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#436911 Scientists Linked Artificial and ...

Posted on March 11, 2020 by Android

Scientists have linked up two silicon-based artificial neurons with a biological one across multiple countries into a fully-functional network. Using standard internet protocols, they established a chain of communication whereby an artificial neuron controls a living, biological one, and passes on the info to another artificial one.

Whoa.

We’ve talked plenty about brain-computer interfaces and novel computer chips that resemble the brain. We’ve covered how those “neuromorphic” chips could link up into tremendously powerful computing entities, using engineered communication nodes called artificial synapses.

As Moore’s law is dying, we even said that neuromorphic computing is one path towards the future of extremely powerful, low energy consumption artificial neural network-based computing—in hardware—that could in theory better link up with the brain. Because the chips “speak” the brain’s language, in theory they could become neuroprosthesis hubs far more advanced and “natural” than anything currently possible.

This month, an international team put all of those ingredients together, turning theory into reality.

The three labs, scattered across Padova, Italy, Zurich, Switzerland, and Southampton, England, collaborated to create a fully self-controlled, hybrid artificial-biological neural network that communicated using biological principles, but over the internet.

The three-neuron network, linked through artificial synapses that emulate the real thing, was able to reproduce a classic neuroscience experiment that’s considered the basis of learning and memory in the brain. In other words, artificial neuron and synapse “chips” have progressed to the point where they can actually use a biological neuron intermediary to form a circuit that, at least partially, behaves like the real thing.

That’s not to say cyborg brains are coming soon. The simulation only recreated a small network that supports excitatory transmission in the hippocampus—a critical region that supports memory—and most brain functions require enormous cross-talk between numerous neurons and circuits. Nevertheless, the study is a jaw-dropping demonstration of how far we’ve come in recreating biological neurons and synapses in artificial hardware.

And perhaps one day, the currently “experimental” neuromorphic hardware will be integrated into broken biological neural circuits as bridges to restore movement, memory, personality, and even a sense of self.

The Artificial Brain Boom
One important thing: this study relies heavily on a decade of research into neuromorphic computing, or the implementation of brain functions inside computer chips.

The best-known example is perhaps IBM’s TrueNorth, which leveraged the brain’s computational principles to build a completely different computer than what we have today. Today’s computers run on a von Neumann architecture, in which memory and processing modules are physically separate. In contrast, the brain’s computing and memory are simultaneously achieved at synapses, small “hubs” on individual neurons that talk to adjacent ones.

Because memory and processing occur on the same site, biological neurons don’t have to shuttle data back and forth between processing and storage compartments, massively reducing processing time and energy use. What’s more, a neuron’s history will also influence how it behaves in the future, increasing flexibility and adaptability compared to computers. With the rise of deep learning, which loosely mimics neural processing as the prima donna of AI, the need to reduce power while boosting speed and flexible learning is becoming ever more tantamount in the AI community.

Neuromorphic computing was partially born out of this need. Most chips utilize special ingredients that change their resistance (or other physical characteristics) to mimic how a neuron might adapt to stimulation. Some chips emulate a whole neuron, that is, how it responds to a history of stimulation—does it get easier or harder to fire? Others imitate synapses themselves, that is, how easily they will pass on the information to another neuron.

Although single neuromorphic chips have proven to be far more efficient and powerful than current computer chips running machine learning algorithms in toy problems, so far few people have tried putting the artificial components together with biological ones in the ultimate test.

That’s what this study did.

A Hybrid Network
Still with me? Let’s talk network.

It’s gonna sound complicated, but remember: learning is the formation of neural networks, and neurons that fire together wire together. To rephrase: when learning, neurons will spontaneously organize into networks so that future instances will re-trigger the entire network. To “wire” together, downstream neurons will become more responsive to their upstream neural partners, so that even a whisper will cause them to activate. In contrast, some types of stimulation will cause the downstream neuron to “chill out” so that only an upstream “shout” will trigger downstream activation.

Both these properties—easier or harder to activate downstream neurons—are essentially how the brain forms connections. The “amping up,” in neuroscience jargon, is long-term potentiation (LTP), whereas the down-tuning is LTD (long-term depression). These two phenomena were first discovered in the rodent hippocampus more than half a century ago, and ever since have been considered as the biological basis of how the brain learns and remembers, and implicated in neurological problems such as addition (seriously, you can’t pass Neuro 101 without learning about LTP and LTD!).

So it’s perhaps especially salient that one of the first artificial-brain hybrid networks recapitulated this classic result.

To visualize: the three-neuron network began in Switzerland, with an artificial neuron with the badass name of “silicon spiking neuron.” That neuron is linked to an artificial synapse, a “memristor” located in the UK, which is then linked to a biological rat neuron cultured in Italy. The rat neuron has a “smart” microelectrode, controlled by the artificial synapse, to stimulate it. This is the artificial-to-biological pathway.

Meanwhile, the rat neuron in Italy also has electrodes that listen in on its electrical signaling. This signaling is passed back to another artificial synapse in the UK, which is then used to control a second artificial neuron back in Switzerland. This is the biological-to-artificial pathway back. As a testimony in how far we’ve come in digitizing neural signaling, all of the biological neural responses are digitized and sent over the internet to control its far-out artificial partner.

Here’s the crux: to demonstrate a functional neural network, just having the biological neuron passively “pass on” electrical stimulation isn’t enough. It has to show the capacity to learn, that is, to be able to mimic the amping up and down-tuning that are LTP and LTD, respectively.

You’ve probably guessed the results: certain stimulation patterns to the first artificial neuron in Switzerland changed how the artificial synapse in the UK operated. This, in turn, changed the stimulation to the biological neuron, so that it either amped up or toned down depending on the input.

Similarly, the response of the biological neuron altered the second artificial synapse, which then controlled the output of the second artificial neuron. Altogether, the biological and artificial components seamlessly linked up, over thousands of miles, into a functional neural circuit.

Cyborg Mind-Meld
So…I’m still picking my jaw up off the floor.

It’s utterly insane seeing a classic neuroscience learning experiment repeated with an integrated network with artificial components. That said, a three-neuron network is far from the thousands of synapses (if not more) needed to truly re-establish a broken neural circuit in the hippocampus, which DARPA has been aiming to do. And LTP/LTD has come under fire recently as the de facto brain mechanism for learning, though so far they remain cemented as neuroscience dogma.

However, this is one of the few studies where you see fields coming together. As Richard Feynman famously said, “What I cannot recreate, I cannot understand.” Even though neuromorphic chips were built on a high-level rather than molecular-level understanding of how neurons work, the study shows that artificial versions can still synapse with their biological counterparts. We’re not just on the right path towards understanding the brain, we’re recreating it, in hardware—if just a little.

While the study doesn’t have immediate use cases, practically it does boost both the neuromorphic computing and neuroprosthetic fields.

“We are very excited with this new development,” said study author Dr. Themis Prodromakis at the University of Southampton. “On one side it sets the basis for a novel scenario that was never encountered during natural evolution, where biological and artificial neurons are linked together and communicate across global networks; laying the foundations for the Internet of Neuro-electronics. On the other hand, it brings new prospects to neuroprosthetic technologies, paving the way towards research into replacing dysfunctional parts of the brain with AI chips.”

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#436559 This Is What an AI Said When Asked to ...

Posted on February 14, 2020 by Android

“What’s past is prologue.” So says the famed quote from Shakespeare’s The Tempest, alleging that we can look to what has already happened as an indication of what will happen next.

This idea could be interpreted as being rather bleak; are we doomed to repeat the errors of the past until we correct them? We certainly do need to learn and re-learn life lessons—whether in our work, relationships, finances, health, or other areas—in order to grow as people.

Zooming out, the same phenomenon exists on a much bigger scale—that of our collective human history. We like to think we’re improving as a species, but haven’t yet come close to doing away with the conflicts and injustices that plagued our ancestors.

Zooming back in (and lightening up) a little, what about the short-term future? What might happen over the course of this year, and what information would we use to make educated guesses about it?

The editorial team at The Economist took a unique approach to answering these questions. On top of their own projections for 2020, including possible scenarios in politics, economics, and the continued development of technologies like artificial intelligence, they looked to an AI to make predictions of its own. What it came up with is intriguing, and a little bit uncanny.

[For the full list of the questions and answers, read The Economist article].

An AI That Reads—Then Writes
Almost exactly a year ago, non-profit OpenAI announced it had built a neural network for natural language processing called GPT-2. The announcement was met with some controversy, as it included the caveat that the tool would not be immediately released to the public due to its potential for misuse. It was then released in phases over the course of several months.

GPT-2’s creators upped the bar on quality when training the neural net; rather than haphazardly feeding it low-quality text, they only used articles that got more than three upvotes on Reddit (admittedly, this doesn’t guarantee high quality across the board—but it’s something).

The training dataset consisted of 40GB of text. For context, 1GB of text is about 900,000 ASCII pages or 130,000 double-spaced Microsoft Word pages.

The tool has no understanding of the text it’s generating, of course. It uses language patterns and word sequences to draw statistical associations between words and phrases, building a sort of guidebook for itself (not unlike the grammar rules and vocabulary words you might study when trying to learn a foreign language). It then uses that guidebook to answer questions or predict what will come after a particular sequence of words.

GPT-2’s creators did note that when the model is trained on specifically-selected datasets for narrower applications, its replies become more convincing.

Some Light Editing
Though the Economist article detailing GPT-2’s predictions describes the text the model generated as “unedited,” this isn’t wholly accurate. In a Medium post, deputy editor Tom Standage describes the methodology he used, and it does involve a degree of tweaking.

Standage wrote an introductory paragraph, followed by his opening question (“Greetings, GPT-2. Have you done an interview before?”). He configured the tool to generate five responses to each subsequent question then selected the best answer, deleting any questions GPT-2 came up with as part of its response.

AI’s View of the World—and Itself
The questions Standage asked ranged from tech-related (the future of AI, technological unemployment, misuse of technology) to political and economic (re-election of Donald Trump, the US-China trade war). Here are a couple of his questions and GPT-2’s answers:

Q: Do fake news stories, generated using AI, pose a threat to democracy? Are we likely to see this tactic being used in the 2020 American presidential elections?
A: Yes, we’ll probably see them. It’s just a matter of when. Fake news stories are generally generated by political campaigns, and have the potential to have a huge impact on the outcome of an election. This is because they spread misinformation, often with no basis in fact.

Q: How do you see the economic outlook for 2020?
A: I think it is very unclear. The outlook for the UK economy is uncertain, but the American economy is very strong. I think we are going to have a lot of turbulence in the world economy.

While some of its answers seemed insightful—when asked about the future of AI, GPT-2 advised that we treat the technology like a tool and “put as much effort into developing [it] as necessary, rather than worrying that it’s going to harm us and destroy our lives”—many of the responses were quite vague. One answer, for example, mentions that there will “probably be major changes in China.” Are we talking political changes? Social? Economic? All of the above?

However, while nothing the AI wrote was particularly illuminating or new, it’s impressive that it was able to expound on the correct topic for each question, and in fully coherent English.

GPT-2 named itself—AI—as the most important disruptive technology to watch in 2020. This is telling; it’s coming from a synthesis of millions of pages of published articles. If GPT-2 thinks AI is more important than, say, gene editing or quantum computing, it could be right. Or it could simply be that AI is at peak hype, and as such more is being written about it than about other technologies.

Equally intriguing was GPT-2’s response when asked whether Donald Trump will win a second term: “I think he will not win a second term. I think he will be defeated in the general election.” Some deeper insight there would be great, but hey—we’ll take it.

Predicting Predictions
Since an AI can read and synthesize vast data sets much faster than we can, it’s being used to predict all kinds of things, from virus outbreaks to crime. But asking it to philosophize on the future based on the (Reddit-curated) past is new, and if you think about it, a pretty fascinating undertaking.

As GPT-2 and tools like it continually improve, we’ll likely see them making more—and better—predictions of the future. In the meantime, let’s hope that the new data these models are trained on—news of what’s happening this week, this month, this year—add to an already-present sense of optimism.

When asked if it had any advice for readers, GPT-2 replied, “The big projects that you think are impossible today are actually possible in the near future.”

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#436530 How Smart Roads Will Make Driving ...

Posted on January 30, 2020 by Android

Roads criss-cross the landscape, but while they provide vital transport links, in many ways they represent a huge amount of wasted space. Advances in “smart road” technology could change that, creating roads that can harvest energy from cars, detect speeding, automatically weigh vehicles, and even communicate with smart cars.

“Smart city” projects are popping up in countries across the world thanks to advances in wireless communication, cloud computing, data analytics, remote sensing, and artificial intelligence. Transportation is a crucial element of most of these plans, but while much of the focus is on public transport solutions, smart roads are increasingly being seen as a crucial feature of these programs.

New technology is making it possible to tackle a host of issues including traffic congestion, accidents, and pollution, say the authors of a paper in the journal Proceedings of the Royal Society A. And they’ve outlined ten of the most promising advances under development or in planning stages that could feature on tomorrow’s roads.

Energy harvesting

A variety of energy harvesting technologies integrated into roads have been proposed as ways to power street lights and traffic signals or provide a boost to the grid. Photovoltaic panels could be built into the road surface to capture sunlight, or piezoelectric materials installed beneath the asphalt could generate current when deformed by vehicles passing overhead.

Musical roads

Countries like Japan, Denmark, the Netherlands, Taiwan, and South Korea have built roads that play music as cars pass by. By varying the spacing of rumble strips, it’s possible to produce a series of different notes as vehicles drive over them. The aim is generally to warn of hazards or help drivers keep to the speed limit.

Automatic weighing

Weight-in-motion technology that measures vehicles’ loads as they drive slowly through a designated lane has been around since the 1970s, but more recently high speed weight-in-motion tech has made it possible to measure vehicles as they travel at regular highway speeds. The latest advance has been integration with automatic licence plate reading and wireless communication to allow continuous remote monitoring both to enforce weight restrictions and monitor wear on roads.

Vehicle charging

The growing popularity of electric vehicles has spurred the development of technology to charge cars and buses as they drive. The most promising of these approaches is magnetic induction, which involves burying cables beneath the road to generate electromagnetic fields that a receiver device in the car then transforms into electrical power to charge batteries.

Smart traffic signs

Traffic signs aren’t always as visible as they should be, and it can often be hard to remember what all of them mean. So there are now proposals for “smart signs” that wirelessly beam a sign’s content to oncoming cars fitted with receivers, which can then alert the driver verbally or on the car’s display. The approach isn’t affected by poor weather and lighting, can be reprogrammed easily, and could do away with the need for complex sign recognition technology in future self-driving cars.

Traffic violation detection and notification

Sensors and cameras can be combined with these same smart signs to detect and automatically notify drivers of traffic violations. The automatic transmission of traffic signals means drivers won’t be able to deny they’ve seen the warnings or been notified of any fines, as a record will be stored on their car’s black box.

Talking cars

Car-to-car communication technology and V2X, which lets cars share information with any other connected device, are becoming increasingly common. Inter-car communication can be used to propagate accidents or traffic jam alerts to prevent congestion, while letting vehicles communicate with infrastructure can help signals dynamically manage timers to keep traffic flowing or automatically collect tolls.

Smart intersections

Combing sensors and cameras with object recognition systems that can detect vehicles and other road users can help increase safety and efficiency at intersections. It can be used to extend green lights for slower road users like pedestrians and cyclists, sense jaywalkers, give priority to emergency vehicles, and dynamically adjust light timers to optimize traffic flow. Information can even be broadcast to oncoming vehicles to highlight blind spots and potential hazards.

Automatic crash detection

There’s a “golden hour” after an accident in which the chance of saving lives is greatly increased. Vehicle communication technology can ensure that notification of a crash reaches the emergency services rapidly, and can also provide vital information about the number and type of vehicles involved, which can help emergency response planning. It can also be used to alert other drivers to slow down or stop to prevent further accidents.

Smart street lights

Street lights are increasingly being embedded with sensors, wireless connectivity, and micro-controllers to enable a variety of smart functions. These include motion activation to save energy, providing wireless access points, air quality monitoring, or parking and litter monitoring. This can also be used to send automatic maintenance requests if a light is faulty, and can even allow neighboring lights to be automatically brightened to compensate.

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#436507 The Weird, the Wacky, the Just Plain ...

Posted on January 12, 2020 by Android

As you know if you’ve ever been to, heard of, or read about the annual Consumer Electronics Show in Vegas, there’s no shortage of tech in any form: gadgets, gizmos, and concepts abound. You probably couldn’t see them all in a month even if you spent all day every day trying.

Given the sheer scale of the show, the number of exhibitors, and the inherent subjectivity of bestowing superlatives, it’s hard to pick out the coolest tech from CES. But I’m going to do it anyway; in no particular order, here are some of the products and concepts that I personally found most intriguing at this year’s event.

e-Novia’s Haptic Gloves
Italian startup e-Novia’s Weart glove uses a ‘sensing core’ to record tactile sensations and an ‘actuation core’ to reproduce those sensations onto the wearer’s skin. Haptic gloves will bring touch to VR and AR experiences, making them that much more life-like. The tech could also be applied to digitization of materials and in gaming and entertainment.

e-Novia’s modular haptic glove
I expected a full glove, but in fact there were two rings that attached to my fingers. Weart co-founder Giovanni Spagnoletti explained that they’re taking a modular approach, so as to better tailor the technology to different experiences. He then walked me through a virtual reality experience that was a sort of simulated science experiment: I had to lift a glass beaker, place it on a stove, pour in an ingredient, open a safe to access some dry ice, add that, and so on. As I went through the steps, I felt the beaker heat up and cool off at the expected times, and felt the liquid moving inside, as well as the pressure of my fingertips against the numbered buttons on the safe.

A virtual (but tactile) science experiment
There was a slight delay between my taking an action and feeling the corresponding tactile sensation, but on the whole, the haptic glove definitely made the experience more realistic—and more fun. Slightly less fun but definitely more significant, Spagnoletti told me Weart is working with a medical group to bring tactile sensations to VR training for surgeons.

Sarcos Robotics’ Exoskeleton
That tire may as well be a feather
Sarcos Robotics unveiled its Guardian XO full-body exoskeleton, which it says can safely lift up to 200 pounds across an extended work session. What’s cool about this particular exoskeleton is that it’s not just a prototype; the company announced a partnership with Delta airlines, which will be trialing the technology for aircraft maintenance, engine repair, and luggage handling. In a demo, I watched a petite female volunteer strap into the exoskeleton and easily lift a 50-pound weight with one hand, and a Sarcos employee lift and attach a heavy component of a propeller; she explained that the strength-augmenting function of the exoskeleton can easily be switched on or off—and the wearer’s hands released—to facilitate multi-step tasks.

Hyundai’s Flying Taxi
Where to?
Hyundai and Uber partnered to unveil an air taxi concept. With a 49-foot wingspan, 4 lift rotors, and 4 tilt rotors, the aircraft would be manned by a pilot and could carry 4 passengers at speeds up to 180 miles per hour. The companies say you’ll be able to ride across your city in one of these by 2030—we’ll see if the regulatory environment, public opinion, and other factors outside of technological capability let that happen.

Mercedes’ Avatar Concept Car
Welcome to the future
As evident from its name, Mercedes’ sweet new Vision AVTR concept car was inspired by the movie Avatar; director James Cameron helped design it. The all-electric car has no steering wheel, transparent doors, seats made of vegan leather, and 33 reptilian-scale-like flaps on the back; its design is meant to connect the driver with both the car and the surrounding environment in a natural, seamless way.

Next-generation scrolling
Offered the chance to ‘drive’ the car, I jumped on it. Placing my hand on the center console started the engine, and within seconds it had synced to my heartbeat, which reverberated through the car. The whole dashboard, from driver door to passenger door, is one big LED display. It showed a virtual landscape I could select by holding up my hand: as I moved my hand from left to right, different images were projected onto my open palm. Closing my hand on an image selected it, and suddenly it looked like I was in the middle of a lush green mountain range. Applying slight forward pressure on the center console made the car advance in the virtual landscape; it was essentially like playing a really cool video game.

Mercedes is aiming to have a carbon-neutral production fleet by 2039, and to reduce the amount of energy it uses during production by 40 percent by 2030. It’s unclear when—or whether—the man-machine-nature connecting features of the Vision AVTR will start showing up in production, but I for one will be on the lookout.

Waverly Labs’ In-Ear Translator
Waverly Labs unveiled its Ambassador translator earlier this year and has it on display at the show. It’s worn on the ear and uses a far-field microphone array with speech recognition to translate real-time conversations in 20 different languages. Besides in-ear audio, translations can also appear as text on an app or be broadcast live in a conference environment.

It’s kind of like a giant talking earring
I stopped by the booth and tested out the translator with Waverly senior software engineer Georgiy Konovalov. We each hooked on an earpiece, and first, he spoke to me in Russian. After a delay of a couple seconds, I heard his words in—slightly robotic, but fully comprehensible—English. Then we switched: I spoke to him in Spanish, my words popped up on his phone screen in Cyrillic, and he translated them back to English for me out loud.

On the whole, the demo was pretty cool. If you’ve ever been lost in a foreign country whose language you don’t speak, imagine how handy a gadget like this would come in. Let’s just hope that once they’re more widespread, these products don’t end up discouraging people from learning languages.

Not to be outdone, Google also announced updates to its Translate product, which is being deployed at information desks in JFK airport’s international terminal, in sports stadiums in Qatar, and by some large hotel chains.

Stratuscent’s Digital Nose
AI is making steady progress towards achieving human-like vision and hearing—but there’s been less work done on mimicking our sense of smell (maybe because it’s less useful in everyday applications). Stratuscent’s digital nose, which it says is based on NASA patents, uses chemical receptors and AI to identify both simple chemicals and complex scents. The company is aiming to create the world’s first comprehensive database of everyday scents, which it says it will use to make “intelligent decisions” for customers. What kind of decisions remains to be seen—and smelled.

Banner Image Credit: The Mercedes Vision AVTR concept car. Photo by Vanessa Bates Ramirez Continue reading →

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#436484 If Machines Want to Make Art, Will ...

Posted on January 2, 2020 by Android

Assuming that the emergence of consciousness in artificial minds is possible, those minds will feel the urge to create art. But will we be able to understand it? To answer this question, we need to consider two subquestions: when does the machine become an author of an artwork? And how can we form an understanding of the art that it makes?

Empathy, we argue, is the force behind our capacity to understand works of art. Think of what happens when you are confronted with an artwork. We maintain that, to understand the piece, you use your own conscious experience to ask what could possibly motivate you to make such an artwork yourself—and then you use that first-person perspective to try to come to a plausible explanation that allows you to relate to the artwork. Your interpretation of the work will be personal and could differ significantly from the artist’s own reasons, but if we share sufficient experiences and cultural references, it might be a plausible one, even for the artist. This is why we can relate so differently to a work of art after learning that it is a forgery or imitation: the artist’s intent to deceive or imitate is very different from the attempt to express something original. Gathering contextual information before jumping to conclusions about other people’s actions—in art, as in life—can enable us to relate better to their intentions.

But the artist and you share something far more important than cultural references: you share a similar kind of body and, with it, a similar kind of embodied perspective. Our subjective human experience stems, among many other things, from being born and slowly educated within a society of fellow humans, from fighting the inevitability of our own death, from cherishing memories, from the lonely curiosity of our own mind, from the omnipresence of the needs and quirks of our biological body, and from the way it dictates the space- and time-scales we can grasp. All conscious machines will have embodied experiences of their own, but in bodies that will be entirely alien to us.

We are able to empathize with nonhuman characters or intelligent machines in human-made fiction because they have been conceived by other human beings from the only subjective perspective accessible to us: “What would it be like for a human to behave as x?” In order to understand machinic art as such—and assuming that we stand a chance of even recognizing it in the first place—we would need a way to conceive a first-person experience of what it is like to be that machine. That is something we cannot do even for beings that are much closer to us. It might very well happen that we understand some actions or artifacts created by machines of their own volition as art, but in doing so we will inevitably anthropomorphize the machine’s intentions. Art made by a machine can be meaningfully interpreted in a way that is plausible only from the perspective of that machine, and any coherent anthropomorphized interpretation will be implausibly alien from the machine perspective. As such, it will be a misinterpretation of the artwork.

But what if we grant the machine privileged access to our ways of reasoning, to the peculiarities of our perception apparatus, to endless examples of human culture? Wouldn’t that enable the machine to make art that a human could understand? Our answer is yes, but this would also make the artworks human—not authentically machinic. All examples so far of “art made by machines” are actually just straightforward examples of human art made with computers, with the artists being the computer programmers. It might seem like a strange claim: how can the programmers be the authors of the artwork if, most of the time, they can’t control—or even anticipate—the actual materializations of the artwork? It turns out that this is a long-standing artistic practice.

Suppose that your local orchestra is playing Beethoven’s Symphony No 7 (1812). Even though Beethoven will not be directly responsible for any of the sounds produced there, you would still say that you are listening to Beethoven. Your experience might depend considerably on the interpretation of the performers, the acoustics of the room, the behavior of fellow audience members or your state of mind. Those and other aspects are the result of choices made by specific individuals or of accidents happening to them. But the author of the music? Ludwig van Beethoven. Let’s say that, as a somewhat odd choice for the program, John Cage’s Imaginary Landscape No 4 (March No 2) (1951) is also played, with 24 performers controlling 12 radios according to a musical score. In this case, the responsibility for the sounds being heard should be attributed to unsuspecting radio hosts, or even to electromagnetic fields. Yet, the shaping of sounds over time—the composition—should be credited to Cage. Each performance of this piece will vary immensely in its sonic materialization, but it will always be a performance of Imaginary Landscape No 4.

Why should we change these principles when artists use computers if, in these respects at least, computer art does not bring anything new to the table? The (human) artists might not be in direct control of the final materializations, or even be able to predict them but, despite that, they are the authors of the work. Various materializations of the same idea—in this case formalized as an algorithm—are instantiations of the same work manifesting different contextual conditions. In fact, a common use of computation in the arts is the production of variations of a process, and artists make extensive use of systems that are sensitive to initial conditions, external inputs, or pseudo-randomness to deliberately avoid repetition of outputs. Having a computer executing a procedure to build an artwork, even if using pseudo-random processes or machine-learning algorithms, is no different than throwing dice to arrange a piece of music, or to pursuing innumerable variations of the same formula. After all, the idea of machines that make art has an artistic tradition that long predates the current trend of artworks made by artificial intelligence.

Machinic art is a term that we believe should be reserved for art made by an artificial mind’s own volition, not for that based on (or directed towards) an anthropocentric view of art. From a human point of view, machinic artworks will still be procedural, algorithmic, and computational. They will be generative, because they will be autonomous from a human artist. And they might be interactive, with humans or other systems. But they will not be the result of a human deferring decisions to a machine, because the first of those—the decision to make art—needs to be the result of a machine’s volition, intentions, and decisions. Only then will we no longer have human art made with computers, but proper machinic art.

The problem is not whether machines will or will not develop a sense of self that leads to an eagerness to create art. The problem is that if—or when—they do, they will have such a different Umwelt that we will be completely unable to relate to it from our own subjective, embodied perspective. Machinic art will always lie beyond our ability to understand it because the boundaries of our comprehension—in art, as in life—are those of the human experience.

This article was originally published at Aeon and has been republished under Creative Commons.

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