Síntesis
La inteligencia artificial (IA) de consumo ya está aquí, trayendo consigo una transformación inevitable que nos obliga a replantear su impacto en nuestros empleos, nuestras vidas y el futuro de la humanidad.
Alejándose del ruido generado tanto por los entusiastas ciegos como por los catastrofistas de la IA, el profesor de Wharton, Ethan Mollick, ofrece una perspectiva provocadora y sumamente práctica. Su enfoque se centra en cómo estas nuevas “herramientas para el pensamiento” pueden transformar nuestro mundo en el día a día. En lugar de temerle, Mollick nos invita a interactuar con la IA asumiendo que es un colaborador: un compañero de trabajo, un co-profesor y un mentor (o coach). Con una visión amplia, estimulante y profundamente optimista, Co-Intelligence nos revela el verdadero poder y las promesas de esta nueva era tecnológica.
Mis subrayados
- There is a sense of poetic irony in the fact that as we move toward a future characterized by greater technological sophistication, we find ourselves contemplating deeply human questions about identity, purpose, and connection.
- Current systems are not good enough to be deep conversation partners, but we may start to see individuals choosing to interact more with AI than with humans.
- … even the most basic facts will be in dispute. This growth of ever more insular information bubbles seems much more likely, accelerating the pre-LLM trend. A final option is that we turn away from online sources of news entirely, because they are so polluted with fake information that they stop becoming useful. Regardless of which direction we head in, even without advances in AI, the way we relate to information will change.
- AI will need to be embraced in classrooms. Some pundits go further, arguing that we need to focus on working with AI. We should, they argue, teach basic AI literacy, and probably “prompt engineering,” about the art and science of creating good prompts for AIs.
- In this case, the nature of jobs will change a lot, as education and skill become less valuable. With lower-cost workers doing the same work in less time, mass unemployment, or at least underemployment, becomes more likely, and we may see the need for policy solutions, like a four-day workweek or universal basic income, that reduce the floor for human welfare.
- The future of understanding how AI impacts work involves understanding how human interaction with AI changes, depending on where tasks are placed on this frontier and how the frontier will change.
- The future of delegation will require further reductions in hallucination rates, and better transparency of AI decision-making, so that we can trust it more.
- When the AI is very good, humans have no reason to work hard and pay attention. They let the AI take over instead of using it as a tool, which can hurt human learning, skill development, and productivity. He called this “falling asleep at the wheel.”
- There is danger in working with AIs—danger that we make ourselves redundant, of course, but also danger that we trust AIs for work too much.
- We are going to need to reconstruct meaning, in art and in the rituals of creative work.
- … lot of work is time-consuming by design. In a world in which the AI gives an instant, pretty good, near universally accessible shortcut, we’ll soon face a crisis of meaning in creative work of all kinds. This is, in part, because we expect creative work to take careful thought and revision, but also that time often operates as a stand-in for work.
- The implications of having AI write our first drafts (even if we do the work ourselves, which is not a given) are huge. One consequence is that we could lose our creativity and originality. When we use AI to generate our first drafts, we tend to anchor on the first idea that the machine produces, which influences our future work. Even if we rewrite the drafts completely, they will still be tainted by the AI’s influence. We will not be able to explore different perspectives and alternatives, which could lead to better solutions and insights.
- … we need people who have deep or broad knowledge of unusual fields to use AI in ways that others cannot, developing unexpected and valuable prompts and testing the limits of how they work.