This Jarvik-7 artificial heart was used in the first bridge operation in 1985 meant to replace a failing heart while the patient waited for a donor organ.
In June, a team of surgeons at Duke University Hospital implanted the latest model of an artificial heart in a 39-year-old man with severe heart failure, a condition in which the heart doesn't pump properly. The man's mechanical heart, made by French company Carmat, is a new generation artificial heart and the first of its kind to be transplanted in the United States. It connects to a portable external power supply and is designed to keep the patient alive until a replacement organ becomes available.
Many patients die while waiting for a heart transplant, but artificial hearts can bridge the gap. Though not a permanent solution for heart failure, artificial hearts have saved countless lives since their first implantation in 1982.
What might surprise you is that the origin of the artificial heart dates back decades before, when an inventive television actor teamed up with a famous doctor to design and patent the first such device.
A man of many talents
Paul Winchell was an entertainer in the 1950s and 60s, rising to fame as a ventriloquist and guest-starring as an actor on programs like "The Ed Sullivan Show" and "Perry Mason." When children's animation boomed in the 1960s, Winchell made a name for himself as a voice actor on shows like "The Smurfs," "Winnie the Pooh," and "The Jetsons." He eventually became famous for originating the voices of Tigger from "Winnie the Pooh" and Gargamel from "The Smurfs," among many others.
But Winchell wasn't just an entertainer: He also had a quiet passion for science and medicine. Between television gigs, Winchell busied himself working as a medical hypnotist and acupuncturist, treating the same Hollywood stars he performed alongside. When he wasn't doing that, Winchell threw himself into engineering and design, building not only the ventriloquism dummies he used on his television appearances but a host of products he'd dreamed up himself. Winchell spent hours tinkering with his own inventions, such as a set of battery-powered gloves and something called a "flameless lighter." Over the course of his life, Winchell designed and patented more than 30 of these products – mostly novelties, but also serious medical devices, such as a portable blood plasma defroster.
| Ventriloquist Paul Winchell with Jerry Mahoney, his dummy, in 1951 |
A meeting of the minds
In the early 1950s, Winchell appeared on a variety show called the "Arthur Murray Dance Party" and faced off in a dance competition with the legendary Ricardo Montalban (Winchell won). At a cast party for the show later that same night, Winchell met Dr. Henry Heimlich – the same doctor who would later become famous for inventing the Heimlich maneuver, who was married to Murray's daughter. The two hit it off immediately, bonding over their shared interest in medicine. Before long, Heimlich invited Winchell to come observe him in the operating room at the hospital where he worked. Winchell jumped at the opportunity, and not long after he became a frequent guest in Heimlich's surgical theatre, fascinated by the mechanics of the human body.
One day while Winchell was observing at the hospital, he witnessed a patient die on the operating table after undergoing open-heart surgery. He was suddenly struck with an idea: If there was some way doctors could keep blood pumping temporarily throughout the body during surgery, patients who underwent risky operations like open-heart surgery might have a better chance of survival. Winchell rushed to Heimlich with the idea – and Heimlich agreed to advise Winchell and look over any design drafts he came up with. So Winchell went to work.
Winchell's heart
As it turned out, building ventriloquism dummies wasn't that different from building an artificial heart, Winchell noted later in his autobiography – the shifting valves and chambers of the mechanical heart were similar to the moving eyes and opening mouths of his puppets. After each design, Winchell would go back to Heimlich and the two would confer, making adjustments along the way to.
By 1956, Winchell had perfected his design: The "heart" consisted of a bag that could be placed inside the human body, connected to a battery-powered motor outside of the body. The motor enabled the bag to pump blood throughout the body, similar to a real human heart. Winchell received a patent for the design in 1963.
At the time, Winchell never quite got the credit he deserved. Years later, researchers at the University of Utah, working on their own artificial heart, came across Winchell's patent and got in touch with Winchell to compare notes. Winchell ended up donating his patent to the team, which included Dr. Richard Jarvik. Jarvik expanded on Winchell's design and created the Jarvik-7 – the world's first artificial heart to be successfully implanted in a human being in 1982.
The Jarvik-7 has since been replaced with newer, more efficient models made up of different synthetic materials, allowing patients to live for longer stretches without the heart clogging or breaking down. With each new generation of hearts, heart failure patients have been able to live relatively normal lives for longer periods of time and with fewer complications than before – and it never would have been possible without the unsung genius of a puppeteer and his love of science.
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.”