With a deadly pandemic sweeping the planet, many are questioning the comfort and security we have taken for granted in the modern world.
A century ago, when an influenza pandemic struck, we barely knew what viruses were.
More than a century after the germ theory, we are still at the mercy of a microbe we can neither treat, nor control, nor immunize against. Even more discouraging is that technology has in some ways exacerbated the problem: cars and air travel allow a new disease to quickly encompass the globe.
Some say we have grown complacent, that we falsely assume the triumphs of the past ensure a happy and prosperous future, that we are oblivious to the possibility of unpredictable "black swan" events that could cause our destruction. Some have begun to lose confidence in progress itself, and despair of the future.
But the new coronavirus should not defeat our spirit—if anything, it should spur us to redouble our efforts, both in the science and technology of medicine, and more broadly in the advance of industry. Because the best way to protect ourselves against future disasters is more progress, faster.
Science and technology have overall made us much better able to deal with disease. In the developed world, we have already tamed most categories of infectious disease. Most bacterial infections, such as tuberculosis or bacterial pneumonia, are cured with antibiotics. Waterborne diseases such as cholera are eliminated through sanitation; insect-borne ones such as malaria through pest control. Those that are not contagious until symptoms appear, such as SARS, can be handled through case isolation and contact tracing. For the rest, such as smallpox, polio, and measles, we develop vaccines, given enough time. COVID-19 could start a pandemic only because it fits a narrow category: a new, viral disease that is highly contagious via pre-symptomatic droplet/aerosol transmission, and that has a high mortality rate compared to seasonal influenza.
A century ago, when an influenza pandemic struck, we barely knew what viruses were; no one had ever seen one. Today we know what COVID-19 is down to its exact genome; in fact, we have sequenced thousands of COVID-19 genomes, and can track its history and its spread through their mutations. We can create vaccines faster today, too: where we once developed them in live animals, we now use cell cultures; where we once had to weaken or inactivate the virus itself, we can now produce vaccines based on the virus's proteins. And even though we don't yet have a treatment, the last century-plus of pharmaceutical research has given us a vast catalog of candidate drugs, already proven safe. Even now, over 50 candidate vaccines and almost 100 candidate treatments are in the research pipeline.
It's not just our knowledge that has advanced, but our methods. When smallpox raged in the 1700s, even the idea of calculating a case-fatality rate was an innovation. When the polio vaccine was trialled in the 1950s, the use of placebo-controlled trials was still controversial. The crucial measure of contagiousness, "R0", was not developed in epidemiology until the 1980s. And today, all of these methods are made orders of magnitude faster and more powerful by statistical and data visualization software.
If you're seeking to avoid COVID-19, the hand sanitizer gel you carry in a pocket or purse did not exist until the 1960s. If you start to show symptoms, the pulse oximeter that tests your blood oxygenation was not developed until the 1970s. If your case worsens, the mechanical ventilator that keeps you alive was invented in the 1950s—in fact, no form of artificial respiration was widely available until the "iron lung" used to treat polio patients in the 1930s. Even the modern emergency medical system did not exist until recently: if during the 1918 flu pandemic you became seriously ill, there was no 911 hotline to call, and any ambulance that showed up would likely have been a modified van or hearse, with no equipment or trained staff.
As many of us "shelter in place", we are far more able to communicate and collaborate, to maintain some semblance of normal life, than we ever would have been. To compare again to 1918: long-distance telephone service barely existed at that time, and only about a third of homes in the US even had electricity; now we can videoconference over Zoom and Skype. And the enormous selection and availability provided by online retail and food delivery have kept us stocked and fed, even when we don't want to venture out to the store.
Let the virus push us to redouble our efforts to make scientific, technological, and industrial progress on all fronts.
"Black swan" calamities can strike without warning at any time. Indeed, humanity has always been subject to them—drought and frost, fire and flood, war and plague. But we are better equipped now to deal with them than ever before. And the more progress we make, the better prepared we'll be for the next one. The accumulation of knowledge, technology, industrial infrastructure, and surplus wealth is the best buffer against any shock—whether a viral pandemic, a nuclear war, or an asteroid impact. In fact, the more worried we are about future crises, the more energetically we should accelerate science, technology and industry.
In this sense, we have grown complacent. We take the modern world for granted, so much so that some question whether further progress is even still needed. The new virus proves how much we do need it, and how far we still have to go. Imagine how different things would be if we had broad-spectrum antiviral drugs, or a way to enhance the immune system to react faster to infection, or a way to detect infection even before symptoms appear. These technologies may seem to belong to a Star Trek future—but so, at one time, did cell phones.
The virus reminds us that nature is indifferent to us, leaving us to fend entirely for ourselves. As we go to war against it, let us not take the need for such a war as reason for despair. Instead, let it push us to redouble our efforts to make scientific, technological, and industrial progress on all fronts. No matter the odds, applied intelligence is our best weapon against disaster.
The Friday Five covers five stories in health research that you may have missed this week. There are plenty of controversies and troubling ethical issues in science – and we get into many of them in our online magazine – but this news roundup focuses on scientific creativity and progress to give you a therapeutic dose of inspiration headed into the weekend.
One day in recent past, scientists at Columbia University’s Creative Machines Lab set up a robotic arm inside a circle of five streaming video cameras and let the robot watch itself move, turn and twist. For about three hours the robot did exactly that—it looked at itself this way and that, like toddlers exploring themselves in a room full of mirrors. By the time the robot stopped, its internal neural network finished learning the relationship between the robot’s motor actions and the volume it occupied in its environment. In other words, the robot built a spatial self-awareness, just like humans do. “We trained its deep neural network to understand how it moved in space,” says Boyuan Chen, one of the scientists who worked on it.
For decades robots have been doing helpful tasks that are too hard, too dangerous, or physically impossible for humans to carry out themselves. Robots are ultimately superior to humans in complex calculations, following rules to a tee and repeating the same steps perfectly. But even the biggest successes for human-robot collaborations—those in manufacturing and automotive industries—still require separating the two for safety reasons. Hardwired for a limited set of tasks, industrial robots don't have the intelligence to know where their robo-parts are in space, how fast they’re moving and when they can endanger a human.
Over the past decade or so, humans have begun to expect more from robots. Engineers have been building smarter versions that can avoid obstacles, follow voice commands, respond to human speech and make simple decisions. Some of them proved invaluable in many natural and man-made disasters like earthquakes, forest fires, nuclear accidents and chemical spills. These disaster recovery robots helped clean up dangerous chemicals, looked for survivors in crumbled buildings, and ventured into radioactive areas to assess damage.
Now roboticists are going a step further, training their creations to do even better: understand their own image in space and interact with humans like humans do. Today, there are already robot-teachers like KeeKo, robot-pets like Moffin, robot-babysitters like iPal, and robotic companions for the elderly like Pepper.
But even these reasonably intelligent creations still have huge limitations, some scientists think. “There are niche applications for the current generations of robots,” says professor Anthony Zador at Cold Spring Harbor Laboratory—but they are not “generalists” who can do varied tasks all on their own, as they mostly lack the abilities to improvise, make decisions based on a multitude of facts or emotions, and adjust to rapidly changing circumstances. “We don’t have general purpose robots that can interact with the world. We’re ages away from that.”
Robotic spatial self-awareness – the achievement by the team at Columbia – is an important step toward creating more intelligent machines. Hod Lipson, professor of mechanical engineering who runs the Columbia lab, says that future robots will need this ability to assist humans better. Knowing how you look and where in space your parts are, decreases the need for human oversight. It also helps the robot to detect and compensate for damage and keep up with its own wear-and-tear. And it allows robots to realize when something is wrong with them or their parts. “We want our robots to learn and continue to grow their minds and bodies on their own,” Chen says. That’s what Zador wants too—and on a much grander level. “I want a robot who can drive my car, take my dog for a walk and have a conversation with me.”
Columbia scientists have trained a robot to become aware of its own "body," so it can map the right path to touch a ball without running into an obstacle, in this case a square.
Jane Nisselson and Yinuo Qin/ Columbia Engineering
Today’s technological advances are making some of these leaps of progress possible. One of them is the so-called Deep Learning—a method that trains artificial intelligence systems to learn and use information similar to how humans do it. Described as a machine learning method based on neural network architectures with multiple layers of processing units, Deep Learning has been used to successfully teach machines to recognize images, understand speech and even write text.
Trained by Google, one of these language machine learning geniuses, BERT, can finish sentences. Another one called GPT3, designed by San Francisco-based company OpenAI, can write little stories. Yet, both of them still make funny mistakes in their linguistic exercises that even a child wouldn’t. According to a paper published by Stanford’s Center for Research on Foundational Models, BERT seems to not understand the word “not.” When asked to fill in the word after “A robin is a __” it correctly answers “bird.” But try inserting the word “not” into that sentence (“A robin is not a __”) and BERT still completes it the same way. Similarly, in one of its stories, GPT3 wrote that if you mix a spoonful of grape juice into your cranberry juice and drink the concoction, you die. It seems that robots, and artificial intelligence systems in general, are still missing some rudimentary facts of life that humans and animals grasp naturally and effortlessly.
How does one give robots a genome? Zador has an idea. We can’t really equip machines with real biological nucleotide-based genes, but we can mimic the neuronal blueprint those genes create.
It's not exactly the robots’ fault. Compared to humans, and all other organisms that have been around for thousands or millions of years, robots are very new. They are missing out on eons of evolutionary data-building. Animals and humans are born with the ability to do certain things because they are pre-wired in them. Flies know how to fly, fish knows how to swim, cats know how to meow, and babies know how to cry. Yet, flies don’t really learn to fly, fish doesn’t learn to swim, cats don’t learn to meow, and babies don’t learn to cry—they are born able to execute such behaviors because they’re preprogrammed to do so. All that happens thanks to the millions of years of evolutions wired into their respective genomes, which give rise to the brain’s neural networks responsible for these behaviors. Robots are the newbies, missing out on that trove of information, Zador argues.
A neuroscience professor who studies how brain circuitry generates various behaviors, Zador has a different approach to developing the robotic mind. Until their creators figure out a way to imbue the bots with that information, robots will remain quite limited in their abilities. Each model will only be able to do certain things it was programmed to do, but it will never go above and beyond its original code. So Zador argues that we have to start giving robots a genome.
How does one do that? Zador has an idea. We can’t really equip machines with real biological nucleotide-based genes, but we can mimic the neuronal blueprint those genes create. Genomes lay out rules for brain development. Specifically, the genome encodes blueprints for wiring up our nervous system—the details of which neurons are connected, the strength of those connections and other specs that will later hold the information learned throughout life. “Our genomes serve as blueprints for building our nervous system and these blueprints give rise to a human brain, which contains about 100 billion neurons,” Zador says.
If you think what a genome is, he explains, it is essentially a very compact and compressed form of information storage. Conceptually, genomes are similar to CliffsNotes and other study guides. When students read these short summaries, they know about what happened in a book, without actually reading that book. And that’s how we should be designing the next generation of robots if we ever want them to act like humans, Zador says. “We should give them a set of behavioral CliffsNotes, which they can then unwrap into brain-like structures.” Robots that have such brain-like structures will acquire a set of basic rules to generate basic behaviors and use them to learn more complex ones.
Currently Zador is in the process of developing algorithms that function like simple rules that generate such behaviors. “My algorithms would write these CliffsNotes, outlining how to solve a particular problem,” he explains. “And then, the neural networks will use these CliffsNotes to figure out which ones are useful and use them in their behaviors.” That’s how all living beings operate. They use the pre-programmed info from their genetics to adapt to their changing environments and learn what’s necessary to survive and thrive in these settings.
For example, a robot’s neural network could draw from CliffsNotes with “genetic” instructions for how to be aware of its own body or learn to adjust its movements. And other, different sets of CliffsNotes may imbue it with the basics of physical safety or the fundamentals of speech.
At the moment, Zador is working on algorithms that are trying to mimic neuronal blueprints for very simple organisms—such as earthworms, which have only 302 neurons and about 7000 synapses compared to the millions we have. That’s how evolution worked, too—expanding the brains from simple creatures to more complex to the Homo Sapiens. But if it took millions of years to arrive at modern humans, how long would it take scientists to forge a robot with human intelligence? That’s a billion-dollar question. Yet, Zador is optimistic. “My hypotheses is that if you can build simple organisms that can interact with the world, then the higher level functions will not be nearly as challenging as they currently are.”