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.”
A drone hovers over Tacuarembó hospital in Uruguay, carrying its precious cargo: breast milk intended for children in remote parts of the country.
But these children need breast milk throughout their first six months, if not longer, to prevent malnutrition and other illnesses that are prevalent in rural Tacuarembó. Ground transport isn't quick or reliable enough to meet this goal. It can take several hours, during which the milk may spoil due to a lack of refrigeration.
The battery-powered drones have been the difference-maker. The project to develop them, financed by the UNICEF Innovation Fund, is the first of its kind in Latin America. To Castelli, it's nothing short of a revolution. Tacuarembó Hospital, along with three rural clinics in the most impoverished part of Uruguay, are its leaders.
"This marks the first occasion when the public health system has been directly impacted [by our technology]," says Sebastián Macías, the CEO and co-founder of Cielum, an engineer at the University Republic, which collaborated on the technology with a Uruguayan company called Cielum and a Swiss company, Rigitech.
The drone can achieve a top speed of up to 68 miles per hour, is capable of flying in light rain, and can withstand winds of up to 30 miles per hour at a maximum altitude of 120 meters.
"We have succeeded in embracing the mothers from rural areas who were previously slipping through the cracks of the system," says Ferreira, the hospital director. He envisions an expansion of the service so it can improve health for children in other rural areas.
Nurses load the drone for breast milk delivery.
Sebastián Macías - Cielum
The star aircraft
The drone, which costs approximately $70,000, was specifically designed for the transportation of biological materials. Constructed from carbon fiber, it's three meters wide, two meters long and weighs 42 pounds when fully loaded. Additionally, it is equipped with a ballistic parachute to ensure a safe descent in case the technology fails in midair. Furthermore, it can achieve a top speed of 68 miles per hour, fly in light rain, and withstand winds of 30 miles per hour at a height of 120 meters.
Inside, the drones feature three refrigerated compartments that maintain a stable temperature and adhere to the United Nations’ standards for transporting perishable products. These compartments accommodate four gallons or 6.5 pounds of cargo. According to Macías, that's more than sufficient to carry a week’s worth of milk for one infant on just two flights, or 3.3 pounds of blood samples collected in a rural clinic.
“From an energy perspective, it serves as an efficient mode of transportation and helps reduce the carbon emissions associated with using an ambulance,” said Macías. Plus, the ambulance can remain available in the town.
Macías, who has led software development for the drone, and three other technicians have been trained to operate it. They ensure that the drone stays on course, monitor weather conditions and implement emergency changes when needed. The software displays the in-flight positions of the drones in relation to other aircraft. All agricultural planes in the region receive notification about the drone's flight path, departure and arrival times, and current location.
The future: doubling the drone's reach
Forty-five days after its inaugural flight, the drone is now making five flights per week. It serves two routes: 34 miles to Curtina and 31 miles to Tambores. The drone reaches Curtina in 50 minutes while ambulances take double that time, partly due to the subpar road conditions. Pueblo Ansina, located 40 miles from the state capital, will soon be introduced as the third destination.
Overall, the drone’s schedule is expected to become much busier, with plans to accomplish 20 weekly flights by the end of October and over 30 in 2024. Given the drone’s speed, Macías is contemplating using it to transport cancer medications as well.
“When it comes to using drones to save lives, for us, the sky is not the limit," says Ciro Ferreira, Tacuarembó hospital director.
In future trips to clinics in San Gregorio de Polanco and Caraguatá, the drone will be pushed to the limit. At these locations, a battery change will be necessary, but it's worth it. The route will cover up to 10 rural Tacuarembó clinics plus one hospital outside Tacuarembó, in Rivera, close to the border with Brazil. Currently, because of a shortage of ambulances, the delivery of pasteurized breast milk to Rivera only occurs every 15 days.
“The expansion to Rivera will include 100,000 more inhabitants, doubling the healthcare reach,” said Ferreira, the director of the Tacuarembó Hospital. In itself, Ferreira's hospital serves the medical needs of 500,000 people as one of the largest in Uruguay's interior.
Alejandro Del Estal, an aeronautical engineer at Rigitech, traveled from Europe to Tacuarembó to oversee the construction of the vertiports – the defined areas that can support drones’ take-off and landing – and the first flights. He pointed out that once the flight network between hospitals and rural polyclinics is complete in Uruguay, it will rank among the five most extensive drone routes in the world for any activity, including healthcare and commercial uses.
Cielum is already working on the long-term sustainability of the project. The aim is to have more drones operating in other rural regions in the western and northern parts of the country. The company has received inquiries from Argentina and Colombia, but, as Macías pointed out, they are exercising caution when making commitments. Expansion will depend on the development of each country’s regulations for airspace use.
For Ferreira, the advantages in Uruguay are evident: "This approach enables us to bridge the geographical gap, enhance healthcare accessibility, and reduce the time required for diagnosing and treating rural inhabitants, all without the necessity of them traveling to the hospital,” he says. "When it comes to using drones to save lives, for us, the sky is not the limit."