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Imagine trying to read War and Peace one letter at a time. The thought alone feels excruciating. But in many ways, this painful idea holds parallels to how human-machine interfaces (HMI) force us to interact with and process data today.
Designed back in the 1970s at Xerox PARC and later refined during the 1980s by Apple, today’s HMI was originally conceived during fundamentally different times, and specifically, before people and machines were generating so much data. Fast forward to 2019, when humans are estimated to produce 44 zettabytes of data—equal to two stacks of books from here to Pluto—and we are still using the same HMI from the 1970s.
These dated interfaces are not equipped to handle today’s exponential rise in data, which has been ushered in by the rapid dematerialization of many physical products into computers and software.
Breakthroughs in perceptual and cognitive computing, especially machine learning algorithms, are enabling technology to process vast volumes of data, and in doing so, they are dramatically amplifying our brain’s abilities. Yet even with these powerful technologies that at times make us feel superhuman, the interfaces are still crippled with poor ergonomics.
Many interfaces are still designed around the concept that human interaction with technology is secondary, not instantaneous. This means that any time someone uses technology, they are inevitably multitasking, because they must simultaneously perform a task and operate the technology.
If our aim, however, is to create technology that truly extends and amplifies our mental abilities so that we can offload important tasks, the technology that helps us must not also overwhelm us in the process. We must reimagine interfaces to work in coherence with how our minds function in the world so that our brains and these tools can work together seamlessly.
Most technology is designed to serve either the mind or the body. It is a problematic divide, because our brains use our entire body to process the world around us. Said differently, our minds and bodies do not operate distinctly. Our minds are embodied.
Studies using MRI scans have shown that when a person feels an emotion in their gut, blood actually moves to that area of the body. The body and the mind are linked in this way, sharing information back and forth continuously.
Current technology presents data to the brain differently from how the brain processes data. Our brains, for example, use sensory data to continually encode and decipher patterns within the neocortex. Our brains do not create a linguistic label for each item, which is how the majority of machine learning systems operate, nor do our brains have an image associated with each of these labels.
Our bodies move information through us instantaneously, in a sense “computing” at the speed of thought. What if our technology could do the same?
Using Cognitive Ergonomics to Design Better Interfaces
Well-designed physical tools, as philosopher Martin Heidegger once meditated on while using the metaphor of a hammer, seem to disappear into the “hand.” They are designed to amplify a human ability and not get in the way during the process.
The aim of physical ergonomics is to understand the mechanical movement of the human body and then adapt a physical system to amplify the human output in accordance. By understanding the movement of the body, physical ergonomics enables ergonomically sound physical affordances—or conditions—so that the mechanical movement of the body and the mechanical movement of the machine can work together harmoniously.
Cognitive ergonomics applied to HMI design uses this same idea of amplifying output, but rather than focusing on physical output, the focus is on mental output. By understanding the raw materials the brain uses to comprehend information and form an output, cognitive ergonomics allows technologists and designers to create technological affordances so that the brain can work seamlessly with interfaces and remove the interruption costs of our current devices. In doing so, the technology itself “disappears,” and a person’s interaction with technology becomes fluid and primary.
By leveraging cognitive ergonomics in HMI design, we can create a generation of interfaces that can process and present data the same way humans process real-world information, meaning through fully-sensory interfaces.
Several brain-machine interfaces are already on the path to achieving this. AlterEgo, a wearable device developed by MIT researchers, uses electrodes to detect and understand nonverbal prompts, which enables the device to read the user’s mind and act as an extension of the user’s cognition.
Another notable example is the BrainGate neural device, created by researchers at Stanford University. Just two months ago, a study was released showing that this brain implant system allowed paralyzed patients to navigate an Android tablet with their thoughts alone.
These are two extraordinary examples of what is possible for the future of HMI, but there is still a long way to go to bring cognitive ergonomics front and center in interface design.
Disruptive Innovation Happens When You Step Outside Your Existing Users
Most of today’s interfaces are designed by a narrow population, made up predominantly of white, non-disabled men who are prolific in the use of technology (you may recall The New York Times viral article from 2016, Artificial Intelligence’s White Guy Problem). If you ask this population if there is a problem with today’s HMIs, most will say no, and this is because the technology has been designed to serve them.
This lack of diversity means a limited perspective is being brought to interface design, which is problematic if we want HMI to evolve and work seamlessly with the brain. To use cognitive ergonomics in interface design, we must first gain a more holistic understanding of how people with different abilities understand the world and how they interact with technology.
Underserved groups, such as people with physical disabilities, operate on what Clayton Christensen coined in The Innovator’s Dilemma as the fringe segment of a market. Developing solutions that cater to fringe groups can in fact disrupt the larger market by opening a downward, much larger market.
Learning From Underserved Populations
When technology fails to serve a group of people, that group must adapt the technology to meet their needs.
The workarounds created are often ingenious, specifically because they have not been arrived at by preferences, but out of necessity that has forced disadvantaged users to approach the technology from a very different vantage point.
When a designer or technologist begins learning from this new viewpoint and understanding challenges through a different lens, they can bring new perspectives to design—perspectives that otherwise can go unseen.
Designers and technologists can also learn from people with physical disabilities who interact with the world by leveraging other senses that help them compensate for one they may lack. For example, some blind people use echolocation to detect objects in their environments.
The BrainPort device developed by Wicab is an incredible example of technology leveraging one human sense to serve or compliment another. The BrainPort device captures environmental information with a wearable video camera and converts this data into soft electrical stimulation sequences that are sent to a device on the user’s tongue—the most sensitive touch receptor in the body. The user learns how to interpret the patterns felt on their tongue, and in doing so, become able to “see” with their tongue.
Key to the future of HMI design is learning how different user groups navigate the world through senses beyond sight. To make cognitive ergonomics work, we must understand how to leverage the senses so we’re not always solely relying on our visual or verbal interactions.
Radical Inclusion for the Future of HMI
Bringing radical inclusion into HMI design is about gaining a broader lens on technology design at large, so that technology can serve everyone better.
Interestingly, cognitive ergonomics and radical inclusion go hand in hand. We can’t design our interfaces with cognitive ergonomics without bringing radical inclusion into the picture, and we also will not arrive at radical inclusion in technology so long as cognitive ergonomics are not considered.
This new mindset is the only way to usher in an era of technology design that amplifies the collective human ability to create a more inclusive future for all.
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The AI Cold War That Could Doom Us All
Nicholas Thompson | Wired
“At the dawn of a new stage in the digital revolution, the world’s two most powerful nations are rapidly retreating into positions of competitive isolation, like players across a Go board. …Is the arc of the digital revolution bending toward tyranny, and is there any way to stop it?”
Finally, the Drug That Keeps You Young
Stephen S. Hall | MIT Technology Review
“The other thing that has changed is that the field of senescence—and the recognition that senescent cells can be such drivers of aging—has finally gained acceptance. Whether those drugs will work in people is still an open question. But the first human trials are under way right now.”
Ginkgo Bioworks Is Turning Human Cells Into On-Demand Factories
Megan Molteni | Wired
“The biotech unicorn is already cranking out an impressive number of microbial biofactories that grow and multiply and burp out fragrances, fertilizers, and soon, psychoactive substances. And they do it at a fraction of the cost of traditional systems. But Kelly is thinking even bigger.”
Thousands of Swedes Are Inserting Microchips Under Their Skin
Maddy Savage | NPR
“Around the size of a grain of rice, the chips typically are inserted into the skin just above each user’s thumb, using a syringe similar to that used for giving vaccinations. The procedure costs about $180. So many Swedes are lining up to get the microchips that the country’s main chipping company says it can’t keep up with the number of requests.”
AI Art at Christie’s Sells for $432,500
Gabe Cohn | The New York Times
“Last Friday, a portrait produced by artificial intelligence was hanging at Christie’s New York opposite an Andy Warhol print and beside a bronze work by Roy Lichtenstein. On Thursday, it sold for well over double the price realized by both those pieces combined.”
Should a Self-Driving Car Kill the Baby or the Grandma? Depends on Where You’re From
Karen Hao | MIT Technology Review
“The researchers never predicted the experiment’s viral reception. Four years after the platform went live, millions of people in 233 countries and territories have logged 40 million decisions, making it one of the largest studies ever done on global moral preferences.”
The Rodney Brooks Rules for Predicting a Technology’s Success
Rodney Brooks | IEEE Spectrum
“Building electric cars and reusable rockets is fairly easy. Building a nuclear fusion reactor, flying cars, self-driving cars, or a Hyperloop system is very hard. What makes the difference?”
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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.
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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In March 2011, Japan was hit by a catastrophic earthquake that triggered a terrible tsunami. Thousands were killed and billions of dollars of damage was done in one of the worst disasters of modern times. For a few perilous weeks, though, the eyes of the world were focused on the Fukushima Daiichi nuclear power plant. Its safety systems were unable to cope with the tsunami damage, and there were widespread fears of another catastrophic meltdown that could spread radiation over several countries, like the Chernobyl disaster in the 1980s. A heroic effort that included dumping seawater into the reactor core prevented an even bigger catastrophe. As it is, a hundred thousand people are still evacuated from the area, and it will likely take many years and hundreds of billions of dollars before the region is safe.
Because radiation is so dangerous to humans, the natural solution to the Fukushima disaster was to send in robots to monitor levels of radiation and attempt to begin the clean-up process. The techno-optimists in Japan had discovered a challenge, deep in the heart of that reactor core, that even their optimism could not solve. The radiation fried the circuits of the robots that were sent in, even those specifically designed and built to deal with the Fukushima catastrophe. The power plant slowly became a vast robot graveyard. While some robots initially saw success in measuring radiation levels around the plant—and, recently, a robot was able to identify the melted uranium fuel at the heart of the disaster—hopes of them playing a substantial role in the clean-up are starting to diminish.
In Tokyo’s neon Shibuya district, it can sometimes seem like it’s brighter at night than it is during the daytime. In karaoke booths on the twelfth floor—because everything is on the twelfth floor—overlooking the brightly-lit streets, businessmen unwind by blasting out pop hits. It can feel like the most artificial place on Earth; your senses are dazzled by the futuristic techno-optimism. Stock footage of the area has become symbolic of futurism and modernity.
Japan has had a reputation for being a nation of futurists for a long time. We’ve already described how tech giant Softbank, headed by visionary founder Masayoshi Son, is investing billions in a technological future, including plans for the world’s largest solar farm.
When Google sold pioneering robotics company Boston Dynamics in 2017, Softbank added it to their portfolio, alongside the famous Nao and Pepper robots. Some may think that Son is taking a gamble in pursuing a robotics project even Google couldn’t succeed in, but this is a man who lost nearly everything in the dot-com crash of 2000. The fact that even this reversal didn’t dent his optimism and faith in technology is telling. But how long can it last?
The failure of Japan’s robots to deal with the immense challenge of Fukushima has sparked something of a crisis of conscience within the industry. Disaster response is an obvious stepping-stone technology for robots. Initially, producing a humanoid robot will be very costly, and the robot will be less capable than a human; building a robot to wait tables might not be particularly economical yet. Building a robot to do jobs that are too dangerous for humans is far more viable. Yet, at Fukushima, in one of the most advanced nations in the world, many of the robots weren’t up to the task.
Nowhere was this crisis more felt than Honda; the company had developed ASIMO, which stunned the world in 2000 and continues to fascinate as an iconic humanoid robot. Despite all this technological advancement, however, Honda knew that ASIMO was still too unreliable for the real world.
It was Fukushima that triggered a sea-change in Honda’s approach to robotics. Two years after the disaster, there were rumblings that Honda was developing a disaster robot, and in October 2017, the prototype was revealed to the public for the first time. It’s not yet ready for deployment in disaster zones, however. Interestingly, the creators chose not to give it dexterous hands but instead to assume that remotely-operated tools fitted to the robot would be a better solution for the range of circumstances it might encounter.
This shift in focus for humanoid robots away from entertainment and amusement like ASIMO, and towards being practically useful, has been mirrored across the world.
In 2015, also inspired by the Fukushima disaster and the lack of disaster-ready robots, the DARPA Robotics Challenge tested humanoid robots with a range of tasks that might be needed in emergency response, such as driving cars, opening doors, and climbing stairs. The Terminator-like ATLAS robot from Boston Dynamics, alongside Korean robot HUBO, took many of the plaudits, and CHIMP also put in an impressive display by being able to right itself after falling.
Yet the DARPA Robotics Challenge showed us just how far the robots are from truly being as useful as we’d like, or maybe even as we would imagine. Many robots took hours to complete the tasks, which were highly idealized to suit them. Climbing stairs proved a particular challenge. Those who watched were more likely to see a robot that had fallen over, struggling to get up, rather than heroic superbots striding in to save the day. The “striding” proved a particular problem, with the fastest robot HUBO managing this by resorting to wheels in its knees when the legs weren’t necessary.
Fukushima may have brought a sea-change over futuristic Japan, but before robots will really begin to enter our everyday lives, they will need to prove their worth. In the interim, aerial drone robots designed to examine infrastructure damage after disasters may well see earlier deployment and more success.
It’s a considerable challenge.
Building a humanoid robot is expensive; if these multi-million-dollar machines can’t help in a crisis, people may begin to question the worth of investing in them in the first place (unless your aim is just to make viral videos). This could lead to a further crisis of confidence among the Japanese, who are starting to rely on humanoid robotics as a solution to the crisis of the aging population. The Japanese government, as part of its robots strategy, has already invested $44 million in their development.
But if they continue to fail when put to the test, that will raise serious concerns. In Tokyo’s Akihabara district, you can see all kinds of flash robotic toys for sale in the neon-lit superstores, and dancing, acting robots like Robothespian can entertain crowds all over the world. But if we want these machines to be anything more than toys—partners, helpers, even saviors—more work needs to be done.
At the same time, those who participated in the DARPA Robotics Challenge in 2015 won’t be too concerned if people were underwhelmed by the performance of their disaster relief robots. Back in 2004, nearly every participant in the DARPA Grand Challenge crashed, caught fire, or failed on the starting line. To an outside observer, the whole thing would have seemed like an unmitigated disaster, and a pointless investment. What was the task in 2004? Developing a self-driving car. A lot can change in a decade.
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The longevity field is bustling but still fragmented, and the “silver tsunami” is coming.
That is the takeaway of The Science of Longevity, the behemoth first volume of a four-part series offering a bird’s-eye view of the longevity industry in 2017. The report, a joint production of the Biogerontology Research Foundation, Deep Knowledge Life Science, Aging Analytics Agency, and Longevity.International, synthesizes the growing array of academic and industry ventures related to aging, healthspan, and everything in between.
This is huge, not only in scale but also in ambition. The report, totally worth a read here, will be followed by four additional volumes in 2018, covering topics ranging from the business side of longevity ventures to financial systems to potential tensions between life extension and religion.
And that’s just the first step. The team hopes to publish updated versions of the report annually, giving scientists, investors, and regulatory agencies an easy way to keep their finger on the longevity pulse.
“In 2018, ‘aging’ remains an unnamed adversary in an undeclared war. For all intents and purposes it is mere abstraction in the eyes of regulatory authorities worldwide,” the authors write.
That needs to change.
People often arrive at the field of aging from disparate areas with wildly diverse opinions and strengths. The report compiles these individual efforts at cracking aging into a systematic resource—a “periodic table” for longevity that clearly lays out emerging trends and promising interventions.
The ultimate goal? A global framework serving as a road map to guide the burgeoning industry. With such a framework in hand, academics and industry alike are finally poised to petition the kind of large-scale investments and regulatory changes needed to tackle aging with a unified front.
Infographic depicting many of the key research hubs and non-profits within the field of geroscience.
Image Credit: Longevity.International
The Aging Globe
The global population is rapidly aging. And our medical and social systems aren’t ready to handle this oncoming “silver tsunami.”
Take the medical field. Many age-related diseases such as Alzheimer’s lack effective treatment options. Others, including high blood pressure, stroke, lung or heart problems, require continuous medication and monitoring, placing enormous strain on medical resources.
What’s more, because disease risk rises exponentially with age, medical care for the elderly becomes a game of whack-a-mole: curing any individual disease such as cancer only increases healthy lifespan by two to three years before another one hits.
That’s why in recent years there’s been increasing support for turning the focus to the root of the problem: aging. Rather than tackling individual diseases, geroscience aims to add healthy years to our lifespan—extending “healthspan,” so to speak.
Despite this relative consensus, the field still faces a roadblock. The US FDA does not yet recognize aging as a bona fide disease. Without such a designation, scientists are banned from testing potential interventions for aging in clinical trials (that said, many have used alternate measures such as age-related biomarkers or Alzheimer’s symptoms as a proxy).
Luckily, the FDA’s stance is set to change. The promising anti-aging drug metformin, for example, is already in clinical trials, examining its effect on a variety of age-related symptoms and diseases. This report, and others to follow, may help push progress along.
“It is critical for investors, policymakers, scientists, NGOs, and influential entities to prioritize the amelioration of the geriatric world scenario and recognize aging as a critical matter of global economic security,” the authors say.
The causes of aging are complex, stubborn, and not all clear.
But the report lays out two main streams of intervention with already promising results.
The first is to understand the root causes of aging and stop them before damage accumulates. It’s like meddling with cogs and other inner workings of a clock to slow it down, the authors say.
The report lays out several treatments to keep an eye on.
Geroprotective drugs is a big one. Often repurposed from drugs already on the market, these traditional small molecule drugs target a wide variety of metabolic pathways that play a role in aging. Think anti-oxidants, anti-inflammatory, and drugs that mimic caloric restriction, a proven way to extend healthspan in animal models.
More exciting are the emerging technologies. One is nanotechnology. Nanoparticles of carbon, “bucky-balls,” for example, have already been shown to fight viral infections and dangerous ion particles, as well as stimulate the immune system and extend lifespan in mice (though others question the validity of the results).
Blood is another promising, if surprising, fountain of youth: recent studies found that molecules in the blood of the young rejuvenate the heart, brain, and muscles of aged rodents, though many of these findings have yet to be replicated.
The second approach is repair and maintenance.
Rather than meddling with inner clockwork, here we force back the hands of a clock to set it back. The main example? Stem cell therapy.
This type of approach would especially benefit the brain, which harbors small, scattered numbers of stem cells that deplete with age. For neurodegenerative diseases like Alzheimer’s, in which neurons progressively die off, stem cell therapy could in theory replace those lost cells and mend those broken circuits.
Once a blue-sky idea, the discovery of induced pluripotent stem cells (iPSCs), where scientists can turn skin and other mature cells back into a stem-like state, hugely propelled the field into near reality. But to date, stem cells haven’t been widely adopted in clinics.
It’s “a toolkit of highly innovative, highly invasive technologies with clinical trials still a great many years off,” the authors say.
But there is a silver lining. The boom in 3D tissue printing offers an alternative approach to stem cells in replacing aging organs. Recent investment from the Methuselah Foundation and other institutions suggests interest remains high despite still being a ways from mainstream use.
A Disruptive Future
“We are finally beginning to see an industry emerge from mankind’s attempts to make sense of the biological chaos,” the authors conclude.
Looking through the trends, they identified several technologies rapidly gaining steam.
One is artificial intelligence, which is already used to bolster drug discovery. Machine learning may also help identify new longevity genes or bring personalized medicine to the clinic based on a patient’s records or biomarkers.
Another is senolytics, a class of drugs that kill off “zombie cells.” Over 10 prospective candidates are already in the pipeline, with some expected to enter the market in less than a decade, the authors say.
Finally, there’s the big gun—gene therapy. The treatment, unlike others mentioned, can directly target the root of any pathology. With a snip (or a swap), genetic tools can turn off damaging genes or switch on ones that promote a youthful profile. It is the most preventative technology at our disposal.
There have already been some success stories in animal models. Using gene therapy, rodents given a boost in telomerase activity, which lengthens the protective caps of DNA strands, live healthier for longer.
“Although it is the prospect farthest from widespread implementation, it may ultimately prove the most influential,” the authors say.
Ultimately, can we stop the silver tsunami before it strikes?
Perhaps not, the authors say. But we do have defenses: the technologies outlined in the report, though still immature, could one day stop the oncoming tidal wave in its tracks.
Now we just have to bring them out of the lab and into the real world. To push the transition along, the team launched Longevity.International, an online meeting ground that unites various stakeholders in the industry.
By providing scientists, entrepreneurs, investors, and policy-makers a platform for learning and discussion, the authors say, we may finally generate enough drive to implement our defenses against aging. The war has begun.
Read the report in full here, and watch out for others coming soon here. The second part of the report profiles 650 (!!!) longevity-focused research hubs, non-profits, scientists, conferences, and literature. It’s an enormously helpful resource—totally worth keeping it in your back pocket for future reference.
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