonomous AI.

The Third Wave: Perception AI
In this wave, AI gets an upgrade with eyes, ears, and myriad other senses, merging the digital world with our physical environments.

As sensors and smart devices proliferate through our homes and cities, we are on the verge of entering a trillion-sensor economy.

Companies like China’s Xiaomi are putting out millions of IoT-connected devices, and teams of researchers have already begun prototyping smart dust—solar cell- and sensor-geared particulates that can store and communicate troves of data anywhere, anytime.

As Kai-Fu explains, perception AI “will bring the convenience and abundance of the online world into our offline reality.” Sensor-enabled hardware devices will turn everything from hospitals to cars to schools into online-merge-offline (OMO) environments.

Imagine walking into a grocery store, scanning your face to pull up your most common purchases, and then picking up a virtual assistant (VA) shopping cart. Having pre-loaded your data, the cart adjusts your usual grocery list with voice input, reminds you to get your spouse’s favorite wine for an upcoming anniversary, and guides you through a personalized store route.

While we haven’t yet leveraged the full potential of perception AI, China and the US are already making incredible strides. Given China’s hardware advantage, Lee predicts China currently has a 60-40 edge over its American tech counterparts.

Now the go-to city for startups building robots, drones, wearable technology, and IoT infrastructure, Shenzhen has turned into a powerhouse for intelligent hardware, as I discussed last week. Turbocharging output of sensors and electronic parts via thousands of factories, Shenzhen’s skilled engineers can prototype and iterate new products at unprecedented scale and speed.

With the added fuel of Chinese government support and a relaxed Chinese attitude toward data privacy, China’s lead may even reach 80-20 in the next five years.

Jumping on this wave are companies like Xiaomi, which aims to turn bathrooms, kitchens, and living rooms into smart OMO environments. Having invested in 220 companies and incubated 29 startups that produce its products, Xiaomi surpassed 85 million intelligent home devices by the end of 2017, making it the world’s largest network of these connected products.

One KFC restaurant in China has even teamed up with Alipay (Alibaba’s mobile payments platform) to pioneer a ‘pay-with-your-face’ feature. Forget cash, cards, and cell phones, and let OMO do the work.

The Fourth Wave: Autonomous AI
But the most monumental—and unpredictable—wave is the fourth and final: autonomous AI.

Integrating all previous waves, autonomous AI gives machines the ability to sense and respond to the world around them, enabling AI to move and act productively.

While today’s machines can outperform us on repetitive tasks in structured and even unstructured environments (think Boston Dynamics’ humanoid Atlas or oncoming autonomous vehicles), machines with the power to see, hear, touch and optimize data will be a whole new ballgame.

Think: swarms of drones that can selectively spray and harvest entire farms with computer vision and remarkable dexterity, heat-resistant drones that can put out forest fires 100X more efficiently, or Level 5 autonomous vehicles that navigate smart roads and traffic systems all on their own.

While autonomous AI will first involve robots that create direct economic value—automating tasks on a one-to-one replacement basis—these intelligent machines will ultimately revamp entire industries from the ground up.

Kai-Fu Lee currently puts America in a commanding lead of 90-10 in autonomous AI, especially when it comes to self-driving vehicles. But Chinese government efforts are quickly ramping up the competition.

Already in China’s Zhejiang province, highway regulators and government officials have plans to build China’s first intelligent superhighway, outfitted with sensors, road-embedded solar panels and wireless communication between cars, roads and drivers.

Aimed at increasing transit efficiency by up to 30 percent while minimizing fatalities, the project may one day allow autonomous electric vehicles to continuously charge as they drive.

A similar government-fueled project involves Beijing’s new neighbor Xiong’an. Projected to take in over $580 billion in infrastructure spending over the next 20 years, Xiong’an New Area could one day become the world’s first city built around autonomous vehicles.

Baidu is already working with Xiong’an’s local government to build out this AI city with an environmental focus. Possibilities include sensor-geared cement, computer vision-enabled traffic lights, intersections with facial recognition, and parking lots-turned parks.

Lastly, Lee predicts China will almost certainly lead the charge in autonomous drones. Already, Shenzhen is home to premier drone maker DJI—a company I’ll be visiting with 24 top executives later this month as part of my annual China Platinum Trip.

Named “the best company I have ever encountered” by Chris Anderson, DJI owns an estimated 50 percent of the North American drone market, supercharged by Shenzhen’s extraordinary maker movement.

While the long-term Sino-US competitive balance in fourth wave AI remains to be seen, one thing is certain: in a matter of decades, we will witness the rise of AI-embedded cityscapes and autonomous machines that can interact with the real world and help solve today’s most pressing grand challenges.

Join Me
Webinar with Dr. Kai-Fu Lee: Dr. Kai-Fu Lee — one of the world’s most respected experts on AI — and I will discuss his latest book AI Superpowers: China, Silicon Valley, and the New World Order. Artificial Intelligence is reshaping the world as we know it. With U.S.-Sino competition heating up, who will own the future of technology? Register here for the free webinar on September 4th, 2018 from 11:00am–12:30pm PST.

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Posted in Human Robots

#431189 Researchers Develop New Tech to Predict ...

It is one of the top 10 deadliest diseases in the United States, and it cannot be cured or prevented. But new studies are finding ways to diagnose Alzheimer’s disease in its earliest stages, while some of the latest research says technologies like artificial intelligence can detect dementia years before the first symptoms occur.
These advances, in turn, will help bolster clinical trials seeking a cure or therapies to slow or prevent the disease. Catching Alzheimer’s disease or other forms of dementia early in their progression can help ease symptoms in some cases.
“Often neurodegeneration is diagnosed late when massive brain damage has already occurred,” says professor Francis L Martin at the University of Central Lancashire in the UK, in an email to Singularity Hub. “As we know more about the molecular basis of the disease, there is the possibility of clinical interventions that might slow or halt the progress of the disease, i.e., before brain damage. Extending cognitive ability for even a number of years would have huge benefit.”
Blood Diamond
Martin is the principal investigator on a project that has developed a technique to analyze blood samples to diagnose Alzheimer’s disease and distinguish between other forms of dementia.
The researchers used sensor-based technology with a diamond core to analyze about 550 blood samples. They identified specific chemical bonds within the blood after passing light through the diamond core and recording its interaction with the sample. The results were then compared against blood samples from cases of Alzheimer’s disease and other neurodegenerative diseases, along with those from healthy individuals.
“From a small drop of blood, we derive a fingerprint spectrum. That fingerprint spectrum contains numerical data, which can be inputted into a computational algorithm we have developed,” Martin explains. “This algorithm is validated for prediction of unknown samples. From this we determine sensitivity and specificity. Although not perfect, my clinical colleagues reliably tell me our results are far better than anything else they have seen.”
Martin says the breakthrough is the result of more than 10 years developing sensor-based technologies for routine screening, monitoring, or diagnosing neurodegenerative diseases and cancers.
“My vision was to develop something low-cost that could be readily applied in a typical clinical setting to handle thousands of samples potentially per day or per week,” he says, adding that the technology also has applications in environmental science and food security.
The new test can also distinguish accurately between Alzheimer’s disease and other forms of neurodegeneration, such as Lewy body dementia, which is one of the most common causes of dementia after Alzheimer’s.
“To this point, other than at post-mortem, there has been no single approach towards classifying these pathologies,” Martin notes. “MRI scanning is often used but is labor-intensive, costly, difficult to apply to dementia patients, and not a routine point-of-care test.”
Crystal Ball
Canadian researchers at McGill University believe they can predict Alzheimer’s disease up to two years before its onset using big data and artificial intelligence. They developed an algorithm capable of recognizing the signatures of dementia using a single amyloid PET scan of the brain of patients at risk of developing the disease.
Alzheimer’s is caused by the accumulation of two proteins—amyloid beta and tau. The latest research suggests that amyloid beta leads to the buildup of tau, which is responsible for damaging nerve cells and connections between cells called synapses.
The work was recently published in the journal Neurobiology of Aging.
“Despite the availability of biomarkers capable of identifying the proteins causative of Alzheimer’s disease in living individuals, the current technologies cannot predict whether carriers of AD pathology in the brain will progress to dementia,” Sulantha Mathotaarachchi, lead author on the paper and an expert in artificial neural networks, tells Singularity Hub by email.
The algorithm, trained on a population with amnestic mild cognitive impairment observed over 24 months, proved accurate 84.5 percent of the time. Mathotaarachchi says the algorithm can be trained on different populations for different observational periods, meaning the system can grow more comprehensive with more data.
“The more biomarkers we incorporate, the more accurate the prediction could be,” Mathotaarachchi adds. “However, right now, acquiring [the] required amount of training data is the biggest challenge. … In Alzheimer’s disease, it is known that the amyloid protein deposition occurs decades before symptoms onset.”
Unfortunately, the same process occurs in normal aging as well. “The challenge is to identify the abnormal patterns of deposition that lead to the disease later on,” he says
One of the key goals of the project is to improve the research in Alzheimer’s disease by ensuring those patients with the highest probability to develop dementia are enrolled in clinical trials. That will increase the efficiency of clinical programs, according to Mathotaarachchi.
“One of the most important outcomes from our study was the pilot, online, real-time prediction tool,” he says. “This can be used as a framework for patient screening before recruiting for clinical trials. … If a disease-modifying therapy becomes available for patients, a predictive tool might have clinical applications as well, by providing to the physician information regarding clinical progression.”
Pixel by Pixel Prediction
Private industry is also working toward improving science’s predictive powers when it comes to detecting dementia early. One startup called Darmiyan out of San Francisco claims its proprietary software can pick up signals before the onset of Alzheimer’s disease by up to 15 years.
Darmiyan didn’t respond to a request for comment for this article. Venture Beat reported that the company’s MRI-analyzing software “detects cell abnormalities at a microscopic level to reveal what a standard MRI scan cannot” and that the “software measures and highlights subtle microscopic changes in the brain tissue represented in every pixel of the MRI image long before any symptoms arise.”
Darmiyan claims to have a 90 percent accuracy rate and says its software has been vetted by top academic institutions like New York University, Rockefeller University, and Stanford, according to Venture Beat. The startup is awaiting FDA approval to proceed further but is reportedly working with pharmaceutical companies like Amgen, Johnson & Johnson, and Pfizer on pilot programs.
“Our technology enables smarter drug selection in preclinical animal studies, better patient selection for clinical trials, and much better drug-effect monitoring,” Darmiyan cofounder and CEO Padideh Kamali-Zare told Venture Beat.
Conclusions
An estimated 5.5 million Americans have Alzheimer’s, and one in 10 people over age 65 have been diagnosed with the disease. By mid-century, the number of Alzheimer’s patients could rise to 16 million. Health care costs in 2017 alone are estimated to be $259 billion, and by 2050 the annual price tag could be more than $1 trillion.
In sum, it’s a disease that cripples people and the economy.
Researchers are always after more data as they look to improve outcomes, with the hope of one day developing a cure or preventing the onset of neurodegeneration altogether. If interested in seeing this medical research progress, you can help by signing up on the Brain Health Registry to improve the quality of clinical trials.
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