The most pragmatic position is to think of A.I. as a tool, not a creature. My attitude doesn’t eliminate the possibility of peril: however we think about it, we can still design and operate our new tech badly, in ways that can hurt us or even lead to our extinction. Mythologizing the technology only makes it more likely that we’ll fail to operate it well—and this kind of thinking limits our imaginations, tying them to yesterday’s dreams. We can work better under the assumption that there is no such thing as A.I. The sooner we understand this, the sooner we’ll start managing our new technology intelligently.
If the new tech isn’t true artificial intelligence, then what is it? In my view, the most accurate way to understand what we are building today is as an innovative form of social collaboration. (...)
Many of the uses of A.I. that I like rest on advantages we gain when computers get less rigid. Digital stuff as we have known it has a brittle quality that forces people to conform to it, rather than assess it. We’ve all endured the agony of watching some poor soul at a doctor’s office struggle to do the expected thing on a front-desk screen. The face contorts; humanity is undermined. The need to conform to digital designs has created an ambient expectation of human subservience. A positive spin on A.I. is that it might spell the end of this torture, if we use it well. We can now imagine a Web site that reformulates itself on the fly for someone who is color-blind, say, or a site that tailors itself to someone’s particular cognitive abilities and styles. A humanist like me wants people to have more control, rather than be overly influenced or guided by technology. Flexibility may give us back some agency.
Still, despite these possible upsides, it’s more than reasonable to worry that the new technology will push us around in ways we don’t like or understand. Recently, some friends of mine circulated a petition asking for a pause on the most ambitious A.I. development. The idea was that we’d work on policy during the pause. The petition was signed by some in our community but not others. I found the notion too hazy—what level of progress would mean that the pause could end? Every week, I receive new but always vague mission statements from organizations seeking to initiate processes to set A.I. policy.
These efforts are well intentioned, but they seem hopeless to me. For years, I worked on the E.U.’s privacy policies, and I came to realize that we don’t know what privacy is. It’s a term we use every day, and it can make sense in context, but we can’t nail it down well enough to generalize. The closest we have come to a definition of privacy is probably “the right to be left alone,” but that seems quaint in an age when we are constantly dependent on digital services. In the context of A.I., “the right to not be manipulated by computation” seems almost correct, but doesn’t quite say everything we’d like it to.
A.I.-policy conversations are dominated by terms like “alignment” (is what an A.I. “wants” aligned with what humans want?), “safety” (can we foresee guardrails that will foil a bad A.I.?), and “fairness” (can we forestall all the ways a program might treat certain people with disfavor?). The community has certainly accomplished much good by pursuing these ideas, but that hasn’t quelled our fears. We end up motivating people to try to circumvent the vague protections we set up. Even though the protections do help, the whole thing becomes a game—like trying to outwit a sneaky genie. The result is that the A.I.-research community communicates the warning that their creations might still kill all of humanity soon, while proposing ever more urgent, but turgid, deliberative processes.
Recently, I tried an informal experiment, calling colleagues and asking them if there’s anything specific on which we can all seem to agree. I’ve found that there is a foundation of agreement. We all seem to agree that deepfakes—false but real-seeming images, videos, and so on—should be labelled as such by the programs that create them. Communications coming from artificial people, and automated interactions that are designed to manipulate the thinking or actions of a human being, should be labelled as well. We also agree that these labels should come with actions that can be taken. People should be able to understand what they’re seeing, and should have reasonable choices in return.
How can all this be done? There is also near-unanimity, I find, that the black-box nature of our current A.I. tools must end. The systems must be made more transparent. We need to get better at saying what is going on inside them and why. This won’t be easy. The problem is that the large-model A.I. systems we are talking about aren’t made of explicit ideas. There is no definite representation of what the system “wants,” no label for when it is doing a particular thing, like manipulating a person. There is only a giant ocean of jello—a vast mathematical mixing. A writers’-rights group has proposed that real human authors be paid in full when tools like GPT are used in the scriptwriting process; after all, the system is drawing on scripts that real people have made. But when we use A.I. to produce film clips, and potentially whole movies, there won’t necessarily be a screenwriting phase. A movie might be produced that appears to have a script, soundtrack, and so on, but it will have been calculated into existence as a whole. Similarly, no sketch precedes the generation of a painting from an illustration A.I. Attempting to open the black box by making a system spit out otherwise unnecessary items like scripts, sketches, or intentions will involve building another black box to interpret the first—an infinite regress.
At the same time, it’s not true that the interior of a big model has to be a trackless wilderness. We may not know what an “idea” is from a formal, computational point of view, but there could be tracks made not of ideas but of people. At some point in the past, a real person created an illustration that was input as data into the model, and, in combination with contributions from other people, this was transformed into a fresh image. Big-model A.I. is made of people—and the way to open the black box is to reveal them.
This concept, which I’ve contributed to developing, is usually called “data dignity.” It appeared, long before the rise of big-model “A.I.,” as an alternative to the familiar arrangement in which people give their data for free in exchange for free services, such as internet searches or social networking. Data dignity is sometimes known as “data as labor” or “plurality research.” The familiar arrangement has turned out to have a dark side: because of “network effects,” a few platforms take over, eliminating smaller players, like local newspapers. Worse, since the immediate online experience is supposed to be free, the only remaining business is the hawking of influence. Users experience what seems to be a communitarian paradise, but they are targeted by stealthy and addictive algorithms that make people vain, irritable, and paranoid.
In a world with data dignity, digital stuff would typically be connected with the humans who want to be known for having made it. In some versions of the idea, people could get paid for what they create, even when it is filtered and recombined through big models, and tech hubs would earn fees for facilitating things that people want to do. Some people are horrified by the idea of capitalism online, but this would be a more honest capitalism. The familiar “free” arrangement has been a disaster.
One of the reasons the tech community worries that A.I. could be an existential threat is that it could be used to toy with people, just as the previous wave of digital technologies have been. Given the power and potential reach of these new systems, it’s not unreasonable to fear extinction as a possible result. Since that danger is widely recognized, the arrival of big-model A.I. could be an occasion to reformat the tech industry for the better.
by Jaron Lanier, New Yorker | Read more:
Image: Nicholas Konrad / The New Yorker
[ed. Perhaps, but with the CIA, Pentagon and other international defense agencies deeply involved (and charging full speed ahead) I'm not too optimistic. See also: Whose Planet Are We On? (TomDispatch):
These efforts are well intentioned, but they seem hopeless to me. For years, I worked on the E.U.’s privacy policies, and I came to realize that we don’t know what privacy is. It’s a term we use every day, and it can make sense in context, but we can’t nail it down well enough to generalize. The closest we have come to a definition of privacy is probably “the right to be left alone,” but that seems quaint in an age when we are constantly dependent on digital services. In the context of A.I., “the right to not be manipulated by computation” seems almost correct, but doesn’t quite say everything we’d like it to.
A.I.-policy conversations are dominated by terms like “alignment” (is what an A.I. “wants” aligned with what humans want?), “safety” (can we foresee guardrails that will foil a bad A.I.?), and “fairness” (can we forestall all the ways a program might treat certain people with disfavor?). The community has certainly accomplished much good by pursuing these ideas, but that hasn’t quelled our fears. We end up motivating people to try to circumvent the vague protections we set up. Even though the protections do help, the whole thing becomes a game—like trying to outwit a sneaky genie. The result is that the A.I.-research community communicates the warning that their creations might still kill all of humanity soon, while proposing ever more urgent, but turgid, deliberative processes.
Recently, I tried an informal experiment, calling colleagues and asking them if there’s anything specific on which we can all seem to agree. I’ve found that there is a foundation of agreement. We all seem to agree that deepfakes—false but real-seeming images, videos, and so on—should be labelled as such by the programs that create them. Communications coming from artificial people, and automated interactions that are designed to manipulate the thinking or actions of a human being, should be labelled as well. We also agree that these labels should come with actions that can be taken. People should be able to understand what they’re seeing, and should have reasonable choices in return.
How can all this be done? There is also near-unanimity, I find, that the black-box nature of our current A.I. tools must end. The systems must be made more transparent. We need to get better at saying what is going on inside them and why. This won’t be easy. The problem is that the large-model A.I. systems we are talking about aren’t made of explicit ideas. There is no definite representation of what the system “wants,” no label for when it is doing a particular thing, like manipulating a person. There is only a giant ocean of jello—a vast mathematical mixing. A writers’-rights group has proposed that real human authors be paid in full when tools like GPT are used in the scriptwriting process; after all, the system is drawing on scripts that real people have made. But when we use A.I. to produce film clips, and potentially whole movies, there won’t necessarily be a screenwriting phase. A movie might be produced that appears to have a script, soundtrack, and so on, but it will have been calculated into existence as a whole. Similarly, no sketch precedes the generation of a painting from an illustration A.I. Attempting to open the black box by making a system spit out otherwise unnecessary items like scripts, sketches, or intentions will involve building another black box to interpret the first—an infinite regress.
At the same time, it’s not true that the interior of a big model has to be a trackless wilderness. We may not know what an “idea” is from a formal, computational point of view, but there could be tracks made not of ideas but of people. At some point in the past, a real person created an illustration that was input as data into the model, and, in combination with contributions from other people, this was transformed into a fresh image. Big-model A.I. is made of people—and the way to open the black box is to reveal them.
This concept, which I’ve contributed to developing, is usually called “data dignity.” It appeared, long before the rise of big-model “A.I.,” as an alternative to the familiar arrangement in which people give their data for free in exchange for free services, such as internet searches or social networking. Data dignity is sometimes known as “data as labor” or “plurality research.” The familiar arrangement has turned out to have a dark side: because of “network effects,” a few platforms take over, eliminating smaller players, like local newspapers. Worse, since the immediate online experience is supposed to be free, the only remaining business is the hawking of influence. Users experience what seems to be a communitarian paradise, but they are targeted by stealthy and addictive algorithms that make people vain, irritable, and paranoid.
In a world with data dignity, digital stuff would typically be connected with the humans who want to be known for having made it. In some versions of the idea, people could get paid for what they create, even when it is filtered and recombined through big models, and tech hubs would earn fees for facilitating things that people want to do. Some people are horrified by the idea of capitalism online, but this would be a more honest capitalism. The familiar “free” arrangement has been a disaster.
One of the reasons the tech community worries that A.I. could be an existential threat is that it could be used to toy with people, just as the previous wave of digital technologies have been. Given the power and potential reach of these new systems, it’s not unreasonable to fear extinction as a possible result. Since that danger is widely recognized, the arrival of big-model A.I. could be an occasion to reformat the tech industry for the better.
by Jaron Lanier, New Yorker | Read more:
Image: Nicholas Konrad / The New Yorker
[ed. Perhaps, but with the CIA, Pentagon and other international defense agencies deeply involved (and charging full speed ahead) I'm not too optimistic. See also: Whose Planet Are We On? (TomDispatch):
"Still, let’s not forget that AI was created by those of us with LTAI [ed. less than artificial intelligence]. If now left to its own devices (with, of course, a helping hand from the powers that be), it seems reasonable to assume that it will, in some way, essentially repeat the human experience. In fact, consider that a guarantee of sorts. That means it will create beauty and wonder and — yes! — horror beyond compare (and perhaps even more efficiently so). Lest you doubt that, just consider which part of humanity already seems the most intent on pushing artificial intelligence to its limits.
Yes, across the planet, departments of “defense” are pouring money into AI research and development, especially the creation of unmanned autonomous vehicles (think: killer robots) and weapons systems of various kinds, as Michael Klare pointed out recently at TomDispatch when it comes to the Pentagon. In fact, it shouldn’t shock you to know that five years ago (yes, five whole years!), the Pentagon was significantly ahead of the game in creating a Joint Artificial Intelligence Center to, as the New York Times put it, “explore the use of artificial intelligence in combat.” There, it might, in the end — and “end” is certainly an operative word here — speed up battlefield action in such a way that we could truly be entering unknown territory. We could, in fact, be entering a realm in which human intelligence in wartime decision-making becomes, at best, a sideline activity. (...)
The Pentagon, however, instantly responded to that call this way, as David Sanger reported in the New York Times: “Pentagon officials, speaking at technology forums, said they thought the idea of a six-month pause in developing the next generations of ChatGPT and similar software was a bad idea: The Chinese won’t wait, and neither will the Russians.” So, full-speed ahead and skip any international attempts to slow down or control the development of the most devastating aspects of AI!"
Yes, across the planet, departments of “defense” are pouring money into AI research and development, especially the creation of unmanned autonomous vehicles (think: killer robots) and weapons systems of various kinds, as Michael Klare pointed out recently at TomDispatch when it comes to the Pentagon. In fact, it shouldn’t shock you to know that five years ago (yes, five whole years!), the Pentagon was significantly ahead of the game in creating a Joint Artificial Intelligence Center to, as the New York Times put it, “explore the use of artificial intelligence in combat.” There, it might, in the end — and “end” is certainly an operative word here — speed up battlefield action in such a way that we could truly be entering unknown territory. We could, in fact, be entering a realm in which human intelligence in wartime decision-making becomes, at best, a sideline activity. (...)
The Pentagon, however, instantly responded to that call this way, as David Sanger reported in the New York Times: “Pentagon officials, speaking at technology forums, said they thought the idea of a six-month pause in developing the next generations of ChatGPT and similar software was a bad idea: The Chinese won’t wait, and neither will the Russians.” So, full-speed ahead and skip any international attempts to slow down or control the development of the most devastating aspects of AI!"
[ed. Last quote is a winner. After thousands of years of war, nuanced reasoning still isn't one of humanity's strong points.]
