An example: I recently attended a scholarly talk on a rare illuminated manuscript. The speaker was as eminent as they come, but the talk was not easy to follow. Frustrated, I opened ChatGPT and started asking it questions about the subject. In the course of that disappointing lecture, I had a rich exchange with the system. I learned what was and wasn’t known about the document, who had done the foundational research, and how scholars had interpreted its iconography and transmission. Was the information perfect? Surely not, but neither is what we get from people. Was it better than the talk I was hearing? By a wide margin. (View Highlight)
Sobre el criterio de evaluar si la IA es mejor que el humano más capacitado disponible.
Now I can hold a sustained, tailored conversation on any of the topics I care about, from agnotology to zoosemiotics, with a system that has effectively achieved Ph.D.-level competence across all of them. I can construct the “book” I want in real time—responsive to my questions, customized to my focus, tuned to the spirit of my inquiry. And the astonishing part is this: the making of books such as those on my shelves, each the labor of years or decades, is quickly becoming a matter of well-designed prompts. The question is no longer whether we can write such books; they can be written endlessly, for us. The question is, do we want to read them? (View Highlight)
the attention economy’s “killer app”: totally algorithmic pseudo-persons who are sensitive, competent, and infinitely patient; know everything about everyone; and will, of course, be turned to the business of extracting money from us. These systems promise a new mode of attention capture—what some are calling the “intimacy economy” (“human fracking” comes closer to the truth). (View Highlight)
What this student had come to say was that she had descended more deeply into her own mind, into her own conceptual powers, while in dialogue with an intelligence toward which she felt no social obligation. No need to accommodate, and no pressure to please. It was a discovery—for her, for me—with widening implications for all of us. “And it was so patient,” she said. “I was asking it about the history of attention, but five minutes in I realized: I don’t think anyone has ever paid such pure attention to me and my thinking and my questions … ever. It’s made me rethink all my interactions with people.” She had gone to the machine to talk about the callow and exploitative dynamics of commodified attention capture—only to discover, in the system’s sweet solicitude, a kind of pure attention she had perhaps never known. Who has? For philosophers like Simone Weil and Iris Murdoch, the capacity to give true attention to another being lies at the absolute center of ethical life. But the sad thing is that we aren’t very good at this. The machines make it look easy. (View Highlight)
Algo de eso lo comparte la experiencia de la psicoterapia, y quizás también los mismos efectos. Considerar a Cristóbal Celis.
The A.I. tools my students and I now engage with are, at core, astoundingly successful applications of probabilistic prediction. They don’t know anything—not in any meaningful sense—and they certainly don’t feel. As they themselves continue to tell us, all they do is guess what letter, what word, what pattern is most likely to satisfy their algorithms in response to given prompts. (View Highlight)
A propósito de la paradoja de entender que no sienten, pero sentir que sí y entrar en esa duda. Test de Turing y zombi filosófico, ¿podemos estar realmente seguros alguna vez de qué el otro siente o es, en algún punto, un acto de fe?
The current systems can be as human as any human I know, if that human is restricted to coming through a screen (and that’s often how we reach other humans these days, for better or worse). (View Highlight)
It’s the calling known as education, which the literary theorist Gayatri Chakravorty Spivak once defined as the “non-coercive rearranging of desire.” (View Highlight)
When we gathered as a class in the wake of the A.I. assignment, hands flew up. One of the first came from Diego, a tall, curly-haired student—and, from what I’d made out in the course of the semester, socially lively on campus. “I guess I just felt more and more hopeless,” he said. “I cannot figure out what I am supposed to do with my life if these things can do anything I can do faster and with way more detail and knowledge.” He said he felt crushed. (View Highlight)
Within five years, it will make little sense for scholars of history to keep producing monographs in the traditional mold—nobody will read them, and systems such as these will be able to generate them, endlessly, at the push of a button. But factory-style scholarly productivity was never the essence of the humanities. The real project was always us: the work of understanding, and not the accumulation of facts. Not “knowledge,” in the sense of yet another sandwich of true statements about the world. That stuff is great—and where science and engineering are concerned it’s pretty much the whole point. But no amount of peer-reviewed scholarship, no data set, can resolve the central questions that confront every human being: How to live? What to do? How to face death? The answers to those questions aren’t out there in the world, waiting to be discovered. They aren’t resolved by “knowledge production.” They are the work of being, not knowing—and knowing alone is utterly unequal to the task. (View Highlight)
But to be human is not to have answers. It is to have questions—and to live with them. The machines can’t do that for us. Not now, not ever. (View Highlight)
In this sense, generative A.I. might count as a conceptual win for my field. Historians have long extolled the “power of the archive.” Little did we know that the engineers would come along and plug it in. And it turns out that a huge amount of what we seek from a human person can be simulated through this Frankensteinian reanimation of our collective dead letters. What a discovery! We have a new whole of ourselves with which to converse now. Let’s take our time; there is plenty to learn. (View Highlight)