Tag Archives: discovery

#434637 AI Is Rapidly Augmenting Healthcare and ...

Posted on February 15, 2019 by Android

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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Posted in Human Robots

#434599 This AI Can Tell Your Age by Analyzing ...

Posted on February 5, 2019 by Android

The plethora of bacteria and other tiny organisms that live in your gut, often referred to as the gut microbiome, don’t just help you digest food and fight disease. As detailed in a new study, they also provide a very accurate biological clock that shows your physical age—a fact that may open up wide-ranging possibilities for health and longevity studies.

Combining Machine Learning and Your Gut
The link between the gut biome and age is described by longevity researcher Alex Zhavoronkov and a team of his colleagues at Insilico Medicine, an artificial intelligence startup focused on drug discovery, biomarker development, and aging research.

Relatively little is known about how our gut biomes transition from one stage to another as we age, or about links between our age and the state of our gut biomes. In their paper, which is awaiting peer review but can be found on the preprint server bioRxiv, the team describes how they examined 3,663 curated samples of gut bacteria from 1,165 healthy people, aged 20-90, from countries in Europe, Asia, and North America. Roughly a third of samples came from the 20-39 age group, a third from individuals between 40-59, and a third from people between 60-90 years old.

A deep learning algorithm was then trained on data on 1,673 different microbial species from 90 percent of the samples. The AI was then tasked with predicting the ages of the remaining 10 percent of participants solely from data on their gut bacteria.

The Accurate Bacterial Clock
The results, described as the first method to predict a human’s chronological age via gut microbiota analysis, showed that the system was able to predict age to within four years based on the gut bacteria data. Furthermore, the results seem to indicate that 39 of the microbial species analyzed are particularly important in relation to accurately predicting age.

The study also showed that our gut microbiomes change over time. While some microbes’ numbers dwindle as we age, others seem to become more abundant. Age is not the only factor that influences the prevalence of different types of bacteria in a person’s digestive system. What you eat, how you sleep, and how physically active you are are all thought to be contributing factors.

Science Magquotes Zhavoronkov as stating that the study could lay the foundation for a “microbiome aging clock” that could serve as a baseline in future research on how a person’s gut ages and how medicine, diet, and alcohol consumption affect longevity.

Living Longer, Better
Studies of our microbiome’s influence on longevity add another dimension to our understanding of how and why we age. Other avenues of study include looking at the length of telomeres, the tips of chromosomes that are believed to play an important role in the aging process, and our DNA.

The same can be said of the role microbiomes play in relation to illnesses and conditions including allergies, diabetes, some types of cancer, and psychological states such as depression. Scientists at Harvard are even developing genetically engineered ‘telephone’ bacteria that would be able to gather precise information about the state of the gut microbiome.

A positive side effect of many of the studies is that alongside dedicated microbiome data collection efforts, they add new data—the food of AI. While we are already gaining a better understanding of the gut biome, it is not a large leap of logic to predict that AI will feast on the new data and assist us in getting an even keener understanding of what is going on in our gut and what it means for our health.

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Posted in Human Robots

#434260 The Most Surprising Tech Breakthroughs ...

Posted on December 30, 2018 by Android

Development across the entire information technology landscape certainly didn’t slow down this year. From CRISPR babies, to the rapid decline of the crypto markets, to a new robot on Mars, and discovery of subatomic particles that could change modern physics as we know it, there was no shortage of headline-grabbing breakthroughs and discoveries.

As 2018 comes to a close, we can pause and reflect on some of the biggest technology breakthroughs and scientific discoveries that occurred this year.

I reached out to a few Singularity University speakers and faculty across the various technology domains we cover asking what they thought the biggest breakthrough was in their area of expertise. The question posed was:

“What, in your opinion, was the biggest development in your area of focus this year? Or, what was the breakthrough you were most surprised by in 2018?”

I can share that for me, hands down, the most surprising development I came across in 2018 was learning that a publicly-traded company that was briefly valued at over $1 billion, and has over 12,000 employees and contractors spread around the world, has no physical office space and the entire business is run and operated from inside an online virtual world. This is Ready Player One stuff happening now.

For the rest, here’s what our experts had to say.

DIGITAL BIOLOGY
Dr. Tiffany Vora | Faculty Director and Vice Chair, Digital Biology and Medicine, Singularity University

“That’s easy: CRISPR babies. I knew it was technically possible, and I’ve spent two years predicting it would happen first in China. I knew it was just a matter of time but I failed to predict the lack of oversight, the dubious consent process, the paucity of publicly-available data, and the targeting of a disease that we already know how to prevent and treat and that the children were at low risk of anyway.

I’m not convinced that this counts as a technical breakthrough, since one of the girls probably isn’t immune to HIV, but it sure was a surprise.”

For more, read Dr. Vora’s summary of this recent stunning news from China regarding CRISPR-editing human embryos.

QUANTUM COMPUTING
Andrew Fursman | Co-Founder/CEO 1Qbit, Faculty, Quantum Computing, Singularity University

“There were two last-minute holiday season surprise quantum computing funding and technology breakthroughs:

First, right before the government shutdown, one priority legislative accomplishment will provide $1.2 billion in quantum computing research over the next five years. Second, there’s the rise of ions as a truly viable, scalable quantum computing architecture.”

*Read this Gizmodo profile on an exciting startup in the space to learn more about this type of quantum computing

ENERGY
Ramez Naam | Chair, Energy and Environmental Systems, Singularity University

“2018 had plenty of energy surprises. In solar, we saw unsubsidized prices in the sunny parts of the world at just over two cents per kwh, or less than half the price of new coal or gas electricity. In the US southwest and Texas, new solar is also now cheaper than new coal or gas. But even more shockingly, in Germany, which is one of the least sunny countries on earth (it gets less sunlight than Canada) the average bid for new solar in a 2018 auction was less than 5 US cents per kwh. That’s as cheap as new natural gas in the US, and far cheaper than coal, gas, or any other new electricity source in most of Europe.

In fact, it’s now cheaper in some parts of the world to build new solar or wind than to run existing coal plants. Think tank Carbon Tracker calculates that, over the next 10 years, it will become cheaper to build new wind or solar than to operate coal power in most of the world, including specifically the US, most of Europe, and—most importantly—India and the world’s dominant burner of coal, China.

Here comes the sun.”

GLOBAL GRAND CHALLENGES
Darlene Damm | Vice Chair, Faculty, Global Grand Challenges, Singularity University

“In 2018 we saw a lot of areas in the Global Grand Challenges move forward—advancements in robotic farming technology and cultured meat, low-cost 3D printed housing, more sophisticated types of online education expanding to every corner of the world, and governments creating new policies to deal with the ethics of the digital world. These were the areas we were watching and had predicted there would be change.

What most surprised me was to see young people, especially teenagers, start to harness technology in powerful ways and use it as a platform to make their voices heard and drive meaningful change in the world. In 2018 we saw teenagers speak out on a number of issues related to their well-being and launch digital movements around issues such as gun and school safety, global warming and environmental issues. We often talk about the harm technology can cause to young people, but on the flip side, it can be a very powerful tool for youth to start changing the world today and something I hope we see more of in the future.”

BUSINESS STRATEGY
Pascal Finette | Chair, Entrepreneurship and Open Innovation, Singularity University

“Without a doubt the rapid and massive adoption of AI, specifically deep learning, across industries, sectors, and organizations. What was a curiosity for most companies at the beginning of the year has quickly made its way into the boardroom and leadership meetings, and all the way down into the innovation and IT department’s agenda. You are hard-pressed to find a mid- to large-sized company today that is not experimenting or implementing AI in various aspects of its business.

On the slightly snarkier side of answering this question: The very rapid decline in interest in blockchain (and cryptocurrencies). The blockchain party was short, ferocious, and ended earlier than most would have anticipated, with a huge hangover for some. The good news—with the hot air dissipated, we can now focus on exploring the unique use cases where blockchain does indeed offer real advantages over centralized approaches.”

*Author note: snark is welcome and appreciated

ROBOTICS
Hod Lipson | Director, Creative Machines Lab, Columbia University

“The biggest surprise for me this year in robotics was learning dexterity. For decades, roboticists have been trying to understand and imitate dexterous manipulation. We humans seem to be able to manipulate objects with our fingers with incredible ease—imagine sifting through a bunch of keys in the dark, or tossing and catching a cube. And while there has been much progress in machine perception, dexterous manipulation remained elusive.

There seemed to be something almost magical in how we humans can physically manipulate the physical world around us. Decades of research in grasping and manipulation, and millions of dollars spent on robot-hand hardware development, has brought us little progress. But in late 2018, the Berkley OpenAI group demonstrated that this hurdle may finally succumb to machine learning as well. Given 200 years worth of practice, machines learned to manipulate a physical object with amazing fluidity. This might be the beginning of a new age for dexterous robotics.”

MACHINE LEARNING
Jeremy Howard | Founding Researcher, fast.ai, Founder/CEO, Enlitic, Faculty Data Science, Singularity University

“The biggest development in machine learning this year has been the development of effective natural language processing (NLP).

The New York Times published an article last month titled “Finally, a Machine That Can Finish Your Sentence,” which argued that NLP neural networks have reached a significant milestone in capability and speed of development. The “finishing your sentence” capability mentioned in the title refers to a type of neural network called a “language model,” which is literally a model that learns how to finish your sentences.

Earlier this year, two systems (one, called ELMO, is from the Allen Institute for AI, and the other, called ULMFiT, was developed by me and Sebastian Ruder) showed that such a model could be fine-tuned to dramatically improve the state-of-the-art in nearly every NLP task that researchers study. This work was further developed by OpenAI, which in turn was greatly scaled up by Google Brain, who created a system called BERT which reached human-level performance on some of NLP’s toughest challenges.

Over the next year, expect to see fine-tuned language models used for everything from understanding medical texts to building disruptive social media troll armies.”

DIGITAL MANUFACTURING
Andre Wegner | Founder/CEO Authentise, Chair, Digital Manufacturing, Singularity University

“Most surprising to me was the extent and speed at which the industry finally opened up.

While previously, only few 3D printing suppliers had APIs and knew what to do with them, 2018 saw nearly every OEM (or original equipment manufacturer) enabling data access and, even more surprisingly, shying away from proprietary standards and adopting MTConnect, as stalwarts such as 3D Systems and Stratasys have been. This means that in two to three years, data access to machines will be easy, commonplace, and free. The value will be in what is being done with that data.

Another example of this openness are the seemingly endless announcements of integrated workflows: GE’s announcement with most major software players to enable integrated solutions, EOS’s announcement with Siemens, and many more. It’s clear that all actors in the additive ecosystem have taken a step forward in terms of openness. The result is a faster pace of innovation, particularly in the software and data domains that are crucial to enabling comprehensive digital workflow to drive agile and resilient manufacturing.

I’m more optimistic we’ll achieve that now than I was at the end of 2017.”

SCIENCE AND DISCOVERY
Paul Saffo | Chair, Future Studies, Singularity University, Distinguished Visiting Scholar, Stanford Media-X Research Network

“The most important development in technology this year isn’t a technology, but rather the astonishing science surprises made possible by recent technology innovations. My short list includes the discovery of the “neptmoon”, a Neptune-scale moon circling a Jupiter-scale planet 8,000 lightyears from us; the successful deployment of the Mars InSight Lander a month ago; and the tantalizing ANITA detection (what could be a new subatomic particle which would in turn blow the standard model wide open). The highest use of invention is to support science discovery, because those discoveries in turn lead us to the future innovations that will improve the state of the world—and fire up our imaginations.”

ROBOTICS
Pablos Holman | Inventor, Hacker, Faculty, Singularity University

“Just five or ten years ago, if you’d asked any of us technologists “What is harder for robots? Eyes, or fingers?” We’d have all said eyes. Robots have extraordinary eyes now, but even in a surgical robot, the fingers are numb and don’t feel anything. Stanford robotics researchers have invented fingertips that can feel, and this will be a kingpin that allows robots to go everywhere they haven’t been yet.”

BLOCKCHAIN
Nathana Sharma | Blockchain, Policy, Law, and Ethics, Faculty, Singularity University

“2017 was the year of peak blockchain hype. 2018 has been a year of resetting expectations and technological development, even as the broader cryptocurrency markets have faced a winter. It’s now about seeing adoption and applications that people want and need to use rise. An incredible piece of news from December 2018 is that Facebook is developing a cryptocurrency for users to make payments through Whatsapp. That’s surprisingly fast mainstream adoption of this new technology, and indicates how powerful it is.”

ARTIFICIAL INTELLIGENCE
Neil Jacobstein | Chair, Artificial Intelligence and Robotics, Singularity University

“I think one of the most visible improvements in AI was illustrated by the Boston Dynamics Parkour video. This was not due to an improvement in brushless motors, accelerometers, or gears. It was due to improvements in AI algorithms and training data. To be fair, the video released was cherry-picked from numerous attempts, many of which ended with a crash. However, the fact that it could be accomplished at all in 2018 was a real win for both AI and robotics.”

NEUROSCIENCE
Divya Chander | Chair, Neuroscience, Singularity University

“2018 ushered in a new era of exponential trends in non-invasive brain modulation. Changing behavior or restoring function takes on a new meaning when invasive interfaces are no longer needed to manipulate neural circuitry. The end of 2018 saw two amazing announcements: the ability to grow neural organoids (mini-brains) in a dish from neural stem cells that started expressing electrical activity, mimicking the brain function of premature babies, and the first (known) application of CRISPR to genetically alter two fetuses grown through IVF. Although this was ostensibly to provide genetic resilience against HIV infections, imagine what would happen if we started tinkering with neural circuitry and intelligence.”

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Posted in Human Robots

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

Posted on December 18, 2018 by Android

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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Posted in Human Robots

#433892 The Spatial Web Will Map Our 3D ...

Posted on November 16, 2018 by Android

The boundaries between digital and physical space are disappearing at a breakneck pace. What was once static and boring is becoming dynamic and magical.

For all of human history, looking at the world through our eyes was the same experience for everyone. Beyond the bounds of an over-active imagination, what you see is the same as what I see.

But all of this is about to change. Over the next two to five years, the world around us is about to light up with layer upon layer of rich, fun, meaningful, engaging, and dynamic data. Data you can see and interact with.

This magical future ahead is called the Spatial Web and will transform every aspect of our lives, from retail and advertising, to work and education, to entertainment and social interaction.

Massive change is underway as a result of a series of converging technologies, from 5G global networks and ubiquitous artificial intelligence, to 30+ billion connected devices (known as the IoT), each of which will generate scores of real-world data every second, everywhere.

The current AI explosion will make everything smart, autonomous, and self-programming. Blockchain and cloud-enabled services will support a secure data layer, putting data back in the hands of users and allowing us to build complex rule-based infrastructure in tomorrow’s virtual worlds.

And with the rise of online-merge-offline (OMO) environments, two-dimensional screens will no longer serve as our exclusive portal to the web. Instead, virtual and augmented reality eyewear will allow us to interface with a digitally-mapped world, richly layered with visual data.

Welcome to the Spatial Web. Over the next few months, I’ll be doing a deep dive into the Spatial Web (a.k.a. Web 3.0), covering what it is, how it works, and its vast implications across industries, from real estate and healthcare to entertainment and the future of work. In this blog, I’ll discuss the what, how, and why of Web 3.0—humanity’s first major foray into our virtual-physical hybrid selves (BTW, this year at Abundance360, we’ll be doing a deep dive into the Spatial Web with the leaders of HTC, Magic Leap, and High-Fidelity).

Let’s dive in.

What is the Spatial Web?
While we humans exist in three dimensions, our web today is flat.

The web was designed for shared information, absorbed through a flat screen. But as proliferating sensors, ubiquitous AI, and interconnected networks blur the lines between our physical and online worlds, we need a spatial web to help us digitally map a three-dimensional world.

To put Web 3.0 in context, let’s take a trip down memory lane. In the late 1980s, the newly-birthed world wide web consisted of static web pages and one-way information—a monumental system of publishing and linking information unlike any unified data system before it. To connect, we had to dial up through unstable modems and struggle through insufferably slow connection speeds.

But emerging from this revolutionary (albeit non-interactive) infodump, Web 2.0 has connected the planet more in one decade than empires did in millennia.

Granting democratized participation through newly interactive sites and applications, today’s web era has turbocharged information-sharing and created ripple effects of scientific discovery, economic growth, and technological progress on an unprecedented scale.

We’ve seen the explosion of social networking sites, wikis, and online collaboration platforms. Consumers have become creators; physically isolated users have been handed a global microphone; and entrepreneurs can now access billions of potential customers.

But if Web 2.0 took the world by storm, the Spatial Web emerging today will leave it in the dust.

While there’s no clear consensus about its definition, the Spatial Web refers to a computing environment that exists in three-dimensional space—a twinning of real and virtual realities—enabled via billions of connected devices and accessed through the interfaces of virtual and augmented reality.

In this way, the Spatial Web will enable us to both build a twin of our physical reality in the virtual realm and bring the digital into our real environments.

It’s the next era of web-like technologies:

Spatial computing technologies, like augmented and virtual reality;
Physical computing technologies, like IoT and robotic sensors;
And decentralized computing: both blockchain—which enables greater security and data authentication—and edge computing, which pushes computing power to where it’s most needed, speeding everything up.

Geared with natural language search, data mining, machine learning, and AI recommendation agents, the Spatial Web is a growing expanse of services and information, navigable with the use of ever-more-sophisticated AI assistants and revolutionary new interfaces.

Where Web 1.0 consisted of static documents and read-only data, Web 2.0 introduced multimedia content, interactive web applications, and social media on two-dimensional screens. But converging technologies are quickly transcending the laptop, and will even disrupt the smartphone in the next decade.

With the rise of wearables, smart glasses, AR / VR interfaces, and the IoT, the Spatial Web will integrate seamlessly into our physical environment, overlaying every conversation, every road, every object, conference room, and classroom with intuitively-presented data and AI-aided interaction.

Think: the Oasis in Ready Player One, where anyone can create digital personas, build and invest in smart assets, do business, complete effortless peer-to-peer transactions, and collect real estate in a virtual world.

Or imagine a virtual replica or “digital twin” of your office, each conference room authenticated on the blockchain, requiring a cryptographic key for entry.

As I’ve discussed with my good friend and “VR guru” Philip Rosedale, I’m absolutely clear that in the not-too-distant future, every physical element of every building in the world is going to be fully digitized, existing as a virtual incarnation or even as N number of these. “Meet me at the top of the Empire State Building?” “Sure, which one?”

This digitization of life means that suddenly every piece of information can become spatial, every environment can be smarter by virtue of AI, and every data point about me and my assets—both virtual and physical—can be reliably stored, secured, enhanced, and monetized.

In essence, the Spatial Web lets us interface with digitally-enhanced versions of our physical environment and build out entirely fictional virtual worlds—capable of running simulations, supporting entire economies, and even birthing new political systems.

But while I’ll get into the weeds of different use cases next week, let’s first concretize.

How Does It Work?
Let’s start with the stack. In the PC days, we had a database accompanied by a program that could ingest that data and present it to us as digestible information on a screen.

Then, in the early days of the web, data migrated to servers. Information was fed through a website, with which you would interface via a browser—whether Mosaic or Mozilla.

And then came the cloud.

Resident at either the edge of the cloud or on your phone, today’s rapidly proliferating apps now allow us to interact with previously read-only data, interfacing through a smartphone. But as Siri and Alexa have brought us verbal interfaces, AI-geared phone cameras can now determine your identity, and sensors are beginning to read our gestures.

And now we’re not only looking at our screens but through them, as the convergence of AI and AR begins to digitally populate our physical worlds.

While Pokémon Go sent millions of mobile game-players on virtual treasure hunts, IKEA is just one of the many companies letting you map virtual furniture within your physical home—simulating everything from cabinets to entire kitchens. No longer the one-sided recipients, we’re beginning to see through sensors, creatively inserting digital content in our everyday environments.

Let’s take a look at how the latest incarnation might work. In this new Web 3.0 stack, my personal AI would act as an intermediary, accessing public or privately-authorized data through the blockchain on my behalf, and then feed it through an interface layer composed of everything from my VR headset, to numerous wearables, to my smart environment (IoT-connected devices or even in-home robots).

But as we attempt to build a smart world with smart infrastructure, smart supply chains and smart everything else, we need a set of basic standards with addresses for people, places, and things. Just like our web today relies on the Internet Protocol (TCP/IP) and other infrastructure, by which your computer is addressed and data packets are transferred, we need infrastructure for the Spatial Web.

And a select group of players is already stepping in to fill this void. Proposing new structural designs for Web 3.0, some are attempting to evolve today’s web model from text-based web pages in 2D to three-dimensional AR and VR web experiences located in both digitally-mapped physical worlds and newly-created virtual ones.

With a spatial programming language analogous to HTML, imagine building a linkable address for any physical or virtual space, granting it a format that then makes it interchangeable and interoperable with all other spaces.

But it doesn’t stop there.

As soon as we populate a virtual room with content, we then need to encode who sees it, who can buy it, who can move it…

And the Spatial Web’s eventual governing system (for posting content on a centralized grid) would allow us to address everything from the room you’re sitting in, to the chair on the other side of the table, to the building across the street.

Just as we have a DNS for the web and the purchasing of web domains, once we give addresses to spaces (akin to granting URLs), we then have the ability to identify and visit addressable locations, physical objects, individuals, or pieces of digital content in cyberspace.

And these not only apply to virtual worlds, but to the real world itself. As new mapping technologies emerge, we can now map rooms, objects, and large-scale environments into virtual space with increasing accuracy.

We might then dictate who gets to move your coffee mug in a virtual conference room, or when a team gets to use the room itself. Rules and permissions would be set in the grid, decentralized governance systems, or in the application layer.

Taken one step further, imagine then monetizing smart spaces and smart assets. If you have booked the virtual conference room, perhaps you’ll let me pay you 0.25 BTC to let me use it instead?

But given the Spatial Web’s enormous technological complexity, what’s allowing it to emerge now?

Why Is It Happening Now?
While countless entrepreneurs have already started harnessing blockchain technologies to build decentralized apps (or dApps), two major developments are allowing today’s birth of Web 3.0:

High-resolution wireless VR/AR headsets are finally catapulting virtual and augmented reality out of a prolonged winter.

The International Data Corporation (IDC) predicts the VR and AR headset market will reach 65.9 million units by 2022. Already in the next 18 months, 2 billion devices will be enabled with AR. And tech giants across the board have long begun investing heavy sums.

In early 2019, HTC is releasing the VIVE Focus, a wireless self-contained VR headset. At the same time, Facebook is charging ahead with its Project Santa Cruz—the Oculus division’s next-generation standalone, wireless VR headset. And Magic Leap has finally rolled out its long-awaited Magic Leap One mixed reality headset.

Mass deployment of 5G will drive 10 to 100-gigabit connection speeds in the next 6 years, matching hardware progress with the needed speed to create virtual worlds.

We’ve already seen tremendous leaps in display technology. But as connectivity speeds converge with accelerating GPUs, we’ll start to experience seamless VR and AR interfaces with ever-expanding virtual worlds.

And with such democratizing speeds, every user will be able to develop in VR.

But accompanying these two catalysts is also an important shift towards the decentralized web and a demand for user-controlled data.

Converging technologies, from immutable ledgers and blockchain to machine learning, are now enabling the more direct, decentralized use of web applications and creation of user content. With no central point of control, middlemen are removed from the equation and anyone can create an address, independently interacting with the network.

Enabled by a permission-less blockchain, any user—regardless of birthplace, gender, ethnicity, wealth, or citizenship—would thus be able to establish digital assets and transfer them seamlessly, granting us a more democratized Internet.

And with data stored on distributed nodes, this also means no single point of failure. One could have multiple backups, accessible only with digital authorization, leaving users immune to any single server failure.

Implications Abound–What’s Next…
With a newly-built stack and an interface built from numerous converging technologies, the Spatial Web will transform every facet of our everyday lives—from the way we organize and access our data, to our social and business interactions, to the way we train employees and educate our children.

We’re about to start spending more time in the virtual world than ever before. Beyond entertainment or gameplay, our livelihoods, work, and even personal decisions are already becoming mediated by a web electrified with AI and newly-emerging interfaces.

In our next blog on the Spatial Web, I’ll do a deep dive into the myriad industry implications of Web 3.0, offering tangible use cases across sectors.

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