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#437103 How to Make Sense of Uncertainty in a ...
As the internet churns with information about Covid-19, about the virus that causes the disease, and about what we’re supposed to do to fight it, it can be difficult to see the forest for the trees. What can we realistically expect for the rest of 2020? And how do we even know what’s realistic?
Today, humanity’s primary, ideal goal is to eliminate the virus, SARS-CoV-2, and Covid-19. Our second-choice goal is to control virus transmission. Either way, we have three big aims: to save lives, to return to public life, and to keep the economy functioning.
To hit our second-choice goal—and maybe even our primary goal—countries are pursuing five major public health strategies. Note that many of these advances cross-fertilize: for example, advances in virus testing and antibody testing will drive data-based prevention efforts.
Five major public health strategies are underway to bring Covid-19 under control and to contain the spread of SARS-CoV-2.
These strategies arise from things we can control based on the things that we know at any given moment. But what about the things we can’t control and don’t yet know?
The biology of the virus and how it interacts with our bodies is what it is, so we should seek to understand it as thoroughly as possible. How long any immunity gained from prior infection lasts—and indeed whether people develop meaningful immunity at all after infection—are open questions urgently in need of greater clarity. Similarly, right now it’s important to focus on understanding rather than making assumptions about environmental factors like seasonality.
But the biggest question on everyone’s lips is, “When?” When will we see therapeutic progress against Covid-19? And when will life get “back to normal”? There are lots of models out there on the internet; which of those models are right? The simple answer is “none of them.” That’s right—it’s almost certain that every model you’ve seen is wrong in at least one detail, if not all of them. But modeling is meant to be a tool for deeper thinking, a way to run mental (and computational) experiments before—and while—taking action. As George E. P. Box famously wrote in 1976, “All models are wrong, but some are useful.”
Here, we’re seeking useful insights, as opposed to exact predictions, which is why we’re pulling back from quantitative details to get at the mindsets that will support agency and hope. To that end, I’ve been putting together timelines that I believe will yield useful expectations for the next year or two—and asking how optimistic I need to be in order to believe a particular timeline.
For a moderately optimistic scenario to be relevant, breakthroughs in science and technology come at paces expected based on previous efforts and assumptions that turn out to be basically correct; accessibility of those breakthroughs increases at a reasonable pace; regulation achieves its desired effects, without major surprises; and compliance with regulations is reasonably high.
In contrast, if I’m being highly optimistic, breakthroughs in science and technology and their accessibility come more quickly than they ever have before; regulation is evidence-based and successful in the first try or two; and compliance with those regulations is high and uniform. If I’m feeling not-so-optimistic, then I anticipate serious setbacks to breakthroughs and accessibility (with the overturning of many important assumptions), repeated failure of regulations to achieve their desired outcomes, and low compliance with those regulations.
The following scenarios outline the things that need to happen in the fight against Covid-19, when I expect to see them, and how confident I feel in those expectations. They focus on North America and Europe because there are data missing about China’s 2019 outbreak and other regions are still early in their outbreaks. Perhaps the most important thing to keep in mind throughout: We know more today than we did yesterday, but we still have much to learn. New knowledge derived from greater study and debate will almost certainly inspire ongoing course corrections.
As you dive into the scenarios below, practice these three mindset shifts. First, defeating Covid-19 will be a marathon, not a sprint. We shouldn’t expect life to look like 2019 for the next year or two—if ever. As Ed Yong wrote recently in The Atlantic, “There won’t be an obvious moment when everything is under control and regular life can safely resume.” Second, remember that you have important things to do for at least a year. And third, we are all in this together. There is no “us” and “them.” We must all be alert, responsive, generous, and strong throughout 2020 and 2021—and willing to throw away our assumptions when scientific evidence invalidates them.
The Middle Way: Moderate Optimism
Let’s start with the case in which I have the most confidence: moderate optimism.
This timeline considers milestones through late 2021, the earliest that I believe vaccines will become available. The “normal” timeline for developing a vaccine for diseases like seasonal flu is 18 months, which leads to my projection that we could potentially have vaccines as soon as 18 months from the first quarter of 2020. While Melinda Gates agrees with that projection, others (including AI) believe that 3 to 5 years is far more realistic, based on past vaccine development and the need to test safety and efficacy in humans. However, repurposing existing vaccines against other diseases—or piggybacking off clever synthetic platforms—could lead to vaccines being available sooner. I tried to balance these considerations for this moderately optimistic scenario. Either way, deploying vaccines at the end of 2021 is probably much later than you may have been led to believe by the hype engine. Again, if you take away only one message from this article, remember that the fight against Covid-19 is a marathon, not a sprint.
Here, I’ve visualized a moderately optimistic scenario as a baseline. Think of these timelines as living guides, as opposed to exact predictions. There are still many unknowns. More or less optimistic views (see below) and new information could shift these timelines forward or back and change the details of the strategies.
Based on current data, I expect that the first wave of Covid-19 cases (where we are now) will continue to subside in many areas, leading governments to ease restrictions in an effort to get people back to work. We’re already seeing movement in that direction, with a variety of benchmarks and changes at state and country levels around the world. But depending on the details of the changes, easing restrictions will probably cause a second wave of sickness (see Germany and Singapore), which should lead governments to reimpose at least some restrictions.
In tandem, therapeutic efforts will be transitioning from emergency treatments to treatments that have been approved based on safety and efficacy data in clinical trials. In a moderately optimistic scenario, assuming clinical trials currently underway yield at least a few positive results, this shift to mostly approved therapies could happen as early as the third or fourth quarter of this year and continue from there. One approval that should come rather quickly is for plasma therapies, in which the blood from people who have recovered from Covid-19 is used as a source of antibodies for people who are currently sick.
Companies around the world are working on both viral and antibody testing, focusing on speed, accuracy, reliability, and wide accessibility. While these tests are currently being run in hospitals and research laboratories, at-home testing is a critical component of the mass testing we’ll need to keep viral spread in check. These are needed to minimize the impact of asymptomatic cases, test the assumption that infection yields resistance to subsequent infection (and whether it lasts), and construct potential immunity passports if this assumption holds. Testing is also needed for contact tracing efforts to prevent further spread and get people back to public life. Finally, it’s crucial to our fundamental understanding of the biology of SARS-CoV-2 and Covid-19.
We need tests that are very reliable, both in the clinic and at home. So, don’t go buying any at-home test kits just yet, even if you find them online. Wait for reliable test kits and deeper understanding of how a test result translates to everyday realities. If we’re moderately optimistic, in-clinic testing will rapidly expand this quarter and/or next, with the possibility of broadly available, high-quality at-home sampling (and perhaps even analysis) thereafter.
Note that testing is not likely to be a “one-and-done” endeavor, as a person’s infection and immunity status change over time. Expect to be testing yourself—and your family—often as we move later into 2020.
Testing data are also going to inform distancing requirements at the country and local levels. In this scenario, restrictions—at some level of stringency—could persist at least through the end of 2020, as most countries are way behind the curve on testing (Iceland is an informative exception). Governments will likely continue to ask citizens to work from home if at all possible; to wear masks or face coverings in public; to employ heightened hygiene and social distancing in workplaces; and to restrict travel and social gatherings. So while it’s likely we’ll be eating in local restaurants again in 2020 in this scenario, at least for a little while, it’s not likely we’ll be heading to big concerts any time soon.
The Extremes: High and Low Optimism
How would high and low levels of optimism change our moderately optimistic timeline? The milestones are the same, but the time required to achieve them is shorter or longer, respectively. Quantifying these shifts is less important than acknowledging and incorporating a range of possibilities into our view. It pays to pay attention to our bias. Here are a few examples of reasonable possibilities that could shift the moderately optimistic timeline.
When vaccines become available
Vaccine repurposing could shorten the time for vaccines to become available; today, many vaccine candidates are in various stages of testing. On the other hand, difficulties in manufacture and distribution, or faster-than-expected mutation of SARS-CoV-2, could slow vaccine development. Given what we know now, I am not strongly concerned about either of these possibilities—drug companies are rapidly expanding their capabilities, and viral mutation isn’t an urgent concern at this time based on sequencing data—but they could happen.
At first, governments will likely supply vaccines to essential workers such as healthcare workers, but it is essential that vaccines become widely available around the world as quickly and as safely as possible. Overall, I suggest a dose of skepticism when reading highly optimistic claims about a vaccine (or multiple vaccines) being available in 2020. Remember, a vaccine is a knockout punch, not a first line of defense for an outbreak.
When testing hits its stride
While I am confident that testing is a critical component of our response to Covid-19, reliability is incredibly important to testing for SARS-CoV-2 and for immunity to the disease, particularly at home. For an individual, a false negative (being told you don’t have antibodies when you really do) could be just as bad as a false positive (being told you do have antibodies when you really don’t). Those errors are compounded when governments are trying to make evidence-based policies for social and physical distancing.
If you’re highly optimistic, high-quality testing will ramp up quickly as companies and scientists innovate rapidly by cleverly combining multiple test modalities, digital signals, and cutting-edge tech like CRISPR. Pop-up testing labs could also take some pressure off hospitals and clinics.
If things don’t go well, reliability issues could hinder testing, manufacturing bottlenecks could limit availability, and both could hamstring efforts to control spread and ease restrictions. And if it turns out that immunity to Covid-19 isn’t working the way we assumed, then we must revisit our assumptions about our path(s) back to public life, as well as our vaccine-development strategies.
How quickly safe and effective treatments appear
Drug development is known to be long, costly, and fraught with failure. It’s not uncommon to see hope in a drug spike early only to be dashed later on down the road. With that in mind, the number of treatments currently under investigation is astonishing, as is the speed through which they’re proceeding through testing. Breakthroughs in a therapeutic area—for example in treating the seriously ill or in reducing viral spread after an infection takes hold—could motivate changes in the focus of distancing regulations.
While speed will save lives, we cannot overlook the importance of knowing a treatment’s efficacy (does it work against Covid-19?) and safety (does it make you sick in a different, or worse, way?). Repurposing drugs that have already been tested for other diseases is speeding innovation here, as is artificial intelligence.
Remarkable collaborations among governments and companies, large and small, are driving innovation in therapeutics and devices such as ventilators for treating the sick.
Whether government policies are effective and responsive
Those of us who have experienced lockdown are eager for it to be over. Businesses, economists, and governments are also eager to relieve the terrible pressure that is being exerted on the global economy. However, lifting restrictions will almost certainly lead to a resurgence in sickness.
Here, the future is hard to model because there are many, many factors at play, and at play differently in different places—including the extent to which individuals actually comply with regulations.
Reliable testing—both in the clinic and at home—is crucial to designing and implementing restrictions, monitoring their effectiveness, and updating them; delays in reliable testing could seriously hamper this design cycle. Lack of trust in governments and/or companies could also suppress uptake. That said, systems are already in place for contact tracing in East Asia. Other governments could learn important lessons, but must also earn—and keep—their citizens’ trust.
Expect to see restrictions descend and then lift in response to changes in the number of Covid-19 cases and in the effectiveness of our prevention strategies. Also expect country-specific and perhaps even area-specific responses that differ from each other. The benefit of this approach? Governments around the world are running perhaps hundreds of real-time experiments and design cycles in balancing health and the economy, and we can learn from the results.
A Way Out
As Jeremy Farrar, head of the Wellcome Trust, told Science magazine, “Science is the exit strategy.” Some of our greatest technological assistance is coming from artificial intelligence, digital tools for collaboration, and advances in biotechnology.
Our exit strategy also needs to include empathy and future visioning—because in the midst of this crisis, we are breaking ground for a new, post-Covid future.
What do we want that future to look like? How will the hard choices we make now about data ethics impact the future of surveillance? Will we continue to embrace inclusiveness and mass collaboration? Perhaps most importantly, will we lay the foundation for successfully confronting future challenges? Whether we’re thinking about the next pandemic (and there will be others) or the cascade of catastrophes that climate change is bringing ever closer—it’s important to remember that we all have the power to become agents of that change.
Special thanks to Ola Kowalewski and Jason Dorrier for significant conversations.
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Posted in Human Robots
#436984 Robots to the Rescue: How They Can Help ...
As the coronavirus pandemic forces people to keep their distance, could this be robots‘ time to shine? A group of scientists think so, and they’re calling for robots to do the “dull, dirty, and dangerous jobs” of infectious disease management.
Social distancing has emerged as one of the most effective strategies for slowing the spread of COVID-19, but it’s also bringing many jobs to a standstill and severely restricting our daily lives. And unfortunately, the one group that can’t rely on its protective benefits are the medical and emergency services workers we’re relying on to save us.
Robots could be a solution, according to the editorial board of Science Robotics, by helping replace humans in a host of critical tasks, from disinfecting hospitals to collecting patient samples and automating lab tests.
According to the authors, the key areas where robots could help are clinical care, logistics, and reconnaissance, which refers to tasks like identifying the infected or making sure people comply with quarantines or social distancing requirements. Outside of the medical sphere, robots could also help keep the economy and infrastructure going by standing in for humans in factories or vital utilities like waste management or power plants.
When it comes to clinical care, robots can play important roles in disease prevention, diagnosis and screening, and patient care, the researchers say. Robots have already been widely deployed to disinfect hospitals and other public spaces either using UV light that kills bugs or by repurposing agricultural robots and drones to spray disinfectant, reducing the exposure of cleaning staff to potentially contaminated surfaces. They are also being used to carry out crucial deliveries of food and medication without exposing humans.
But they could also play an important role in tracking the disease, say the researchers. Thermal cameras combined with image recognition algorithms are already being used to detect potential cases at places like airports, but incorporating them into mobile robots or drones could greatly expand the coverage of screening programs.
A more complex challenge—but one that could significantly reduce medical workers’ exposure to the virus—would be to design robots that could automate the collection of nasal swabs used to test for COVID-19. Similarly automated blood collection for tests could be of significant help, and researchers are already investigating using ultrasound to help robots locate veins to draw blood from.
Convincing people it’s safe to let a robot stick a swab up their nose or jab a needle in their arm might be a hard sell right now, but a potentially more realistic scenario would be to get robots to carry out laboratory tests on collected samples to reduce exposure to lab technicians. Commercial laboratory automation systems already exist, so this might be a more achievable near-term goal.
Not all solutions need to be automated, though. While autonomous systems will be helpful for reducing the workload of stretched health workers, remote systems can still provide useful distancing. Remote control robotics systems are already becoming increasingly common in the delicate business of surgery, so it would be entirely feasible to create remote systems to carry out more prosaic medical tasks.
Such systems would make it possible for experts to contribute remotely in many different places without having to travel. And robotic systems could combine medical tasks like patient monitoring with equally important social interaction for people who may have been shut off from human contact.
In a teleconference last week Guang-Zhong Yang, a medical roboticist from Carnegie Mellon University and founding editor of Science Robotics, highlighted the importance of including both doctors and patients in the design of these robots to ensure they are safe and effective, but also to make sure people trust them to observe social protocols and not invade their privacy.
But Yang also stressed the importance of putting the pieces in place to enable the rapid development and deployment of solutions. During the 2015 Ebola outbreak, the White House Office of Science and Technology Policy and the National Science Foundation organized workshops to identify where robotics could help deal with epidemics.
But once the threat receded, attention shifted elsewhere, and by the time the next pandemic came around little progress had been made on potential solutions. The result is that it’s unclear how much help robots will really be able to provide to the COVID-19 response.
That means it’s crucial to invest in a sustained research effort into this field, say the paper’s authors, with more funding and multidisciplinary research partnerships between government agencies and industry so that next time around we will be prepared.
“These events are rare and then it’s just that people start to direct their efforts to other applications,” said Yang. “So I think this time we really need to nail it, because without a sustained approach to this history will repeat itself and robots won’t be ready.”
Image Credit: ABB’s YuMi collaborative robot. Image courtesy of ABB Continue reading →
Posted in Human Robots
#436504 20 Technology Metatrends That Will ...
In the decade ahead, waves of exponential technological advancements are stacking atop one another, eclipsing decades of breakthroughs in scale and impact.
Emerging from these waves are 20 “metatrends” likely to revolutionize entire industries (old and new), redefine tomorrow’s generation of businesses and contemporary challenges, and transform our livelihoods from the bottom up.
Among these metatrends are augmented human longevity, the surging smart economy, AI-human collaboration, urbanized cellular agriculture, and high-bandwidth brain-computer interfaces, just to name a few.
It is here that master entrepreneurs and their teams must see beyond the immediate implications of a given technology, capturing second-order, Google-sized business opportunities on the horizon.
Welcome to a new decade of runaway technological booms, historic watershed moments, and extraordinary abundance.
Let’s dive in.
20 Metatrends for the 2020s
(1) Continued increase in global abundance: The number of individuals in extreme poverty continues to drop, as the middle-income population continues to rise. This metatrend is driven by the convergence of high-bandwidth and low-cost communication, ubiquitous AI on the cloud, and growing access to AI-aided education and AI-driven healthcare. Everyday goods and services (finance, insurance, education, and entertainment) are being digitized and becoming fully demonetized, available to the rising billion on mobile devices.
(2) Global gigabit connectivity will connect everyone and everything, everywhere, at ultra-low cost: The deployment of both licensed and unlicensed 5G, plus the launch of a multitude of global satellite networks (OneWeb, Starlink, etc.), allow for ubiquitous, low-cost communications for everyone, everywhere, not to mention the connection of trillions of devices. And today’s skyrocketing connectivity is bringing online an additional three billion individuals, driving tens of trillions of dollars into the global economy. This metatrend is driven by the convergence of low-cost space launches, hardware advancements, 5G networks, artificial intelligence, materials science, and surging computing power.
(3) The average human healthspan will increase by 10+ years: A dozen game-changing biotech and pharmaceutical solutions (currently in Phase 1, 2, or 3 clinical trials) will reach consumers this decade, adding an additional decade to the human healthspan. Technologies include stem cell supply restoration, wnt pathway manipulation, senolytic medicines, a new generation of endo-vaccines, GDF-11, and supplementation of NMD/NAD+, among several others. And as machine learning continues to mature, AI is set to unleash countless new drug candidates, ready for clinical trials. This metatrend is driven by the convergence of genome sequencing, CRISPR technologies, AI, quantum computing, and cellular medicine.
(4) An age of capital abundance will see increasing access to capital everywhere: From 2016 – 2018 (and likely in 2019), humanity hit all-time highs in the global flow of seed capital, venture capital, and sovereign wealth fund investments. While this trend will witness some ups and downs in the wake of future recessions, it is expected to continue its overall upward trajectory. Capital abundance leads to the funding and testing of ‘crazy’ entrepreneurial ideas, which in turn accelerate innovation. Already, $300 billion in crowdfunding is anticipated by 2025, democratizing capital access for entrepreneurs worldwide. This metatrend is driven by the convergence of global connectivity, dematerialization, demonetization, and democratization.
(5) Augmented reality and the spatial web will achieve ubiquitous deployment: The combination of augmented reality (yielding Web 3.0, or the spatial web) and 5G networks (offering 100Mb/s – 10Gb/s connection speeds) will transform how we live our everyday lives, impacting every industry from retail and advertising to education and entertainment. Consumers will play, learn, and shop throughout the day in a newly intelligent, virtually overlaid world. This metatrend will be driven by the convergence of hardware advancements, 5G networks, artificial intelligence, materials science, and surging computing power.
(6) Everything is smart, embedded with intelligence: The price of specialized machine learning chips is dropping rapidly with a rise in global demand. Combined with the explosion of low-cost microscopic sensors and the deployment of high-bandwidth networks, we’re heading into a decade wherein every device becomes intelligent. Your child’s toy remembers her face and name. Your kids’ drone safely and diligently follows and videos all the children at the birthday party. Appliances respond to voice commands and anticipate your needs.
(7) AI will achieve human-level intelligence: As predicted by technologist and futurist Ray Kurzweil, artificial intelligence will reach human-level performance this decade (by 2030). Through the 2020s, AI algorithms and machine learning tools will be increasingly made open source, available on the cloud, allowing any individual with an internet connection to supplement their cognitive ability, augment their problem-solving capacity, and build new ventures at a fraction of the current cost. This metatrend will be driven by the convergence of global high-bandwidth connectivity, neural networks, and cloud computing. Every industry, spanning industrial design, healthcare, education, and entertainment, will be impacted.
(8) AI-human collaboration will skyrocket across all professions: The rise of “AI as a Service” (AIaaS) platforms will enable humans to partner with AI in every aspect of their work, at every level, in every industry. AIs will become entrenched in everyday business operations, serving as cognitive collaborators to employees—supporting creative tasks, generating new ideas, and tackling previously unattainable innovations. In some fields, partnership with AI will even become a requirement. For example: in the future, making certain diagnoses without the consultation of AI may be deemed malpractice.
(9) Most individuals adapt a JARVIS-like “software shell” to improve their quality of life: As services like Alexa, Google Home, and Apple Homepod expand in functionality, such services will eventually travel beyond the home and become your cognitive prosthetic 24/7. Imagine a secure JARVIS-like software shell that you give permission to listen to all your conversations, read your email, monitor your blood chemistry, etc. With access to such data, these AI-enabled software shells will learn your preferences, anticipate your needs and behavior, shop for you, monitor your health, and help you problem-solve in support of your mid- and long-term goals.
(10) Globally abundant, cheap renewable energy: Continued advancements in solar, wind, geothermal, hydroelectric, nuclear, and localized grids will drive humanity towards cheap, abundant, and ubiquitous renewable energy. The price per kilowatt-hour will drop below one cent per kilowatt-hour for renewables, just as storage drops below a mere three cents per kilowatt-hour, resulting in the majority displacement of fossil fuels globally. And as the world’s poorest countries are also the world’s sunniest, the democratization of both new and traditional storage technologies will grant energy abundance to those already bathed in sunlight.
(11) The insurance industry transforms from “recovery after risk” to “prevention of risk”: Today, fire insurance pays you after your house burns down; life insurance pays your next-of-kin after you die; and health insurance (which is really sick insurance) pays only after you get sick. This next decade, a new generation of insurance providers will leverage the convergence of machine learning, ubiquitous sensors, low-cost genome sequencing, and robotics to detect risk, prevent disaster, and guarantee safety before any costs are incurred.
(12) Autonomous vehicles and flying cars will redefine human travel (soon to be far faster and cheaper): Fully autonomous vehicles, car-as-a-service fleets, and aerial ride-sharing (flying cars) will be fully operational in most major metropolitan cities in the coming decade. The cost of transportation will plummet 3-4X, transforming real estate, finance, insurance, the materials economy, and urban planning. Where you live and work, and how you spend your time, will all be fundamentally reshaped by this future of human travel. Your kids and elderly parents will never drive. This metatrend will be driven by the convergence of machine learning, sensors, materials science, battery storage improvements, and ubiquitous gigabit connections.
(13) On-demand production and on-demand delivery will birth an “instant economy of things”: Urban dwellers will learn to expect “instant fulfillment” of their retail orders as drone and robotic last-mile delivery services carry products from local supply depots directly to your doorstep. Further riding the deployment of regional on-demand digital manufacturing (3D printing farms), individualized products can be obtained within hours, anywhere, anytime. This metatrend is driven by the convergence of networks, 3D printing, robotics, and artificial intelligence.
(14) Ability to sense and know anything, anytime, anywhere: We’re rapidly approaching the era wherein 100 billion sensors (the Internet of Everything) is monitoring and sensing (imaging, listening, measuring) every facet of our environments, all the time. Global imaging satellites, drones, autonomous car LIDARs, and forward-looking augmented reality (AR) headset cameras are all part of a global sensor matrix, together allowing us to know anything, anytime, anywhere. This metatrend is driven by the convergence of terrestrial, atmospheric and space-based sensors, vast data networks, and machine learning. In this future, it’s not “what you know,” but rather “the quality of the questions you ask” that will be most important.
(15) Disruption of advertising: As AI becomes increasingly embedded in everyday life, your custom AI will soon understand what you want better than you do. In turn, we will begin to both trust and rely upon our AIs to make most of our buying decisions, turning over shopping to AI-enabled personal assistants. Your AI might make purchases based upon your past desires, current shortages, conversations you’ve allowed your AI to listen to, or by tracking where your pupils focus on a virtual interface (i.e. what catches your attention). As a result, the advertising industry—which normally competes for your attention (whether at the Superbowl or through search engines)—will have a hard time influencing your AI. This metatrend is driven by the convergence of machine learning, sensors, augmented reality, and 5G/networks.
(16) Cellular agriculture moves from the lab into inner cities, providing high-quality protein that is cheaper and healthier: This next decade will witness the birth of the most ethical, nutritious, and environmentally sustainable protein production system devised by humankind. Stem cell-based ‘cellular agriculture’ will allow the production of beef, chicken, and fish anywhere, on-demand, with far higher nutritional content, and a vastly lower environmental footprint than traditional livestock options. This metatrend is enabled by the convergence of biotechnology, materials science, machine learning, and AgTech.
(17) High-bandwidth brain-computer interfaces (BCIs) will come online for public use: Technologist and futurist Ray Kurzweil has predicted that in the mid-2030s, we will begin connecting the human neocortex to the cloud. This next decade will see tremendous progress in that direction, first serving those with spinal cord injuries, whereby patients will regain both sensory capacity and motor control. Yet beyond assisting those with motor function loss, several BCI pioneers are now attempting to supplement their baseline cognitive abilities, a pursuit with the potential to increase their sensorium, memory, and even intelligence. This metatrend is fueled by the convergence of materials science, machine learning, and robotics.
(18) High-resolution VR will transform both retail and real estate shopping: High-resolution, lightweight virtual reality headsets will allow individuals at home to shop for everything from clothing to real estate from the convenience of their living room. Need a new outfit? Your AI knows your detailed body measurements and can whip up a fashion show featuring your avatar wearing the latest 20 designs on a runway. Want to see how your furniture might look inside a house you’re viewing online? No problem! Your AI can populate the property with your virtualized inventory and give you a guided tour. This metatrend is enabled by the convergence of: VR, machine learning, and high-bandwidth networks.
(19) Increased focus on sustainability and the environment: An increase in global environmental awareness and concern over global warming will drive companies to invest in sustainability, both from a necessity standpoint and for marketing purposes. Breakthroughs in materials science, enabled by AI, will allow companies to drive tremendous reductions in waste and environmental contamination. One company’s waste will become another company’s profit center. This metatrend is enabled by the convergence of materials science, artificial intelligence, and broadband networks.
(20) CRISPR and gene therapies will minimize disease: A vast range of infectious diseases, ranging from AIDS to Ebola, are now curable. In addition, gene-editing technologies continue to advance in precision and ease of use, allowing families to treat and ultimately cure hundreds of inheritable genetic diseases. This metatrend is driven by the convergence of various biotechnologies (CRISPR, gene therapy), genome sequencing, and artificial intelligence.
Join Me
(1) A360 Executive Mastermind: If you’re an exponentially and abundance-minded entrepreneur who would like coaching directly from me, consider joining my Abundance 360 Mastermind, a highly selective community of 360 CEOs and entrepreneurs who I coach for 3 days every January in Beverly Hills, Ca. Through A360, I provide my members with context and clarity about how converging exponential technologies will transform every industry. I’m committed to running A360 for the course of an ongoing 25-year journey as a “countdown to the Singularity.”
If you’d like to learn more and consider joining our 2020 membership, apply here.
(2) Abundance-Digital Online Community: I’ve also created a Digital/Online community of bold, abundance-minded entrepreneurs called Abundance-Digital. Abundance-Digital is Singularity University’s ‘onramp’ for exponential entrepreneurs — those who want to get involved and play at a higher level. Click here to learn more.
(Both A360 and Abundance-Digital are part of Singularity University — your participation opens you to a global community.)
This article originally appeared on diamandis.com. Read the original article here.
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Posted in Human Robots
#436437 Why AI Will Be the Best Tool for ...
Dmitry Kaminskiy speaks as though he were trying to unload everything he knows about the science and economics of longevity—from senolytics research that seeks to stop aging cells from spewing inflammatory proteins and other molecules to the trillion-dollar life extension industry that he and his colleagues are trying to foster—in one sitting.
At the heart of the discussion with Singularity Hub is the idea that artificial intelligence will be the engine that drives breakthroughs in how we approach healthcare and healthy aging—a concept with little traction even just five years ago.
“At that time, it was considered too futuristic that artificial intelligence and data science … might be more accurate compared to any hypothesis of human doctors,” said Kaminskiy, co-founder and managing partner at Deep Knowledge Ventures, an investment firm that is betting big on AI and longevity.
How times have changed. Artificial intelligence in healthcare is attracting more investments and deals than just about any sector of the economy, according to data research firm CB Insights. In the most recent third quarter, AI healthcare startups raised nearly $1.6 billion, buoyed by a $550 million mega-round from London-based Babylon Health, which uses AI to collect data from patients, analyze the information, find comparable matches, then make recommendations.
Even without the big bump from Babylon Health, AI healthcare startups raised more than $1 billion last quarter, including two companies focused on longevity therapeutics: Juvenescence and Insilico Medicine.
The latter has risen to prominence for its novel use of reinforcement learning and general adversarial networks (GANs) to accelerate the drug discovery process. Insilico Medicine recently published a seminal paper that demonstrated how such an AI system could generate a drug candidate in just 46 days. Co-founder and CEO Alex Zhavoronkov said he believes there is no greater goal in healthcare today—or, really, any venture—than extending the healthy years of the human lifespan.
“I don’t think that there is anything more important than that,” he told Singularity Hub, explaining that an unhealthy society is detrimental to a healthy economy. “I think that it’s very, very important to extend healthy, productive lifespan just to fix the economy.”
An Aging Crisis
The surge of interest in longevity is coming at a time when life expectancy in the US is actually dropping, despite the fact that we spend more money on healthcare than any other nation.
A new paper in the Journal of the American Medical Association found that after six decades of gains, life expectancy for Americans has decreased since 2014, particularly among young and middle-aged adults. While some of the causes are societal, such as drug overdoses and suicide, others are health-related.
While average life expectancy in the US is 78, Kaminskiy noted that healthy life expectancy is about ten years less.
To Zhavoronkov’s point about the economy (a topic of great interest to Kaminskiy as well), the US spent $1.1 trillion on chronic diseases in 2016, according to a report from the Milken Institute, with diabetes, cardiovascular conditions, and Alzheimer’s among the most costly expenses to the healthcare system. When the indirect costs of lost economic productivity are included, the total price tag of chronic diseases in the US is $3.7 trillion, nearly 20 percent of GDP.
“So this is the major negative feedback on the national economy and creating a lot of negative social [and] financial issues,” Kaminskiy said.
Investing in Longevity
That has convinced Kaminskiy that an economy focused on extending healthy human lifespans—including the financial instruments and institutions required to support a long-lived population—is the best way forward.
He has co-authored a book on the topic with Margaretta Colangelo, another managing partner at Deep Knowledge Ventures, which has launched a specialized investment fund, Longevity.Capital, focused on the longevity industry. Kaminskiy estimates that there are now about 20 such investment funds dedicated to funding life extension companies.
In November at the inaugural AI for Longevity Summit in London, he and his collaborators also introduced the Longevity AI Consortium, an academic-industry initiative at King’s College London. Eventually, the research center will include an AI Longevity Accelerator program to serve as a bridge between startups and UK investors.
Deep Knowledge Ventures has committed about £7 million ($9 million) over the next three years to the accelerator program, as well as establishing similar consortiums in other regions of the world, according to Franco Cortese, a partner at Longevity.Capital and director of the Aging Analytics Agency, which has produced a series of reports on longevity.
A Cure for What Ages You
One of the most recent is an overview of Biomarkers for Longevity. A biomarker, in the case of longevity, is a measurable component of health that can indicate a disease state or a more general decline in health associated with aging. Examples range from something as simple as BMI as an indicator of obesity, which is associated with a number of chronic diseases, to sophisticated measurements of telomeres, the protective ends of chromosomes that shorten as we age.
While some researchers are working on moonshot therapies to reverse or slow aging—with a few even arguing we could expand human life on the order of centuries—Kaminskiy said he believes understanding biomarkers of aging could make more radical interventions unnecessary.
In this vision of healthcare, people would be able to monitor their health 24-7, with sensors attuned to various biomarkers that could indicate the onset of everything from the flu to diabetes. AI would be instrumental in not just ingesting the billions of data points required to develop such a system, but also what therapies, treatments, or micro-doses of a drug or supplement would be required to maintain homeostasis.
“Consider it like Tesla with many, many detectors, analyzing the behavior of the car in real time, and a cloud computing system monitoring those signals in real time with high frequency,” Kaminskiy explained. “So the same shall be applied for humans.”
And only sophisticated algorithms, Kaminskiy argued, can make longevity healthcare work on a mass scale but at the individual level. Precision medicine becomes preventive medicine. Healthcare truly becomes a system to support health rather than a way to fight disease.
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#436094 Agility Robotics Unveils Upgraded Digit ...
Last time we saw Agility Robotics’ Digit biped, it was picking up a box from a Ford delivery van and autonomously dropping it off on a porch, while at the same time managing to not trip over stairs, grass, or small children. As a demo, it was pretty impressive, but of course there’s an enormous gap between making a video of a robot doing a successful autonomous delivery and letting that robot out into the semi-structured world and expecting it to reliably do a good job.
Agility Robotics is aware of this, of course, and over the last six months they’ve been making substantial improvements to Digit to make it more capable and robust. A new video posted today shows what’s new with the latest version of Digit—Digit v2.
We appreciate Agility Robotics foregoing music in the video, which lets us hear exactly what Digit sounds like in operation. The most noticeable changes are in Digit’s feet, torso, and arms, and I was particularly impressed to see Digit reposition the box on the table before grasping it to make sure that it could get a good grip. Otherwise, it’s hard to tell what’s new, so we asked Agility Robotics’ CEO Damion Shelton to get us up to speed.
IEEE Spectrum: Can you summarize the differences between Digit v1 and v2? We’re particularly interested in the new feet.
Damion Shelton: The feet now include a roll degree of freedom, so that Digit can resist lateral forces without needing to side step. This allows Digit v2 to balance on one foot statically, which Digit v1 and Cassie could not do. The larger foot also dramatically decreases load per unit area, for improved performance on very soft surfaces like sand.
The perception stack includes four Intel RealSense cameras used for obstacle detection and pick/place, plus the lidar. In Digit v1, the perception systems were brought up incrementally over time for development purposes. In Digit v2, all perception systems are active from the beginning and tied to a dedicated computer. The perception system is used for a number of additional things beyond manipulation, which we’ll start to show in the next few weeks.
The torso changes are a bit more behind-the-scenes. All of the electronics in it are now fully custom, thermally managed, and environmentally sealed. We’ve also included power and ethernet to a payload bay that can fit either a NUC or Jetson module (or other customer payload).
What exactly are we seeing in the video in terms of Digit’s autonomous capabilities?
At the moment this is a demonstration of shared autonomy. Picking and placing the box is fully autonomous. Balance and footstep placement are fully autonomous, but guidance and obstacle avoidance are under local teleop. It’s no longer a radio controller as in early videos; we’re not ready to reveal our current controller design but it’s a reasonably significant upgrade. This is v2 hardware, so there’s one more full version in development prior to the 2020 launch, which will expand the autonomy envelope significantly.
“This is a demonstration of shared autonomy. Picking and placing the box is fully autonomous. Balance and footstep placement are fully autonomous, but guidance and obstacle avoidance are under local teleop. It’s no longer a radio controller as in early videos; we’re not ready to reveal our current controller design but it’s a reasonably significant upgrade”
—Damion Shelton, Agility Robotics
What are some unique features or capabilities of Digit v2 that might not be obvious from the video?
For those who’ve used Cassie robots, the power-up and power-down ergonomics are a lot more user friendly. Digit can be disassembled into carry-on luggage sized pieces (give or take) in under 5 minutes for easy transport. The battery charges in-situ using a normal laptop-style charger.
I’m curious about this “stompy” sort of gait that we see in Digit and many other bipedal robots—are there significant challenges or drawbacks to implementing a more human-like (and presumably quieter) heel-toe gait?
There are no drawbacks other than increased complexity in controls and foot design. With Digit v2, the larger surface area helps with the noise, and v2 has similar or better passive-dynamic performance as compared to Cassie or Digit v1. The foot design is brand new, and new behaviors like heel-toe are an active area of development.
How close is Digit v2 to a system that you’d be comfortable operating commercially?
We’re on track for a 2020 launch for Digit v3. Changes from v2 to v3 are mostly bug-fix in nature, with a few regulatory upgrades like full battery certification. Safety is a major concern for us, and we have launch customers that will be operating Digit in a safe environment, with a phased approach to relaxing operational constraints. Digit operates almost exclusively under force control (as with cobots more generally), but at the moment we’ll err on the side of caution during operation until we have the stats to back up safety and reliability. The legged robot industry has too much potential for us to screw it up by behaving irresponsibly.
It will be a while before Digit (or any other humanoid robot) is operating fully autonomously in crowds of people, but there are so many large market opportunities (think indoor factory/warehouse environments) to address prior to that point that we expect to mature the operational safety side of things well in advance of having saturated the more robot-tolerant markets.
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Posted in Human Robots