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.”
Recent immigration restrictions have left many foreign researchers' projects and careers in limbo—and some in jeopardy.
"I spent about $4,000 on lawyer fees and another $1,200 to pay for the motions I filed," she recalls. "I had to borrow money from my parents and my cousin because without my salary I just didn't have the $7,000 at hand." But the already narrow window of opportunity slammed completely shut when the Trump administration suspended issuing new visas for foreign researchers in June. All Mohan's attempts were denied. In August, she had to leave the country. "Given the recent work visa ban by the administration, all my options in the U.S. are closed," she wrote a bitter note on Twitter. "I have to uproot my entire life in NY for the past 6 years and leave." She eventually found a temporary position in Calcutta, where she can continue research.
Mohan is hardly alone in her visa saga. Many foreign scholars on H- and J-type visas and other permits that let them remain employed in America had been struggling to keep their rights to continue research, which in certain cases is crucial to battling the pandemic. Some had to leave the country, some filed every possible extension to buy time, and others are stuck in their home countries, unable to return. The already cumbersome process of applying for visas and extensions became crippled during the lockdowns. But in June, when President Trump extended and expanded immigration restrictions to cut the number of immigrant workers entering the U.S., the new limits left researchers' projects and careers in limbo—and some in jeopardy.
"We have been a beneficiary of this flow of human capacity and resource investment for many generations—and this is now threatened."
Rakesh Ramachandran, whose computational biology work contributed to one of the first coronavirus studies to map out its protein structures—is stranded in India. In early March, he had travelled there to attend a conference and visit the American consulate to stamp his H1 visa for a renewal, already granted. The pandemic shut down both the conference and the consulates, and Ramachandran hasn't been able to come back since. The consulates finally opened in September, but so far the online portal has no available appointment slots. "I'm told to keep trying," Ramachandran says.
The visa restrictions affected researchers worldwide, regardless of disciplines or countries. A Ph.D. student in neuroscience, Morgane Leroux had to do her experiments with mice at Gladstone Institutes in America and analyze the data back home at Sorbonne University in France. She had finished her first round of experiments when the lockdowns forced her to return to Paris, and she hasn't been able to come back to resume her work since. "I can't continue the experiments, which is really frustrating," she says, especially because she doesn't know what it means for her Ph.D. "I may have to entirely change my subject," she says, which she doesn't want to do—it would be a waste of time and money.
But besides wreaking havoc in scholars' personal lives and careers, the visa restrictions had—and will continue to have—tremendous deleterious effects on America's research and its global scientific competitiveness. "It's incredibly short-sighted and self-destructing to restrict the immigration of scientists into the U.S.," says Benjamin G. Neel, who directs the Laura and Isaac Perlmutter Cancer Center at New York University. "If they can't come here, they will go elsewhere," he says, causing a brain drain.
Neel in his lab with postdocs
(Courtesy of Neel)
Neel felt the outcomes of the shortsighted policies firsthand. In the past few months, his lab lost two postdoctoral researchers who had made major strides in understanding the biology of several particularly stubborn, treatment-resistant malignancies. One postdoc studied the underlying mechanisms responsible for 90 percent of pancreatic cancers and half of the colon ones. The other one devised a new system of modeling ovarian cancer in mice to test new therapeutic drug combinations for the deadliest tumor types—but had to return home to China.
"By working around the clock, she was able to get her paper accepted, but she hasn't been able to train us to use this new system, which can set us back six months," Neel says.
Her discoveries also helped the lab secure about $900,000 in grants for new research. Losing people like this is "literally killing the goose that lays the golden eggs," Neel adds. "If you want to make America poor again, this is the way to do it."
Cassidy R. Sugimoto at Indiana University Bloomington, who studies how scientific knowledge is produced and disseminated, says that scientists are the most productive when they are free to move, exchange ideas, and work at labs with the best equipment. Restricting that freedom reduces their achievement.
"Several empirical studied demonstrated the benefits to the U.S. by attracting and retaining foreign scientists. The disproportional number of our Nobel Prize winners were not only foreign-born but also foreign-educated," she says. Scientific advancement bolsters the country's economic prowess, too, so turning scholars away is bad for the economy long-term. "We have been a beneficiary of this flow of human capacity and resource investment for many generations—and this is now threatened," Sugimoto adds—because scientists will look elsewhere. "We are seeing them shifting to other countries that are more hospitable, both ideologically and in terms of health security. Many visiting scholars, postdocs, and graduate students who would otherwise come to the United States are now moving to Canada."
It's not only the Ph.D. students and postdocs who are affected. In some cases, even well-established professors who have already made their marks in the field and direct their own labs at prestigious research institutions may have to pack up and leave the country in the next few months. One scientist who directs a prominent neuroscience lab is betting on his visa renewal and a green card application, but if that's denied, the entire lab may be in jeopardy, as many grants hinge on his ability to stay employed in America.
"It's devastating to even think that it can happen," he says—after years of efforts invested. "I can't even comprehend how it would feel. It would be terrifying and really sad." (He asked to withhold his name for fear that it may adversely affect his applications.) Another scientist who originally shared her story for this article, later changed her mind and withdrew, worrying that speaking out may hurt the entire project, a high-profile COVID-19 effort. It's not how things should work in a democratic country, scientists admit, but that's the reality.
Still, some foreign scholars are speaking up. Mehmet Doğan, a physicist at University of California Berkeley who has been fighting a visa extension battle all year, says it's important to push back in an organized fashion with petitions and engage legislators. "This administration was very creative in finding subtle and not so subtle ways to make our lives more difficult," Doğan says. He adds that the newest rules, proposed by the Department of Homeland Security on September 24, could further limit the time scholars can stay, forcing them into continuous extension battles. That's why the upcoming election might be a turning point for foreign academics. "This election will decide if many of us will see the U.S. as the place to stay and work or whether we look at other countries," Doğan says, echoing the worries of Neel, Sugimoto, and others in academia.
Dogan on Zoom talking to his fellow union members of the Academic Researchers United, a union of almost 5,000 Academic Researchers.
(Credit: Ceyda Durmaz Dogan)
If this year has shown us anything, it is that viruses and pandemics know no borders as they sweep across the globe. Likewise, science can't be restrained by borders either. "Science is an international endeavor," says Neel—and right now humankind now needs unified scientific research more than ever, unhindered by immigration hurdles and visa wars. Humanity's wellbeing in America and beyond depends on it.
[Editor's Note: To read other articles in this special magazine issue, visit the beautifully designed e-reader version.]