Tag Archives: healthcare

#434637 AI Is Rapidly Augmenting Healthcare and ...

When it comes to the future of healthcare, perhaps the only technology more powerful than CRISPR is artificial intelligence.

Over the past five years, healthcare AI startups around the globe raised over $4.3 billion across 576 deals, topping all other industries in AI deal activity.

During this same period, the FDA has given 70 AI healthcare tools and devices ‘fast-tracked approval’ because of their ability to save both lives and money.

The pace of AI-augmented healthcare innovation is only accelerating.

In Part 3 of this blog series on longevity and vitality, I cover the different ways in which AI is augmenting our healthcare system, enabling us to live longer and healthier lives.

In this blog, I’ll expand on:

Machine learning and drug design
Artificial intelligence and big data in medicine
Healthcare, AI & China

Let’s dive in.

Machine Learning in Drug Design
What if AI systems, specifically neural networks, could predict the design of novel molecules (i.e. medicines) capable of targeting and curing any disease?

Imagine leveraging cutting-edge artificial intelligence to accomplish with 50 people what the pharmaceutical industry can barely do with an army of 5,000.

And what if these molecules, accurately engineered by AIs, always worked? Such a feat would revolutionize our $1.3 trillion global pharmaceutical industry, which currently holds a dismal record of 1 in 10 target drugs ever reaching human trials.

It’s no wonder that drug development is massively expensive and slow. It takes over 10 years to bring a new drug to market, with costs ranging from $2.5 billion to $12 billion.

This inefficient, slow-to-innovate, and risk-averse industry is a sitting duck for disruption in the years ahead.

One of the hottest startups in digital drug discovery today is Insilico Medicine. Leveraging AI in its end-to-end drug discovery pipeline, Insilico Medicine aims to extend healthy longevity through drug discovery and aging research.

Their comprehensive drug discovery engine uses millions of samples and multiple data types to discover signatures of disease, identify the most promising protein targets, and generate perfect molecules for these targets. These molecules either already exist or can be generated de novo with the desired set of parameters.

In late 2018, Insilico’s CEO Dr. Alex Zhavoronkov announced the groundbreaking result of generating novel molecules for a challenging protein target with an unprecedented hit rate in under 46 days. This included both synthesis of the molecules and experimental validation in a biological test system—an impressive feat made possible by converging exponential technologies.

Underpinning Insilico’s drug discovery pipeline is a novel machine learning technique called Generative Adversarial Networks (GANs), used in combination with deep reinforcement learning.

Generating novel molecular structures for diseases both with and without known targets, Insilico is now pursuing drug discovery in aging, cancer, fibrosis, Parkinson’s disease, Alzheimer’s disease, ALS, diabetes, and many others. Once rolled out, the implications will be profound.

Dr. Zhavoronkov’s ultimate goal is to develop a fully-automated Health-as-a-Service (HaaS) and Longevity-as-a-Service (LaaS) engine.

Once plugged into the services of companies from Alibaba to Alphabet, such an engine would enable personalized solutions for online users, helping them prevent diseases and maintain optimal health.

Insilico, alongside other companies tackling AI-powered drug discovery, truly represents the application of the 6 D’s. What was once a prohibitively expensive and human-intensive process is now rapidly becoming digitized, dematerialized, demonetized and, perhaps most importantly, democratized.

Companies like Insilico can now do with a fraction of the cost and personnel what the pharmaceutical industry can barely accomplish with thousands of employees and a hefty bill to foot.

As I discussed in my blog on ‘The Next Hundred-Billion-Dollar Opportunity,’ Google’s DeepMind has now turned its neural networks to healthcare, entering the digitized drug discovery arena.

In 2017, DeepMind achieved a phenomenal feat by matching the fidelity of medical experts in correctly diagnosing over 50 eye disorders.

And just a year later, DeepMind announced a new deep learning tool called AlphaFold. By predicting the elusive ways in which various proteins fold on the basis of their amino acid sequences, AlphaFold may soon have a tremendous impact in aiding drug discovery and fighting some of today’s most intractable diseases.

Artificial Intelligence and Data Crunching
AI is especially powerful in analyzing massive quantities of data to uncover patterns and insights that can save lives. Take WAVE, for instance. Every year, over 400,000 patients die prematurely in US hospitals as a result of heart attack or respiratory failure.

Yet these patients don’t die without leaving plenty of clues. Given information overload, however, human physicians and nurses alone have no way of processing and analyzing all necessary data in time to save these patients’ lives.

Enter WAVE, an algorithm that can process enough data to offer a six-hour early warning of patient deterioration.

Just last year, the FDA approved WAVE as an AI-based predictive patient surveillance system to predict and thereby prevent sudden death.

Another highly valuable yet difficult-to-parse mountain of medical data comprises the 2.5 million medical papers published each year.

For some time, it has become physically impossible for a human physician to read—let alone remember—all of the relevant published data.

To counter this compounding conundrum, Johnson & Johnson is teaching IBM Watson to read and understand scientific papers that detail clinical trial outcomes.

Enriching Watson’s data sources, Apple is also partnering with IBM to provide access to health data from mobile apps.

One such Watson system contains 40 million documents, ingesting an average of 27,000 new documents per day, and providing insights for thousands of users.

After only one year, Watson’s successful diagnosis rate of lung cancer has reached 90 percent, compared to the 50 percent success rate of human doctors.

But what about the vast amount of unstructured medical patient data that populates today’s ancient medical system? This includes medical notes, prescriptions, audio interview transcripts, and pathology and radiology reports.

In late 2018, Amazon announced a new HIPAA-eligible machine learning service that digests and parses unstructured data into categories, such as patient diagnoses, treatments, dosages, symptoms and signs.

Taha Kass-Hout, Amazon’s senior leader in health care and artificial intelligence, told the Wall Street Journal that internal tests demonstrated that the software even performs as well as or better than other published efforts.

On the heels of this announcement, Amazon confirmed it was teaming up with the Fred Hutchinson Cancer Research Center to evaluate “millions of clinical notes to extract and index medical conditions.”

Having already driven extraordinary algorithmic success rates in other fields, data is the healthcare industry’s goldmine for future innovation.

Healthcare, AI & China
In 2017, the Chinese government published its ambitious national plan to become a global leader in AI research by 2030, with healthcare listed as one of four core research areas during the first wave of the plan.

Just a year earlier, China began centralizing healthcare data, tackling a major roadblock to developing longevity and healthcare technologies (particularly AI systems): scattered, dispersed, and unlabeled patient data.

Backed by the Chinese government, China’s largest tech companies—particularly Tencent—have now made strong entrances into healthcare.

Just recently, Tencent participated in a $154 million megaround for China-based healthcare AI unicorn iCarbonX.

Hoping to develop a complete digital representation of your biological self, iCarbonX has acquired numerous US personalized medicine startups.

Considering Tencent’s own Miying healthcare AI platform—aimed at assisting healthcare institutions in AI-driven cancer diagnostics—Tencent is quickly expanding into the drug discovery space, participating in two multimillion-dollar, US-based AI drug discovery deals just this year.

China’s biggest, second-order move into the healthtech space comes through Tencent’s WeChat. In the course of a mere few years, already 60 percent of the 38,000 medical institutions registered on WeChat allow patients to digitally book appointments through Tencent’s mobile platform. At the same time, 2,000 Chinese hospitals accept WeChat payments.

Tencent has additionally partnered with the U.K.’s Babylon Health, a virtual healthcare assistant startup whose app now allows Chinese WeChat users to message their symptoms and receive immediate medical feedback.

Similarly, Alibaba’s healthtech focus started in 2016 when it released its cloud-based AI medical platform, ET Medical Brain, to augment healthcare processes through everything from diagnostics to intelligent scheduling.

Conclusion
As Nvidia CEO Jensen Huang has stated, “Software ate the world, but AI is going to eat software.” Extrapolating this statement to a more immediate implication, AI will first eat healthcare, resulting in dramatic acceleration of longevity research and an amplification of the human healthspan.

Next week, I’ll continue to explore this concept of AI systems in healthcare.

Particularly, I’ll expand on how we’re acquiring and using the data for these doctor-augmenting AI systems: from ubiquitous biosensors, to the mobile healthcare revolution, and finally, to the transformative power of the health nucleus.

As AI and other exponential technologies increase our healthspan by 30 to 40 years, how will you leverage these same exponential technologies to take on your moonshots and live out your massively transformative purpose?

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#434580 How Genome Sequencing and Senolytics Can ...

The causes of aging are extremely complex and unclear. With the dramatic demonetization of genome reading and editing over the past decade, and Big Pharma, startups, and the FDA starting to face aging as a disease, we are starting to find practical ways to extend our healthspan.

Here, in Part 2 of a series of blogs on longevity and vitality, I explore how genome sequencing and editing, along with new classes of anti-aging drugs, are augmenting our biology to further extend our healthy lives.

In this blog I’ll cover two classes of emerging technologies:

Genome Sequencing and Editing;
Senolytics, Nutraceuticals & Pharmaceuticals.

Let’s dive in.

Genome Sequencing & Editing
Your genome is the software that runs your body.

A sequence of 3.2 billion letters makes you “you.” These base pairs of A’s, T’s, C’s, and G’s determine your hair color, your height, your personality, your propensity to disease, your lifespan, and so on.

Until recently, it’s been very difficult to rapidly and cheaply “read” these letters—and even more difficult to understand what they mean.

Since 2001, the cost to sequence a whole human genome has plummeted exponentially, outpacing Moore’s Law threefold. From an initial cost of $3.7 billion, it dropped to $10 million in 2006, and to $5,000 in 2012.

Today, the cost of genome sequencing has dropped below $500, and according to Illumina, the world’s leading sequencing company, the process will soon cost about $100 and take about an hour to complete.

This represents one of the most powerful and transformative technology revolutions in healthcare.

When we understand your genome, we’ll be able to understand how to optimize “you.”

We’ll know the perfect foods, the perfect drugs, the perfect exercise regimen, and the perfect supplements, just for you.
We’ll understand what microbiome types, or gut flora, are ideal for you (more on this in a later blog).
We’ll accurately predict how specific sedatives and medicines will impact you.
We’ll learn which diseases and illnesses you’re most likely to develop and, more importantly, how to best prevent them from developing in the first place (rather than trying to cure them after the fact).

CRISPR Gene Editing
In addition to reading the human genome, scientists can now edit a genome using a naturally-occurring biological system discovered in 1987 called CRISPR/Cas9.

Short for Clustered Regularly Interspaced Short Palindromic Repeats and CRISPR-associated protein 9, the editing system was adapted from a naturally-occurring defense system found in bacteria.

Here’s how it works:

The bacteria capture snippets of DNA from invading viruses (or bacteriophage) and use them to create DNA segments known as CRISPR arrays.
The CRISPR arrays allow the bacteria to “remember” the viruses (or closely related ones), and defend against future invasions.
If the viruses attack again, the bacteria produce RNA segments from the CRISPR arrays to target the viruses’ DNA. The bacteria then use Cas9 to cut the DNA apart, which disables the virus.

Most importantly, CRISPR is cheap, quick, easy to use, and more accurate than all previous gene editing methods. As a result, CRISPR/Cas9 has swept through labs around the world as the way to edit a genome.

A short search in the literature will show an exponential rise in the number of CRISPR-related publications and patents.

2018: Filled With CRISPR Breakthroughs
Early results are impressive. Researchers from the University of Chicago recently used CRISPR to genetically engineer cocaine resistance into mice.

Researchers at the University of Texas Southwestern Medical Center used CRISPR to reverse the gene defect causing Duchenne muscular dystrophy (DMD) in dogs (DMD is the most common fatal genetic disease in children).

With great power comes great responsibility, and moral and ethical dilemmas.

In 2015, Chinese scientists sparked global controversy when they first edited human embryo cells in the lab with the goal of modifying genes that would make the child resistant to smallpox, HIV, and cholera.

Three years later, in November 2018, researcher He Jiankui informed the world that the first set of CRISPR-engineered female twins had been delivered.

To accomplish his goal, Jiankui deleted a region of a receptor on the surface of white blood cells known as CCR5, introducing a rare, natural genetic variation that makes it more difficult for HIV to infect its favorite target, white blood cells.

Setting aside the significant ethical conversations, CRISPR will soon provide us the tools to eliminate diseases, create hardier offspring, produce new environmentally resistant crops, and even wipe out pathogens.

Senolytics, Nutraceuticals & Pharmaceuticals
Over the arc of your life, the cells in your body divide until they reach what is known as the Hayflick limit, or the number of times a normal human cell population will divide before cell division stops, which is typically about 50 divisions.

What normally follows next is programmed cell death or destruction by the immune system. A very small fraction of cells, however, become senescent cells and evade this fate to linger indefinitely.

These lingering cells secrete a potent mix of molecules that triggers chronic inflammation, damages the surrounding tissue structures, and changes the behavior of nearby cells for the worse.

Senescent cells appear to be one of the root causes of aging, causing everything from fibrosis and blood vessel calcification, to localized inflammatory conditions such as osteoarthritis, to diminished lung function.

Fortunately, both the scientific and entrepreneurial communities have begun to work on senolytic therapies, moving the technology for selectively destroying senescent cells out of the laboratory and into a half-dozen startup companies.

Prominent companies in the field include the following:

Unity Biotechnology is developing senolytic medicines to selectively eliminate senescent cells with an initial focus on delivering localized therapy in osteoarthritis, ophthalmology and pulmonary disease.
Oisin Biotechnologiesis pioneering a programmable gene therapy that can destroy cells based on their internal biochemistry.
SIWA Therapeuticsis working on an immunotherapy approach to the problem of senescent cells.

In recent years, researchers have identified or designed a handful of senolytic compounds that can curb aging by regulating senescent cells. Two of these drugs that have gained mainstay research traction are rapamycin and metformin.

Rapamycin
Originally extracted from bacteria found on Easter Island, Rapamycin acts on the m-TOR (mechanistic target of rapamycin) pathway to selectively block a key protein that facilitates cell division.

Currently, rapamycin derivatives are widely used as immunosuppression in organ and bone marrow transplants. Research now suggests that use results in prolonged lifespan and enhanced cognitive and immune function.

PureTech Health subsidiary resTORbio (which started 2018 by going public) is working on a rapamycin-based drug intended to enhance immunity and reduce infection. Their clinical-stage RTB101 drug works by inhibiting part of the mTOR pathway.

Results of the drug’s recent clinical trial include:

Decreased incidence of infection
Improved influenza vaccination response
A 30.6 percent decrease in respiratory tract infections

Impressive, to say the least.

Metformin
Metformin is a widely-used generic drug for mitigating liver sugar production in Type 2 diabetes patients.

Researchers have found that Metformin also reduces oxidative stress and inflammation, which otherwise increase as we age.

There is strong evidence that Metformin can augment cellular regeneration and dramatically mitigate cellular senescence by reducing both oxidative stress and inflammation.

Over 100 studies registered on ClinicalTrials.gov are currently following up on strong evidence of Metformin’s protective effect against cancer.

Nutraceuticals and NAD+
Beyond cellular senescence, certain critical nutrients and proteins tend to decline as a function of age. Nutraceuticals combat aging by supplementing and replenishing these declining nutrient levels.

NAD+ exists in every cell, participating in every process from DNA repair to creating the energy vital for cellular processes. It’s been shown that NAD+ levels decline as we age.

The Elysium Health Basis supplement aims to elevate NAD+ levels in the body to extend one’s lifespan. Elysium’s clinical study reports that Basis increases NAD+ levels consistently by a sustained 40 percent.

Conclusion
These are just a taste of the tremendous momentum that longevity and aging technology has right now. As artificial intelligence and quantum computing transform how we decode our DNA and how we discover drugs, genetics and pharmaceuticals will become truly personalized.

The next blog in this series will demonstrate how artificial intelligence is converging with genetics and pharmaceuticals to transform how we approach longevity, aging, and vitality.

We are edging closer to a dramatically extended healthspan—where 100 is the new 60. What will you create, where will you explore, and how will you spend your time if you are able to add an additional 40 healthy years to your life?

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Posted in Human Robots

#434534 To Extend Our Longevity, First We Must ...

Healthcare today is reactive, retrospective, bureaucratic, and expensive. It’s sick care, not healthcare.

But that is radically changing at an exponential rate.

Through this multi-part blog series on longevity, I’ll take a deep dive into aging, longevity, and healthcare technologies that are working together to dramatically extend the human lifespan, disrupting the $3 trillion healthcare system in the process.

I’ll begin the series by explaining the nine hallmarks of aging, as explained in this journal article. Next, I’ll break down the emerging technologies and initiatives working to combat these nine hallmarks. Finally, I’ll explore the transformative implications of dramatically extending the human health span.

In this blog I’ll cover:

Why the healthcare system is broken
Why, despite this, we live in the healthiest time in human history
The nine mechanisms of aging

Let’s dive in.

The System is Broken—Here’s the Data:

Doctors spend $210 billion per year on procedures that aren’t based on patient need, but fear of liability.
Americans spend, on average, $8,915 per person on healthcare—more than any other country on Earth.
Prescription drugs cost around 50 percent more in the US than in other industrialized countries.
At current rates, by 2025, nearly 25 percent of the US GDP will be spent on healthcare.
It takes 12 years and $359 million, on average, to take a new drug from the lab to a patient.
Only 5 in 5,000 of these new drugs proceed to human testing. From there, only 1 of those 5 is actually approved for human use.

And Yet, We Live in the Healthiest Time in Human History
Consider these insights, which I adapted from Max Roser’s excellent database Our World in Data:

Right now, the countries with the lowest life expectancy in the world still have higher life expectancies than the countries with the highest life expectancy did in 1800.
In 1841, a 5-year-old had a life expectancy of 55 years. Today, a 5-year-old can expect to live 82 years—an increase of 27 years.
We’re seeing a dramatic increase in healthspan. In 1845, a newborn would expect to live to 40 years old. For a 70-year-old, that number became 79. Now, people of all ages can expect to live to be 81 to 86 years old.
100 years ago, 1 of 3 children would die before the age of 5. As of 2015, the child mortality rate fell to just 4.3 percent.
The cancer mortality rate has declined 27 percent over the past 25 years.

Figure: Around the globe, life expectancy has doubled since the 1800s. | Image from Life Expectancy by Max Roser – Our World in Data / CC BY SA
Figure: A dramatic reduction in child mortality in 1800 vs. in 2015. | Image from Child Mortality by Max Roser – Our World in Data / CC BY SA
The 9 Mechanisms of Aging
*This section was adapted from CB INSIGHTS: The Future Of Aging.

Longevity, healthcare, and aging are intimately linked.

With better healthcare, we can better treat some of the leading causes of death, impacting how long we live.

By investigating how to treat diseases, we’ll inevitably better understand what causes these diseases in the first place, which directly correlates to why we age.

Following are the nine hallmarks of aging. I’ll share examples of health and longevity technologies addressing each of these later in this blog series.

Genomic instability: As we age, the environment and normal cellular processes cause damage to our genes. Activities like flying at high altitude, for example, expose us to increased radiation or free radicals. This damage compounds over the course of life and is known to accelerate aging.
Telomere attrition: Each strand of DNA in the body (known as chromosomes) is capped by telomeres. These short snippets of DNA repeated thousands of times are designed to protect the bulk of the chromosome. Telomeres shorten as our DNA replicates; if a telomere reaches a certain critical shortness, a cell will stop dividing, resulting in increased incidence of disease.
Epigenetic alterations: Over time, environmental factors will change how genes are expressed, i.e., how certain sequences of DNA are read and the instruction set implemented.
Loss of proteostasis: Over time, different proteins in our body will no longer fold and function as they are supposed to, resulting in diseases ranging from cancer to neurological disorders.
Deregulated nutrient-sensing: Nutrient levels in the body can influence various metabolic pathways. Among the affected parts of these pathways are proteins like IGF-1, mTOR, sirtuins, and AMPK. Changing levels of these proteins’ pathways has implications on longevity.
Mitochondrial dysfunction: Mitochondria (our cellular power plants) begin to decline in performance as we age. Decreased performance results in excess fatigue and other symptoms of chronic illnesses associated with aging.
Cellular senescence: As cells age, they stop dividing and cannot be removed from the body. They build up and typically cause increased inflammation.
Stem cell exhaustion: As we age, our supply of stem cells begins to diminish as much as 100 to 10,000-fold in different tissues and organs. In addition, stem cells undergo genetic mutations, which reduce their quality and effectiveness at renovating and repairing the body.
Altered intercellular communication: The communication mechanisms that cells use are disrupted as cells age, resulting in decreased ability to transmit information between cells.

Conclusion
Over the past 200 years, we have seen an abundance of healthcare technologies enable a massive lifespan boom.

Now, exponential technologies like artificial intelligence, 3D printing and sensors, as well as tremendous advancements in genomics, stem cell research, chemistry, and many other fields, are beginning to tackle the fundamental issues of why we age.

In the next blog in this series, we will dive into how genome sequencing and editing, along with new classes of drugs, are augmenting our biology to further extend our healthy lives.

What will you be able to achieve with an extra 30 to 50 healthy years (or longer) in your lifespan? Personally, I’m excited for a near-infinite lifespan to take on moonshots.

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#434246 How AR and VR Will Shape the Future of ...

How we work and play is about to transform.

After a prolonged technology “winter”—or what I like to call the ‘deceptive growth’ phase of any exponential technology—the hardware and software that power virtual (VR) and augmented reality (AR) applications are accelerating at an extraordinary rate.

Unprecedented new applications in almost every industry are exploding onto the scene.

Both VR and AR, combined with artificial intelligence, will significantly disrupt the “middleman” and make our lives “auto-magical.” The implications will touch every aspect of our lives, from education and real estate to healthcare and manufacturing.

The Future of Work
How and where we work is already changing, thanks to exponential technologies like artificial intelligence and robotics.

But virtual and augmented reality are taking the future workplace to an entirely new level.

Virtual Reality Case Study: eXp Realty

I recently interviewed Glenn Sanford, who founded eXp Realty in 2008 (imagine: a real estate company on the heels of the housing market collapse) and is the CEO of eXp World Holdings.

Ten years later, eXp Realty has an army of 14,000 agents across all 50 US states, three Canadian provinces, and 400 MLS market areas… all without a single traditional staffed office.

In a bid to transition from 2D interfaces to immersive, 3D work experiences, virtual platform VirBELA built out the company’s office space in VR, unlocking indefinite scaling potential and an extraordinary new precedent.

Real estate agents, managers, and even clients gather in a unique virtual campus, replete with a sports field, library, and lobby. It’s all accessible via head-mounted displays, but most agents join with a computer browser. Surprisingly, the campus-style setup enables the same type of water-cooler conversations I see every day at the XPRIZE headquarters.

With this centralized VR campus, eXp Realty has essentially thrown out overhead costs and entered a lucrative market without the same constraints of brick-and-mortar businesses.

Delocalize with VR, and you can now hire anyone with internet access (right next door or on the other side of the planet), redesign your corporate office every month, throw in an ocean-view office or impromptu conference room for client meetings, and forget about guzzled-up hours in traffic.

As a leader, what happens when you can scalably expand and connect your workforce, not to mention your customer base, without the excess overhead of office space and furniture? Your organization can run faster and farther than your competition.

But beyond the indefinite scalability achieved through digitizing your workplace, VR’s implications extend to the lives of your employees and even the future of urban planning:

Home Prices: As virtual headquarters and office branches take hold of the 21st-century workplace, those who work on campuses like eXp Realty’s won’t need to commute to work. As a result, VR has the potential to dramatically influence real estate prices—after all, if you don’t need to drive to an office, your home search isn’t limited to a specific set of neighborhoods anymore.

Transportation: In major cities like Los Angeles and San Francisco, the implications are tremendous. Analysts have revealed that it’s already cheaper to use ride-sharing services like Uber and Lyft than to own a car in many major cities. And once autonomous “Car-as-a-Service” platforms proliferate, associated transportation costs like parking fees, fuel, and auto repairs will no longer fall on the individual, if not entirely disappear.

Augmented Reality: Annotate and Interact with Your Workplace

As I discussed in a recent Spatial Web blog, not only will Web 3.0 and VR advancements allow us to build out virtual worlds, but we’ll soon be able to digitally map our real-world physical offices or entire commercial high-rises.

Enter a professional world electrified by augmented reality.

Our workplaces are practically littered with information. File cabinets abound with archival data and relevant documents, and company databases continue to grow at a breakneck pace. And, as all of us are increasingly aware, cybersecurity and robust data permission systems remain a major concern for CEOs and national security officials alike.

What if we could link that information to specific locations, people, time frames, and even moving objects?

As data gets added and linked to any given employee’s office, conference room, or security system, we might then access online-merge-offline environments and information through augmented reality.

Imagine showing up at your building’s concierge and your AR glasses automatically check you into the building, authenticating your identity and pulling up any reminders you’ve linked to that specific location.

You stop by a friend’s office, and his smart security system lets you know he’ll arrive in an hour. Need to book a public conference room that’s already been scheduled by another firm’s marketing team? Offer to pay them a fee and, once accepted, a smart transaction will automatically deliver a payment to their company account.

With blockchain-verified digital identities, spatially logged data, and virtually manifest information, business logistics take a fraction of the time, operations grow seamless, and corporate data will be safer than ever.

Or better yet, imagine precise and high-dexterity work environments populated with interactive annotations that guide an artisan, surgeon, or engineer through meticulous handiwork.

Take, for instance, AR service 3D4Medical, which annotates virtual anatomy in midair. And as augmented reality hardware continues to advance, we might envision a future wherein surgeons perform operations on annotated organs and magnified incision sites, or one in which quantum computer engineers can magnify and annotate mechanical parts, speeding up reaction times and vastly improving precision.

The Future of Free Time and Play
In Abundance, I wrote about today’s rapidly demonetizing cost of living. In 2011, almost 75 percent of the average American’s income was spent on housing, transportation, food, personal insurance, health, and entertainment. What the headlines don’t mention: this is a dramatic improvement over the last 50 years. We’re spending less on basic necessities and working fewer hours than previous generations.

Chart depicts the average weekly work hours for full-time production employees in non-agricultural activities. Source: Diamandis.com data
Technology continues to change this, continues to take care of us and do our work for us. One phrase that describes this is “technological socialism,” where it’s technology, not the government, that takes care of us.

Extrapolating from the data, I believe we are heading towards a post-scarcity economy. Perhaps we won’t need to work at all, because we’ll own and operate our own fleet of robots or AI systems that do our work for us.

As living expenses demonetize and workplace automation increases, what will we do with this abundance of time? How will our children and grandchildren connect and find their purpose if they don’t have to work for a living?

As I write this on a Saturday afternoon and watch my two seven-year-old boys immersed in Minecraft, building and exploring worlds of their own creation, I can’t help but imagine that this future is about to enter its disruptive phase.

Exponential technologies are enabling a new wave of highly immersive games, virtual worlds, and online communities. We’ve likely all heard of the Oasis from Ready Player One. But far beyond what we know today as ‘gaming,’ VR is fast becoming a home to immersive storytelling, interactive films, and virtual world creation.

Within the virtual world space, let’s take one of today’s greatest precursors, the aforementioned game Minecraft.

For reference, Minecraft is over eight times the size of planet Earth. And in their free time, my kids would rather build in Minecraft than almost any other activity. I think of it as their primary passion: to create worlds, explore worlds, and be challenged in worlds.

And in the near future, we’re all going to become creators of or participants in virtual worlds, each populated with assets and storylines interoperable with other virtual environments.

But while the technological methods are new, this concept has been alive and well for generations. Whether you got lost in the world of Heidi or Harry Potter, grew up reading comic books or watching television, we’ve all been playing in imaginary worlds, with characters and story arcs populating our minds. That’s the nature of childhood.

In the past, however, your ability to edit was limited, especially if a given story came in some form of 2D media. I couldn’t edit where Tom Sawyer was going or change what Iron Man was doing. But as a slew of new software advancements underlying VR and AR allow us to interact with characters and gain (albeit limited) agency (for now), both new and legacy stories will become subjects of our creation and playgrounds for virtual interaction.

Take VR/AR storytelling startup Fable Studio’s Wolves in the Walls film. Debuting at the 2018 Sundance Film Festival, Fable’s immersive story is adapted from Neil Gaiman’s book and tracks the protagonist, Lucy, whose programming allows her to respond differently based on what her viewers do.

And while Lucy can merely hand virtual cameras to her viewers among other limited tasks, Fable Studio’s founder Edward Saatchi sees this project as just the beginning.

Imagine a virtual character—either in augmented or virtual reality—geared with AI capabilities, that now can not only participate in a fictional storyline but interact and dialogue directly with you in a host of virtual and digitally overlayed environments.

Or imagine engaging with a less-structured environment, like the Star Wars cantina, populated with strangers and friends to provide an entirely novel social media experience.

Already, we’ve seen characters like that of Pokémon brought into the real world with Pokémon Go, populating cities and real spaces with holograms and tasks. And just as augmented reality has the power to turn our physical environments into digital gaming platforms, advanced AR could bring on a new era of in-home entertainment.

Imagine transforming your home into a narrative environment for your kids or overlaying your office interior design with Picasso paintings and gothic architecture. As computer vision rapidly grows capable of identifying objects and mapping virtual overlays atop them, we might also one day be able to project home theaters or live sports within our homes, broadcasting full holograms that allow us to zoom into the action and place ourselves within it.

Increasingly honed and commercialized, augmented and virtual reality are on the cusp of revolutionizing the way we play, tell stories, create worlds, and interact with both fictional characters and each other.

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#434151 Life-or-Death Algorithms: The Black Box ...

When it comes to applications for machine learning, few can be more widely hyped than medicine. This is hardly surprising: it’s a huge industry that generates a phenomenal amount of data and revenue, where technological advances can improve or save the lives of millions of people. Hardly a week passes without a study that suggests algorithms will soon be better than experts at detecting pneumonia, or Alzheimer’s—diseases in complex organs ranging from the eye to the heart.

The problems of overcrowded hospitals and overworked medical staff plague public healthcare systems like Britain’s NHS and lead to rising costs for private healthcare systems. Here, again, algorithms offer a tantalizing solution. How many of those doctor’s visits really need to happen? How many could be replaced by an interaction with an intelligent chatbot—especially if it can be combined with portable diagnostic tests, utilizing the latest in biotechnology? That way, unnecessary visits could be reduced, and patients could be diagnosed and referred to specialists more quickly without waiting for an initial consultation.

As ever with artificial intelligence algorithms, the aim is not to replace doctors, but to give them tools to reduce the mundane or repetitive parts of the job. With an AI that can examine thousands of scans in a minute, the “dull drudgery” is left to machines, and the doctors are freed to concentrate on the parts of the job that require more complex, subtle, experience-based judgement of the best treatments and the needs of the patient.

High Stakes
But, as ever with AI algorithms, there are risks involved with relying on them—even for tasks that are considered mundane. The problems of black-box algorithms that make inexplicable decisions are bad enough when you’re trying to understand why that automated hiring chatbot was unimpressed by your job interview performance. In a healthcare context, where the decisions made could mean life or death, the consequences of algorithmic failure could be grave.

A new paper in Science Translational Medicine, by Nicholson Price, explores some of the promises and pitfalls of using these algorithms in the data-rich medical environment.

Neural networks excel at churning through vast quantities of training data and making connections, absorbing the underlying patterns or logic for the system in hidden layers of linear algebra; whether it’s detecting skin cancer from photographs or learning to write in pseudo-Shakespearean script. They are terrible, however, at explaining the underlying logic behind the relationships that they’ve found: there is often little more than a string of numbers, the statistical “weights” between the layers. They struggle to distinguish between correlation and causation.

This raises interesting dilemmas for healthcare providers. The dream of big data in medicine is to feed a neural network on “huge troves of health data, finding complex, implicit relationships and making individualized assessments for patients.” What if, inevitably, such an algorithm proves to be unreasonably effective at diagnosing a medical condition or prescribing a treatment, but you have no scientific understanding of how this link actually works?

Too Many Threads to Unravel?
The statistical models that underlie such neural networks often assume that variables are independent of each other, but in a complex, interacting system like the human body, this is not always the case.

In some ways, this is a familiar concept in medical science—there are many phenomena and links which have been observed for decades but are still poorly understood on a biological level. Paracetamol is one of the most commonly-prescribed painkillers, but there’s still robust debate about how it actually works. Medical practitioners may be keen to deploy whatever tool is most effective, regardless of whether it’s based on a deeper scientific understanding. Fans of the Copenhagen interpretation of quantum mechanics might spin this as “Shut up and medicate!”

But as in that field, there’s a debate to be had about whether this approach risks losing sight of a deeper understanding that will ultimately prove more fruitful—for example, for drug discovery.

Away from the philosophical weeds, there are more practical problems: if you don’t understand how a black-box medical algorithm is operating, how should you approach the issues of clinical trials and regulation?

Price points out that, in the US, the “21st-Century Cures Act” allows the FDA to regulate any algorithm that analyzes images, or doesn’t allow a provider to review the basis for its conclusions: this could completely exclude “black-box” algorithms of the kind described above from use.

Transparency about how the algorithm functions—the data it looks at, and the thresholds for drawing conclusions or providing medical advice—may be required, but could also conflict with the profit motive and the desire for secrecy in healthcare startups.

One solution might be to screen algorithms that can’t explain themselves, or don’t rely on well-understood medical science, from use before they enter the healthcare market. But this could prevent people from reaping the benefits that they can provide.

Evaluating Algorithms
New healthcare algorithms will be unable to do what physicists did with quantum mechanics, and point to a track record of success, because they will not have been deployed in the field. And, as Price notes, many algorithms will improve as they’re deployed in the field for a greater amount of time, and can harvest and learn from the performance data that’s actually used. So how can we choose between the most promising approaches?

Creating a standardized clinical trial and validation system that’s equally valid across algorithms that function in different ways, or use different input or training data, will be a difficult task. Clinical trials that rely on small sample sizes, such as for algorithms that attempt to personalize treatment to individuals, will also prove difficult. With a small sample size and little scientific understanding, it’s hard to tell whether the algorithm succeeded or failed because it’s bad at its job or by chance.

Add learning into the mix and the picture gets more complex. “Perhaps more importantly, to the extent that an ideal black-box algorithm is plastic and frequently updated, the clinical trial validation model breaks down further, because the model depends on a static product subject to stable validation.” As Price describes, the current system for testing and validation of medical products needs some adaptation to deal with this new software before it can successfully test and validate the new algorithms.

Striking a Balance
The story in healthcare reflects the AI story in so many other fields, and the complexities involved perhaps illustrate why even an illustrious company like IBM appears to be struggling to turn its famed Watson AI into a viable product in the healthcare space.

A balance must be struck, both in our rush to exploit big data and the eerie power of neural networks, and to automate thinking. We must be aware of the biases and flaws of this approach to problem-solving: to realize that it is not a foolproof panacea.

But we also need to embrace these technologies where they can be a useful complement to the skills, insights, and deeper understanding that humans can provide. Much like a neural network, our industries need to train themselves to enhance this cooperation in the future.

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