Your Digital Avatar May One Day Get Sick Before You Do
Artificial intelligence is everywhere, just not in the way you think it is.
These networks, loosely designed after the human brain, are interconnected computers that have the ability to "learn."
"There's the perception of AI in the glossy magazines," says Anders Kofod-Petersen, a professor of Artificial Intelligence at the Norwegian University of Science and Technology. "That's the sci-fi version. It resembles the small guy in the movie AI. It might be benevolent or it might be evil, but it's generally intelligent and conscious."
"And this is, of course, as far from the truth as you can possibly get."
What Exactly Is Artificial Intelligence, Anyway?
Let's start with how you got to this piece. You likely came to it through social media. Your Facebook account, Twitter feed, or perhaps a Google search. AI influences all of those things, machine learning helping to run the algorithms that decide what you see, when, and where. AI isn't the little humanoid figure; it's the system that controls the figure.
"AI is being confused with robotics," Eleonore Pauwels, Director of the Anticipatory Intelligence Lab with the Science and Technology Innovation Program at the Wilson Center, says. "What AI is right now is a data optimization system, a very powerful data optimization system."
The revolution in recent years hasn't come from the method scientists and other researchers use. The general ideas and philosophies have been around since the late 1960s. Instead, the big change has been the dramatic increase in computing power, primarily due to the development of neural networks. These networks, loosely designed after the human brain, are interconnected computers that have the ability to "learn." An AI, for example, can be taught to spot a picture of a cat by looking at hundreds of thousands of pictures that have been labeled "cat" and "learning" what a cat looks like. Or an AI can beat a human at Go, an achievement that just five years ago Kofod-Petersen thought wouldn't be accomplished for decades.
"It's very difficult to argue that something is intelligent if it can't learn, and these algorithms are getting pretty good at learning stuff. What they are not good at is learning how to learn."
Medicine is the field where this expertise in perception tasks might have the most influence. It's already having an impact as iPhones use AI to detect cancer, Apple watches alert the wearer to a heart problem, AI spots tuberculosis and the spread of breast cancer with a higher accuracy than human doctors, and more. Every few months, another study demonstrates more possibility. (The New Yorker published an article about medicine and AI last year, so you know it's a serious topic.)
But this is only the beginning. "I personally think genomics and precision medicine is where AI is going to be the biggest game-changer," Pauwels says. "It's going to completely change how we think about health, our genomes, and how we think about our relationship between our genotype and phenotype."
The Fundamental Breakthrough That Must Be Solved
To get there, however, researchers will need to make another breakthrough, and there's debate about how long that will take. Kofod-Petersen explains: "If we want to move from this narrow intelligence to this broader intelligence, that's a very difficult problem. It basically boils down to that we haven't got a clue about what intelligence actually is. We don't know what intelligence means in a biological sense. We think we might recognize it but we're not completely sure. There isn't a working definition. We kind of agree with the biologists that learning is an aspect of it. It's very difficult to argue that something is intelligent if it can't learn, and these algorithms are getting pretty good at learning stuff. What they are not good at is learning how to learn. They can learn specific tasks but we haven't approached how to teach them to learn to learn."
In other words, current AI is very, very good at identifying that a picture of a cat is, in fact, a cat – and getting better at doing so at an incredibly rapid pace – but the system only knows what a "cat" is because that's what a programmer told it a furry thing with whiskers and two pointy ears is called. If the programmer instead decided to label the training images as "dogs," the AI wouldn't say "no, that's a cat." Instead, it would simply call a furry thing with whiskers and two pointy ears a dog. AI systems lack the explicit inference that humans do effortlessly, almost without thinking.
Pauwels believes that the next step is for AI to transition from supervised to unsupervised learning. The latter means that the AI isn't answering questions that a programmer asks it ("Is this a cat?"). Instead, it's almost like it's looking at the data it has, coming up with its own questions and hypothesis, and answering them or putting them to the test. Combining this ability with the frankly insane processing power of the computer system could result in game-changing discoveries.
In the not-too-distant future, a doctor could run diagnostics on a digital avatar, watching which medical conditions present themselves before the person gets sick in real life.
One company in China plans to develop a way to create a digital avatar of an individual person, then simulate that person's health and medical information into the future. In the not-too-distant future, a doctor could run diagnostics on a digital avatar, watching which medical conditions presented themselves – cancer or a heart condition or anything, really – and help the real-life version prevent those conditions from beginning or treating them before they became a life-threatening issue.
That, obviously, would be an incredibly powerful technology, and it's just one of the many possibilities that unsupervised AI presents. It's also terrifying in the potential for misuse. Even the term "unsupervised AI" brings to mind a dystopian landscape where AI takes over and enslaves humanity. (Pick your favorite movie. There are dozens.) This is a concern, something for developers, programmers, and scientists to consider as they build the systems of the future.
The Ethical Problem That Deserves More Attention
But the more immediate concern about AI is much more mundane. We think of AI as an unbiased system. That's incorrect. Algorithms, after all, are designed by someone or a team, and those people have explicit or implicit biases. Intentionally, or more likely not, they introduce these biases into the very code that forms the basis for the AI. Current systems have a bias against people of color. Facebook tried to rectify the situation and failed. These are two small examples of a larger, potentially systemic problem.
It's vital and necessary for the people developing AI today to be aware of these issues. And, yes, avoid sending us to the brink of a James Cameron movie. But AI is too powerful a tool to ignore. Today, it's identifying cats and on the verge of detecting cancer. In not too many tomorrows, it will be on the forefront of medical innovation. If we are careful, aware, and smart, it will help simulate results, create designer drugs, and revolutionize individualize medicine. "AI is the only way to get there," Pauwels says.
Story by Freethink
Try burning an iron metal ingot and you’ll have to wait a long time — but grind it into a powder and it will readily burst into flames. That’s how sparklers work: metal dust burning in a beautiful display of light and heat. But could we burn iron for more than fun? Could this simple material become a cheap, clean, carbon-free fuel?
In new experiments — conducted on rockets, in microgravity — Canadian and Dutch researchers are looking at ways of boosting the efficiency of burning iron, with a view to turning this abundant material — the fourth most common in the Earth’s crust, about about 5% of its mass — into an alternative energy source.
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.
Tom Oxley is building what he calls a “natural highway into the brain” that lets people use their minds to control their phones and computers. The device, called the Stentrode, could improve the lives of hundreds of thousands of people living with spinal cord paralysis, ALS and other neurodegenerative diseases.
Leaps.org talked with Dr. Oxley for today’s podcast. A fascinating thing about the Stentrode is that it works very differently from other “brain computer interfaces” you may be familiar with, like Elon Musk’s Neuralink. Some BCIs are implanted by surgeons directly into a person’s brain, but the Stentrode is much less invasive. Dr. Oxley’s company, Synchron, opts for a “natural” approach, using stents in blood vessels to access the brain. This offers some major advantages to the handful of people who’ve already started to use the Stentrode.
The audio improves about 10 minutes into the episode. (There was a minor headset issue early on, but everything is audible throughout.) Dr. Oxley’s work creates game-changing opportunities for patients desperate for new options. His take on where we're headed with BCIs is must listening for anyone who cares about the future of health and technology.
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.
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 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/