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.”
Many women want non-hormonal birth control. A 22-year-old's findings were used to launch a company that could, within the decade, bring a new kind of contraceptive to the marketplace.
Now a scientist with NextVivo, Shrestha is not directly involved in the development of the contraceptive that is based on her research. But, back in 2016 when she was going through her own problems with hormonal contraceptives, she “was very personally invested” in her research project, Shrestha says. She was coping with a long list of negative effects from an implanted hormonal IUD. According to the Mayo Clinic, those can include severe pelvic pain, headaches, acute acne, breast tenderness, irregular bleeding and mood swings. After a year, she had the IUD removed, but it took another full year before all the side effects finally subsided; she also watched her sister suffer the “same tribulations” after trying a hormonal IUD, she says.
For contraceptive use either daily or monthly, Shrestha says, “You want the antibody to be very potent and also cheap.” That was her goal when she launched her study.
Shrestha unshelved antibody research that had been sitting idle for decades. It was in the late 80s that scientists in Japan first tried to develop anti-sperm antibodies for contraceptive use. But, 35 years ago, “Antibody production had not been streamlined as it is now, so antibodies were very expensive,” Shrestha explains. So, they shifted away from birth control, opting to focus on developing antibodies for vaccines.
Over the course of the last three decades, different teams of researchers have been working to make the antibody more effective, bringing the cost down, though it’s still expensive, according to Shrestha. For contraceptive use either daily or monthly, she says, “You want the antibody to be very potent and also cheap.” That was her goal when she launched her study.
The problem
The problem with contraceptives for women, Shrestha says, is that all but a few of them are hormone-based or have other negative side effects. In fact, some studies and reports show that millions of women risk unintended pregnancy because of medical contraindications with hormone-based contraceptives or to avoid the risks and side effects. While there are about a dozen contraceptive choices for women, there are two for men: the condom, considered 98% effective if used correctly, and vasectomy, 99% effective. Neither of these choices are hormone-based.
On the non-hormonal side for women, there is the diaphragm which is considered only 87 percent effective. It works better with the addition of spermicides — Nonoxynol-9, or N-9 — however, they are detergents; they not only kill the sperm, they also erode the vaginal epithelium. And, there’s the non-hormonal IUD which is 99% effective. However, the IUD needs to be inserted by a medical professional, and it has a number of negative side effects, including painful cramping at a higher frequency and extremely heavy or “abnormal” and unpredictable menstrual flows.
The hormonal version of the IUD, also considered 99% effective, is the one Shrestha used which caused her two years of pain. Of course, there’s the pill, which needs to be taken daily, and the birth control ring which is worn 24/7. Both cause side effects similar to the other hormonal contraceptives on the market. The ring is considered 93% effective mostly because of user error; the pill is considered 99% effective if taken correctly.
“That’s where we saw this opening or gap for women. We want a safe, non-hormonal contraceptive,” Shrestha says. Compounding the lack of good choices, is poor access to quality sex education and family planning information, according to the non-profit Urban Institute. A focus group survey suggested that the sex education women received “often lacked substance, leaving them feeling unprepared to make smart decisions about their sexual health and safety,” wrote the authors of the Urban Institute report. In fact, nearly half (45%, or 2.8 million) of the pregnancies that occur each year in the US are unintended, reports the Guttmacher Institute. Globally the numbers are similar. According to a new report by the United Nations, each year there are 121 million unintended pregnancies, worldwide.
The science
The early work on antibodies as a contraceptive had been inspired by women with infertility. It turns out that 9 to 12 percent of women who are treated for infertility have antibodies that develop naturally and work against sperm. Shrestha was encouraged that the antibodies were specific to the target — sperm — and therefore “very safe to use in women.” She aimed to make the antibodies more stable, more effective and less expensive so they could be more easily manufactured.
Since antibodies tend to stick to things that you tell them to stick to, the idea was, basically, to engineer antibodies to stick to sperm so they would stop swimming. Shrestha and her colleagues took the binding arm of an antibody that they’d isolated from an infertile woman. Then, targeting a unique surface antigen present on human sperm, they engineered a panel of antibodies with as many as six to 10 binding arms — “almost like tongs with prongs on the tongs, that bind the sperm,” explains Shrestha. “We decided to add those grabbers on top of it, behind it. So it went from having two prongs to almost 10. And the whole goal was to have so many arms binding the sperm that it clumps it” into a “dollop,” explains Shrestha, who earned a patent on her research.
Suruchi Shrestha works in the lab with a colleague. In 2016, her research on antibodies for birth control was inspired by her own experience with side effects from an implanted hormonal IUD.
UNC - Chapel Hill
The sperm stays right where it met the antibody, never reaching the egg for fertilization. Eventually, and naturally, “Our vaginal system will just flush it out,” Shrestha explains.
“She showed in her early studies that [she] definitely got the sperm immotile, so they didn't move. And that was a really promising start,” says Jasmine Edelstein, a scientist with an expertise in antibody engineering who was not involved in this research. Shrestha’s team at UNC reproduced the effect in the sheep, notes Edelstein, who works at the startup Be Biopharma. In fact, Shrestha’s anti-sperm antibodies that caused the sperm to agglutinate, or clump together, were 99.9% effective when delivered topically to the sheep’s reproductive tracts.
The future
Going forward, Shrestha thinks the ideal approach would be delivering the antibodies through a vaginal ring. “We want to use it at the source of the spark,” Shrestha says, as opposed to less direct methods, such as taking a pill. The ring would dissolve after one month, she explains, “and then you get another one.”
Engineered to have a long shelf life, the anti-sperm antibody ring could be purchased without a prescription, and women could insert it themselves, without a doctor. “That's our hope, so that it is accessible,” Shrestha says. “Anybody can just go and grab it and not worry about pregnancy or unintended pregnancy.”
Her patented research has been licensed by several biotech companies for clinical trials. A number of Shrestha’s co-authors, including her lab advisor, Sam Lai, have launched a company, Mucommune, to continue developing the contraceptives based on these antibodies.
And, results from a small clinical trial run by researchers at Boston University Chobanian & Avedisian School of Medicine show that a dissolvable vaginal film with antibodies was safe when tested on healthy women of reproductive age. That same group of researchers earlier this year received a $7.2 million grant from the National Institute of Health for further research on monoclonal antibody-based contraceptives, which have also been shown to block transmission of viruses, like HIV.
“As the costs come down, this becomes a more realistic option potentially for women,” says Edelstein. “The impact could be tremendous.”
