Tag Archives: machine
#436484 If Machines Want to Make Art, Will ...
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.
Image Credit: Rene Böhmer / Unsplash Continue reading
Posted in Human Robots
#436466 How Two Robots Learned to Grill and ...
The list of things robots can do seems to be growing by the week. They can play sports, help us explore outer space and the deep sea, take over some of our boring everyday tasks, and even assemble Ikea furniture.
Now they can add one more accomplishment to the list: grilling and serving a hot dog.
It seems like a pretty straightforward task, and as far as grilling goes, hot dogs are about as easy as it gets (along with, maybe, burgers? Hot dogs require more rotation, but it’s easier to tell when they’re done since they’re lighter in color).
Let’s paint a picture: you’re manning the grill at your family’s annual Fourth of July celebration. You’ve got a 10-pack of plump, juicy beef franks and a hungry crowd of relatives whose food-to-alcohol ratio is getting pretty skewed—they need some solid calories, pronto. What are the steps you need to take to get those franks from package to plate?
Each one needs to be placed on the grill, rotated every couple minutes for even cooking, removed from the grill when you deem it’s done, then—if you’re the kind of guy or gal who goes the extra mile—placed in a bun and dressed with ketchup, mustard, pickles, and the like before being handed over to salivating, too-loud Uncle Hector or sweet, bored Cousin Margaret.
While carrying out your grillmaster duties, you know better than to drop the hot dogs on the ground, leave them cooking on one side for too long, squeeze them to the point of breaking or bursting, and any other hot-dog-ruining amateur moves.
But for a robot, that’s a lot to figure out, especially if they have no prior knowledge of grilling hot dogs (which, well, most robots don’t).
As described in a paper published in this week’s Science Robotics, a team from Boston University programmed two robotic arms to use reinforcement learning—a branch of machine learning in which software gathers information about its environment then learns from it by replaying its experiences and incorporating rewards—to cook and serve hot dogs.
The team used a set of formulas to specify and combine tasks (“pick up hot dog and place on the grill”), meet safety requirements (“always avoid collisions”), and incorporate general prior knowledge (“you cannot pick up another hot dog if you are already holding one”).
Baxter and Jaco—as the two robots were dubbed—were trained through computer simulations. The paper’s authors emphasized their use of what they call a “formal specification language” for training the software, with the aim of generating easily-interpretable task descriptions. In reinforcement learning, they explain, being able to understand how a reward function influences an AI’s learning process is a key component in understanding the system’s behavior—but most systems lack this quality, and are thus likely to be lumped into the ‘black box’ of AI.
The robots’ decisions throughout the hot dog prep process—when to turn a hot dog, when to take it off the grill, and so on—are, the authors write, “easily interpretable from the beginning because the language is very similar to plain English.”
Besides being a step towards more explainable AI systems, Baxter and Jaco are another example of fast-food robots—following in the footsteps of their burger and pizza counterparts—that may take over some repetitive manual tasks currently performed by human workers. As robots’ capabilities improve through incremental progress like this, they’ll be able to take on additional tasks.
In a not-so-distant future, then, you just may find yourself throwing back drinks with Uncle Hector and Cousin Margaret while your robotic replacement mans the grill, churning out hot dogs that are perfectly cooked every time.
Image Credit: Image by Muhammad Ribkhan from Pixabay Continue reading
Posted in Human Robots