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.”
Sparklers produce a beautiful display of light and heat by burning metal dust, which contains iron. The recent work of Canadian and Dutch researchers suggests we can use iron as a cheap, carbon-free fuel.
Iron as a fuel
Iron is abundantly available and cheap. More importantly, the byproduct of burning iron is rust (iron oxide), a solid material that is easy to collect and recycle. Neither burning iron nor converting its oxide back produces any carbon in the process.
Iron oxide is potentially renewable by reacting with electricity or hydrogen to become iron again.
Iron has a high energy density: it requires almost the same volume as gasoline to produce the same amount of energy. However, iron has poor specific energy: it’s a lot heavier than gas to produce the same amount of energy. (Think of picking up a jug of gasoline, and then imagine trying to pick up a similar sized chunk of iron.) Therefore, its weight is prohibitive for many applications. Burning iron to run a car isn’t very practical if the iron fuel weighs as much as the car itself.
In its powdered form, however, iron offers more promise as a high-density energy carrier or storage system. Iron-burning furnaces could provide direct heat for industry, home heating, or to generate electricity.
Plus, iron oxide is potentially renewable by reacting with electricity or hydrogen to become iron again (as long as you’ve got a source of clean electricity or green hydrogen). When there’s excess electricity available from renewables like solar and wind, for example, rust could be converted back into iron powder, and then burned on demand to release that energy again.
However, these methods of recycling rust are very energy intensive and inefficient, currently, so improvements to the efficiency of burning iron itself may be crucial to making such a circular system viable.
The science of discrete burning
Powdered particles have a high surface area to volume ratio, which means it is easier to ignite them. This is true for metals as well.
Under the right circumstances, powdered iron can burn in a manner known as discrete burning. In its most ideal form, the flame completely consumes one particle before the heat radiating from it combusts other particles in its vicinity. By studying this process, researchers can better understand and model how iron combusts, allowing them to design better iron-burning furnaces.
Discrete burning is difficult to achieve on Earth. Perfect discrete burning requires a specific particle density and oxygen concentration. When the particles are too close and compacted, the fire jumps to neighboring particles before fully consuming a particle, resulting in a more chaotic and less controlled burn.
Presently, the rate at which powdered iron particles burn or how they release heat in different conditions is poorly understood. This hinders the development of technologies to efficiently utilize iron as a large-scale fuel.
Burning metal in microgravity
In April, the European Space Agency (ESA) launched a suborbital “sounding” rocket, carrying three experimental setups. As the rocket traced its parabolic trajectory through the atmosphere, the experiments got a few minutes in free fall, simulating microgravity.
One of the experiments on this mission studied how iron powder burns in the absence of gravity.
In microgravity, particles float in a more uniformly distributed cloud. This allows researchers to model the flow of iron particles and how a flame propagates through a cloud of iron particles in different oxygen concentrations.
Existing fossil fuel power plants could potentially be retrofitted to run on iron fuel.
Insights into how flames propagate through iron powder under different conditions could help design much more efficient iron-burning furnaces.
Clean and carbon-free energy on Earth
Various businesses are looking at ways to incorporate iron fuels into their processes. In particular, it could serve as a cleaner way to supply industrial heat by burning iron to heat water.
For example, Dutch brewery Swinkels Family Brewers, in collaboration with the Eindhoven University of Technology, switched to iron fuel as the heat source to power its brewing process, accounting for 15 million glasses of beer annually. Dutch startup RIFT is running proof-of-concept iron fuel power plants in Helmond and Arnhem.
As researchers continue to improve the efficiency of burning iron, its applicability will extend to other use cases as well. But is the infrastructure in place for this transition?
Often, the transition to new energy sources is slowed by the need to create new infrastructure to utilize them. Fortunately, this isn’t the case with switching from fossil fuels to iron. Since the ideal temperature to burn iron is similar to that for hydrocarbons, existing fossil fuel power plants could potentially be retrofitted to run on iron fuel.
This article originally appeared on Freethink, home of the brightest minds and biggest ideas of all time.
Leaps.org talks with Dr. Tom Oxley, founding CEO of Synchron, a company that's taking a unique - and less invasive - approach to "brain-computer interfaces" for patients with ALS and other mobility challenges.
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In our conversation, Dr. Oxley talks about “Bluetooth brain”; the critical role of AI in the present and future of BCIs; how BCIs compare to voice command technology; regulatory frameworks for revolutionary technologies; specific people with paralysis who’ve been able to regain some independence thanks to the Stentrode; what it means to be a neurointerventionist; how to scale BCIs for more people to use them; the risks of BCIs malfunctioning; organic implants; and how BCIs help us understand the brain, among other topics.
Dr. Oxley received his PhD in neuro engineering from the University of Melbourne in Australia. He is the founding CEO of Synchron and an associate professor and the head of the vascular bionics laboratory at the University of Melbourne. He’s also a clinical instructor in the Deepartment of Neurosurgery at Mount Sinai Hospital. Dr. Oxley has completed more than 1,600 endovascular neurosurgical procedures on patients, including people with aneurysms and strokes, and has authored over 100 peer reviewed articles.
Links:
Synchron website - https://synchron.com/
Assessment of Safety of a Fully Implanted Endovascular Brain-Computer Interface for Severe Paralysis in 4 Patients (paper co-authored by Tom Oxley) - https://jamanetwork.com/journals/jamaneurology/art...
More research related to Synchron's work - https://synchron.com/research
Tom Oxley on LinkedIn - https://www.linkedin.com/in/tomoxl
Tom Oxley on Twitter - https://twitter.com/tomoxl?lang=en
Tom Oxley TED - https://www.ted.com/talks/tom_oxley_a_brain_implant_that_turns_your_thoughts_into_text?language=en
Tom Oxley website - https://tomoxl.com/
Novel brain implant helps paralyzed woman speak using digital avatar - https://engineering.berkeley.edu/news/2023/08/novel-brain-implant-helps-paralyzed-woman-speak-using-a-digital-avatar/
Edward Chang lab - https://changlab.ucsf.edu/
BCIs convert brain activity into text at 62 words per minute - https://med.stanford.edu/neurosurgery/news/2023/he...
Leaps.org: The Mind-Blowing Promise of Neural Implants - https://leaps.org/the-mind-blowing-promise-of-neural-implants/
Tom Oxley