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#433386 What We Have to Gain From Making ...

The borders between the real world and the digital world keep crumbling, and the latter’s importance in both our personal and professional lives keeps growing. Some describe the melding of virtual and real worlds as part of the fourth industrial revolution. Said revolution’s full impact on us as individuals, our companies, communities, and societies is still unknown.

Greg Cross, chief business officer of New Zealand-based AI company Soul Machines, thinks one inescapable consequence of these crumbling borders is people spending more and more time interacting with technology. In a presentation at Singularity University’s Global Summit in San Francisco last month, Cross unveiled Soul Machines’ latest work and shared his views on the current state of human-like AI and where the technology may go in the near future.

Humanizing Technology Interaction
Cross started by introducing Rachel, one of Soul Machines’ “emotionally responsive digital humans.” The company has built 15 different digital humans of various sexes, groups, and ethnicities. Rachel, along with her “sisters” and “brothers,” has a virtual nervous system based on neural networks and biological models of different paths in the human brain. The system is controlled by virtual neurotransmitters and hormones akin to dopamine, serotonin, and oxytocin, which influence learning and behavior.

As a result, each digital human can have its own unique set of “feelings” and responses to interactions. People interact with them via visual and audio sensors, and the machines respond in real time.

“Over the last 20 or 30 years, the way we think about machines and the way we interact with machines has changed,” Cross said. “We’ve always had this view that they should actually be more human-like.”

The realism of the digital humans’ graphic representations comes thanks to the work of Soul Machines’ other co-founder, Dr. Mark Sager, who has won two Academy Awards for his work on some computer-generated movies, including James Cameron’s Avatar.

Cross pointed out, for example, that rather than being unrealistically flawless and clear, Rachel’s skin has blemishes and sun spots, just like real human skin would.

The Next Human-Machine Frontier
When people interact with each other face to face, emotional and intellectual engagement both heavily influence the interaction. What would it look like for machines to bring those same emotional and intellectual capacities to our interactions with them, and how would this type of interaction affect the way we use, relate to, and feel about AI?

Cross and his colleagues believe that humanizing artificial intelligence will make the technology more useful to humanity, and prompt people to use AI in more beneficial ways.

“What we think is a very important view as we move forward is that these machines can be more helpful to us. They can be more useful to us. They can be more interesting to us if they’re actually more like us,” Cross said.

It is an approach that seems to resonate with companies and organizations. For example, in the UK, where NatWest Bank is testing out Cora as a digital employee to help answer customer queries. In Germany, Daimler Financial Group plans to employ Sarah as something “similar to a personal concierge” for its customers. According to Cross, Daimler is looking at other ways it could deploy digital humans across the organization, from building digital service people, digital sales people, and maybe in the future, digital chauffeurs.

Soul Machines’ latest creation is Will, a digital teacher that can interact with children through a desktop, tablet, or mobile device and help them learn about renewable energy. Cross sees other social uses for digital humans, including potentially serving as doctors to rural communities.

Our Digital Friends—and Twins
Soul Machines is not alone in its quest to humanize technology. It is a direction many technology companies, including the likes of Amazon, also seem to be pursuing. Amazon is working on building a home robot that, according to Bloomberg, “could be a sort of mobile Alexa.”

Finding a more human form for technology seems like a particularly pervasive pursuit in Japan. Not just when it comes to its many, many robots, but also virtual assistants like Gatebox.

The Japanese approach was perhaps best summed up by famous android researcher Dr. Hiroshi Ishiguro, who I interviewed last year: “The human brain is set up to recognize and interact with humans. So, it makes sense to focus on developing the body for the AI mind, as well as the AI. I believe that the final goal for both Japanese and other companies and scientists is to create human-like interaction.”

During Cross’s presentation, Rob Nail, CEO and associate founder of Singularity University, joined him on the stage, extending an invitation to Rachel to be SU’s first fully digital faculty member. Rachel accepted, and though she’s the only digital faculty right now, she predicted this won’t be the case for long.

“In 10 years, all of you will have digital versions of yourself, just like me, to take on specific tasks and make your life a whole lot easier,” she said. “This is great news for me. I’ll have millions of digital friends.”

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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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#433270 Robots as tools and partners in ...

In future decades, the need for effective strategies for medical rehabilitation will increase significantly, because patients' rate of survival after diseases with severe functional deficits, such as a stroke, will increase. Socially assistive robots (SARs) are already being used in rehabilitation for this reason. In the journal Science Robotics, a research team led by neuroscientist Dr. Philipp Kellmeyer of the Freiburg University Medical Center and Prof. Dr. Oliver Müller from the Department of Philosophy of the University of Freiburg, analyzes the improvements necessary to make SARs valuable and trustworthy assistants for medical therapies. Continue reading

Posted in Human Robots

#432880 Google’s Duplex Raises the Question: ...

By now, you’ve probably seen Google’s new Duplex software, which promises to call people on your behalf to book appointments for haircuts and the like. As yet, it only exists in demo form, but already it seems like Google has made a big stride towards capturing a market that plenty of companies have had their eye on for quite some time. This software is impressive, but it raises questions.

Many of you will be familiar with the stilted, robotic conversations you can have with early chatbots that are, essentially, glorified menus. Instead of pressing 1 to confirm or 2 to re-enter, some of these bots would allow for simple commands like “Yes” or “No,” replacing the buttons with limited ability to recognize a few words. Using them was often a far more frustrating experience than attempting to use a menu—there are few things more irritating than a robot saying, “Sorry, your response was not recognized.”

Google Duplex scheduling a hair salon appointment:

Google Duplex calling a restaurant:

Even getting the response recognized is hard enough. After all, there are countless different nuances and accents to baffle voice recognition software, and endless turns of phrase that amount to saying the same thing that can confound natural language processing (NLP), especially if you like your phrasing quirky.

You may think that standard customer-service type conversations all travel the same route, using similar words and phrasing. But when there are over 80,000 ways to order coffee, and making a mistake is frowned upon, even simple tasks require high accuracy over a huge dataset.

Advances in audio processing, neural networks, and NLP, as well as raw computing power, have meant that basic recognition of what someone is trying to say is less of an issue. Soundhound’s virtual assistant prides itself on being able to process complicated requests (perhaps needlessly complicated).

The deeper issue, as with all attempts to develop conversational machines, is one of understanding context. There are so many ways a conversation can go that attempting to construct a conversation two or three layers deep quickly runs into problems. Multiply the thousands of things people might say by the thousands they might say next, and the combinatorics of the challenge runs away from most chatbots, leaving them as either glorified menus, gimmicks, or rather bizarre to talk to.

Yet Google, who surely remembers from Glass the risk of premature debuts for technology, especially the kind that ask you to rethink how you interact with or trust in software, must have faith in Duplex to show it on the world stage. We know that startups like Semantic Machines and x.ai have received serious funding to perform very similar functions, using natural-language conversations to perform computing tasks, schedule meetings, book hotels, or purchase items.

It’s no great leap to imagine Google will soon do the same, bringing us closer to a world of onboard computing, where Lens labels the world around us and their assistant arranges it for us (all the while gathering more and more data it can convert into personalized ads). The early demos showed some clever tricks for keeping the conversation within a fairly narrow realm where the AI should be comfortable and competent, and the blog post that accompanied the release shows just how much effort has gone into the technology.

Yet given the privacy and ethics funk the tech industry finds itself in, and people’s general unease about AI, the main reaction to Duplex’s impressive demo was concern. The voice sounded too natural, bringing to mind Lyrebird and their warnings of deepfakes. You might trust “Do the Right Thing” Google with this technology, but it could usher in an era when automated robo-callers are far more convincing.

A more human-like voice may sound like a perfectly innocuous improvement, but the fact that the assistant interjects naturalistic “umm” and “mm-hm” responses to more perfectly mimic a human rubbed a lot of people the wrong way. This wasn’t just a voice assistant trying to sound less grinding and robotic; it was actively trying to deceive people into thinking they were talking to a human.

Google is running the risk of trying to get to conversational AI by going straight through the uncanny valley.

“Google’s experiments do appear to have been designed to deceive,” said Dr. Thomas King of the Oxford Internet Institute’s Digital Ethics Lab, according to Techcrunch. “Their main hypothesis was ‘can you distinguish this from a real person?’ In this case it’s unclear why their hypothesis was about deception and not the user experience… there should be some kind of mechanism there to let people know what it is they are speaking to.”

From Google’s perspective, being able to say “90 percent of callers can’t tell the difference between this and a human personal assistant” is an excellent marketing ploy, even though statistics about how many interactions are successful might be more relevant.

In fact, Duplex runs contrary to pretty much every major recommendation about ethics for the use of robotics or artificial intelligence, not to mention certain eavesdropping laws. Transparency is key to holding machines (and the people who design them) accountable, especially when it comes to decision-making.

Then there are the more subtle social issues. One prominent effect social media has had is to allow people to silo themselves; in echo chambers of like-minded individuals, it’s hard to see how other opinions exist. Technology exacerbates this by removing the evolutionary cues that go along with face-to-face interaction. Confronted with a pair of human eyes, people are more generous. Confronted with a Twitter avatar or a Facebook interface, people hurl abuse and criticism they’d never dream of using in a public setting.

Now that we can use technology to interact with ever fewer people, will it change us? Is it fair to offload the burden of dealing with a robot onto the poor human at the other end of the line, who might have to deal with dozens of such calls a day? Google has said that if the AI is in trouble, it will put you through to a human, which might help save receptionists from the hell of trying to explain a concept to dozens of dumbfounded AI assistants all day. But there’s always the risk that failures will be blamed on the person and not the machine.

As AI advances, could we end up treating the dwindling number of people in these “customer-facing” roles as the buggiest part of a fully automatic service? Will people start accusing each other of being robots on the phone, as well as on Twitter?

Google has provided plenty of reassurances about how the system will be used. They have said they will ensure that the system is identified, and it’s hardly difficult to resolve this problem; a slight change in the script from their demo would do it. For now, consumers will likely appreciate moves that make it clear whether the “intelligent agents” that make major decisions for us, that we interact with daily, and that hide behind social media avatars or phone numbers are real or artificial.

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Posted in Human Robots

#432691 Is the Secret to Significantly Longer ...

Once upon a time, a powerful Sumerian king named Gilgamesh went on a quest, as such characters often do in these stories of myth and legend. Gilgamesh had witnessed the death of his best friend, Enkidu, and, fearing a similar fate, went in search of immortality. The great king failed to find the secret of eternal life but took solace that his deeds would live well beyond his mortal years.

Fast-forward four thousand years, give or take a century, and Gilgamesh (as famous as any B-list celebrity today, despite the passage of time) would probably be heartened to learn that many others have taken up his search for longevity. Today, though, instead of battling epic monsters and the machinations of fickle gods, those seeking to enhance and extend life are cutting-edge scientists and visionary entrepreneurs who are helping unlock the secrets of human biology.

Chief among them is Aubrey de Grey, a biomedical gerontologist who founded the SENS Research Foundation, a Silicon Valley-based research organization that seeks to advance the application of regenerative medicine to age-related diseases. SENS stands for Strategies for Engineered Negligible Senescence, a term coined by de Grey to describe a broad array (seven, to be precise) of medical interventions that attempt to repair or prevent different types of molecular and cellular damage that eventually lead to age-related diseases like cancer and Alzheimer’s.

Many of the strategies focus on senescent cells, which accumulate in tissues and organs as people age. Not quite dead, senescent cells stop dividing but are still metabolically active, spewing out all sorts of proteins and other molecules that can cause inflammation and other problems. In a young body, that’s usually not a problem (and probably part of general biological maintenance), as a healthy immune system can go to work to put out most fires.

However, as we age, senescent cells continue to accumulate, and at some point the immune system retires from fire watch. Welcome to old age.

Of Mice and Men
Researchers like de Grey believe that treating the cellular underpinnings of aging could not only prevent disease but significantly extend human lifespans. How long? Well, if you’re talking to de Grey, Biblical proportions—on the order of centuries.

De Grey says that science has made great strides toward that end in the last 15 years, such as the ability to copy mitochondrial DNA to the nucleus. Mitochondria serve as the power plant of the cell but are highly susceptible to mutations that lead to cellular degeneration. Copying the mitochondrial DNA into the nucleus would help protect it from damage.

Another achievement occurred about six years ago when scientists first figured out how to kill senescent cells. That discovery led to a spate of new experiments in mice indicating that removing these ticking-time-bomb cells prevented disease and even extended their lifespans. Now the anti-aging therapy is about to be tested in humans.

“As for the next few years, I think the stream of advances is likely to become a flood—once the first steps are made, things get progressively easier and faster,” de Grey tells Singularity Hub. “I think there’s a good chance that we will achieve really dramatic rejuvenation of mice within only six to eight years: maybe taking middle-aged mice and doubling their remaining lifespan, which is an order of magnitude more than can be done today.”

Not Horsing Around
Richard G.A. Faragher, a professor of biogerontology at the University of Brighton in the United Kingdom, recently made discoveries in the lab regarding the rejuvenation of senescent cells with chemical compounds found in foods like chocolate and red wine. He hopes to apply his findings to an animal model in the future—in this case,horses.

“We have been very fortunate in receiving some funding from an animal welfare charity to look at potential treatments for older horses,” he explains to Singularity Hub in an email. “I think this is a great idea. Many aspects of the physiology we are studying are common between horses and humans.”

What Faragher and his colleagues demonstrated in a paper published in BMC Cell Biology last year was that resveralogues, chemicals based on resveratrol, were able to reactivate a protein called a splicing factor that is involved in gene regulation. Within hours, the chemicals caused the cells to rejuvenate and start dividing like younger cells.

“If treatments work in our old pony systems, then I am sure they could be translated into clinical trials in humans,” Faragher says. “How long is purely a matter of money. Given suitable funding, I would hope to see a trial within five years.”

Show Them the Money
Faragher argues that the recent breakthroughs aren’t because a result of emerging technologies like artificial intelligence or the gene-editing tool CRISPR, but a paradigm shift in how scientists understand the underpinnings of cellular aging. Solving the “aging problem” isn’t a question of technology but of money, he says.

“Frankly, when AI and CRISPR have removed cystic fibrosis, Duchenne muscular dystrophy or Gaucher syndrome, I’ll be much more willing to hear tales of amazing progress. Go fix a single, highly penetrant genetic disease in the population using this flashy stuff and then we’ll talk,” he says. “My faith resides in the most potent technological development of all: money.”

De Grey is less flippant about the role that technology will play in the quest to defeat aging. AI, CRISPR, protein engineering, advances in stem cell therapies, and immune system engineering—all will have a part.

“There is not really anything distinctive about the ways in which these technologies will contribute,” he says. “What’s distinctive is that we will need all of these technologies, because there are so many different types of damage to repair and they each require different tricks.”

It’s in the Blood
A startup in the San Francisco Bay Area believes machines can play a big role in discovering the right combination of factors that lead to longer and healthier lives—and then develop drugs that exploit those findings.

BioAge Labs raised nearly $11 million last year for its machine learning platform that crunches big data sets to find blood factors, such as proteins or metabolites, that are tied to a person’s underlying biological age. The startup claims that these factors can predict how long a person will live.

“Our interest in this comes out of research into parabiosis, where joining the circulatory systems of old and young mice—so that they share the same blood—has been demonstrated to make old mice healthier and more robust,” Dr. Eric Morgen, chief medical officer at BioAge, tells Singularity Hub.

Based on that idea, he explains, it should be possible to alter those good or bad factors to produce a rejuvenating effect.

“Our main focus at BioAge is to identify these types of factors in our human cohort data, characterize the important molecular pathways they are involved in, and then drug those pathways,” he says. “This is a really hard problem, and we use machine learning to mine these complex datasets to determine which individual factors and molecular pathways best reflect biological age.”

Saving for the Future
Of course, there’s no telling when any of these anti-aging therapies will come to market. That’s why Forever Labs, a biotechnology startup out of Ann Arbor, Michigan, wants your stem cells now. The company offers a service to cryogenically freeze stem cells taken from bone marrow.

The theory behind the procedure, according to Forever Labs CEO Steven Clausnitzer, is based on research showing that stem cells may be a key component for repairing cellular damage. That’s because stem cells can develop into many different cell types and can divide endlessly to replenish other cells. Clausnitzer notes that there are upwards of a thousand clinical studies looking at using stem cells to treat age-related conditions such as cardiovascular disease.

However, stem cells come with their own expiration date, which usually coincides with the age that most people start experiencing serious health problems. Stem cells harvested from bone marrow at a younger age can potentially provide a therapeutic resource in the future.

“We believe strongly that by having access to your own best possible selves, you’re going to be well positioned to lead healthier, longer lives,” he tells Singularity Hub.

“There’s a compelling argument to be made that if you started to maintain the bone marrow population, the amount of nuclear cells in your bone marrow, and to re-up them so that they aren’t declining with age, it stands to reason that you could absolutely mitigate things like cardiovascular disease and stroke and Alzheimer’s,” he adds.

Clausnitzer notes that the stored stem cells can be used today in developing therapies to treat chronic conditions such as osteoarthritis. However, the more exciting prospect—and the reason he put his own 38-year-old stem cells on ice—is that he believes future stem cell therapies can help stave off the ravages of age-related disease.

“I can start reintroducing them not to treat age-related disease but to treat the decline in the stem-cell niche itself, so that I don’t ever get an age-related disease,” he says. “I don’t think that it equates to immortality, but it certainly is a step in that direction.”

Indecisive on Immortality
The societal implications of a longer-living human species are a guessing game at this point. We do know that by mid-century, the global population of those aged 65 and older will reach 1.6 billion, while those older than 80 will hit nearly 450 million, according to the National Academies of Science. If many of those people could enjoy healthy lives in their twilight years, an enormous medical cost could be avoided.

Faragher is certainly working toward a future where human health is ubiquitous. Human immortality is another question entirely.

“The longer lifespans become, the more heavily we may need to control birth rates and thus we may have fewer new minds. This could have a heavy ‘opportunity cost’ in terms of progress,” he says.

And does anyone truly want to live forever?

“There have been happy moments in my life but I have also suffered some traumatic disappointments. No [drug] will wash those experiences out of me,” Faragher says. “I no longer view my future with unqualified enthusiasm, and I do not think I am the only middle-aged man to feel that way. I don’t think it is an accident that so many ‘immortalists’ are young.

“They should be careful what they wish for.”

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Posted in Human Robots