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#437216 New Report: Tech Could Fuel an Age of ...

Posted on June 29, 2020 by Android

With rapid technological progress running headlong into dramatic climate change and widening inequality, most experts agree the coming decade will be tumultuous. But a new report predicts it could actually make or break civilization as we know it.

The idea that humanity is facing a major shake-up this century is not new. The Fourth Industrial Revolution being brought about by technologies like AI, gene editing, robotics, and 3D printing is predicted to cause dramatic social, political, and economic upheaval in the coming decades.

But according to think tank RethinkX, thinking about the coming transition as just another industrial revolution is too simplistic. In a report released last week called Rethinking Humanity, the authors argue that we are about to see a reordering of our relationship with the world as fundamental as when hunter-gatherers came together to build the first civilizations.

At the core of their argument is the fact that since the first large human settlements appeared 10,000 years ago, civilization has been built on the back of our ability to extract resources from nature, be they food, energy, or materials. This led to a competitive landscape where the governing logic is grow or die, which has driven all civilizations to date.

That could be about to change thanks to emerging technologies that will fundamentally disrupt the five foundational sectors underpinning society: information, energy, food, transportation, and materials. They predict that across all five, costs will fall by 10 times or more, while production processes will become 10 times more efficient and will use 90 percent fewer natural resources with 10 to 100 times less waste.

They say that this transformation has already happened in information, where the internet has dramatically reduced barriers to communication and knowledge. They predict the combination of cheap solar and grid storage will soon see energy costs drop as low as one cent per kilowatt hour, and they envisage widespread adoption of autonomous electric vehicles and the replacement of car ownership with ride-sharing.

The authors laid out their vision for the future of food in another report last year, where they predicted that traditional agriculture would soon be replaced by industrial-scale brewing of single-celled organisms genetically modified to produce all the nutrients we need. In a similar vein, they believe the same processes combined with additive manufacturing and “nanotechnologies” will allow us to build all the materials required for the modern world from the molecule up rather than extracting scarce natural resources.

They believe this could allow us to shift from a system of production based on extraction to one built on creation, as limitless renewable energy makes it possible to build everything we need from scratch and barriers to movement and information disappear. As a result, a lifestyle worthy of the “American Dream” could be available to anyone for as little as $250/month by 2030.

This will require a fundamental reimagining of our societies, though. All great civilizations have eventually hit fundamental limits on their growth and we are no different, as demonstrated by our growing impact on the environment and the increasing concentration of wealth. Historically this stage of development has lead to a doubling down on old tactics in search of short-term gains, but this invariably leads to the collapse of the civilization.

The authors argue that we’re in a unique position. Because of the technological disruption detailed above, we have the ability to break through the limits on our growth. But only if we change what the authors call our “Organizing System.” They describe this as “the prevailing models of thought, belief systems, myths, values, abstractions, and conceptual frameworks that help explain how the world works and our relationship to it.”

They say that the current hierarchical, centralized system based on nation-states is unfit for the new system of production that is emerging. The cracks are already starting to appear, with problems like disinformation campaigns, fake news, and growing polarization demonstrating how ill-suited our institutions are for dealing with the distributed nature of today’s information systems. And as this same disruption comes to the other foundational sectors the shockwaves could lead to the collapse of civilization as we know it.

Their solution is a conscious shift towards a new way of organizing the world. As emerging technology allows communities to become self-sufficient, flows of physical resources will be replaced by flows of information, and we will require a decentralized but highly networked Organizing System.

The report includes detailed recommendations on how to usher this in. Examples include giving individuals control and ownership of data rights; developing new models for community ownership of energy, information, and transportation networks; and allowing states and cities far greater autonomy on policies like immigration, taxation, education, and public expenditure.

How easy it will be to get people on board with such a shift is another matter. The authors say it may require us to re-examine the foundations of our society, like representative democracy, capitalism, and nation-states. While they acknowledge that these ideas are deeply entrenched, they appear to believe we can reason our way around them.

That seems optimistic. Cultural and societal change can be glacial, and efforts to impose it top-down through reason and logic are rarely successful. The report seems to brush over many of the messy realities of humanity, such as the huge sway that tradition and religion hold over the vast majority of people.

It also doesn’t deal with the uneven distribution of the technology that is supposed to catapult us into this new age. And while the predicted revolutions in transportation, energy, and information do seem inevitable, the idea that in the next decade or two we’ll be able to produce any material we desire using cheap and abundant stock materials seems like a stretch.

Despite the techno-utopianism though, many of the ideas in the report hold promise for building societies that are better adapted for the disruptive new age we are about to enter.

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Posted in Human Robots

#437120 The New Indiana Jones? AI. Here’s How ...

Posted on May 7, 2020 by Android

Archaeologists have uncovered scores of long-abandoned settlements along coastal Madagascar that reveal environmental connections to modern-day communities. They have detected the nearly indiscernible bumps of earthen mounds left behind by prehistoric North American cultures. Still other researchers have mapped Bronze Age river systems in the Indus Valley, one of the cradles of civilization.

All of these recent discoveries are examples of landscape archaeology. They’re also examples of how artificial intelligence is helping scientists hunt for new archaeological digs on a scale and at a pace unimaginable even a decade ago.

“AI in archaeology has been increasing substantially over the past few years,” said Dylan Davis, a PhD candidate in the Department of Anthropology at Penn State University. “One of the major uses of AI in archaeology is for the detection of new archaeological sites.”

The near-ubiquitous availability of satellite data and other types of aerial imagery for many parts of the world has been both a boon and a bane to archaeologists. They can cover far more ground, but the job of manually mowing their way across digitized landscapes is still time-consuming and laborious. Machine learning algorithms offer a way to parse through complex data far more quickly.

AI Gives Archaeologists a Bird’s Eye View
Davis developed an automated algorithm for identifying large earthen and shell mounds built by native populations long before Europeans arrived with far-off visions of skyscrapers and superhighways in their eyes. The sites still hidden in places like the South Carolina wilderness contain a wealth of information about how people lived, even what they ate, and the ways they interacted with the local environment and other cultures.

In this particular case, the imagery comes from LiDAR, which uses light pulses that can penetrate tree canopies to map forest floors. The team taught the computer the shape, size, and texture characteristics of the mounds so it could identify potential sites from the digital 3D datasets that it analyzed.

“The process resulted in several thousand possible features that my colleagues and I checked by hand,” Davis told Singularity Hub. “While not entirely automated, this saved the equivalent of years of manual labor that would have been required for analyzing the whole LiDAR image by hand.”

In Madagascar—where Davis is studying human settlement history across the world’s fourth largest island over a timescale of millennia—he developed a predictive algorithm to help locate archaeological sites using freely available satellite imagery. His team was able to survey and identify more than 70 new archaeological sites—and potentially hundreds more—across an area of more than 1,000 square kilometers during the course of about a year.

Machines Learning From the Past Prepare Us for the Future
One impetus behind the rapid identification of archaeological sites is that many are under threat from climate change, such as coastal erosion from sea level rise, or other human impacts. Meanwhile, traditional archaeological approaches are expensive and laborious—serious handicaps in a race against time.

“It is imperative to record as many archaeological sites as we can in a short period of time. That is why AI and machine learning are useful for my research,” Davis said.

Studying the rise and fall of past civilizations can also teach modern humans a thing or two about how to grapple with these current challenges.

Researchers at the Institut Català d’Arqueologia Clàssica (ICAC) turned to machine-learning algorithms to reconstruct more than 20,000 kilometers of paleo-rivers along the Indus Valley civilization of what is now part of modern Pakistan and India. Such AI-powered mapping techniques wouldn’t be possible using satellite images alone.

That effort helped locate many previously unknown archaeological sites and unlocked new insights into those Bronze Age cultures. However, the analytics can also assist governments with important water resource management today, according to Hèctor A. Orengo Romeu, co-director of the Landscape Archaeology Research Group at ICAC.

“Our analyses can contribute to the forecasts of the evolution of aquifers in the area and provide valuable information on aspects such as the variability of agricultural productivity or the influence of climate change on the expansion of the Thar desert, in addition to providing cultural management tools to the government,” he said.

Leveraging AI for Language and Lots More
While landscape archaeology is one major application of AI in archaeology, it’s far from the only one. In 2000, only about a half-dozen scientific papers referred to the use of AI, according to the Web of Science, reputedly the world’s largest global citation database. Last year, more than 65 papers were published concerning the use of machine intelligence technologies in archaeology, with a significant uptick beginning in 2015.

AI methods, for instance, are being used to understand the chemical makeup of artifacts like pottery and ceramics, according to Davis. “This can help identify where these materials were made and how far they were transported. It can also help us to understand the extent of past trading networks.”

Linguistic anthropologists have also used machine intelligence methods to trace the evolution of different languages, Davis said. “Using AI, we can learn when and where languages emerged around the world.”

In other cases, AI has helped reconstruct or decipher ancient texts. Last year, researchers at Google’s DeepMind used a deep neural network called PYTHIA to recreate missing inscriptions in ancient Greek from damaged surfaces of objects made of stone or ceramics.

Named after the Oracle at Delphi, PYTHIA “takes a sequence of damaged text as input, and is trained to predict character sequences comprising hypothesised restorations of ancient Greek inscriptions,” the researchers reported.

In a similar fashion, Chinese scientists applied a convolutional neural network (CNN) to untangle another ancient tongue once found on turtle shells and ox bones. The CNN managed to classify oracle bone morphology in order to piece together fragments of these divination objects, some with inscriptions that represent the earliest evidence of China’s recorded history.

“Differentiating the materials of oracle bones is one of the most basic steps for oracle bone morphology—we need to first make sure we don’t assemble pieces of ox bones with tortoise shells,” lead author of the study, associate professor Shanxiong Chen at China’s Southwest University, told Synced, an online tech publication in China.

AI Helps Archaeologists Get the Scoop…
And then there are applications of AI in archaeology that are simply … interesting. Just last month, researchers published a paper about a machine learning method trained to differentiate between human and canine paleofeces.

The algorithm, dubbed CoproID, compares the gut microbiome DNA found in the ancient material with DNA found in modern feces, enabling it to get the scoop on the origin of the poop.

Also known as coprolites, paleo-feces from humans and dogs are often found in the same archaeological sites. Scientists need to know which is which if they’re trying to understand something like past diets or disease.

“CoproID is the first line of identification in coprolite analysis to confirm that what we’re looking for is actually human, or a dog if we’re interested in dogs,” Maxime Borry, a bioinformatics PhD student at the Max Planck Institute for the Science of Human History, told Vice.

…But Machine Intelligence Is Just Another Tool
There is obviously quite a bit of work that can be automated through AI. But there’s no reason for archaeologists to hit the unemployment line any time soon. There are also plenty of instances where machines can’t yet match humans in identifying objects or patterns. At other times, it’s just faster doing the analysis yourself, Davis noted.

“For ‘big data’ tasks like detecting archaeological materials over a continental scale, AI is useful,” he said. “But for some tasks, it is sometimes more time-consuming to train an entire computer algorithm to complete a task that you can do on your own in an hour.”

Still, there’s no telling what the future will hold for studying the past using artificial intelligence.

“We have already started to see real improvements in the accuracy and reliability of these approaches, but there is a lot more to do,” Davis said. “Hopefully, we start to see these methods being directly applied to a variety of interesting questions around the world, as these methods can produce datasets that would have been impossible a few decades ago.”

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Posted in Human Robots

#437103 How to Make Sense of Uncertainty in a ...

Posted on May 1, 2020 by Android

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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#436488 Tech’s Biggest Leaps From the Last 10 ...

Posted on January 3, 2020 by Android

As we enter our third decade in the 21st century, it seems appropriate to reflect on the ways technology developed and note the breakthroughs that were achieved in the last 10 years.

The 2010s saw IBM’s Watson win a game of Jeopardy, ushering in mainstream awareness of machine learning, along with DeepMind’s AlphaGO becoming the world’s Go champion. It was the decade that industrial tools like drones, 3D printers, genetic sequencing, and virtual reality (VR) all became consumer products. And it was a decade in which some alarming trends related to surveillance, targeted misinformation, and deepfakes came online.

For better or worse, the past decade was a breathtaking era in human history in which the idea of exponential growth in information technologies powered by computation became a mainstream concept.

As I did last year for 2018 only, I’ve asked a collection of experts across the Singularity University faculty to help frame the biggest breakthroughs and moments that gave shape to the past 10 years. I asked them what, in their opinion, was the most important breakthrough in their respective fields over the past decade.

My own answer to this question, focused in the space of augmented and virtual reality, would be the stunning announcement in March of 2014 that Facebook acquired Oculus VR for $2 billion. Although VR technology had been around for a while, it was at this precise moment that VR arrived as a consumer technology platform. Facebook, largely fueled by the singular interest of CEO Mark Zuckerberg, has funded the development of this industry, keeping alive the hope that consumer VR can become a sustainable business. In the meantime, VR has continued to grow in sophistication and usefulness, though it has yet to truly take off as a mainstream concept. That will hopefully be a development for the 2020s.

Below is a decade in review across the technology areas that are giving shape to our modern world, as described by the SU community of experts.

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

In my mind, this decade of astounding breakthroughs in the life sciences and medicine rests on the achievement of the $1,000 human genome in 2016. More-than-exponentially falling costs of DNA sequencing have driven advances in medicine, agriculture, ecology, genome editing, synthetic biology, the battle against climate change, and our fundamental understanding of life and its breathtaking connections. The “digital” revolution in DNA constituted an important model for harnessing other types of biological information, from personalized bio data to massive datasets spanning populations and species.

Crucially, by aggressively driving down the cost of such analyses, researchers and entrepreneurs democratized access to the source code of life—with attendant financial, cultural, and ethical consequences. Exciting, but take heed: Veritas Genetics spearheaded a $600 genome in 2019, only to have to shutter USA operations due to a money trail tangled with the trade war with China. Stay tuned through the early 2020s to see the pricing of DNA sequencing fall even further … and to experience the many ways that cheaper, faster harvesting of biological data will enrich your daily life.

Cryptocurrency
Alex Gladstein | Chief Strategy Officer, Human Rights Foundation

The past decade has seen Bitcoin go from just an idea on an obscure online message board to a global financial network carrying more than 100 billion dollars in value. And we’re just getting started. One recent defining moment in the cryptocurrency space has been a stunning trend underway in Venezuela, where today, the daily dollar-denominated value of Bitcoin traded now far exceeds the daily dollar-denominated value traded on the Caracas Stock Exchange. It’s just one country, but it’s a significant country, and a paradigm shift.

Governments and corporations are following Bitcoin’s success too, and are looking to launch their own digital currencies. China will launch its “DC/EP” project in the coming months, and Facebook is trying to kickstart its Libra project. There are technical and regulatory uncertainties for both, but one thing is for certain: the era of digital currency has arrived.

Business Strategy and Entrepreneurship
Pascal Finnette | Chair, Entrepreneurship and Open Innovation, Singularity University

For me, without a doubt, the most interesting and quite possibly ground-shifting development in the fields of entrepreneurship and corporate innovation in the last ten years is the rapid maturing of customer-driven product development frameworks such as Lean Startup, and its subsequent adoption by corporates for their own innovation purposes.

Tools and frameworks like the Business Model Canvas, agile (software) development and the aforementioned Lean Startup methodology fundamentally shifted the way we think and go about building products, services, and companies, with many of these tools bursting onto the startup scene in the late 2000s and early 2010s.

As these tools matured they found mass adoption not only in startups around the world, but incumbent companies who eagerly adopted them to increase their own innovation velocity and success.

Energy
Ramez Naam | Co-Chair, Energy and Environment, Singularity University

The 2010s were the decade that saw clean electricity, energy storage, and electric vehicles break through price and performance barriers around the world. Solar, wind, batteries, and EVs started this decade as technologies that had to be subsidized. That was the first phase of their existence. Now they’re entering their third, most disruptive phase, where shifting to clean energy and mobility is cheaper than continuing to use existing coal, gas, or oil infrastructure.

Consider that at the start of 2010, there was no place on earth where building new solar or wind was cheaper than building new coal or gas power generation. By 2015, in some of the sunniest and windiest places on earth, solar and wind had entered their second phase, where they were cost-competitive for new power. And then, in 2018 and 2019, we started to see the edge of the third phase, as building new solar and wind, in some parts of the world, was cheaper than operating existing coal or gas power plants.

Food Technology
Liz Specht, Ph. D | Associate Director of Science & Technology, The Good Food Institute

The arrival of mainstream plant-based meat is easily the food tech advance of the decade. Meat analogs have, of course, been around forever. But only in the last decade have companies like Beyond Meat and Impossible Foods decided to cut animals out of the process and build no-compromise meat directly from plants.

Plant-based meat is already transforming the fast-food industry. For example, the introduction of the Impossible Whopper led Burger King to their most profitable quarter in many years. But the global food industry as a whole is shifting as well. Tyson, JBS, Nestle, Cargill, and many others are all embracing plant-based meat.

Augmented and Virtual Reality
Jody Medich | CEO, Superhuman-x

The breakthrough moment for augmented and virtual reality came in 2013 when Palmer Lucky took apart an Android smartphone and added optic lenses to make the first version of the Oculus Rift. Prior to that moment, we struggled with miniaturizing the components needed to develop low-latency head-worn devices. But thanks to the smartphone race started in 2006 with the iPhone, we finally had a suite of sensors, chips, displays, and computing power small enough to put on the head.

What will the next 10 years bring? Look for AR/VR to explode in a big way. We are right on the cusp of that tipping point when the tech is finally “good enough” for our linear expectations. Given all it can do today, we can’t even picture what’s possible. Just as today we can’t function without our phones, by 2029 we’ll feel lost without some AR/VR product. It will be the way we interact with computing, smart objects, and AI. Tim Cook, Apple CEO, predicts it will replace all of today’s computing devices. I can’t wait.

Philosophy of Technology
Alix Rübsaam | Faculty Fellow, Singularity University, Philosophy of Technology/Ethics of AI

The last decade has seen a significant shift in our general attitude towards the algorithms that we now know dictate much of our surroundings. Looking back at the beginning of the decade, it seems we were blissfully unaware of how the data we freely and willingly surrendered would feed the algorithms that would come to shape every aspect of our daily lives: the news we consume, the products we purchase, the opinions we hold, etc.

If I were to isolate a single publication that contributed greatly to the shift in public discourse on algorithms, it would have to be Cathy O’Neil’s Weapons of Math Destruction from 2016. It remains a comprehensive, readable, and highly informative insight into how algorithms dictate our finances, our jobs, where we go to school, or if we can get health insurance. Its publication represents a pivotal moment when the general public started to question whether we should be OK with outsourcing decision making to these opaque systems.

The ubiquity of ethical guidelines for AI and algorithms published just in the last year (perhaps most comprehensively by the AI Now Institute) fully demonstrates the shift in public opinion of this decade.

Data Science
Ola Kowalewski | Faculty Fellow, Singularity University, Data Innovation

In the last decade we entered the era of internet and smartphone ubiquity. The number of internet users doubled, with nearly 60 percent of the global population connected online and now over 35 percent of the globe owns a smartphone. With billions of people in a state of constant connectedness and therefore in a state of constant surveillance, the companies that have built the tech infrastructure and information pipelines have dominated the global economy. This shift from tech companies being the underdogs to arguably the world’s major powers sets the landscape we enter for the next decade.

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

The biggest breakthrough over the last decade in social impact and technology is that the social impact sector switched from seeing technology as something problematic to avoid, to one of the most effective ways to create social change. We now see people using exponential technologies to solve all sorts of social challenges in areas ranging from disaster response to hunger to shelter.

The world’s leading social organizations, such as UNICEF and the World Food Programme, have launched their own venture funds and accelerators, and the United Nations recently declared that digitization is revolutionizing global development.

Digital Biology
Raymond McCauley | Chair, Digital Biology, Singularity University, Co-Founder & Chief Architect, BioCurious; Principal, Exponential Biosciences

CRISPR is bringing about a revolution in genetic engineering. It’s obvious, and it’s huge. What may not be so obvious is the widespread adoption of genetic testing. And this may have an even longer-lasting effect. It’s used to test new babies, to solve medical mysteries, and to catch serial killers. Thanks to holiday ads from 23andMe and Ancestry.com, it’s everywhere. Testing your DNA is now a common over-the-counter product. People are using it to set their diet, to pick drugs, and even for dating (or at least picking healthy mates).

And we’re just in the early stages. Further down the line, doing large-scale studies on more people, with more data, will lead to the use of polygenic risk scores to help us rank our genetic potential for everything from getting cancer to being a genius. Can you imagine what it would be like for parents to pick new babies, GATTACA-style, to get the smartest kids? You don’t have to; it’s already happening.

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

The convergence of exponentially improved computing power, the deep learning algorithm, and access to massive data resulted in a series of AI breakthroughs over the past decade. These included: vastly improved accuracy in identifying images, making self driving cars practical, beating several world champions in Go, and identifying gender, smoking status, and age from retinal fundus photographs.

Combined, these breakthroughs convinced researchers and investors that after 50+ years of research and development, AI was ready for prime-time applications. Now, virtually every field of human endeavor is being revolutionized by machine learning. We still have a long way to go to achieve human-level intelligence and beyond, but the pace of worldwide improvement is blistering.

Hod Lipson | Professor of Engineering and Data Science, Columbia University

The biggest moment in AI in the past decade (and in its entire history, in my humble opinion) was midnight, Pacific time, September 30, 2012: the moment when machines finally opened their eyes. It was the moment when deep learning took off, breaking stagnant decades of machine blindness, when AI couldn’t reliably tell apart even a cat from a dog. That seemingly trivial accomplishment—a task any one-year-old child can do—has had a ripple effect on AI applications from driverless cars to health diagnostics. And this is just the beginning of what is sure to be a Cambrian explosion of AI.

Neuroscience
Divya Chander | Chair, Neuroscience, Singularity University

If the 2000s were the decade of brain mapping, then the 2010s were the decade of brain writing. Optogenetics, a technique for precisely mapping and controlling neurons and neural circuits using genetically-directed light, saw incredible growth in the 2010s.

Also in the last 10 years, neuromodulation, or the ability to rewire the brain using both invasive and non-invasive interfaces and energy, has exploded in use and form. For instance, the Braingate consortium showed us how electrode arrays implanted into the motor cortex could be used by paralyzed people to use their thoughts to direct a robotic arm. These technologies, alone or in combination with robotics, exoskeletons, and flexible, implantable, electronics also make possible a future of human augmentation.

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#436482 50+ Reasons Our Favorite Emerging ...

Posted on December 31, 2019 by Android

For most of history, technology was about atoms, the manipulation of physical stuff to extend humankind’s reach. But in the last five or six decades, atoms have partnered with bits, the elemental “particles” of the digital world as we know it today. As computing has advanced at the accelerating pace described by Moore’s Law, technological progress has become increasingly digitized.

SpaceX lands and reuses rockets and self-driving cars do away with drivers thanks to automation, sensors, and software. Businesses find and hire talent from anywhere in the world, and for better and worse, a notable fraction of the world learns and socializes online. From the sequencing of DNA to artificial intelligence and from 3D printing to robotics, more and more new technologies are moving at a digital pace and quickly emerging to reshape the world around us.

In 2019, stories charting the advances of some of these digital technologies consistently made headlines. Below is, what is at best, an incomplete list of some of the big stories that caught our eye this year. With so much happening, it’s likely we’ve missed some notable headlines and advances—as well as some of your personal favorites. In either instance, share your thoughts and candidates for the biggest stories and breakthroughs on Facebook and Twitter.

With that said, let’s dive straight into the year.

Artificial Intelligence
No technology garnered as much attention as AI in 2019. With good reason. Intelligent computer systems are transitioning from research labs to everyday life. Healthcare, weather forecasting, business process automation, traffic congestion—you name it, and machine learning algorithms are likely beginning to work on it. Yet, AI has also been hyped up and overmarketed, and the latest round of AI technology, deep learning, is likely only one piece of the AI puzzle.

This year, Open AI’s game-playing algorithms beat some of the world’s best Dota 2 players, DeepMind notched impressive wins in Starcraft, and Carnegie Mellon University’s Libratus “crushed” pros at six-player Texas Hold‘em.
Speaking of games, AI’s mastery of the incredibly complex game of Go prompted a former world champion to quit, stating that AI ‘”cannot be defeated.”
But it isn’t just fun and games. Practical, powerful applications that make the best of AI’s pattern recognition abilities are on the way. Insilico Medicine, for example, used machine learning to help discover and design a new drug in just 46 days, and DeepMind is focused on using AI to crack protein folding.
Of course, AI can be a double-edged sword. When it comes to deepfakes and fake news, for example, AI makes both easier to create and detect, and early in the year, OpenAI created and announced a powerful AI text generator but delayed releasing it for fear of malicious use.
Recognizing AI’s power for good and ill, the OECD, EU, World Economic Forum, and China all took a stab at defining an ethical framework for the development and deployment of AI.

Computing Systems
Processors and chips kickstarted the digital boom and are still the bedrock of continued growth. While progress in traditional silicon-based chips continues, it’s slowing and getting more expensive. Some say we’re reaching the end of Moore’s Law. While that may be the case for traditional chips, specialized chips and entirely new kinds of computing are waiting in the wings.

In fall 2019, Google confirmed its quantum computer had achieved “quantum supremacy,” a term that means a quantum computer can perform a calculation a normal computer cannot. IBM pushed back on the claim, and it should be noted the calculation was highly specialized. But while it’s still early days, there does appear to be some real progress (and more to come).
Should quantum computing become truly practical, “the implications are staggering.” It could impact machine learning, medicine, chemistry, and materials science, just to name a few areas.
Specialized chips continue to take aim at machine learning—a giant new chip with over a trillion transistors, for example, may make machine learning algorithms significantly more efficient.
Cellular computers also saw advances in 2019 thanks to CRISPR. And the year witnessed the emergence of the first reprogrammable DNA computer and new chips inspired by the brain.
The development of hardware computing platforms is intrinsically linked to software. 2019 saw a continued move from big technology companies towards open sourcing (at least parts of) their software, potentially democratizing the use of advanced systems.

Networks
Increasing interconnectedness has, in many ways, defined the 21st century so far. Your phone is no longer just a phone. It’s access to the world’s population and accumulated knowledge—and it fits in your pocket. Pretty neat. This is all thanks to networks, which had some notable advances in 2019.

The biggest network development of the year may well be the arrival of the first 5G networks.
5G’s faster speeds promise advances across many emerging technologies.
Self-driving vehicles, for example, may become both smarter and safer thanks to 5G C-V2X networks. (Don’t worry with trying to remember that. If they catch on, they’ll hopefully get a better name.)
Wi-Fi may have heard the news and said “hold my beer,” as 2019 saw the introduction of Wi-Fi 6. Perhaps the most important upgrade, among others, is that Wi-Fi 6 ensures that the ever-growing number of network connected devices get higher data rates.
Networks also went to space in 2019, as SpaceX began launching its Starlink constellation of broadband satellites. In typical fashion, Elon Musk showed off the network’s ability to bounce data around the world by sending a Tweet.

Augmented Reality and Virtual Reality
Forget Pokemon Go (unless you want to add me as a friend in the game—in which case don’t forget Pokemon Go). 2019 saw AR and VR advance, even as Magic Leap, the most hyped of the lot, struggled to live up to outsized expectations and sell headsets.

Mixed reality AR and VR technologies, along with the explosive growth of sensor-based data about the world around us, is creating a one-to-one “Mirror World” of our physical reality—a digital world you can overlay on our own or dive into immersively thanks to AR and VR.
Facebook launched Replica, for example, which is a photorealistic virtual twin of the real world that, among other things, will help train AIs to better navigate their physical surroundings.
Our other senses (beyond eyes) may also become part of the Mirror World through the use of peripherals like a newly developed synthetic skin that aim to bring a sense of touch to VR.
AR and VR equipment is also becoming cheaper—with more producers entering the space—and more user-friendly. Instead of a wired headset requiring an expensive gaming PC, the new Oculus Quest is a wireless, self-contained step toward the mainstream.
Niche uses also continue to gain traction, from Google Glass’s Enterprise edition to the growth of AR and VR in professional education—including on-the-job-training and roleplaying emotionally difficult work encounters, like firing an employee.

Digital Biology and Biotech
The digitization of biology is happening at an incredible rate. With wild new research coming to light every year and just about every tech giant pouring money into new solutions and startups, we’re likely to see amazing advances in 2020 added to those we saw in 2019.

None were, perhaps, more visible than the success of protein-rich, plant-based substitutes for various meats. This was the year Beyond Meat was the top IPO on the NASDAQ stock exchange and people stood in line for the plant-based Impossible Whopper and KFC’s Beyond Chicken.
In the healthcare space, a report about three people with HIV who became virus free thanks to a bone marrow transplants of stem cells caused a huge stir. The research is still in relatively early stages, and isn’t suitable for most people, but it does provides a glimmer of hope.
CRISPR technology, which almost deserves its own section, progressed by leaps and bounds. One tweak made CRISPR up to 50 times more accurate, while the latest new CRISPR-based system, CRISPR prime, was described as a “word processor” for gene editing.
Many areas of healthcare stand to gain from CRISPR. For instance, cancer treatment, were a first safety test showed ‘promising’ results.
CRISPR’s many potential uses, however, also include some weird/morally questionable areas, which was exemplified by one the year’s stranger CRISPR-related stories about a human-monkey hybrid embryo in China.
Incidentally, China could be poised to take the lead on CRISPR thanks to massive investments and research programs.
As a consequence of quick advances in gene editing, we are approaching a point where we will be able to design our own biology—but first we need to have a serious conversation as a society about the ethics of gene editing and what lines should be drawn.

3D Printing
3D printing has quietly been growing both market size and the objects the printers are capable of producing. While both are impressive, perhaps the biggest story of 2019 is their increased speed.

One example was a boat that was printed in just three days, which also set three new world records for 3D printing.
3D printing is also spreading in the construction industry. In Mexico, the technology is being used to construct 50 new homes with subsidized mortgages of just $20/month.
3D printers also took care of all parts of a 640 square-meter home in Dubai.
Generally speaking, the use of 3D printing to make parts for everything from rocket engines (even entire rockets) to trains to cars illustrates the sturdiness of the technology, anno 2019.
In healthcare, 3D printing is also advancing the cause of bio-printed organs and, in one example, was used to print vascularized parts of a human heart.

Robotics
Living in Japan, I get to see Pepper, Aibo, and other robots on pretty much a daily basis. The novelty of that experience is spreading to other countries, and robots are becoming a more visible addition to both our professional and private lives.

We can’t talk about robots and 2019 without mentioning Boston Dynamics’ Spot robot, which went on sale for the general public.
Meanwhile, Google, Boston Dynamics’ former owner, rebooted their robotics division with a more down-to-earth focus on everyday uses they hope to commercialize.
SoftBank’s Pepper robot is working as a concierge and receptionist in various countries. It is also being used as a home companion. Not satisfied, Pepper rounded off 2019 by heading to the gym—to coach runners.
Indeed, there’s a growing list of sports where robots perform as well—or better—than humans.
2019 also saw robots launch an assault on the kitchen, including the likes of Samsung’s robot chef, and invade the front yard, with iRobot’s Terra robotic lawnmower.
In the borderlands of robotics, full-body robotic exoskeletons got a bit more practical, as the (by all accounts) user-friendly, battery-powered Sarcos Robotics Guardian XO went commercial.

Autonomous Vehicles
Self-driving cars did not—if you will forgive the play on words—stay quite on track during 2019. The fallout from Uber’s 2018 fatal crash marred part of the year, while some big players ratcheted back expectations on a quick shift to the driverless future. Still, self-driving cars, trucks, and other autonomous systems did make progress this year.

Winner of my unofficial award for best name in self-driving goes to Optimus Ride. The company also illustrates that self-driving may not be about creating a one-size-fits-all solution but catering to specific markets.
Self-driving trucks had a good year, with tests across many countries and states. One of the year’s odder stories was a self-driving truck traversing the US with a delivery of butter.
A step above the competition may be the future slogan (or perhaps not) of Boeing’s self-piloted air taxi that saw its maiden test flight in 2019. It joins a growing list of companies looking to create autonomous, flying passenger vehicles.
2019 was also the year where companies seemed to go all in on last-mile autonomous vehicles. Who wins that particular competition could well emerge during 2020.

Blockchain and Digital Currencies
Bitcoin continues to be the cryptocurrency equivalent of a rollercoaster, but the underlying blockchain technology is progressing more steadily. Together, they may turn parts of our financial systems cashless and digital—though how and when remains a slightly open question.

One indication of this was Facebook’s hugely controversial announcement of Libra, its proposed cryptocurrency. The company faced immediate pushback and saw a host of partners jump ship. Still, it brought the tech into mainstream conversations as never before and is putting the pressure on governments and central banks to explore their own digital currencies.
Deloitte’s in-depth survey of the state of blockchain highlighted how the technology has moved from fintech into just about any industry you can think of.
One of the biggest issues facing the spread of many digital currencies—Bitcoin in particular, you could argue—is how much energy it consumes to mine them. 2019 saw the emergence of several new digital currencies with a much smaller energy footprint.
2019 was also a year where we saw a new kind of digital currency, stablecoins, rise to prominence. As the name indicates, stablecoins are a group of digital currencies whose price fluctuations are more stable than the likes of Bitcoin.
In a geopolitical sense, 2019 was a year of China playing catch-up. Having initially banned blockchain, the country turned 180 degrees and announced that it was “quite close” to releasing a digital currency and a wave of blockchain-programs.

Renewable Energy and Energy Storage
While not every government on the planet seems to be a fan of renewable energy, it keeps on outperforming fossil fuel after fossil fuel in places well suited to it—even without support from some of said governments.

One of the reasons for renewable energy’s continued growth is that energy efficiency levels keep on improving.
As a result, an increased number of coal plants are being forced to close due to an inability to compete, and the UK went coal-free for a record two weeks.
We are also seeing more and more financial institutions refusing to fund fossil fuel projects. One such example is the European Investment Bank.
Renewable energy’s advance is tied at the hip to the rise of energy storage, which also had a breakout 2019, in part thanks to investments from the likes of Bill Gates.
The size and capabilities of energy storage also grew in 2019. The best illustration came from Australia were Tesla’s mega-battery proved that energy storage has reached a stage where it can prop up entire energy grids.

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