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#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
#436261 AI and the future of work: The prospects ...
AI experts gathered at MIT last week, with the aim of predicting the role artificial intelligence will play in the future of work. Will it be the enemy of the human worker? Will it prove to be a savior? Or will it be just another innovation—like electricity or the internet?
As IEEE Spectrum previously reported, this conference (“AI and the Future of Work Congress”), held at MIT’s Kresge Auditorium, offered sometimes pessimistic outlooks on the job- and industry-destroying path that AI and automation seems to be taking: Self-driving technology will put truck drivers out of work; smart law clerk algorithms will put paralegals out of work; robots will (continue to) put factory and warehouse workers out of work.
Andrew McAfee, co-director of MIT’s Initiative on the Digital Economy, said even just in the past couple years, he’s noticed a shift in the public’s perception of AI. “I remember from previous versions of this conference, it felt like we had to make the case that we’re living in a period of accelerating change and that AI’s going to have a big impact,” he said. “Nobody had to make that case today.”
Elisabeth Reynolds, executive director of MIT’s Task Force on the Work of the Future, noted that following the path of least resistance is not a viable way forward. “If we do nothing, we’re in trouble,” she said. “The future will not take care of itself. We have to do something about it.”
Panelists and speakers spoke about championing productive uses of AI in the workplace, which ultimately benefit both employees and customers.
As one example, Zeynep Ton, professor at MIT Sloan School of Management, highlighted retailer Sam’s Club’s recent rollout of a program called Sam’s Garage. Previously customers shopping for tires for their car spent somewhere between 30 and 45 minutes with a Sam’s Club associate paging through manuals and looking up specs on websites.
But with an AI algorithm, they were able to cut that spec hunting time down to 2.2 minutes. “Now instead of wasting their time trying to figure out the different tires, they can field the different options and talk about which one would work best [for the customer],” she said. “This is a great example of solving a real problem, including [enhancing] the experience of the associate as well as the customer.”
“We think of it as an AI-first world that’s coming,” said Scott Prevost, VP of engineering at Adobe. Prevost said AI agents in Adobe’s software will behave something like a creative assistant or intern who will take care of more mundane tasks for you.
“We need a mindset change. That it is not just about minimizing costs or maximizing tax benefits, but really worrying about what kind of society we’re creating and what kind of environment we’re creating if we keep on just automating and [eliminating] good jobs.”
—Daron Acemoglu, MIT Institute Professor of Economics
Prevost cited an internal survey of Adobe customers that found 74 percent of respondents’ time was spent doing repetitive work—the kind that might be automated by an AI script or smart agent.
“It used to be you’d have the resources to work on three ideas [for a creative pitch or presentation],” Prevost said. “But if the AI can do a lot of the production work, then you can have 10 or 100. Which means you can actually explore some of the further out ideas. It’s also lowering the bar for everyday people to create really compelling output.”
In addition to changing the nature of work, noted a number of speakers at the event, AI is also directly transforming the workforce.
Jacob Hsu, CEO of the recruitment company Catalyte spoke about using AI as a job placement tool. The company seeks to fill myriad positions including auto mechanics, baristas, and office workers—with its sights on candidates including young people and mid-career job changers. To find them, it advertises on Craigslist, social media, and traditional media.
The prospects who sign up with Catalyte take a battery of tests. The company’s AI algorithms then match each prospect’s skills with the field best suited for their talents.
“We want to be like the Harry Potter Sorting Hat,” Hsu said.
Guillermo Miranda, IBM’s global head of corporate social responsibility, said IBM has increasingly been hiring based not on credentials but on skills. For instance, he said, as much as 50 per cent of the company’s new hires in some divisions do not have a traditional four-year college degree. “As a company, we need to be much more clear about hiring by skills,” he said. “It takes discipline. It takes conviction. It takes a little bit of enforcing with H.R. by the business leaders. But if you hire by skills, it works.”
Ardine Williams, Amazon’s VP of workforce development, said the e-commerce giant has been experimenting with developing skills of the employees at its warehouses (a.k.a. fulfillment centers) with an eye toward putting them in a position to get higher-paying work with other companies.
She described an agreement Amazon had made in its Dallas fulfillment center with aircraft maker Sikorsky, which had been experiencing a shortage of skilled workers for its nearby factory. So Amazon offered to its employees a free certification training to seek higher-paying work at Sikorsky.
“I do that because now I have an attraction mechanism—like a G.I. Bill,” Williams said. The program is also only available for employees who have worked at least a year with Amazon. So their program offers medium-term job retention, while ultimately moving workers up the wage ladder.
Radha Basu, CEO of AI data company iMerit, said her firm aggressively hires from the pool of women and under-resourced minority communities in the U.S. and India. The company specializes in turning unstructured data (e.g. video or audio feeds) into tagged and annotated data for machine learning, natural language processing, or computer vision applications.
“There is a motivation with these young people to learn these things,” she said. “It comes with no baggage.”
Alastair Fitzpayne, executive director of The Aspen Institute’s Future of Work Initiative, said the future of work ultimately means, in bottom-line terms, the future of human capital. “We have an R&D tax credit,” he said. “We’ve had it for decades. It provides credit for companies that make new investment in research and development. But we have nothing on the human capital side that’s analogous.”
So a company that’s making a big investment in worker training does it on their own dime, without any of the tax benefits that they might accrue if they, say, spent it on new equipment or new technology. Fitzpayne said a simple tweak to the R&D tax credit could make a big difference by incentivizing new investment programs in worker training. Which still means Amazon’s pre-existing worker training programs—for a company that already famously pays no taxes—would not count.
“We need a different way of developing new technologies,” said Daron Acemoglu, MIT Institute Professor of Economics. He pointed to the clean energy sector as an example. First a consensus around the problem needs to emerge. Then a broadly agreed-upon set of goals and measurements needs to be developed (e.g., that AI and automation would, for instance, create at least X new jobs for every Y jobs that it eliminates).
Then it just needs to be implemented.
“We need to build a consensus that, along the path we’re following at the moment, there are going to be increasing problems for labor,” Acemoglu said. “We need a mindset change. That it is not just about minimizing costs or maximizing tax benefits, but really worrying about what kind of society we’re creating and what kind of environment we’re creating if we keep on just automating and [eliminating] good jobs.” Continue reading
Posted in Human Robots