Paul Alexander spent more than 70 years confined to an iron lung after a polio infection left him paralyzed at age 6. Here, Alexander uses a mirror attached to the top of his iron lung to view his surroundings.
Paul Alexander was one of several thousand who were infected and paralyzed by polio in 1952. That year, a polio epidemic swept the United States, forcing businesses to close and polio wards in hospitals all over the country to fill up with sick children. When Paul caught polio in the summer of 1952, doctors urged his parents to let him rest and recover at home, since the hospital in his home suburb of Dallas, Texas was already overrun with polio patients.
Paul rested in bed for a few days with aching limbs and a fever. But his condition quickly got worse. Within a week, Paul could no longer speak or swallow, and his parents rushed him to the local hospital where the doctors performed an emergency procedure to help him breathe. Paul woke from the surgery three days later, and found himself unable to move and lying inside an iron lung in the polio ward, surrounded by rows of other paralyzed children.
Hospitals were commonly filled with polio patients who had been paralyzed by the virus before a vaccine became widely available in 1955. Associated Press
But against all odds, Paul lived. And with help from a physical therapist, Paul was able to thrive—sometimes for small periods outside the iron lung.
The way Paul did this was to practice glossopharyngeal breathing (or as Paul called it, “frog breathing”), where he would trap air in his mouth and force it down his throat and into his lungs by flattening his tongue. This breathing technique, taught to him by his physical therapist, would allow Paul to leave the iron lung for increasing periods of time.
With help from his iron lung (and for small periods of time without it), Paul managed to live a full, happy, and sometimes record-breaking life. At 21, Paul became the first person in Dallas, Texas to graduate high school without attending class in person, owing his success to memorization rather than taking notes. After high school, Paul received a scholarship to Southern Methodist University and pursued his dream of becoming a trial lawyer and successfully represented clients in court.
Paul Alexander, pictured here in his early 20s, mastered a type of breathing technique that allowed him to spend short amounts of time outside his iron lung. Paul Alexander
Throughout his long life, Paul was also able to fly on a plane, visit the beach, adopt a dog, fall in love, and write a memoir using a plastic stick to tap out a draft on a keyboard. In recent years, Paul joined TikTok and became a viral sensation with more than 330,000 followers. In one of his first videos, Paul advocated for vaccination and warned against another polio epidemic.
Paul was reportedly hospitalized with COVID-19 at the end of February and died on March 11th, 2024. He currently holds the Guiness World Record for longest survival inside an iron lung—71 years.
Polio thankfully no longer circulates in the United States, or in most of the world, thanks to vaccines. But Paul continues to serve as a reminder of the importance of vaccination—and the power of the human spirit.
““I’ve got some big dreams. I’m not going to accept from anybody their limitations,” he said in a 2022 interview with CNN. “My life is incredible.”
Giving robots self-awareness as they move through space - and maybe even providing them with gene-like methods for storing rules of behavior - could be important steps toward creating more intelligent machines.
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.”