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#434673 The World’s Most Valuable AI ...

It recognizes our faces. It knows the videos we might like. And it can even, perhaps, recommend the best course of action to take to maximize our personal health.

Artificial intelligence and its subset of disciplines—such as machine learning, natural language processing, and computer vision—are seemingly becoming integrated into our daily lives whether we like it or not. What was once sci-fi is now ubiquitous research and development in company and university labs around the world.

Similarly, the startups working on many of these AI technologies have seen their proverbial stock rise. More than 30 of these companies are now valued at over a billion dollars, according to data research firm CB Insights, which itself employs algorithms to provide insights into the tech business world.

Private companies with a billion-dollar valuation were so uncommon not that long ago that they were dubbed unicorns. Now there are 325 of these once-rare creatures, with a combined valuation north of a trillion dollars, as CB Insights maintains a running count of this exclusive Unicorn Club.

The subset of AI startups accounts for about 10 percent of the total membership, growing rapidly in just 4 years from 0 to 32. Last year, an unprecedented 17 AI startups broke the billion-dollar barrier, with 2018 also a record year for venture capital into private US AI companies at $9.3 billion, CB Insights reported.

What exactly is all this money funding?

AI Keeps an Eye Out for You
Let’s start with the bad news first.

Facial recognition is probably one of the most ubiquitous applications of AI today. It’s actually a decades-old technology often credited to a man named Woodrow Bledsoe, who used an instrument called a RAND tablet that could semi-autonomously match faces from a database. That was in the 1960s.

Today, most of us are familiar with facial recognition as a way to unlock our smartphones. But the technology has gained notoriety as a surveillance tool of law enforcement, particularly in China.

It’s no secret that the facial recognition algorithms developed by several of the AI unicorns from China—SenseTime, CloudWalk, and Face++ (also known as Megvii)—are used to monitor the country’s 1.3 billion citizens. Police there are even equipped with AI-powered eyeglasses for such purposes.

A fourth billion-dollar Chinese startup, Yitu Technologies, also produces a platform for facial recognition in the security realm, and develops AI systems in healthcare on top of that. For example, its CARE.AITM Intelligent 4D Imaging System for Chest CT can reputedly identify in real time a variety of lesions for the possible early detection of cancer.

The AI Doctor Is In
As Peter Diamandis recently noted, AI is rapidly augmenting healthcare and longevity. He mentioned another AI unicorn from China in this regard—iCarbonX, which plans to use machines to develop personalized health plans for every individual.

A couple of AI unicorns on the hardware side of healthcare are OrCam Technologies and Butterfly. The former, an Israeli company, has developed a wearable device for the vision impaired called MyEye that attaches to one’s eyeglasses. The device can identify people and products, as well as read text, conveying the information through discrete audio.

Butterfly Network, out of Connecticut, has completely upended the healthcare market with a handheld ultrasound machine that works with a smartphone.

“Orcam and Butterfly are amazing examples of how machine learning can be integrated into solutions that provide a step-function improvement over state of the art in ultra-competitive markets,” noted Andrew Byrnes, investment director at Comet Labs, a venture capital firm focused on AI and robotics, in an email exchange with Singularity Hub.

AI in the Driver’s Seat
Comet Labs’ portfolio includes two AI unicorns, Megvii and Pony.ai.

The latter is one of three billion-dollar startups developing the AI technology behind self-driving cars, with the other two being Momenta.ai and Zoox.

Founded in 2016 near San Francisco (with another headquarters in China), Pony.ai debuted its latest self-driving system, called PonyAlpha, last year. The platform uses multiple sensors (LiDAR, cameras, and radar) to navigate its environment, but its “sensor fusion technology” makes things simple by choosing the most reliable sensor data for any given driving scenario.

Zoox is another San Francisco area startup founded a couple of years earlier. In late 2018, it got the green light from the state of California to be the first autonomous vehicle company to transport a passenger as part of a pilot program. Meanwhile, China-based Momenta.ai is testing level four autonomy for its self-driving system. Autonomous driving levels are ranked zero to five, with level five being equal to a human behind the wheel.

The hype around autonomous driving is currently in overdrive, and Byrnes thinks regulatory roadblocks will keep most self-driving cars in idle for the foreseeable future. The exception, he said, is China, which is adopting a “systems” approach to autonomy for passenger transport.

“If [autonomous mobility] solves bigger problems like traffic that can elicit government backing, then that has the potential to go big fast,” he said. “This is why we believe Pony.ai will be a winner in the space.”

AI in the Back Office
An AI-powered technology that perhaps only fans of the cult classic Office Space might appreciate has suddenly taken the business world by storm—robotic process automation (RPA).

RPA companies take the mundane back office work, such as filling out invoices or processing insurance claims, and turn it over to bots. The intelligent part comes into play because these bots can tackle unstructured data, such as text in an email or even video and pictures, in order to accomplish an increasing variety of tasks.

Both Automation Anywhere and UiPath are older companies, founded in 2003 and 2005, respectively. However, since just 2017, they have raised nearly a combined $1 billion in disclosed capital.

Cybersecurity Embraces AI
Cybersecurity is another industry where AI is driving investment into startups. Sporting imposing names like CrowdStrike, Darktrace, and Tanium, these cybersecurity companies employ different machine-learning techniques to protect computers and other IT assets beyond the latest software update or virus scan.

Darktrace, for instance, takes its inspiration from the human immune system. Its algorithms can purportedly “learn” the unique pattern of each device and user on a network, detecting emerging problems before things spin out of control.

All three companies are used by major corporations and governments around the world. CrowdStrike itself made headlines a few years ago when it linked the hacking of the Democratic National Committee email servers to the Russian government.

Looking Forward
I could go on, and introduce you to the world’s most valuable startup, a Chinese company called Bytedance that is valued at $75 billion for news curation and an app to create 15-second viral videos. But that’s probably not where VC firms like Comet Labs are generally putting their money.

Byrnes sees real value in startups that are taking “data-driven approaches to problems specific to unique industries.” Take the example of Chicago-based unicorn Uptake Technologies, which analyzes incoming data from machines, from wind turbines to tractors, to predict problems before they occur with the machinery. A not-yet unicorn called PingThings in the Comet Labs portfolio does similar predictive analytics for the energy utilities sector.

“One question we like asking is, ‘What does the state of the art look like in your industry in three to five years?’” Byrnes said. “We ask that a lot, then we go out and find the technology-focused teams building those things.”

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#434658 The Next Data-Driven Healthtech ...

Increasing your healthspan (i.e. making 100 years old the new 60) will depend to a large degree on artificial intelligence. And, as we saw in last week’s blog, healthcare AI systems are extremely data-hungry.

Fortunately, a slew of new sensors and data acquisition methods—including over 122 million wearables shipped in 2018—are bursting onto the scene to meet the massive demand for medical data.

From ubiquitous biosensors, to the mobile healthcare revolution, to the transformative power of the Health Nucleus, converging exponential technologies are fundamentally transforming our approach to healthcare.

In Part 4 of this blog series on Longevity & Vitality, I expand on how we’re acquiring the data to fuel today’s AI healthcare revolution.

In this blog, I’ll explore:

How the Health Nucleus is transforming “sick care” to healthcare
Sensors, wearables, and nanobots
The advent of mobile health

Let’s dive in.

Health Nucleus: Transforming ‘Sick Care’ to Healthcare
Much of today’s healthcare system is actually sick care. Most of us assume that we’re perfectly healthy, with nothing going on inside our bodies, until the day we travel to the hospital writhing in pain only to discover a serious or life-threatening condition.

Chances are that your ailment didn’t materialize that morning; rather, it’s been growing or developing for some time. You simply weren’t aware of it. At that point, once you’re diagnosed as “sick,” our medical system engages to take care of you.

What if, instead of this retrospective and reactive approach, you were constantly monitored, so that you could know the moment anything was out of whack?

Better yet, what if you more closely monitored those aspects of your body that your gene sequence predicted might cause you difficulty? Think: your heart, your kidneys, your breasts. Such a system becomes personalized, predictive, and possibly preventative.

This is the mission of the Health Nucleus platform built by Human Longevity, Inc. (HLI). While not continuous—that will come later, with the next generation of wearable and implantable sensors—the Health Nucleus was designed to ‘digitize’ you once per year to help you determine whether anything is going on inside your body that requires immediate attention.

The Health Nucleus visit provides you with the following tests during a half-day visit:

Whole genome sequencing (30x coverage)
Whole body (non-contrast) MRI
Brain magnetic resonance imaging/angiography (MRI/MRA)
CT (computed tomography) of the heart and lungs
Coronary artery calcium scoring
Electrocardiogram
Echocardiogram
Continuous cardiac monitoring
Clinical laboratory tests and metabolomics

In late 2018, HLI published the results of the first 1,190 clients through the Health Nucleus. The results were eye-opening—especially since these patients were all financially well-off, and already had access to the best doctors.

Following are the physiological and genomic findings in these clients who self-selected to undergo evaluation at HLI’s Health Nucleus.

Physiological Findings [TG]

Two percent had previously unknown tumors detected by MRI
2.5 percent had previously undetected aneurysms detected by MRI
Eight percent had cardiac arrhythmia found on cardiac rhythm monitoring, not previously known
Nine percent had moderate-severe coronary artery disease risk, not previously known
16 percent discovered previously unknown cardiac structure/function abnormalities
30 percent had elevated liver fat, not previously known

Genomic Findings [TG]

24 percent of clients uncovered a rare (unknown) genetic mutation found on WGS
63 percent of clients had a rare genetic mutation with a corresponding phenotypic finding

In summary, HLI’s published results found that 14.4 percent of clients had significant findings that are actionable, requiring immediate or near-term follow-up and intervention.

Long-term value findings were found in 40 percent of the clients we screened. Long-term clinical findings include discoveries that require medical attention or monitoring but are not immediately life-threatening.

The bottom line: most people truly don’t know their actual state of health. The ability to take a fully digital deep dive into your health status at least once per year will enable you to detect disease at stage zero or stage one, when it is most curable.

Sensors, Wearables, and Nanobots
Wearables, connected devices, and quantified self apps will allow us to continuously collect enormous amounts of useful health information.

Wearables like the Quanttus wristband and Vital Connect can transmit your electrocardiogram data, vital signs, posture, and stress levels anywhere on the planet.

In April 2017, we were proud to grant $2.5 million in prize money to the winning team in the Qualcomm Tricorder XPRIZE, Final Frontier Medical Devices.

Using a group of noninvasive sensors that collect data on vital signs, body chemistry, and biological functions, Final Frontier integrates this data in their powerful, AI-based DxtER diagnostic engine for rapid, high-precision assessments.

Their engine combines learnings from clinical emergency medicine and data analysis from actual patients.

Google is developing a full range of internal and external sensors (e.g. smart contact lenses) that can monitor the wearer’s vitals, ranging from blood sugar levels to blood chemistry.

In September 2018, Apple announced its Series 4 Apple Watch, including an FDA-approved mobile, on-the-fly ECG. Granted its first FDA approval, Apple appears to be moving deeper into the sensing healthcare market.

Further, Apple is reportedly now developing sensors that can non-invasively monitor blood sugar levels in real time for diabetic treatment. IoT-connected sensors are also entering the world of prescription drugs.

Last year, the FDA approved the first sensor-embedded pill, Abilify MyCite. This new class of digital pills can now communicate medication data to a user-controlled app, to which doctors may be granted access for remote monitoring.

Perhaps what is most impressive about the next generation of wearables and implantables is the density of sensors, processing, networking, and battery capability that we can now cheaply and compactly integrate.

Take the second-generation OURA ring, for example, which focuses on sleep measurement and management.

The OURA ring looks like a slightly thick wedding band, yet contains an impressive array of sensors and capabilities, including:

Two infrared LED
One infrared sensor
Three temperature sensors
One accelerometer
A six-axis gyro
A curved battery with a seven-day life
The memory, processing, and transmission capability required to connect with your smartphone

Disrupting Medical Imaging Hardware
In 2018, we saw lab breakthroughs that will drive the cost of an ultrasound sensor to below $100, in a packaging smaller than most bandages, powered by a smartphone. Dramatically disrupting ultrasound is just the beginning.

Nanobots and Nanonetworks
While wearables have long been able to track and transmit our steps, heart rate, and other health data, smart nanobots and ingestible sensors will soon be able to monitor countless new parameters and even help diagnose disease.

Some of the most exciting breakthroughs in smart nanotechnology from the past year include:

Researchers from the École Polytechnique Fédérale de Lausanne (EPFL) and the Swiss Federal Institute of Technology in Zurich (ETH Zurich) demonstrated artificial microrobots that can swim and navigate through different fluids, independent of additional sensors, electronics, or power transmission.

Researchers at the University of Chicago proposed specific arrangements of DNA-based molecular logic gates to capture the information contained in the temporal portion of our cells’ communication mechanisms. Accessing the otherwise-lost time-dependent information of these cellular signals is akin to knowing the tune of a song, rather than solely the lyrics.

MIT researchers built micron-scale robots able to sense, record, and store information about their environment. These tiny robots, about 100 micrometers in diameter (approximately the size of a human egg cell), can also carry out pre-programmed computational tasks.

Engineers at University of California, San Diego developed ultrasound-powered nanorobots that swim efficiently through your blood, removing harmful bacteria and the toxins they produce.

But it doesn’t stop there.

As nanosensor and nanonetworking capabilities develop, these tiny bots may soon communicate with each other, enabling the targeted delivery of drugs and autonomous corrective action.

Mobile Health
The OURA ring and the Series 4 Apple Watch are just the tip of the spear when it comes to our future of mobile health. This field, predicted to become a $102 billion market by 2022, puts an on-demand virtual doctor in your back pocket.

Step aside, WebMD.

In true exponential technology fashion, mobile device penetration has increased dramatically, while image recognition error rates and sensor costs have sharply declined.

As a result, AI-powered medical chatbots are flooding the market; diagnostic apps can identify anything from a rash to diabetic retinopathy; and with the advent of global connectivity, mHealth platforms enable real-time health data collection, transmission, and remote diagnosis by medical professionals.

Already available to residents across North London, Babylon Health offers immediate medical advice through AI-powered chatbots and video consultations with doctors via its app.

Babylon now aims to build up its AI for advanced diagnostics and even prescription. Others, like Woebot, take on mental health, using cognitive behavioral therapy in communications over Facebook messenger with patients suffering from depression.

In addition to phone apps and add-ons that test for fertility or autism, the now-FDA-approved Clarius L7 Linear Array Ultrasound Scanner can connect directly to iOS and Android devices and perform wireless ultrasounds at a moment’s notice.

Next, Healthy.io, an Israeli startup, uses your smartphone and computer vision to analyze traditional urine test strips—all you need to do is take a few photos.

With mHealth platforms like ClickMedix, which connects remotely-located patients to medical providers through real-time health data collection and transmission, what’s to stop us from delivering needed treatments through drone delivery or robotic telesurgery?

Welcome to the age of smartphone-as-a-medical-device.

Conclusion
With these DIY data collection and diagnostic tools, we save on transportation costs (time and money), and time bottlenecks.

No longer will you need to wait for your urine or blood results to go through the current information chain: samples will be sent to the lab, analyzed by a technician, results interpreted by your doctor, and only then relayed to you.

Just like the “sage-on-the-stage” issue with today’s education system, healthcare has a “doctor-on-the-dais” problem. Current medical procedures are too complicated and expensive for a layperson to perform and analyze on their own.

The coming abundance of healthcare data promises to transform how we approach healthcare, putting the power of exponential technologies in the patient’s hands and revolutionizing how we live.

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#434655 Purposeful Evolution: Creating an ...

More often than not, we fall into the trap of trying to predict and anticipate the future, forgetting that the future is up to us to envision and create. In the words of Buckminster Fuller, “We are called to be architects of the future, not its victims.”

But how, exactly, do we create a “good” future? What does such a future look like to begin with?

In Future Consciousness: The Path to Purposeful Evolution, Tom Lombardo analytically deconstructs how we can flourish in the flow of evolution and create a prosperous future for humanity. Scientifically informed, the books taps into themes that are constructive and profound, from both eastern and western philosophies.

As the executive director of the Center for Future Consciousness and an executive board member and fellow of the World Futures Studies Federation, Lombardo has dedicated his life and career to studying how we can create a “realistic, constructive, and ethical future.”

In a conversation with Singularity Hub, Lombardo discussed purposeful evolution, ethical use of technology, and the power of optimism.

Raya Bidshahri: Tell me more about the title of your book. What is future consciousness and what role does it play in what you call purposeful evolution?

Tom Lombardo: Humans have the unique capacity to purposefully evolve themselves because they possess future consciousness. Future consciousness contains all of the cognitive, motivational, and emotional aspects of the human mind that pertain to the future. It’s because we can imagine and think about the future that we can manipulate and direct our future evolution purposefully. Future consciousness empowers us to become self-responsible in our own evolutionary future. This is a jump in the process of evolution itself.

RB: In several places in the book, you discuss the importance of various eastern philosophies. What can we learn from the east that is often missing from western models?

TL: The key idea in the east that I have been intrigued by for decades is the Taoist Yin Yang, which is the idea that reality should be conceptualized as interdependent reciprocities.

In the west we think dualistically, or we attempt to think in terms of one end of the duality to the exclusion of the other, such as whole versus parts or consciousness versus physical matter. Yin Yang thinking is seeing how both sides of a “duality,” even though they appear to be opposites, are interdependent; you can’t have one without the other. You can’t have order without chaos, consciousness without the physical world, individuals without the whole, humanity without technology, and vice versa for all these complementary pairs.

RB: You talk about the importance of chaos and destruction in the trajectory of human progress. In your own words, “Creativity frequently involves destruction as a prelude to the emergence of some new reality.” Why is this an important principle for readers to keep in mind, especially in the context of today’s world?

TL: In order for there to be progress, there often has to be a disintegration of aspects of the old. Although progress and evolution involve a process of building up, growth isn’t entirely cumulative; it’s also transformative. Things fall apart and come back together again.

Throughout history, we have seen a transformation of what are the most dominant human professions or vocations. At some point, almost everybody worked in agriculture, but most of those agricultural activities were replaced by machines, and a lot of people moved over to industry. Now we’re seeing that jobs and functions are increasingly automated in industry, and humans are being pushed into vocations that involve higher cognitive and artistic skills, services, information technology, and so on.

RB: You raise valid concerns about the dark side of technological progress, especially when it’s combined with mass consumerism, materialism, and anti-intellectualism. How do we counter these destructive forces as we shape the future of humanity?

TL: We can counter such forces by always thoughtfully considering how our technologies are affecting the ongoing purposeful evolution of our conscious minds, bodies, and societies. We should ask ourselves what are the ethical values that are being served by the development of various technologies.

For example, we often hear the criticism that technologies that are driven by pure capitalism degrade human life and only benefit the few people who invented and market them. So we need to also think about what good these new technologies can serve. It’s what I mean when I talk about the “wise cyborg.” A wise cyborg is somebody who uses technology to serve wisdom, or values connected with wisdom.

RB: Creating an ideal future isn’t just about progress in technology, but also progress in morality. How we do decide what a “good” future is? What are some philosophical tools we can use to determine a code of ethics that is as objective as possible?

TL: Let’s keep in mind that ethics will always have some level of subjectivity. That being said, the way to determine a good future is to base it on the best theory of reality that we have, which is that we are evolutionary beings in an evolutionary universe and we are interdependent with everything else in that universe. Our ethics should acknowledge that we are fluid and interactive.

Hence, the “good” can’t be something static, and it can’t be something that pertains to me and not everybody else. It can’t be something that only applies to humans and ignores all other life on Earth, and it must be a mode of change rather than something stable.

RB: You present a consciousness-centered approach to creating a good future for humanity. What are some of the values we should develop in order to create a prosperous future?

TL: A sense of self-responsibility for the future is critical. This means realizing that the “good future” is something we have to take upon ourselves to create; we can’t let something or somebody else do that. We need to feel responsible both for our own futures and for the future around us.

Another one is going to be an informed and hopeful optimism about the future, because both optimism and pessimism have self-fulfilling prophecy effects. If you hope for the best, you are more likely to look deeply into your reality and increase the chance of it coming out that way. In fact, all of the positive emotions that have to do with future consciousness actually make people more intelligent and creative.

Some other important character virtues are discipline and tenacity, deep purpose, the love of learning and thinking, and creativity.

RB: Are you optimistic about the future? If so, what informs your optimism?

I justify my optimism the same way that I have seen Ray Kurzweil, Peter Diamandis, Kevin Kelly, and Steven Pinker justify theirs. If we look at the history of human civilization and even the history of nature, we see a progressive motion forward toward greater complexity and even greater intelligence. There’s lots of ups and downs, and catastrophes along the way, but the facts of nature and human history support the long-term expectation of continued evolution into the future.

You don’t have to be unrealistic to be optimistic. It’s also, psychologically, the more empowering position. That’s the position we should take if we want to maximize the chances of our individual or collective reality turning out better.

A lot of pessimists are pessimistic because they’re afraid of the future. There are lots of reasons to be afraid, but all in all, fear disempowers, whereas hope empowers.

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#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?

Join Me
Abundance-Digital Online Community: I’ve created a Digital/Online community of bold, abundance-minded entrepreneurs called Abundance-Digital. Abundance-Digital is my ‘onramp’ for exponential entrepreneurs – those who want to get involved and play at a higher level. Click here to learn more.

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Posted in Human Robots

#434616 What Games Are Humans Still Better at ...

Artificial intelligence (AI) systems’ rapid advances are continually crossing rows off the list of things humans do better than our computer compatriots.

AI has bested us at board games like chess and Go, and set astronomically high scores in classic computer games like Ms. Pacman. More complex games form part of AI’s next frontier.

While a team of AI bots developed by OpenAI, known as the OpenAI Five, ultimately lost to a team of professional players last year, they have since been running rampant against human opponents in Dota 2. Not to be outdone, Google’s DeepMind AI recently took on—and beat—several professional players at StarCraft II.

These victories beg the questions: what games are humans still better at than AI? And for how long?

The Making Of AlphaStar
DeepMind’s results provide a good starting point in a search for answers. The version of its AI for StarCraft II, dubbed AlphaStar, learned to play the games through supervised learning and reinforcement learning.

First, AI agents were trained by analyzing and copying human players, learning basic strategies. The initial agents then played each other in a sort of virtual death match where the strongest agents stayed on. New iterations of the agents were developed and entered the competition. Over time, the agents became better and better at the game, learning new strategies and tactics along the way.

One of the advantages of AI is that it can go through this kind of process at superspeed and quickly develop better agents. DeepMind researchers estimate that the AlphaStar agents went through the equivalent of roughly 200 years of game time in about 14 days.

Cheating or One Hand Behind the Back?
The AlphaStar AI agents faced off against human professional players in a series of games streamed on YouTube and Twitch. The AIs trounced their human opponents, winning ten games on the trot, before pro player Grzegorz “MaNa” Komincz managed to salvage some pride for humanity by winning the final game. Experts commenting on AlphaStar’s performance used words like “phenomenal” and “superhuman”—which was, to a degree, where things got a bit problematic.

AlphaStar proved particularly skilled at controlling and directing units in battle, known as micromanagement. One reason was that it viewed the whole game map at once—something a human player is not able to do—which made it seemingly able to control units in different areas at the same time. DeepMind researchers said the AIs only focused on a single part of the map at any given time, but interestingly, AlphaStar’s AI agent was limited to a more restricted camera view during the match “MaNA” won.

Potentially offsetting some of this advantage was the fact that AlphaStar was also restricted in certain ways. For example, it was prevented from performing more clicks per minute than a human player would be able to.

Where AIs Struggle
Games like StarCraft II and Dota 2 throw a lot of challenges at AIs. Complex game theory/ strategies, operating with imperfect/incomplete information, undertaking multi-variable and long-term planning, real-time decision-making, navigating a large action space, and making a multitude of possible decisions at every point in time are just the tip of the iceberg. The AIs’ performance in both games was impressive, but also highlighted some of the areas where they could be said to struggle.

In Dota 2 and StarCraft II, AI bots have seemed more vulnerable in longer games, or when confronted with surprising, unfamiliar strategies. They seem to struggle with complexity over time and improvisation/adapting to quick changes. This could be tied to how AIs learn. Even within the first few hours of performing a task, humans tend to gain a sense of familiarity and skill that takes an AI much longer. We are also better at transferring skill from one area to another. In other words, experience playing Dota 2 can help us become good at StarCraft II relatively quickly. This is not the case for AI—yet.

Dwindling Superiority
While the battle between AI and humans for absolute superiority is still on in Dota 2 and StarCraft II, it looks likely that AI will soon reign supreme. Similar things are happening to other types of games.

In 2017, a team from Carnegie Mellon University pitted its Libratus AI against four professionals. After 20 days of No Limit Texas Hold’em, Libratus was up by $1.7 million. Another likely candidate is the destroyer of family harmony at Christmas: Monopoly.

Poker involves bluffing, while Monopoly involves negotiation—skills you might not think AI would be particularly suited to handle. However, an AI experiment at Facebook showed that AI bots are more than capable of undertaking such tasks. The bots proved skilled negotiators, and developed negotiating strategies like pretending interest in one object while they were interested in another altogether—bluffing.

So, what games are we still better at than AI? There is no precise answer, but the list is getting shorter at a rapid pace.

The Aim Of the Game
While AI’s mastery of games might at first glance seem an odd area to focus research on, the belief is that the way AI learn to master a game is transferrable to other areas.

For example, the Libratus poker-playing AI employed strategies that could work in financial trading or political negotiations. The same applies to AlphaStar. As Oriol Vinyals, co-leader of the AlphaStar project, told The Verge:

“First and foremost, the mission at DeepMind is to build an artificial general intelligence. […] To do so, it’s important to benchmark how our agents perform on a wide variety of tasks.”

A 2017 survey of more than 350 AI researchers predicts AI could be a better driver than humans within ten years. By the middle of the century, AI will be able to write a best-selling novel, and a few years later, it will be better than humans at surgery. By the year 2060, AI may do everything better than us.

Whether you think this is a good or a bad thing, it’s worth noting that AI has an often overlooked ability to help us see things differently. When DeepMind’s AlphaGo beat human Go champion Lee Sedol, the Go community learned from it, too. Lee himself went on a win streak after the match with AlphaGo. The same is now happening within the Dota 2 and StarCraft II communities that are studying the human vs. AI games intensely.

More than anything, AI’s recent gaming triumphs illustrate how quickly artificial intelligence is developing. In 1997, Dr. Piet Hut, an astrophysicist at the Institute for Advanced Study at Princeton and a GO enthusiast, told the New York Times that:

”It may be a hundred years before a computer beats humans at Go—maybe even longer.”

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