Tag Archives: solution
#437491 3.2 Billion Images and 720,000 Hours of ...
Twitter over the weekend “tagged” as manipulated a video showing US Democratic presidential candidate Joe Biden supposedly forgetting which state he’s in while addressing a crowd.
Biden’s “hello Minnesota” greeting contrasted with prominent signage reading “Tampa, Florida” and “Text FL to 30330.”
The Associated Press’s fact check confirmed the signs were added digitally and the original footage was indeed from a Minnesota rally. But by the time the misleading video was removed it already had more than one million views, The Guardian reports.
A FALSE video claiming Biden forgot what state he was in was viewed more than 1 million times on Twitter in the past 24 hours
In the video, Biden says “Hello, Minnesota.”
The event did indeed happen in MN — signs on stage read MN
But false video edited signs to read Florida pic.twitter.com/LdHQVaky8v
— Donie O'Sullivan (@donie) November 1, 2020
If you use social media, the chances are you see (and forward) some of the more than 3.2 billion images and 720,000 hours of video shared daily. When faced with such a glut of content, how can we know what’s real and what’s not?
While one part of the solution is an increased use of content verification tools, it’s equally important we all boost our digital media literacy. Ultimately, one of the best lines of defense—and the only one you can control—is you.
Seeing Shouldn’t Always Be Believing
Misinformation (when you accidentally share false content) and disinformation (when you intentionally share it) in any medium can erode trust in civil institutions such as news organizations, coalitions and social movements. However, fake photos and videos are often the most potent.
For those with a vested political interest, creating, sharing and/or editing false images can distract, confuse and manipulate viewers to sow discord and uncertainty (especially in already polarized environments). Posters and platforms can also make money from the sharing of fake, sensationalist content.
Only 11-25 percent of journalists globally use social media content verification tools, according to the International Centre for Journalists.
Could You Spot a Doctored Image?
Consider this photo of Martin Luther King Jr.
Dr. Martin Luther King Jr. Giving the middle finger #DopeHistoricPics pic.twitter.com/5W38DRaLHr
— Dope Historic Pics (@dopehistoricpic) December 20, 2013
This altered image clones part of the background over King Jr’s finger, so it looks like he’s flipping off the camera. It has been shared as genuine on Twitter, Reddit, and white supremacist websites.
In the original 1964 photo, King flashed the “V for victory” sign after learning the US Senate had passed the civil rights bill.
“Those who love peace must learn to organize as effectively as those who love war.”
Dr. Martin Luther King Jr.
This photo was taken on June 19th, 1964, showing Dr King giving a peace sign after hearing that the civil rights bill had passed the senate. @snopes pic.twitter.com/LXHmwMYZS5
— Willie's Reserve (@WilliesReserve) January 21, 2019
Beyond adding or removing elements, there’s a whole category of photo manipulation in which images are fused together.
Earlier this year, a photo of an armed man was photoshopped by Fox News, which overlaid the man onto other scenes without disclosing the edits, the Seattle Times reported.
You mean this guy who’s been photoshopped into three separate photos released by Fox News? pic.twitter.com/fAXpIKu77a
— Zander Yates ザンダーイェーツ (@ZanderYates) June 13, 2020
Similarly, the image below was shared thousands of times on social media in January, during Australia’s Black Summer bushfires. The AFP’s fact check confirmed it is not authentic and is actually a combination of several separate photos.
Image is more powerful than screams of Greta. A silent girl is holding a koala. She looks straight at you from the waters of the ocean where they found a refuge. She is wearing a breathing mask. A wall of fire is behind them. I do not know the name of the photographer #Australia pic.twitter.com/CrTX3lltdh
— EVC Music (@EVCMusicUK) January 6, 2020
Fully and Partially Synthetic Content
Online, you’ll also find sophisticated “deepfake” videos showing (usually famous) people saying or doing things they never did. Less advanced versions can be created using apps such as Zao and Reface.
Or, if you don’t want to use your photo for a profile picture, you can default to one of several websites offering hundreds of thousands of AI-generated, photorealistic images of people.
These people don’t exist, they’re just images generated by artificial intelligence. Generated Photos, CC BY
Editing Pixel Values and the (not so) Simple Crop
Cropping can greatly alter the context of a photo, too.
We saw this in 2017, when a US government employee edited official pictures of Donald Trump’s inauguration to make the crowd appear bigger, according to The Guardian. The staffer cropped out the empty space “where the crowd ended” for a set of pictures for Trump.
Views of the crowds at the inaugurations of former US President Barack Obama in 2009 (left) and President Donald Trump in 2017 (right). AP
But what about edits that only alter pixel values such as color, saturation, or contrast?
One historical example illustrates the consequences of this. In 1994, Time magazine’s cover of OJ Simpson considerably “darkened” Simpson in his police mugshot. This added fuel to a case already plagued by racial tension, to which the magazine responded, “No racial implication was intended, by Time or by the artist.”
Tools for Debunking Digital Fakery
For those of us who don’t want to be duped by visual mis/disinformation, there are tools available—although each comes with its own limitations (something we discuss in our recent paper).
Invisible digital watermarking has been proposed as a solution. However, it isn’t widespread and requires buy-in from both content publishers and distributors.
Reverse image search (such as Google’s) is often free and can be helpful for identifying earlier, potentially more authentic copies of images online. That said, it’s not foolproof because it:
Relies on unedited copies of the media already being online.
Doesn’t search the entire web.
Doesn’t always allow filtering by publication time. Some reverse image search services such as TinEye support this function, but Google’s doesn’t.
Returns only exact matches or near-matches, so it’s not thorough. For instance, editing an image and then flipping its orientation can fool Google into thinking it’s an entirely different one.
Most Reliable Tools Are Sophisticated
Meanwhile, manual forensic detection methods for visual mis/disinformation focus mostly on edits visible to the naked eye, or rely on examining features that aren’t included in every image (such as shadows). They’re also time-consuming, expensive, and need specialized expertise.
Still, you can access work in this field by visiting sites such as Snopes.com—which has a growing repository of “fauxtography.”
Computer vision and machine learning also offer relatively advanced detection capabilities for images and videos. But they too require technical expertise to operate and understand.
Moreover, improving them involves using large volumes of “training data,” but the image repositories used for this usually don’t contain the real-world images seen in the news.
If you use an image verification tool such as the REVEAL project’s image verification assistant, you might need an expert to help interpret the results.
The good news, however, is that before turning to any of the above tools, there are some simple questions you can ask yourself to potentially figure out whether a photo or video on social media is fake. Think:
Was it originally made for social media?
How widely and for how long was it circulated?
What responses did it receive?
Who were the intended audiences?
Quite often, the logical conclusions drawn from the answers will be enough to weed out inauthentic visuals. You can access the full list of questions, put together by Manchester Metropolitan University experts, here.
This article is republished from The Conversation under a Creative Commons license. Read the original article.
Image Credit: Simon Steinberger from Pixabay Continue reading
Posted in Human Robots
#437301 The Global Work Crisis: Automation, the ...
The alarm bell rings. You open your eyes, come to your senses, and slide from dream state to consciousness. You hit the snooze button, and eventually crawl out of bed to the start of yet another working day.
This daily narrative is experienced by billions of people all over the world. We work, we eat, we sleep, and we repeat. As our lives pass day by day, the beating drums of the weekly routine take over and years pass until we reach our goal of retirement.
A Crisis of Work
We repeat the routine so that we can pay our bills, set our kids up for success, and provide for our families. And after a while, we start to forget what we would do with our lives if we didn’t have to go back to work.
In the end, we look back at our careers and reflect on what we’ve achieved. It may have been the hundreds of human interactions we’ve had; the thousands of emails read and replied to; the millions of minutes of physical labor—all to keep the global economy ticking along.
According to Gallup’s World Poll, only 15 percent of people worldwide are actually engaged with their jobs. The current state of “work” is not working for most people. In fact, it seems we as a species are trapped by a global work crisis, which condemns people to cast away their time just to get by in their day-to-day lives.
Technologies like artificial intelligence and automation may help relieve the work burdens of millions of people—but to benefit from their impact, we need to start changing our social structures and the way we think about work now.
The Specter of Automation
Automation has been ongoing since the Industrial Revolution. In recent decades it has taken on a more elegant guise, first with physical robots in production plants, and more recently with software automation entering most offices.
The driving goal behind much of this automation has always been productivity and hence, profits: technology that can act as a multiplier on what a single human can achieve in a day is of huge value to any company. Powered by this strong financial incentive, the quest for automation is growing ever more pervasive.
But if automation accelerates or even continues at its current pace and there aren’t strong social safety nets in place to catch the people who are negatively impacted (such as by losing their jobs), there could be a host of knock-on effects, including more concentrated wealth among a shrinking elite, more strain on government social support, an increase in depression and drug dependence, and even violent social unrest.
It seems as though we are rushing headlong into a major crisis, driven by the engine of accelerating automation. But what if instead of automation challenging our fragile status quo, we view it as the solution that can free us from the shackles of the Work Crisis?
The Way Out
In order to undertake this paradigm shift, we need to consider what society could potentially look like, as well as the problems associated with making this change. In the context of these crises, our primary aim should be for a system where people are not obligated to work to generate the means to survive. This removal of work should not threaten access to food, water, shelter, education, healthcare, energy, or human value. In our current system, work is the gatekeeper to these essentials: one can only access these (and even then often in a limited form), if one has a “job” that affords them.
Changing this system is thus a monumental task. This comes with two primary challenges: providing people without jobs with financial security, and ensuring they maintain a sense of their human value and worth. There are several measures that could be implemented to help meet these challenges, each with important steps for society to consider.
Universal basic income (UBI)
UBI is rapidly gaining support, and it would allow people to become shareholders in the fruits of automation, which would then be distributed more broadly.
UBI trials have been conducted in various countries around the world, including Finland, Kenya, and Spain. The findings have generally been positive on the health and well-being of the participants, and showed no evidence that UBI disincentivizes work, a common concern among the idea’s critics. The most recent popular voice for UBI has been that of former US presidential candidate Andrew Yang, who now runs a non-profit called Humanity Forward.
UBI could also remove wasteful bureaucracy in administering welfare payments (since everyone receives the same amount, there’s no need to prevent false claims), and promote the pursuit of projects aligned with peoples’ skill sets and passions, as well as quantifying the value of tasks not recognized by economic measures like Gross Domestic Product (GDP). This includes looking after children and the elderly at home.
How a UBI can be initiated with political will and social backing and paid for by governments has been hotly debated by economists and UBI enthusiasts. Variables like how much the UBI payments should be, whether to implement taxes such as Yang’s proposed valued added tax (VAT), whether to replace existing welfare payments, the impact on inflation, and the impact on “jobs” from people who would otherwise look for work require additional discussion. However, some have predicted the inevitability of UBI as a result of automation.
Universal healthcare
Another major component of any society is the healthcare of its citizens. A move away from work would further require the implementation of a universal healthcare system to decouple healthcare from jobs. Currently in the US, and indeed many other economies, healthcare is tied to employment.
Universal healthcare such as Medicare in Australia is evidence for the adage “prevention is better than cure,” when comparing the cost of healthcare in the US with Australia on a per capita basis. This has already presented itself as an advancement in the way healthcare is considered. There are further benefits of a healthier population, including less time and money spent on “sick-care.” Healthy people are more likely and more able to achieve their full potential.
Reshape the economy away from work-based value
One of the greatest challenges in a departure from work is for people to find value elsewhere in life. Many people view their identities as being inextricably tied to their jobs, and life without a job is therefore a threat to one’s sense of existence. This presents a shift that must be made at both a societal and personal level.
A person can only seek alternate value in life when afforded the time to do so. To this end, we need to start reducing “work-for-a-living” hours towards zero, which is a trend we are already seeing in Europe. This should not come at the cost of reducing wages pro rata, but rather could be complemented by UBI or additional schemes where people receive dividends for work done by automation. This transition makes even more sense when coupled with the idea of deviating from using GDP as a measure of societal growth, and instead adopting a well-being index based on universal human values like health, community, happiness, and peace.
The crux of this issue is in transitioning away from the view that work gives life meaning and life is about using work to survive, towards a view of living a life that itself is fulfilling and meaningful. This speaks directly to notions from Maslow’s hierarchy of needs, where work largely addresses psychological and safety needs such as shelter, food, and financial well-being. More people should have a chance to grow beyond the most basic needs and engage in self-actualization and transcendence.
The question is largely around what would provide people with a sense of value, and the answers would differ as much as people do; self-mastery, building relationships and contributing to community growth, fostering creativity, and even engaging in the enjoyable aspects of existing jobs could all come into play.
Universal education
With a move towards a society that promotes the values of living a good life, the education system would have to evolve as well. Researchers have long argued for a more nimble education system, but universities and even most online courses currently exist for the dominant purpose of ensuring people are adequately skilled to contribute to the economy. These “job factories” only exacerbate the Work Crisis. In fact, the response often given by educational institutions to the challenge posed by automation is to find new ways of upskilling students, such as ensuring they are all able to code. As alluded to earlier, this is a limited and unimaginative solution to the problem we are facing.
Instead, education should be centered on helping people acknowledge the current crisis of work and automation, teach them how to derive value that is decoupled from work, and enable people to embrace progress as we transition to the new economy.
Disrupting the Status Quo
While we seldom stop to think about it, much of the suffering faced by humanity is brought about by the systemic foe that is the Work Crisis. The way we think about work has brought society far and enabled tremendous developments, but at the same time it has failed many people. Now the status quo is threatened by those very developments as we progress to an era where machines are likely to take over many job functions.
This impending paradigm shift could be a threat to the stability of our fragile system, but only if it is not fully anticipated. If we prepare for it appropriately, it could instead be the key not just to our survival, but to a better future for all.
Image Credit: mostafa meraji from Pixabay Continue reading
Posted in Human Robots
#437276 Cars Will Soon Be Able to Sense and ...
Imagine you’re on your daily commute to work, driving along a crowded highway while trying to resist looking at your phone. You’re already a little stressed out because you didn’t sleep well, woke up late, and have an important meeting in a couple hours, but you just don’t feel like your best self.
Suddenly another car cuts you off, coming way too close to your front bumper as it changes lanes. Your already-simmering emotions leap into overdrive, and you lay on the horn and shout curses no one can hear.
Except someone—or, rather, something—can hear: your car. Hearing your angry words, aggressive tone, and raised voice, and seeing your furrowed brow, the onboard computer goes into “soothe” mode, as it’s been programmed to do when it detects that you’re angry. It plays relaxing music at just the right volume, releases a puff of light lavender-scented essential oil, and maybe even says some meditative quotes to calm you down.
What do you think—creepy? Helpful? Awesome? Weird? Would you actually calm down, or get even more angry that a car is telling you what to do?
Scenarios like this (maybe without the lavender oil part) may not be imaginary for much longer, especially if companies working to integrate emotion-reading artificial intelligence into new cars have their way. And it wouldn’t just be a matter of your car soothing you when you’re upset—depending what sort of regulations are enacted, the car’s sensors, camera, and microphone could collect all kinds of data about you and sell it to third parties.
Computers and Feelings
Just as AI systems can be trained to tell the difference between a picture of a dog and one of a cat, they can learn to differentiate between an angry tone of voice or facial expression and a happy one. In fact, there’s a whole branch of machine intelligence devoted to creating systems that can recognize and react to human emotions; it’s called affective computing.
Emotion-reading AIs learn what different emotions look and sound like from large sets of labeled data; “smile = happy,” “tears = sad,” “shouting = angry,” and so on. The most sophisticated systems can likely even pick up on the micro-expressions that flash across our faces before we consciously have a chance to control them, as detailed by Daniel Goleman in his groundbreaking book Emotional Intelligence.
Affective computing company Affectiva, a spinoff from MIT Media Lab, says its algorithms are trained on 5,313,751 face videos (videos of people’s faces as they do an activity, have a conversation, or react to stimuli) representing about 2 billion facial frames. Fascinatingly, Affectiva claims its software can even account for cultural differences in emotional expression (for example, it’s more normalized in Western cultures to be very emotionally expressive, whereas Asian cultures tend to favor stoicism and politeness), as well as gender differences.
But Why?
As reported in Motherboard, companies like Affectiva, Cerence, Xperi, and Eyeris have plans in the works to partner with automakers and install emotion-reading AI systems in new cars. Regulations passed last year in Europe and a bill just introduced this month in the US senate are helping make the idea of “driver monitoring” less weird, mainly by emphasizing the safety benefits of preemptive warning systems for tired or distracted drivers (remember that part in the beginning about sneaking glances at your phone? Yeah, that).
Drowsiness and distraction can’t really be called emotions, though—so why are they being lumped under an umbrella that has a lot of other implications, including what many may consider an eerily Big Brother-esque violation of privacy?
Our emotions, in fact, are among the most private things about us, since we are the only ones who know their true nature. We’ve developed the ability to hide and disguise our emotions, and this can be a useful skill at work, in relationships, and in scenarios that require negotiation or putting on a game face.
And I don’t know about you, but I’ve had more than one good cry in my car. It’s kind of the perfect place for it; private, secluded, soundproof.
Putting systems into cars that can recognize and collect data about our emotions under the guise of preventing accidents due to the state of mind of being distracted or the physical state of being sleepy, then, seems a bit like a bait and switch.
A Highway to Privacy Invasion?
European regulations will help keep driver data from being used for any purpose other than ensuring a safer ride. But the US is lagging behind on the privacy front, with car companies largely free from any enforceable laws that would keep them from using driver data as they please.
Affectiva lists the following as use cases for occupant monitoring in cars: personalizing content recommendations, providing alternate route recommendations, adapting environmental conditions like lighting and heating, and understanding user frustration with virtual assistants and designing those assistants to be emotion-aware so that they’re less frustrating.
Our phones already do the first two (though, granted, we’re not supposed to look at them while we drive—but most cars now let you use bluetooth to display your phone’s content on the dashboard), and the third is simply a matter of reaching a hand out to turn a dial or press a button. The last seems like a solution for a problem that wouldn’t exist without said… solution.
Despite how unnecessary and unsettling it may seem, though, emotion-reading AI isn’t going away, in cars or other products and services where it might provide value.
Besides automotive AI, Affectiva also makes software for clients in the advertising space. With consent, the built-in camera on users’ laptops records them while they watch ads, gauging their emotional response, what kind of marketing is most likely to engage them, and how likely they are to buy a given product. Emotion-recognition tech is also being used or considered for use in mental health applications, call centers, fraud monitoring, and education, among others.
In a 2015 TED talk, Affectiva co-founder Rana El-Kaliouby told her audience that we’re living in a world increasingly devoid of emotion, and her goal was to bring emotions back into our digital experiences. Soon they’ll be in our cars, too; whether the benefits will outweigh the costs remains to be seen.
Image Credit: Free-Photos from Pixabay Continue reading
Posted in Human Robots
#437216 New Report: Tech Could Fuel an Age of ...
With rapid technological progress running headlong into dramatic climate change and widening inequality, most experts agree the coming decade will be tumultuous. But a new report predicts it could actually make or break civilization as we know it.
The idea that humanity is facing a major shake-up this century is not new. The Fourth Industrial Revolution being brought about by technologies like AI, gene editing, robotics, and 3D printing is predicted to cause dramatic social, political, and economic upheaval in the coming decades.
But according to think tank RethinkX, thinking about the coming transition as just another industrial revolution is too simplistic. In a report released last week called Rethinking Humanity, the authors argue that we are about to see a reordering of our relationship with the world as fundamental as when hunter-gatherers came together to build the first civilizations.
At the core of their argument is the fact that since the first large human settlements appeared 10,000 years ago, civilization has been built on the back of our ability to extract resources from nature, be they food, energy, or materials. This led to a competitive landscape where the governing logic is grow or die, which has driven all civilizations to date.
That could be about to change thanks to emerging technologies that will fundamentally disrupt the five foundational sectors underpinning society: information, energy, food, transportation, and materials. They predict that across all five, costs will fall by 10 times or more, while production processes will become 10 times more efficient and will use 90 percent fewer natural resources with 10 to 100 times less waste.
They say that this transformation has already happened in information, where the internet has dramatically reduced barriers to communication and knowledge. They predict the combination of cheap solar and grid storage will soon see energy costs drop as low as one cent per kilowatt hour, and they envisage widespread adoption of autonomous electric vehicles and the replacement of car ownership with ride-sharing.
The authors laid out their vision for the future of food in another report last year, where they predicted that traditional agriculture would soon be replaced by industrial-scale brewing of single-celled organisms genetically modified to produce all the nutrients we need. In a similar vein, they believe the same processes combined with additive manufacturing and “nanotechnologies” will allow us to build all the materials required for the modern world from the molecule up rather than extracting scarce natural resources.
They believe this could allow us to shift from a system of production based on extraction to one built on creation, as limitless renewable energy makes it possible to build everything we need from scratch and barriers to movement and information disappear. As a result, a lifestyle worthy of the “American Dream” could be available to anyone for as little as $250/month by 2030.
This will require a fundamental reimagining of our societies, though. All great civilizations have eventually hit fundamental limits on their growth and we are no different, as demonstrated by our growing impact on the environment and the increasing concentration of wealth. Historically this stage of development has lead to a doubling down on old tactics in search of short-term gains, but this invariably leads to the collapse of the civilization.
The authors argue that we’re in a unique position. Because of the technological disruption detailed above, we have the ability to break through the limits on our growth. But only if we change what the authors call our “Organizing System.” They describe this as “the prevailing models of thought, belief systems, myths, values, abstractions, and conceptual frameworks that help explain how the world works and our relationship to it.”
They say that the current hierarchical, centralized system based on nation-states is unfit for the new system of production that is emerging. The cracks are already starting to appear, with problems like disinformation campaigns, fake news, and growing polarization demonstrating how ill-suited our institutions are for dealing with the distributed nature of today’s information systems. And as this same disruption comes to the other foundational sectors the shockwaves could lead to the collapse of civilization as we know it.
Their solution is a conscious shift towards a new way of organizing the world. As emerging technology allows communities to become self-sufficient, flows of physical resources will be replaced by flows of information, and we will require a decentralized but highly networked Organizing System.
The report includes detailed recommendations on how to usher this in. Examples include giving individuals control and ownership of data rights; developing new models for community ownership of energy, information, and transportation networks; and allowing states and cities far greater autonomy on policies like immigration, taxation, education, and public expenditure.
How easy it will be to get people on board with such a shift is another matter. The authors say it may require us to re-examine the foundations of our society, like representative democracy, capitalism, and nation-states. While they acknowledge that these ideas are deeply entrenched, they appear to believe we can reason our way around them.
That seems optimistic. Cultural and societal change can be glacial, and efforts to impose it top-down through reason and logic are rarely successful. The report seems to brush over many of the messy realities of humanity, such as the huge sway that tradition and religion hold over the vast majority of people.
It also doesn’t deal with the uneven distribution of the technology that is supposed to catapult us into this new age. And while the predicted revolutions in transportation, energy, and information do seem inevitable, the idea that in the next decade or two we’ll be able to produce any material we desire using cheap and abundant stock materials seems like a stretch.
Despite the techno-utopianism though, many of the ideas in the report hold promise for building societies that are better adapted for the disruptive new age we are about to enter.
Image Credit: Futuristic Society/flickr Continue reading
Posted in Human Robots
#437202 Scientists Used Dopamine to Seamlessly ...
In just half a decade, neuromorphic devices—or brain-inspired computing—already seem quaint. The current darling? Artificial-biological hybrid computing, uniting both man-made computer chips and biological neurons seamlessly into semi-living circuits.
It sounds crazy, but a new study in Nature Materials shows that it’s possible to get an artificial neuron to communicate directly with a biological one using not just electricity, but dopamine—a chemical the brain naturally uses to change how neural circuits behave, most known for signaling reward.
Because these chemicals, known as “neurotransmitters,” are how biological neurons functionally link up in the brain, the study is a dramatic demonstration that it’s possible to connect artificial components with biological brain cells into a functional circuit.
The team isn’t the first to pursue hybrid neural circuits. Previously, a different team hooked up two silicon-based artificial neurons with a biological one into a circuit using electrical protocols alone. Although a powerful demonstration of hybrid computing, the study relied on only one-half of the brain’s computational ability: electrical computing.
The new study now tackles the other half: chemical computing. It adds a layer of compatibility that lays the groundwork not just for brain-inspired computers, but also for brain-machine interfaces and—perhaps—a sort of “cyborg” future. After all, if your brain can’t tell the difference between an artificial neuron and your own, could you? And even if you did, would you care?
Of course, that scenario is far in the future—if ever. For now, the team, led by Dr. Alberto Salleo, professor of materials science and engineering at Stanford University, collectively breathed a sigh of relief that the hybrid circuit worked.
“It’s a demonstration that this communication melding chemistry and electricity is possible,” said Salleo. “You could say it’s a first step toward a brain-machine interface, but it’s a tiny, tiny very first step.”
Neuromorphic Computing
The study grew from years of work into neuromorphic computing, or data processing inspired by the brain.
The blue-sky idea was inspired by the brain’s massive parallel computing capabilities, along with vast energy savings. By mimicking these properties, scientists reasoned, we could potentially turbo-charge computing. Neuromorphic devices basically embody artificial neural networks in physical form—wouldn’t hardware that mimics how the brain processes information be even more efficient and powerful?
These explorations led to novel neuromorphic chips, or artificial neurons that “fire” like biological ones. Additional work found that it’s possible to link these chips up into powerful circuits that run deep learning with ease, with bioengineered communication nodes called artificial synapses.
As a potential computing hardware replacement, these systems have proven to be incredibly promising. Yet scientists soon wondered: given their similarity to biological brains, can we use them as “replacement parts” for brains that suffer from traumatic injuries, aging, or degeneration? Can we hook up neuromorphic components to the brain to restore its capabilities?
Buzz & Chemistry
Theoretically, the answer’s yes.
But there’s a huge problem: current brain-machine interfaces only use electrical signals to mimic neural computation. The brain, in contrast, has two tricks up its sleeve: electricity and chemicals, or electrochemical.
Within a neuron, electricity travels up its incoming branches, through the bulbous body, then down the output branches. When electrical signals reach the neuron’s outgoing “piers,” dotted along the output branch, however, they hit a snag. A small gap exists between neurons, so to get to the other side, the electrical signals generally need to be converted into little bubble ships, packed with chemicals, and set sail to the other neuronal shore.
In other words, without chemical signals, the brain can’t function normally. These neurotransmitters don’t just passively carry information. Dopamine, for example, can dramatically change how a neural circuit functions. For an artificial-biological hybrid neural system, the absence of chemistry is like nixing international cargo vessels and only sticking with land-based trains and highways.
“To emulate biological synaptic behavior, the connectivity of the neuromorphic device must be dynamically regulated by the local neurotransmitter activity,” the team said.
Let’s Get Electro-Chemical
The new study started with two neurons: the upstream, an immortalized biological cell that releases dopamine; and the downstream, an artificial neuron that the team previously introduced in 2017, made of a mix of biocompatible and electrical-conducting materials.
Rather than the classic neuron shape, picture more of a sandwich with a chunk bitten out in the middle (yup, I’m totally serious). Each of the remaining parts of the sandwich is a soft electrode, made of biological polymers. The “bitten out” part has a conductive solution that can pass on electrical signals.
The biological cell sits close to the first electrode. When activated, it dumps out boats of dopamine, which drift to the electrode and chemically react with it—mimicking the process of dopamine docking onto a biological neuron. This, in turn, generates a current that’s passed on to the second electrode through the conductive solution channel. When this current reaches the second electrode, it changes the electrode’s conductance—that is, how well it can pass on electrical information. This second step is analogous to docked dopamine “ships” changing how likely it is that a biological neuron will fire in the future.
In other words, dopamine release from the biological neuron interacts with the artificial one, so that the chemicals change how the downstream neuron behaves in a somewhat lasting way—a loose mimic of what happens inside the brain during learning.
But that’s not all. Chemical signaling is especially powerful in the brain because it’s flexible. Dopamine, for example, only grabs onto the downstream neurons for a bit before it returns back to its upstream neuron—that is, recycled or destroyed. This means that its effect is temporary, giving the neural circuit breathing room to readjust its activity.
The Stanford team also tried reconstructing this quirk in their hybrid circuit. They crafted a microfluidic channel that shuttles both dopamine and its byproduct away from the artificial neurons after they’ve done their job for recycling.
Putting It All Together
After confirming that biological cells can survive happily on top of the artificial one, the team performed a few tests to see if the hybrid circuit could “learn.”
They used electrical methods to first activate the biological dopamine neuron, and watched the artificial one. Before the experiment, the team wasn’t quite sure what to expect. Theoretically, it made sense that dopamine would change the artificial neuron’s conductance, similar to learning. But “it was hard to know whether we’d achieve the outcome we predicted on paper until we saw it happen in the lab,” said study author Scott Keene.
On the first try, however, the team found that the burst of chemical signaling was able to change the artificial neuron’s conductance long-term, similar to the neuroscience dogma “neurons that fire together, wire together.” Activating the upstream biological neuron with chemicals also changed the artificial neuron’s conductance in a way that mimicked learning.
“That’s when we realized the potential this has for emulating the long-term learning process of a synapse,” said Keene.
Visualizing under an electron microscope, the team found that, similar to its biological counterpart, the hybrid synapse was able to efficiently recycle dopamine with timescales similar to the brain after some calibration. By playing with how much dopamine accumulates at the artificial neuron, the team found that they loosely mimic a learning rule called spike learning—a darling of machine learning inspired by the brain’s computation.
A Hybrid Future?
Unfortunately for cyborg enthusiasts, the work is still in its infancy.
For one, the artificial neurons are still rather bulky compared to biological ones. This means that they can’t capture and translate information from a single “boat” of dopamine. It’s also unclear if, and how, a hybrid synapse can work inside a living brain. Given the billions of synapses firing away in our heads, it’ll be a challenge to find-and-replace those that need replacement, and be able to control our memories and behaviors similar to natural ones.
That said, we’re inching ever closer to full-capability artificial-biological hybrid circuits.
“The neurotransmitter-mediated neuromorphic device presented in this work constitutes a fundamental building block for artificial neural networks that can be directly modulated based on biological feedback from live neurons,” the authors concluded. “[It] is a crucial first step in realizing next-generation adaptive biohybrid interfaces.”
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