内容来源：https://www.geekwire.com/2025/me-myself-and-ai-host-sam-ransbotham-on-finding-the-real-value-in-ai-even-when-its-wrong/

内容总结：

波士顿学院商业分析学教授萨姆·兰斯博瑟姆近日在教学中观察到，人工智能正在课堂引发新型"数字鸿沟"，而这一现象与设备普及度无关——真正令人担忧的是学生与技术互动深度的两极分化。

这位同时主持《我、自己和AI》播客的学者指出，尽管校方为所有学生免费提供顶尖AI工具以保障教育公平，但部分学生仅满足于向机器"敷衍交差"，另一部分则能借助技术完成突破性创作。他强调："对工具的理解程度直接决定产出质量，浅尝辄止只能获得平庸成果，而深度探索方能创造卓越价值。"

兰斯博瑟姆用"奔向平庸的竞赛"形容当前困境："如果目标只是及格线，AI确实能让人快速达标。但波士顿学院的校训是'永争卓越'，而非'安于平庸'。"他警告，轻易达成平庸的能力可能正在侵蚀学生追求卓越的驱动力。

针对AI价值评估体系，这位学者以维基百科的崛起为例，指出传统经济指标难以衡量技术革命带来的社会增益。在实际应用层面，他更看重AI的信息提炼能力而非内容生成："与其关注创作功能，我更依赖它从海量信息中提取精华，为每天24小时扩容。"

尽管时常遭遇AI的荒谬输出，兰斯博瑟姆仍保持乐观："即使它给出完全错误的答案，这种错误也能激发我思考其谬误根源，进而获得新发现。"他主持的播客正致力于在AI的过度鼓吹与全盘否定之间，寻找理性客观的技术信号。

中文翻译：

波士顿学院商业分析学教授萨姆·兰斯博瑟姆正在见证机器学习课堂上的两极现象——既为之振奋又深感忧虑。

有些学生运用人工智能工具创造出惊人成果，从这项技术中的获益远超预期。但另一些情况却令人担忧：学生们正沦为"向机器交作业的传声筒"。

这形成了一种新型数字鸿沟，却与寻常认知大相径庭。

波士顿学院免费为学生提供顶尖工具，确保社会经济地位不会成为课堂表现的分水岭。但这位主持MIT斯隆管理学院《我、自我与AI》播客的教授担忧的是"技术兴趣的鸿沟"。

"对工具和技术的理解越深刻，所能激发的价值就越大。"他解释道，"浅尝辄止只能获得浮光掠影的成果，深度挖掘方能触及核心。"

问题在于："这正在演变为奔向平庸的竞赛。若以平庸为目标，确实能快速抵达。"

"波士顿学院的校训是'永争卓越'，而非'甘于平庸'。"他补充道，"轻易达到平庸的便利性，正在侵蚀学生追求卓越的能力。"

在GeekWire播客与《我、自我与AI》合作的特别节目中，我们对比了两档播客的观察笔记，分享对AI新兴趋势与长期影响的见解。本次双播客联动分为上下两集，完整对话可前往《我、自我与AI》播客收听。

以下为本集精彩观点：

AI的衡量困境：十余年前深入研究维基百科的兰斯博瑟姆，在当下看到了历史重演。在大英百科全书时代，企业雇佣员工编纂书籍、支付印刷费用，创造着可量化的经济价值。而维基百科的出现终结了这种模式。

传统经济价值虽然消失，但"有理性的人会认为拥有维基百科的世界比大英百科全书时代更糟糕吗？"这意味着传统经济指标无法完全捕捉维基百科为社会创造的价值净值，AI正面临同样的衡量困境。

"数据让你更洞察自身行为与文档资料，从而做出更优决策。"他反问道，"但这该如何量化？"

内容总结与生成之争：对兰斯博瑟姆而言，AI的必备功能并非内容创作，而是帮助他在24小时内高效提炼信息。

"人们大量讨论生成能力，但我发现更多时候是在运用其总结提炼的功能。"

在错误中寻找价值：尽管担忧学生利用AI甘于平庸，他仍对人们运用AI工具取得的成就保持乐观。

"经常遇到AI输出完全错误荒谬的内容，但这些垃圾信息会激发思考：它为何出错？如何突破这种局限？"

噪音中的信号：《我、自我与AI》播客的宗旨正是穿透关于人工智能的两极叙事。

"现在既有对AI的过度炒作，也有大量否定质疑。而在两个极端之间，存在着真实的信号与真相。"

欢迎在Apple、Spotify等平台订阅GeekWire收听完整内容，并关注《我、自我与AI》播客获取更多对话。

英文来源：

Sam Ransbotham teaches a class in machine learning as a professor of business analytics at Boston College, and what he’s witnessing in the classroom both excites and terrifies him.
Some students are using AI tools to create and accomplish amazing things, learning and getting more out of the technology than he could have imagined. But in other situations, he sees a concerning trend: students “phoning things into the machine.”
The result is a new kind of digital divide — but it’s not the one you’d expect.
Boston College provides premier tools to students at no cost, to ensure that socioeconomics aren’t the differentiator in the classroom. But Ransbotham, who hosts the “Me, Myself and AI” podcast from MIT Sloan Management Review, worries about “a divide in technology interest.”
“The deeper that someone is able to understand tools and technology, the more that they’re able to get out of those tools,” he explained. “A cursory usage of a tool will get a cursory result, and a deeper use will get a deeper result.”
The problem? “It’s a race to mediocre. If mediocre is what you’re shooting for, then it’s really quick to get to mediocre.”
He explained, “Boston College’s motto is ‘Ever to Excel.’ It’s not ‘Ever to Mediocre.’ And the ability of students to get to excellence can be hampered by their ease of getting to mediocre.”
That’s one of the topics on this special episode of the GeekWire Podcast, a collaboration with Me, Myself and AI. Sam and I compare notes from our podcasts and share our own observations on emerging trends and long-term implications of AI. This is a two-part series across our podcasts — you can find the rest of our conversation on the Me, Myself and AI feed.
Continue reading for takeaways from this episode.
AI has a measurement problem: Sam, who researched Wikipedia extensively more than a decade ago, sees parallels to the present day. Before Wikipedia, Encyclopedia Britannica was a company with employees that produced books, paid a printer, and created measurable economic value. Then Wikipedia came along, and Encyclopedia Britannica didn’t last.
Its economic value was lost. But as he puts it: “Would any rational person say that the world is a worse place because we now have Wikipedia versus Encyclopedia Britannica?”
In other words, traditional economic metrics don’t fully capture the net gain in value that Wikipedia created for society. He sees the same measurement problem with AI.
“The data gives better insights about what you’re doing, about the documents you have, and you can make a slightly better decision,” he said. “How do you measure that?”
Content summarization vs. generation: Sam’s “gotta have it” AI feature isn’t about creating content — it’s about distilling information to fit more into his 24 hours.
“We talk a lot about generation and the generational capabilities, what these things can create,” he said. “I find myself using it far more for what it can summarize, what it can distill.”
Finding value in AI, even when it’s wrong: Despite his concerns about students using AI to achieve mediocrity, Sam remains optimistic about what people can accomplish with AI tools.
“Often I find that the tool is completely wrong and ridiculous and it says just absolute garbage,” he said. “But that garbage sparks me to think about something — the way that it’s wrong pushes me to think: why is that wrong? … and how can I push on that?”
Searching for the signal in the noise: Sam described the goal of the Me, Myself and AI podcast as cutting through the polarizing narratives about artificial intelligence.
“There’s a lot of hype about artificial intelligence,” he said. “There’s a lot of naysaying about artificial intelligence. And somewhere between those, there is some signal, and some truth.”
Listen to the full episode above, subscribe to GeekWire in Apple, Spotify, or wherever you listen, and find the rest of our conversation on the Me, Myself and AI podcast feed.