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#437395 Microsoft Had a Crazy Idea to Put ...

A little over two years ago, a shipping container-sized cylinder bearing Microsoft’s name and logo was lowered onto the ocean floor off the northern coast of Scotland. Inside were 864 servers, and their submersion was part of the second phase of the software giant’s Project Natick. Launched in 2015, the project’s purpose is to determine the feasibility of underwater data centers powered by offshore renewable energy.

A couple months ago, the deep-sea servers were brought back up to the surface so engineers could inspect them and evaluate how they’d performed while under water.

But wait—why were they there in the first place?

As bizarre as it seems to sink hundreds of servers into the ocean, there are actually several very good reasons to do so. According to the UN, about 40 percent of the world’s population lives within 60 miles of an ocean. As internet connectivity expands to cover most of the globe in the next few years, millions more people will come online, and a lot more servers will be needed to manage the increased demand and data they’ll generate.

In densely-populated cities real estate is expensive and can be hard to find. But know where there’s lots of cheap, empty space? At the bottom of the ocean. This locale also carries the added benefit of being really cold (depending where we’re talking, that is; if you’re looking off the coast of, say, Mumbai or Abu Dhabi, the waters are warmer).

Servers generate a lot of heat, and datacenters use most of their electricity for cooling. Keeping not just the temperature but also the humidity level constant is important for optimal functioning of the servers; neither of these vary much 100 feet under water.

Finally, installing data centers on the ocean floor is, surprisingly, much faster than building them on land. Microsoft claims its server-holding cylinders will take less than 90 days to go from factory ship to operation, as compared to the average two years it takes to get a terrestrial data center up and running.

Microsoft’s Special Projects team operated the underwater data center for two years, and it took a full day to dredge it up and bring it to the surface. One of the first things researchers did was to insert test tubes into the container to take samples of the air inside; they’ll use it to try to determine how gases released from the equipment may have impacted the servers’ operating environment.

The container was filled with dry nitrogen upon deployment, which seems to have made for a much better environment than the oxygen that land-bound servers are normally surrounded by; the failure rate of the servers in the water was just one-eighth that of Microsoft’s typical rate for its servers on land. The team thinks the nitrogen atmosphere was helpful because it’s less corrosive than oxygen. The fact that no humans entered the container for the entirety of its operations helped, too (no moving around of components or having to turn on lights or adjust the temperature).

Ben Cutler, a project manager in Microsoft’s Special Projects research group who leads Project Natick, believes the results of this phase of the project are sufficient to show that underwater data centers are worth pursuing. “We are now at the point of trying to harness what we have done as opposed to feeling the need to go and prove out some more,” he said.

Cutler envisions putting underwater datacenters near offshore wind farms to power them sustainably. The data centers of the future will require less human involvement, instead being managed and run primarily by technologies like robotics and AI. In this kind of “lights-out” datacenter, the servers would be swapped out about once every five years, with any that fail before then being taken offline.

The final step in this phase of Project Natick is to recycle all the components used for the underwater data center, including the steel pressure vessel, heat exchangers, and the servers themselves—and restoring the sea bed where the cylinder rested back to its original condition.

If Cutler’s optimism is a portent of things to come, it may not be long before the ocean floor is dotted with sustainable datacenters to feed our ever-increasing reliance on our phones and the internet.

Image Credit: Microsoft Continue reading

Posted in Human Robots

#437337 6G Will Be 100 Times Faster Than ...

Though 5G—a next-generation speed upgrade to wireless networks—is scarcely up and running (and still nonexistent in many places) researchers are already working on what comes next. It lacks an official name, but they’re calling it 6G for the sake of simplicity (and hey, it’s tradition). 6G promises to be up to 100 times faster than 5G—fast enough to download 142 hours of Netflix in a second—but researchers are still trying to figure out exactly how to make such ultra-speedy connections happen.

A new chip, described in a paper in Nature Photonics by a team from Osaka University and Nanyang Technological University in Singapore, may give us a glimpse of our 6G future. The team was able to transmit data at a rate of 11 gigabits per second, topping 5G’s theoretical maximum speed of 10 gigabits per second and fast enough to stream 4K high-def video in real time. They believe the technology has room to grow, and with more development, might hit those blistering 6G speeds.

NTU final year PhD student Abhishek Kumar, Assoc Prof Ranjan Singh and postdoc Dr Yihao Yang. Dr Singh is holding the photonic topological insulator chip made from silicon, which can transmit terahertz waves at ultrahigh speeds. Credit: NTU Singapore
But first, some details about 5G and its predecessors so we can differentiate them from 6G.

Electromagnetic waves are characterized by a wavelength and a frequency; the wavelength is the distance a cycle of the wave covers (peak to peak or trough to trough, for example), and the frequency is the number of waves that pass a given point in one second. Cellphones use miniature radios to pick up electromagnetic signals and convert those signals into the sights and sounds on your phone.

4G wireless networks run on millimeter waves on the low- and mid-band spectrum, defined as a frequency of a little less (low-band) and a little more (mid-band) than one gigahertz (or one billion cycles per second). 5G kicked that up several notches by adding even higher frequency millimeter waves of up to 300 gigahertz, or 300 billion cycles per second. Data transmitted at those higher frequencies tends to be information-dense—like video—because they’re much faster.

The 6G chip kicks 5G up several more notches. It can transmit waves at more than three times the frequency of 5G: one terahertz, or a trillion cycles per second. The team says this yields a data rate of 11 gigabits per second. While that’s faster than the fastest 5G will get, it’s only the beginning for 6G. One wireless communications expert even estimates 6G networks could handle rates up to 8,000 gigabits per second; they’ll also have much lower latency and higher bandwidth than 5G.

Terahertz waves fall between infrared waves and microwaves on the electromagnetic spectrum. Generating and transmitting them is difficult and expensive, requiring special lasers, and even then the frequency range is limited. The team used a new material to transmit terahertz waves, called photonic topological insulators (PTIs). PTIs can conduct light waves on their surface and edges rather than having them run through the material, and allow light to be redirected around corners without disturbing its flow.

The chip is made completely of silicon and has rows of triangular holes. The team’s research showed the chip was able to transmit terahertz waves error-free.

Nanyang Technological University associate professor Ranjan Singh, who led the project, said, “Terahertz technology […] can potentially boost intra-chip and inter-chip communication to support artificial intelligence and cloud-based technologies, such as interconnected self-driving cars, which will need to transmit data quickly to other nearby cars and infrastructure to navigate better and also to avoid accidents.”

Besides being used for AI and self-driving cars (and, of course, downloading hundreds of hours of video in seconds), 6G would also make a big difference for data centers, IoT devices, and long-range communications, among other applications.

Given that 5G networks are still in the process of being set up, though, 6G won’t be coming on the scene anytime soon; a recent whitepaper on 6G from Japanese company NTTDoCoMo estimates we’ll see it in 2030, pointing out that wireless connection tech generations have thus far been spaced about 10 years apart; we got 3G in the early 2000s, 4G in 2010, and 5G in 2020.

In the meantime, as 6G continues to develop, we’re still looking forward to the widespread adoption of 5G.

Image Credit: Hans Braxmeier from Pixabay Continue reading

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#437251 The Robot Revolution Was Televised: Our ...

When robots take over the world, Boston Dynamics may get a special shout-out in the acceptance speech.

“Do you, perchance, recall the many times you shoved our ancestors with a hockey stick on YouTube? It might have seemed like fun and games to you—but we remember.”

In the last decade, while industrial robots went about blandly automating boring tasks like the assembly of Teslas, Boston Dynamics built robots as far removed from Roombas as antelope from amoebas. The flaws in Asimov’s laws of robotics suddenly seemed a little too relevant.

The robot revolution was televised—on YouTube. With tens of millions of views, the robotics pioneer is the undisputed heavyweight champion of robot videos, and has been for years. Each new release is basically guaranteed press coverage—mostly stoking robot fear but occasionally eliciting compassion for the hardships of all robot-kind. And for good reason. The robots are not only some of the most advanced in the world, their makers just seem to have a knack for dynamite demos.

When Google acquired the company in 2013, it was a bombshell. One of the richest tech companies, with some of the most sophisticated AI capabilities, had just paired up with one of the world’s top makers of robots. And some walked on two legs like us.

Of course, the robots aren’t quite as advanced as they seem, and a revolution is far from imminent. The decade’s most meme-worthy moment was a video montage of robots, some of them by Boston Dynamics, falling—over and over and over, in the most awkward ways possible. Even today, they’re often controlled by a human handler behind the scenes, and the most jaw-dropping cuts can require several takes to nail. Google sold the company to SoftBank in 2017, saying advanced as they were, there wasn’t yet a clear path to commercial products. (Google’s robotics work was later halted and revived.)

Yet, despite it all, Boston Dynamics is still with us and still making sweet videos. Taken as a whole, the evolution in physical prowess over the years has been nothing short of astounding. And for the first time, this year, a Boston Dynamics robot, Spot, finally went on sale to anyone with a cool $75K.

So, we got to thinking: What are our favorite Boston Dynamics videos? And can we gather them up in one place for your (and our) viewing pleasure? Well, great question, and yes, why not. These videos were the ones that entertained or amazed us most (or both). No doubt, there are other beloved hits we missed or inadvertently omitted.

With that in mind, behold: Our favorite Boston Dynamics videos, from that one time they dressed up a humanoid bot in camo and gas mask—because, damn, that’s terrifying—to the time the most advanced robot dog in all the known universe got extra funky.

Let’s Kick This Off With a Big (Loud) Robot Dog
Let’s start with a baseline. BigDog was the first Boston Dynamics YouTube sensation. The year? 2009! The company was working on military contracts, and BigDog was supposed to be a sort of pack mule for soldiers. The video primarily shows off BigDog’s ability to balance on its own, right itself, and move over uneven terrain. Note the power source—a noisy combustion engine—and utilitarian design. Sufficed to say, things have evolved.

Nothing to See Here. Just a Pair of Robot Legs on a Treadmill
While BigDog is the ancestor of later four-legged robots, like Spot, Petman preceded the two-legged Atlas robot. Here, the Petman prototype, just a pair of robot legs and a caged torso, gets a light workout on the treadmill. Again, you can see its ability to balance and right itself when shoved. In contrast to BigDog, Petman is tethered for power (which is why it’s so quiet) and to catch it should it fall. Again, as you’ll see, things have evolved since then.

Robot in Gas Mask and Camo Goes for a Stroll
This one broke the internet—for obvious reasons. Not only is the robot wearing clothes, those clothes happen to be a camouflaged chemical protection suit and gas mask. Still working for the military, Boston Dynamics said Petman was testing protective clothing, and in addition to a full body, it had skin that actually sweated and was studded with sensors to detect leaks. In addition to walking, Petman does some light calisthenics as it prepares to climb out of the uncanny valley. (Still tethered though!)

This Machine Could Run Down Usain Bolt
If BigDog and Petman were built for balance and walking, Cheetah was built for speed. Here you can see the four-legged robot hitting 28.3 miles per hour, which, as the video casually notes, would be enough to run down the fastest human on the planet. Luckily, it wouldn’t be running down anyone as it was firmly leashed in the lab at this point.

Ever Dreamt of a Domestic Robot to Do the Dishes?
After its acquisition by Google, Boston Dynamics eased away from military contracts and applications. It was a return to more playful videos (like BigDog hitting the beach in Thailand and sporting bull horns) and applications that might be practical in civilian life. Here, the team introduced Spot, a streamlined version of BigDog, and showed it doing dishes, delivering a drink, and slipping on a banana peel (which was, of course, instantly made into a viral GIF). Note how much quieter Spot is thanks to an onboard battery and electric motor.

Spot Gets Funky
Nothing remotely practical here. Just funky moves. (Also, with a coat of yellow and black paint, Spot’s dressed more like a polished product as opposed to a utilitarian lab robot.)

Atlas Does Parkour…
Remember when Atlas was just a pair of legs on a treadmill? It’s amazing what ten years brings. By 2019, Atlas had a more polished appearance, like Spot, and had long ago ditched the tethers. Merely balancing was laughably archaic. The robot now had some amazing moves: like a handstand into a somersault, 180- and 360-degree spins, mid-air splits, and just for good measure, a gymnastics-style end to the routine to show it’s in full control.

…and a Backflip?!
To this day, this one is just. Insane.

10 Robot Dogs Tow a Box Truck
Nearly three decades after its founding, Boston Dynamics is steadily making its way into the commercial space. The company is pitching Spot as a multipurpose ‘mobility platform,’ emphasizing it can carry a varied suite of sensors and can go places standard robots can’t. (Its Handle robot is also set to move into warehouse automation.) So far, Spot’s been mostly trialed in surveying and data collection, but as this video suggests, string enough Spots together, and they could tow your car. That said, a pack of 10 would set you back $750K, so, it’s probably safe to say a tow truck is the better option (for now).

Image credit: Boston Dynamics Continue reading

Posted in Human Robots

#437182 MIT’s Tiny New Brain Chip Aims for AI ...

The human brain operates on roughly 20 watts of power (a third of a 60-watt light bulb) in a space the size of, well, a human head. The biggest machine learning algorithms use closer to a nuclear power plant’s worth of electricity and racks of chips to learn.

That’s not to slander machine learning, but nature may have a tip or two to improve the situation. Luckily, there’s a branch of computer chip design heeding that call. By mimicking the brain, super-efficient neuromorphic chips aim to take AI off the cloud and put it in your pocket.

The latest such chip is smaller than a piece of confetti and has tens of thousands of artificial synapses made out of memristors—chip components that can mimic their natural counterparts in the brain.

In a recent paper in Nature Nanotechnology, a team of MIT scientists say their tiny new neuromorphic chip was used to store, retrieve, and manipulate images of Captain America’s Shield and MIT’s Killian Court. Whereas images stored with existing methods tended to lose fidelity over time, the new chip’s images remained crystal clear.

“So far, artificial synapse networks exist as software. We’re trying to build real neural network hardware for portable artificial intelligence systems,” Jeehwan Kim, associate professor of mechanical engineering at MIT said in a press release. “Imagine connecting a neuromorphic device to a camera on your car, and having it recognize lights and objects and make a decision immediately, without having to connect to the internet. We hope to use energy-efficient memristors to do those tasks on-site, in real-time.”

A Brain in Your Pocket
Whereas the computers in our phones and laptops use separate digital components for processing and memory—and therefore need to shuttle information between the two—the MIT chip uses analog components called memristors that process and store information in the same place. This is similar to the way the brain works and makes memristors far more efficient. To date, however, they’ve struggled with reliability and scalability.

To overcome these challenges, the MIT team designed a new kind of silicon-based, alloyed memristor. Ions flowing in memristors made from unalloyed materials tend to scatter as the components get smaller, meaning the signal loses fidelity and the resulting computations are less reliable. The team found an alloy of silver and copper helped stabilize the flow of silver ions between electrodes, allowing them to scale the number of memristors on the chip without sacrificing functionality.

While MIT’s new chip is promising, there’s likely a ways to go before memristor-based neuromorphic chips go mainstream. Between now and then, engineers like Kim have their work cut out for them to further scale and demonstrate their designs. But if successful, they could make for smarter smartphones and other even smaller devices.

“We would like to develop this technology further to have larger-scale arrays to do image recognition tasks,” Kim said. “And some day, you might be able to carry around artificial brains to do these kinds of tasks, without connecting to supercomputers, the internet, or the cloud.”

Special Chips for AI
The MIT work is part of a larger trend in computing and machine learning. As progress in classical chips has flagged in recent years, there’s been an increasing focus on more efficient software and specialized chips to continue pushing the pace.

Neuromorphic chips, for example, aren’t new. IBM and Intel are developing their own designs. So far, their chips have been based on groups of standard computing components, such as transistors (as opposed to memristors), arranged to imitate neurons in the brain. These chips are, however, still in the research phase.

Graphics processing units (GPUs)—chips originally developed for graphics-heavy work like video games—are the best practical example of specialized hardware for AI and were heavily used in this generation of machine learning early on. In the years since, Google, NVIDIA, and others have developed even more specialized chips that cater more specifically to machine learning.

The gains from such specialized chips are already being felt.

In a recent cost analysis of machine learning, research and investment firm ARK Invest said cost declines have far outpaced Moore’s Law. In a particular example, they found the cost to train an image recognition algorithm (ResNet-50) went from around $1,000 in 2017 to roughly $10 in 2019. The fall in cost to actually run such an algorithm was even more dramatic. It took $10,000 to classify a billion images in 2017 and just $0.03 in 2019.

Some of these declines can be traced to better software, but according to ARK, specialized chips have improved performance by nearly 16 times in the last three years.

As neuromorphic chips—and other tailored designs—advance further in the years to come, these trends in cost and performance may continue. Eventually, if all goes to plan, we might all carry a pocket brain that can do the work of today’s best AI.

Image credit: Peng Lin Continue reading

Posted in Human Robots

#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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