“I know we’re all anti-AI here,” a friend texted in the group chat. “We should co-write an article on how AI has no place in education,” a professional acquaintance suggested during a collaboration session.
This was becoming an awkward time to announce my return to AI… specifically, to focus on AI in education. My friends are right. There is a lot that is terrible about AI. Part of why I haven’t written a blog post in the last 5 months is that I feel so discouraged by how over-saturated the internet is now with writing by and about AI. I still spend weeks and sometimes months researching and writing my posts, but fewer and fewer people even see them in a world awash in slop. Execs make claims about AI capabilities that are so overhyped they verge on fraud.
People of all ages are outsourcing their thinking to AI. However, skills atrophy when you stop using them, and reading, writing, and understanding texts are core parts of being human.
Professor friends share how widespread AI use has upended their curricula, with students submitting essays generated by AI, or reading scripts generated by AI for their presentations. Maintainers of open-source code repositories are flooded with low-quality pull requests of AI-generated code.
Most AI-powered education products are terrible, chasing after gameable metrics . Rather than entice kids to get absorbed in real novels, one AI-powered school shows kids AI-generated passages and then quizzes them with AI-generated multiple choice questions. Other AI education products are downright scams, such as the one that Los Angeles Unified School District wasted $3 million on before the founder was charged with fraud and identity theft .
A decade of AI worries
I have spent a decade worrying about where AI was headed. In 2016, Jeremy Howard and I co-founded fast.ai, inspired by the power of neural networks and alarmed by the direction of the major AI companies.
Ten years ago, AI development was the domain of a small, homogeneous elite group making decisions with wide-ranging impact. We tried to counter that concentration of power by getting a more diverse group of people with unlikely backgrounds involved in the field. And yet today, a handful of billionaires running the top AI labs hold more power than ever before.
The big AI labs behaved as though computing power and money were limitless. Most of the world cannot afford that assumption. Jeremy and I wanted researchers to treat constraints as a source of creativity, rather than something they could spend their way around.
I wanted ethics to be part of how data scientists were trained, rather than an optional discussion after the technical work was done. At the University of San Francisco, I founded the Center for Applied Data Ethics, created a data ethics course, and we made it a requirement for the MS in Data Science program. Yet AI ethics is still often treated as a marketing exercise, or focuses on theoretical questions over actual human suffering.
以上为主要内容节选（长篇引用）；完整文章请见原文链接。
阅读原文 ↗ 内容来自 Hacker News Frontpage（社区）